2025-08-26T19:42:37.1653894Z Current runner version: '2.328.0' 2025-08-26T19:42:37.1663506Z Runner name: 'i-0d10cabc7fe6d3867' 2025-08-26T19:42:37.1664856Z Runner group name: 'Default' 2025-08-26T19:42:37.1666269Z Machine name: 'ip-10-1-64-236' 2025-08-26T19:42:37.1671364Z ##[group]GITHUB_TOKEN Permissions 2025-08-26T19:42:37.1674624Z Contents: read 2025-08-26T19:42:37.1675488Z Metadata: read 2025-08-26T19:42:37.1676491Z ##[endgroup] 2025-08-26T19:42:37.1680429Z Secret source: Actions 2025-08-26T19:42:37.1681717Z Prepare workflow directory 2025-08-26T19:42:37.2306284Z Prepare all required actions 2025-08-26T19:42:37.2348286Z Getting action download info 2025-08-26T19:42:37.4832943Z Download action repository 'pytorch/test-infra@main' (SHA:0192d56cb596bb73b125bd368553908cc5c513f0) 2025-08-26T19:42:39.3978988Z Download action repository 'pytorch/pytorch@main' (SHA:77bc959fe122bfd131e339ca36cab445a1860806) 2025-08-26T19:42:53.0055786Z Download action repository 'actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065' (SHA:a26af69be951a213d495a4c3e4e4022e16d87065) 2025-08-26T19:42:53.3559114Z Download action repository 'aws-actions/configure-aws-credentials@ececac1a45f3b08a01d2dd070d28d111c5fe6722' (SHA:ececac1a45f3b08a01d2dd070d28d111c5fe6722) 2025-08-26T19:42:53.5428718Z Download action repository 'aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076' (SHA:062b18b96a7aff071d4dc91bc00c4c1a7945b076) 2025-08-26T19:42:53.7177442Z Download action repository 'seemethere/upload-artifact-s3@baba72d0712b404f646cebe0730933554ebce96a' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-08-26T19:42:54.0130008Z Getting action download info 2025-08-26T19:42:54.1265551Z Download action repository 'actions/checkout@v4' (SHA:08eba0b27e820071cde6df949e0beb9ba4906955) 2025-08-26T19:42:54.4349958Z Getting action download info 2025-08-26T19:42:54.5460709Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-08-26T19:42:54.7802668Z Getting action download info 2025-08-26T19:42:54.8819178Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-08-26T19:42:55.0711837Z Getting action download info 2025-08-26T19:42:55.2163179Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/heads/main (262640fd220236042fbf4443cc163c8838c84c3d) 2025-08-26T19:42:55.2167312Z ##[group] Inputs 2025-08-26T19:42:55.2167723Z build-environment: linux-jammy-py3.13-clang12 2025-08-26T19:42:55.2170953Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]} 2025-08-26T19:42:55.2174498Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:42:55.2175326Z sync-tag: 2025-08-26T19:42:55.2176139Z timeout-minutes: 240 2025-08-26T19:42:55.2191443Z use-gha: 2025-08-26T19:42:55.2191948Z dashboard-tag: 2025-08-26T19:42:55.2192215Z s3-bucket: gha-artifacts 2025-08-26T19:42:55.2192498Z aws-role-to-assume: 2025-08-26T19:42:55.2193292Z disable-monitor: false 2025-08-26T19:42:55.2193916Z monitor-log-interval: 5 2025-08-26T19:42:55.2194321Z monitor-data-collect-interval: 1 2025-08-26T19:42:55.2194661Z ##[endgroup] 2025-08-26T19:42:55.2195111Z Complete job name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:42:55.2676217Z A job started hook has been configured by the self-hosted runner administrator 2025-08-26T19:42:55.2783021Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-08-26T19:42:55.2795038Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:42:55.2795800Z ##[endgroup] 2025-08-26T19:42:56.5656904Z Runner Type: lf.linux.2xlarge 2025-08-26T19:42:56.5657456Z Instance Type: c5.2xlarge 2025-08-26T19:42:56.5657739Z AMI Name: unknown 2025-08-26T19:42:56.5684466Z AMI ID: ami-05ffe3c48a9991133 2025-08-26T19:43:02.3330619Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-08-26T19:43:02.3331102Z with: 2025-08-26T19:43:02.3331725Z github-secret: *** 2025-08-26T19:43:02.3332471Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-08-26T19:43:02.3333304Z activate-with-label: false 2025-08-26T19:43:02.3333581Z label: with-ssh 2025-08-26T19:43:02.3333844Z remove-existing-keys: true 2025-08-26T19:43:02.3334135Z fail-silently: true 2025-08-26T19:43:02.3334396Z env: 2025-08-26T19:43:02.3334608Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:43:02.3334884Z ##[endgroup] 2025-08-26T19:43:02.4495097Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-08-26T19:43:02.4496757Z Not on pull request and ciflow reference could not be extracted, skipping adding ssh keys 2025-08-26T19:43:02.4660858Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-08-26T19:43:02.4661332Z with: 2025-08-26T19:43:02.4661555Z no-sudo: true 2025-08-26T19:43:02.4661811Z submodules: recursive 2025-08-26T19:43:02.4662098Z fetch-depth: 0 2025-08-26T19:43:02.4662326Z env: 2025-08-26T19:43:02.4662571Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:43:02.4662842Z ##[endgroup] 2025-08-26T19:43:02.4753012Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-26T19:43:02.4754067Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-26T19:43:02.4762262Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:43:02.4762664Z env: 2025-08-26T19:43:02.4762907Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:43:02.4763232Z ##[endgroup] 2025-08-26T19:43:02.4857566Z ##[group]Run # Use all available CPUs for fetching 2025-08-26T19:43:02.4858054Z # Use all available CPUs for fetching 2025-08-26T19:43:02.4858421Z cd "${GITHUB_WORKSPACE}" 2025-08-26T19:43:02.4858781Z git config --global fetch.parallel 0 2025-08-26T19:43:02.4859185Z git config --global submodule.fetchJobs 0 2025-08-26T19:43:02.4859547Z  2025-08-26T19:43:02.4860002Z # Clean workspace. The default checkout action should also do this, but 2025-08-26T19:43:02.4860687Z # do it here as well just in case 2025-08-26T19:43:02.4861024Z if [[ -d .git ]]; then 2025-08-26T19:43:02.4861333Z  if [ -z "${NO_SUDO}" ]; then 2025-08-26T19:43:02.4861646Z  sudo git clean -ffdx 2025-08-26T19:43:02.4861943Z  else 2025-08-26T19:43:02.4862188Z  git clean -ffdx 2025-08-26T19:43:02.4862463Z  fi 2025-08-26T19:43:02.4862675Z fi 2025-08-26T19:43:02.4868325Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:43:02.4868727Z env: 2025-08-26T19:43:02.4868961Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:43:02.4869237Z NO_SUDO: true 2025-08-26T19:43:02.4869461Z ##[endgroup] 2025-08-26T19:43:02.5042386Z ##[group]Run actions/checkout@v4 2025-08-26T19:43:02.5042968Z with: 2025-08-26T19:43:02.5043406Z ref: 262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:43:02.5044295Z fetch-depth: 0 2025-08-26T19:43:02.5044539Z submodules: recursive 2025-08-26T19:43:02.5044815Z show-progress: false 2025-08-26T19:43:02.5045100Z repository: pytorch/pytorch 2025-08-26T19:43:02.5045625Z token: *** 2025-08-26T19:43:02.5045863Z ssh-strict: true 2025-08-26T19:43:02.5046096Z ssh-user: git 2025-08-26T19:43:02.5046353Z persist-credentials: true 2025-08-26T19:43:02.5046636Z clean: true 2025-08-26T19:43:02.5046891Z sparse-checkout-cone-mode: true 2025-08-26T19:43:02.5047191Z fetch-tags: false 2025-08-26T19:43:02.5047433Z lfs: false 2025-08-26T19:43:02.5047671Z set-safe-directory: true 2025-08-26T19:43:02.5047951Z env: 2025-08-26T19:43:02.5048171Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:43:02.5048442Z ##[endgroup] 2025-08-26T19:43:02.6161518Z Syncing repository: pytorch/pytorch 2025-08-26T19:43:02.6162975Z ##[group]Getting Git version info 2025-08-26T19:43:02.6163488Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-08-26T19:43:02.6164215Z [command]/usr/bin/git version 2025-08-26T19:43:02.6164514Z git version 2.47.1 2025-08-26T19:43:02.6173156Z ##[endgroup] 2025-08-26T19:43:02.6185797Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/9f3421ca-9377-416b-858b-17eff36f0aef/.gitconfig' 2025-08-26T19:43:02.6208074Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/9f3421ca-9377-416b-858b-17eff36f0aef' before making global git config changes 2025-08-26T19:43:02.6209999Z Adding repository directory to the temporary git global config as a safe directory 2025-08-26T19:43:02.6214903Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-26T19:43:02.6252445Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-08-26T19:43:02.6256138Z ##[group]Initializing the repository 2025-08-26T19:43:02.6261150Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-26T19:43:02.6291889Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-08-26T19:43:02.6293075Z hint: is subject to change. To configure the initial branch name to use in all 2025-08-26T19:43:02.6294089Z hint: of your new repositories, which will suppress this warning, call: 2025-08-26T19:43:02.6294827Z hint: 2025-08-26T19:43:02.6295327Z hint: git config --global init.defaultBranch 2025-08-26T19:43:02.6295977Z hint: 2025-08-26T19:43:02.6296596Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-08-26T19:43:02.6297670Z hint: 'development'. The just-created branch can be renamed via this command: 2025-08-26T19:43:02.6298451Z hint: 2025-08-26T19:43:02.6298839Z hint: git branch -m 2025-08-26T19:43:02.6299804Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-08-26T19:43:02.6305582Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-08-26T19:43:02.6330870Z ##[endgroup] 2025-08-26T19:43:02.6331685Z ##[group]Disabling automatic garbage collection 2025-08-26T19:43:02.6335738Z [command]/usr/bin/git config --local gc.auto 0 2025-08-26T19:43:02.6360395Z ##[endgroup] 2025-08-26T19:43:02.6361101Z ##[group]Setting up auth 2025-08-26T19:43:02.6367891Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-08-26T19:43:02.6396037Z [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' || :" 2025-08-26T19:43:02.6678004Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-08-26T19:43:02.6705486Z [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' || :" 2025-08-26T19:43:02.6966753Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-08-26T19:43:02.7012971Z ##[endgroup] 2025-08-26T19:43:02.7020694Z ##[group]Fetching the repository 2025-08-26T19:43:02.7021531Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-08-26T19:43:57.2348708Z From https://github.com/pytorch/pytorch 2025-08-26T19:43:57.2350603Z * [new branch] 160583 -> origin/160583 2025-08-26T19:43:57.2351482Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-08-26T19:43:57.2352328Z * [new branch] 5addvllmbuild -> origin/5addvllmbuild 2025-08-26T19:43:57.2353424Z * [new branch] AaronWang04_addmmfusion_perftest -> origin/AaronWang04_addmmfusion_perftest 2025-08-26T19:43:57.2354240Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-08-26T19:43:57.2355177Z * [new branch] ISSUE-154849 -> origin/ISSUE-154849 2025-08-26T19:43:57.2356291Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-08-26T19:43:57.2357444Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-08-26T19:43:57.2358494Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-08-26T19:43:57.2359462Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-08-26T19:43:57.2361081Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-08-26T19:43:57.2362090Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-08-26T19:43:57.2362937Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-08-26T19:43:57.2363651Z * [new branch] VLA_exp -> origin/VLA_exp 2025-08-26T19:43:57.2364660Z * [new branch] add-missing-args-normalization -> origin/add-missing-args-normalization 2025-08-26T19:43:57.2365526Z * [new branch] add-user-guide-structure -> origin/add-user-guide-structure 2025-08-26T19:43:57.2366568Z * [new branch] addVllmPin -> origin/addVllmPin 2025-08-26T19:43:57.2367503Z * [new branch] add_compile_benchmarking -> origin/add_compile_benchmarking 2025-08-26T19:43:57.2368541Z * [new branch] add_windows_testing_back -> origin/add_windows_testing_back 2025-08-26T19:43:57.2369398Z * [new branch] addbuildvllm -> origin/addbuildvllm 2025-08-26T19:43:57.2370626Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-08-26T19:43:57.2371773Z * [new branch] addsimde -> origin/addsimde 2025-08-26T19:43:57.2373648Z * [new branch] adi/acl_upgrade -> origin/adi/acl_upgrade 2025-08-26T19:43:57.2374642Z * [new branch] adi/test -> origin/adi/test 2025-08-26T19:43:57.2375752Z * [new branch] adi/test_bgemm -> origin/adi/test_bgemm 2025-08-26T19:43:57.2376907Z * [new branch] adi/test_fusions -> origin/adi/test_fusions 2025-08-26T19:43:57.2378267Z * [new branch] adi/test_onednn_v3.9 -> origin/adi/test_onednn_v3.9 2025-08-26T19:43:57.2379468Z * [new branch] adi/test_presve_change -> origin/adi/test_presve_change 2025-08-26T19:43:57.2380709Z * [new branch] adi/test_timm -> origin/adi/test_timm 2025-08-26T19:43:57.2382217Z * [new branch] adi/testpresve_change -> origin/adi/testpresve_change 2025-08-26T19:43:57.2384349Z * [new branch] aditew01/test/vec_bf16 -> origin/aditew01/test/vec_bf16 2025-08-26T19:43:57.2385419Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-08-26T19:43:57.2386555Z * [new branch] alt-disable -> origin/alt-disable 2025-08-26T19:43:57.2388546Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-08-26T19:43:57.2389571Z * [new branch] angelayi/aoti_inductor_fx -> origin/angelayi/aoti_inductor_fx 2025-08-26T19:43:57.2391026Z * [new branch] angelayi/assert_tensor_metadata_device -> origin/angelayi/assert_tensor_metadata_device 2025-08-26T19:43:57.2392240Z * [new branch] angelayi/benchmark -> origin/angelayi/benchmark 2025-08-26T19:43:57.2393476Z * [new branch] angelayi/benchmark2 -> origin/angelayi/benchmark2 2025-08-26T19:43:57.2395175Z * [new branch] angelayi/change_pytree_serialization -> origin/angelayi/change_pytree_serialization 2025-08-26T19:43:57.2396346Z * [new branch] angelayi/cpp_loader -> origin/angelayi/cpp_loader 2025-08-26T19:43:57.2398117Z * [new branch] angelayi/custom_op_subgraph -> origin/angelayi/custom_op_subgraph 2025-08-26T19:43:57.2399037Z * [new branch] angelayi/customop -> origin/angelayi/customop 2025-08-26T19:43:57.2400404Z * [new branch] angelayi/is_symbolic_tracing -> origin/angelayi/is_symbolic_tracing 2025-08-26T19:43:57.2401576Z * [new branch] angelayi/logging.bak -> origin/angelayi/logging.bak 2025-08-26T19:43:57.2402800Z * [new branch] angelayi/logging2 -> origin/angelayi/logging2 2025-08-26T19:43:57.2404243Z * [new branch] angelayi/no_so_weight -> origin/angelayi/no_so_weight 2025-08-26T19:43:57.2405051Z * [new branch] angelayi/opoverload -> origin/angelayi/opoverload 2025-08-26T19:43:57.2406372Z * [new branch] angelayi/pytree -> origin/angelayi/pytree 2025-08-26T19:43:57.2407469Z * [new branch] angelayi/save_error -> origin/angelayi/save_error 2025-08-26T19:43:57.2408644Z * [new branch] angelayi/scan_layers -> origin/angelayi/scan_layers 2025-08-26T19:43:57.2409801Z * [new branch] angelayi/symint_input -> origin/angelayi/symint_input 2025-08-26T19:43:57.2411095Z * [new branch] angelayi/tensor_nn_module_meta -> origin/angelayi/tensor_nn_module_meta 2025-08-26T19:43:57.2412219Z * [new branch] angelayi/test_cpp -> origin/angelayi/test_cpp 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2025-08-26T19:43:57.8823799Z * [new branch] xmfan/disable_duck_shape -> origin/xmfan/disable_duck_shape 2025-08-26T19:43:57.8824918Z * [new branch] xmfan/fca_cpp_node_passthrough -> origin/xmfan/fca_cpp_node_passthrough 2025-08-26T19:43:57.8825864Z * [new branch] xmfan/issue_123374 -> origin/xmfan/issue_123374 2025-08-26T19:43:57.8827086Z * [new branch] xmfan/post_3945954741e2d37023c5d6954f9483008e0892f9 -> origin/xmfan/post_3945954741e2d37023c5d6954f9483008e0892f9 2025-08-26T19:43:57.8828016Z * [new branch] xmfan/pre_3945954741e2d37023c5d6954f9483008e0892f9 -> origin/xmfan/pre_3945954741e2d37023c5d6954f9483008e0892f9 2025-08-26T19:43:57.8828771Z * [new branch] xmfan/segfault_test -> origin/xmfan/segfault_test 2025-08-26T19:43:57.8829797Z * [new branch] xmfan/single_step -> origin/xmfan/single_step 2025-08-26T19:43:57.8830809Z * [new branch] xmfan/sth_0829 -> origin/xmfan/sth_0829 2025-08-26T19:43:57.8831951Z * [new branch] xmfan/test -> origin/xmfan/test 2025-08-26T19:43:57.8833484Z * [new branch] yguo/debug-0226-constexpr -> origin/yguo/debug-0226-constexpr 2025-08-26T19:43:57.8834368Z * [new branch] yguo/new_latest_changes -> origin/yguo/new_latest_changes 2025-08-26T19:43:57.8835367Z * [new branch] yguo/patch_constexpr_changes -> origin/yguo/patch_constexpr_changes 2025-08-26T19:43:57.8836429Z * [new branch] yihan_quantization -> origin/yihan_quantization 2025-08-26T19:43:57.8837899Z * [new branch] yiming/add_jit_trace_benchmark -> origin/yiming/add_jit_trace_benchmark 2025-08-26T19:43:57.8838797Z * [new branch] yiming/add_nativert_benchmark -> origin/yiming/add_nativert_benchmark 2025-08-26T19:43:57.8839705Z * [new branch] yiming/bootcamp -> origin/yiming/bootcamp 2025-08-26T19:43:57.8841197Z * [new branch] zainr/canary-test -> origin/zainr/canary-test 2025-08-26T19:43:57.8842296Z * [new branch] zainr/cleanup-gh-runners -> origin/zainr/cleanup-gh-runners 2025-08-26T19:43:57.8843120Z * [new branch] zainr/git-push-v2 -> origin/zainr/git-push-v2 2025-08-26T19:43:57.8844139Z * [new branch] zainr/pull-migration-c -> origin/zainr/pull-migration-c 2025-08-26T19:43:57.8845434Z * [new branch] zainr/test2 -> origin/zainr/test2 2025-08-26T19:43:57.8846589Z * [new branch] zainr/unstable -> origin/zainr/unstable 2025-08-26T19:43:57.8847395Z * [new branch] zainr/unstable-xla -> origin/zainr/unstable-xla 2025-08-26T19:43:57.8848373Z * [new branch] zainr/uv-pip-fix -> origin/zainr/uv-pip-fix 2025-08-26T19:43:57.8849646Z * [new branch] zainr/vs-aarch64 -> origin/zainr/vs-aarch64 2025-08-26T19:43:57.8850948Z * [new branch] zasdfgbnm-patch-3 -> origin/zasdfgbnm-patch-3 2025-08-26T19:43:57.8852062Z * [new branch] zb2p -> origin/zb2p 2025-08-26T19:43:57.8853325Z * [new branch] zdevito-patch-1 -> origin/zdevito-patch-1 2025-08-26T19:43:57.8854517Z * [new branch] zero_grad_optimization -> origin/zero_grad_optimization 2025-08-26T19:43:57.8855587Z * [new branch] zeros-and-scatter-part2 -> origin/zeros-and-scatter-part2 2025-08-26T19:43:57.8857298Z * [new branch] zhxchen17/scratch/0 -> origin/zhxchen17/scratch/0 2025-08-26T19:43:57.8858829Z * [new branch] zhxhcen17/moodycamel -> origin/zhxhcen17/moodycamel 2025-08-26T19:43:57.8860265Z * [new branch] zxiiro/main -> origin/zxiiro/main 2025-08-26T19:43:57.8861491Z * [new branch] zxiiro/test -> origin/zxiiro/test 2025-08-26T19:43:57.8862719Z * [new tag] bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug -> bc2caa7fdf006894eff7af936babde69ab5a40f8-huydhn-debug 2025-08-26T19:43:57.8863386Z * [new tag] ci/binaries/77164 -> ci/binaries/77164 2025-08-26T19:43:57.8864529Z * [new tag] ciflow/binaries/153920 -> ciflow/binaries/153920 2025-08-26T19:43:57.8865038Z * [new tag] ciflow/binaries/158104 -> ciflow/binaries/158104 2025-08-26T19:43:57.8865848Z * [new tag] ciflow/binaries/160229 -> ciflow/binaries/160229 2025-08-26T19:43:57.8866634Z * [new tag] ciflow/binaries/160853 -> ciflow/binaries/160853 2025-08-26T19:43:57.8867382Z * [new tag] ciflow/binaries/161257 -> ciflow/binaries/161257 2025-08-26T19:43:57.8868214Z * [new tag] ciflow/binaries_libtorch/156049 -> ciflow/binaries_libtorch/156049 2025-08-26T19:43:57.8869432Z * [new tag] ciflow/binaries_wheel/156049 -> ciflow/binaries_wheel/156049 2025-08-26T19:43:57.8870239Z * [new tag] ciflow/binaries_wheel/158733 -> ciflow/binaries_wheel/158733 2025-08-26T19:43:57.8870991Z * [new tag] ciflow/binaries_wheel/160207 -> ciflow/binaries_wheel/160207 2025-08-26T19:43:57.8871937Z * [new tag] ciflow/h100-symm-mem/151845 -> ciflow/h100-symm-mem/151845 2025-08-26T19:43:57.8872705Z * [new tag] ciflow/h100-symm-mem/155923 -> ciflow/h100-symm-mem/155923 2025-08-26T19:43:57.8873152Z * [new tag] ciflow/h100-symm-mem/157635 -> ciflow/h100-symm-mem/157635 2025-08-26T19:43:57.8873913Z * [new tag] ciflow/h100-symm-mem/159562 -> ciflow/h100-symm-mem/159562 2025-08-26T19:43:57.8874565Z * [new tag] ciflow/h100-symm-mem/159889 -> ciflow/h100-symm-mem/159889 2025-08-26T19:43:57.8875228Z * [new tag] ciflow/h100-symm-mem/160825 -> ciflow/h100-symm-mem/160825 2025-08-26T19:43:57.8875864Z * [new tag] ciflow/h100-symm-mem/161008 -> ciflow/h100-symm-mem/161008 2025-08-26T19:43:57.8876547Z * [new tag] ciflow/h100-symm-mem/161090 -> ciflow/h100-symm-mem/161090 2025-08-26T19:43:57.8877184Z * [new tag] ciflow/h100-symm-mem/161214 -> ciflow/h100-symm-mem/161214 2025-08-26T19:43:57.8877864Z * [new tag] ciflow/h100-symm-mem/161217 -> ciflow/h100-symm-mem/161217 2025-08-26T19:43:57.8878422Z * [new tag] ciflow/h100-symm-mem/161232 -> ciflow/h100-symm-mem/161232 2025-08-26T19:43:57.8879625Z * [new tag] ciflow/h100-symm-mem/161257 -> ciflow/h100-symm-mem/161257 2025-08-26T19:43:57.8880218Z * [new tag] ciflow/h100-symm-mem/161309 -> ciflow/h100-symm-mem/161309 2025-08-26T19:43:57.8881282Z * [new tag] ciflow/h100-symm-mem/161470 -> ciflow/h100-symm-mem/161470 2025-08-26T19:43:57.8881811Z * [new tag] ciflow/h100-symm-mem/161471 -> ciflow/h100-symm-mem/161471 2025-08-26T19:43:57.8882516Z * [new tag] ciflow/h100-symm-mem/161532 -> ciflow/h100-symm-mem/161532 2025-08-26T19:43:57.8883210Z * [new tag] ciflow/h100-symm-mem/161533 -> ciflow/h100-symm-mem/161533 2025-08-26T19:43:57.8884089Z * [new tag] ciflow/h100/159158 -> ciflow/h100/159158 2025-08-26T19:43:57.8884626Z * [new tag] ciflow/h100/161225 -> ciflow/h100/161225 2025-08-26T19:43:57.8885783Z * [new tag] ciflow/inductor-perf-test-nightly-rocm/151845 -> ciflow/inductor-perf-test-nightly-rocm/151845 2025-08-26T19:43:57.8886505Z * [new tag] ciflow/inductor-perf-test-nightly-x86-zen/161512 -> ciflow/inductor-perf-test-nightly-x86-zen/161512 2025-08-26T19:43:57.8887553Z * [new tag] ciflow/inductor-periodic/158137 -> ciflow/inductor-periodic/158137 2025-08-26T19:43:57.8888110Z * [new tag] ciflow/inductor-periodic/160807 -> ciflow/inductor-periodic/160807 2025-08-26T19:43:57.8888802Z * [new tag] ciflow/inductor-periodic/161461 -> ciflow/inductor-periodic/161461 2025-08-26T19:43:57.8889857Z * [new tag] ciflow/inductor-periodic/2f0de0ff9361ca4f2b1e6f9edbc600b5fb6abcd6 -> ciflow/inductor-periodic/2f0de0ff9361ca4f2b1e6f9edbc600b5fb6abcd6 2025-08-26T19:43:57.8891264Z * [new tag] ciflow/inductor-periodic/3e5b021f217a42ae55dc690083f67a28126808ed -> ciflow/inductor-periodic/3e5b021f217a42ae55dc690083f67a28126808ed 2025-08-26T19:43:57.8892358Z * [new tag] ciflow/inductor-periodic/f912c93344caa74e24c8164a2e25fe84a8203073 -> ciflow/inductor-periodic/f912c93344caa74e24c8164a2e25fe84a8203073 2025-08-26T19:43:57.8892996Z * [new tag] ciflow/inductor-rocm/151845 -> ciflow/inductor-rocm/151845 2025-08-26T19:43:57.8893720Z * [new tag] ciflow/inductor-rocm/159158 -> ciflow/inductor-rocm/159158 2025-08-26T19:43:57.8894549Z * [new tag] ciflow/inductor-rocm/160671 -> ciflow/inductor-rocm/160671 2025-08-26T19:43:57.8895946Z * [new tag] ciflow/inductor-rocm/161180 -> ciflow/inductor-rocm/161180 2025-08-26T19:43:57.8896423Z * [new tag] ciflow/inductor-rocm/161225 -> ciflow/inductor-rocm/161225 2025-08-26T19:43:57.8897377Z * [new tag] ciflow/inductor-rocm/161521 -> ciflow/inductor-rocm/161521 2025-08-26T19:43:57.8898379Z * [new tag] ciflow/inductor-windows/160406 -> ciflow/inductor-windows/160406 2025-08-26T19:43:57.8899071Z * [new tag] ciflow/inductor/148492 -> ciflow/inductor/148492 2025-08-26T19:43:57.8899664Z * [new tag] ciflow/inductor/151845 -> ciflow/inductor/151845 2025-08-26T19:43:57.8900290Z * [new tag] ciflow/inductor/154694 -> ciflow/inductor/154694 2025-08-26T19:43:57.8901070Z * [new tag] ciflow/inductor/155072 -> ciflow/inductor/155072 2025-08-26T19:43:57.8901782Z * [new tag] ciflow/inductor/155152 -> ciflow/inductor/155152 2025-08-26T19:43:57.8902449Z * [new tag] ciflow/inductor/155153 -> ciflow/inductor/155153 2025-08-26T19:43:57.8903079Z * [new tag] ciflow/inductor/155154 -> ciflow/inductor/155154 2025-08-26T19:43:57.8903715Z * [new tag] ciflow/inductor/155501 -> ciflow/inductor/155501 2025-08-26T19:43:57.8904356Z * [new tag] ciflow/inductor/155502 -> ciflow/inductor/155502 2025-08-26T19:43:57.8905008Z * [new tag] ciflow/inductor/155503 -> ciflow/inductor/155503 2025-08-26T19:43:57.8905778Z * [new tag] ciflow/inductor/155557 -> ciflow/inductor/155557 2025-08-26T19:43:57.8906598Z * [new tag] ciflow/inductor/155608 -> ciflow/inductor/155608 2025-08-26T19:43:57.8907599Z * [new tag] ciflow/inductor/155923 -> ciflow/inductor/155923 2025-08-26T19:43:57.8908272Z * [new tag] ciflow/inductor/155928 -> ciflow/inductor/155928 2025-08-26T19:43:57.8909409Z * [new tag] ciflow/inductor/156875 -> ciflow/inductor/156875 2025-08-26T19:43:57.8910475Z * [new tag] ciflow/inductor/156967 -> ciflow/inductor/156967 2025-08-26T19:43:57.8911050Z * [new tag] ciflow/inductor/157298 -> ciflow/inductor/157298 2025-08-26T19:43:57.8911743Z * [new tag] ciflow/inductor/157572 -> ciflow/inductor/157572 2025-08-26T19:43:57.8912428Z * [new tag] ciflow/inductor/157635 -> ciflow/inductor/157635 2025-08-26T19:43:57.8913345Z * [new tag] ciflow/inductor/157743 -> ciflow/inductor/157743 2025-08-26T19:43:57.8914012Z * [new tag] ciflow/inductor/157767 -> ciflow/inductor/157767 2025-08-26T19:43:57.8914698Z * [new tag] ciflow/inductor/157944 -> ciflow/inductor/157944 2025-08-26T19:43:57.8915358Z * [new tag] ciflow/inductor/158061 -> ciflow/inductor/158061 2025-08-26T19:43:57.8916070Z * [new tag] ciflow/inductor/158097 -> ciflow/inductor/158097 2025-08-26T19:43:57.8916739Z * [new tag] ciflow/inductor/158098 -> ciflow/inductor/158098 2025-08-26T19:43:57.8917495Z * [new tag] ciflow/inductor/158104 -> ciflow/inductor/158104 2025-08-26T19:43:57.8918148Z * [new tag] ciflow/inductor/158137 -> ciflow/inductor/158137 2025-08-26T19:43:57.8918868Z * [new tag] ciflow/inductor/158321 -> ciflow/inductor/158321 2025-08-26T19:43:57.8919533Z * [new tag] ciflow/inductor/158609 -> ciflow/inductor/158609 2025-08-26T19:43:57.8920490Z * [new tag] ciflow/inductor/158932 -> ciflow/inductor/158932 2025-08-26T19:43:57.8921055Z * [new tag] ciflow/inductor/159003 -> ciflow/inductor/159003 2025-08-26T19:43:57.8921768Z * [new tag] ciflow/inductor/159158 -> ciflow/inductor/159158 2025-08-26T19:43:57.8922609Z * [new tag] ciflow/inductor/159274 -> ciflow/inductor/159274 2025-08-26T19:43:57.8923266Z * [new tag] ciflow/inductor/159387 -> ciflow/inductor/159387 2025-08-26T19:43:57.8924138Z * [new tag] ciflow/inductor/159473 -> ciflow/inductor/159473 2025-08-26T19:43:57.8924771Z * [new tag] ciflow/inductor/159664 -> ciflow/inductor/159664 2025-08-26T19:43:57.8925810Z * [new tag] ciflow/inductor/159778 -> ciflow/inductor/159778 2025-08-26T19:43:57.8926341Z * [new tag] ciflow/inductor/159835 -> ciflow/inductor/159835 2025-08-26T19:43:57.8927052Z * [new tag] ciflow/inductor/159889 -> ciflow/inductor/159889 2025-08-26T19:43:57.8927979Z * [new tag] ciflow/inductor/159923 -> ciflow/inductor/159923 2025-08-26T19:43:57.8928705Z * [new tag] ciflow/inductor/159944 -> ciflow/inductor/159944 2025-08-26T19:43:57.8929416Z * [new tag] ciflow/inductor/160080 -> ciflow/inductor/160080 2025-08-26T19:43:57.8930437Z * [new tag] ciflow/inductor/160111 -> ciflow/inductor/160111 2025-08-26T19:43:57.8931256Z * [new tag] ciflow/inductor/160156 -> ciflow/inductor/160156 2025-08-26T19:43:57.8932007Z * [new tag] ciflow/inductor/160180 -> ciflow/inductor/160180 2025-08-26T19:43:57.8932811Z * [new tag] ciflow/inductor/160198 -> ciflow/inductor/160198 2025-08-26T19:43:57.8933747Z * [new tag] ciflow/inductor/160258 -> ciflow/inductor/160258 2025-08-26T19:43:57.8934307Z * [new tag] ciflow/inductor/160266 -> ciflow/inductor/160266 2025-08-26T19:43:57.8935022Z * [new tag] ciflow/inductor/160282 -> ciflow/inductor/160282 2025-08-26T19:43:57.8935788Z * [new tag] ciflow/inductor/160323 -> ciflow/inductor/160323 2025-08-26T19:43:57.8936795Z * [new tag] ciflow/inductor/160324 -> ciflow/inductor/160324 2025-08-26T19:43:57.8937743Z * [new tag] ciflow/inductor/160325 -> ciflow/inductor/160325 2025-08-26T19:43:57.8938525Z * [new tag] ciflow/inductor/160326 -> ciflow/inductor/160326 2025-08-26T19:43:57.8939326Z * [new tag] ciflow/inductor/160327 -> ciflow/inductor/160327 2025-08-26T19:43:57.8940066Z * [new tag] ciflow/inductor/160328 -> ciflow/inductor/160328 2025-08-26T19:43:57.8941147Z * [new tag] ciflow/inductor/160329 -> ciflow/inductor/160329 2025-08-26T19:43:57.8942065Z * [new tag] ciflow/inductor/160431 -> ciflow/inductor/160431 2025-08-26T19:43:57.8942708Z * [new tag] ciflow/inductor/160448 -> ciflow/inductor/160448 2025-08-26T19:43:57.8943322Z * [new tag] ciflow/inductor/160449 -> ciflow/inductor/160449 2025-08-26T19:43:57.8944033Z * [new tag] ciflow/inductor/160467 -> ciflow/inductor/160467 2025-08-26T19:43:57.8944702Z * [new tag] ciflow/inductor/160470 -> ciflow/inductor/160470 2025-08-26T19:43:57.8945381Z * [new tag] ciflow/inductor/160483 -> ciflow/inductor/160483 2025-08-26T19:43:57.8946070Z * [new tag] ciflow/inductor/160527 -> ciflow/inductor/160527 2025-08-26T19:43:57.8947098Z * [new tag] ciflow/inductor/160532 -> ciflow/inductor/160532 2025-08-26T19:43:57.8948581Z * [new tag] ciflow/inductor/160539 -> ciflow/inductor/160539 2025-08-26T19:43:57.8949123Z * [new tag] ciflow/inductor/160580 -> ciflow/inductor/160580 2025-08-26T19:43:57.8949840Z * [new tag] ciflow/inductor/160601 -> ciflow/inductor/160601 2025-08-26T19:43:57.8950517Z * [new tag] ciflow/inductor/160611 -> ciflow/inductor/160611 2025-08-26T19:43:57.8951312Z * [new tag] ciflow/inductor/160669 -> ciflow/inductor/160669 2025-08-26T19:43:57.8951991Z * [new tag] ciflow/inductor/160670 -> ciflow/inductor/160670 2025-08-26T19:43:57.8952691Z * [new tag] ciflow/inductor/160671 -> ciflow/inductor/160671 2025-08-26T19:43:57.8953637Z * [new tag] ciflow/inductor/160677 -> ciflow/inductor/160677 2025-08-26T19:43:57.8954200Z * [new tag] ciflow/inductor/160690 -> ciflow/inductor/160690 2025-08-26T19:43:57.8954878Z * [new tag] ciflow/inductor/160763 -> ciflow/inductor/160763 2025-08-26T19:43:57.8955588Z * [new tag] ciflow/inductor/160772 -> ciflow/inductor/160772 2025-08-26T19:43:57.8956507Z * [new tag] ciflow/inductor/160798 -> ciflow/inductor/160798 2025-08-26T19:43:57.8957442Z * [new tag] ciflow/inductor/160807 -> ciflow/inductor/160807 2025-08-26T19:43:57.8958129Z * [new tag] ciflow/inductor/160836 -> ciflow/inductor/160836 2025-08-26T19:43:57.8958806Z * [new tag] ciflow/inductor/160861 -> ciflow/inductor/160861 2025-08-26T19:43:57.8960058Z * [new tag] ciflow/inductor/160883 -> ciflow/inductor/160883 2025-08-26T19:43:57.8960590Z * [new tag] ciflow/inductor/160888 -> ciflow/inductor/160888 2025-08-26T19:43:57.8961280Z * [new tag] ciflow/inductor/160903 -> ciflow/inductor/160903 2025-08-26T19:43:57.8961962Z * [new tag] ciflow/inductor/160913 -> ciflow/inductor/160913 2025-08-26T19:43:57.8962777Z * [new tag] ciflow/inductor/160941 -> ciflow/inductor/160941 2025-08-26T19:43:57.8963457Z * [new tag] ciflow/inductor/160943 -> ciflow/inductor/160943 2025-08-26T19:43:57.8964130Z * [new tag] ciflow/inductor/160991 -> ciflow/inductor/160991 2025-08-26T19:43:57.8965070Z * [new tag] ciflow/inductor/160997 -> ciflow/inductor/160997 2025-08-26T19:43:57.8965657Z * [new tag] ciflow/inductor/161003 -> ciflow/inductor/161003 2025-08-26T19:43:57.8966308Z * [new tag] ciflow/inductor/161026 -> ciflow/inductor/161026 2025-08-26T19:43:57.8966978Z * [new tag] ciflow/inductor/161032 -> ciflow/inductor/161032 2025-08-26T19:43:57.8967937Z * [new tag] ciflow/inductor/161040 -> ciflow/inductor/161040 2025-08-26T19:43:57.8968509Z * [new tag] ciflow/inductor/161055 -> ciflow/inductor/161055 2025-08-26T19:43:57.8969243Z * [new tag] ciflow/inductor/161062 -> ciflow/inductor/161062 2025-08-26T19:43:57.8969894Z * [new tag] ciflow/inductor/161069 -> ciflow/inductor/161069 2025-08-26T19:43:57.8970607Z * [new tag] ciflow/inductor/161092 -> ciflow/inductor/161092 2025-08-26T19:43:57.8971320Z * [new tag] ciflow/inductor/161093 -> ciflow/inductor/161093 2025-08-26T19:43:57.8971990Z * [new tag] ciflow/inductor/161097 -> ciflow/inductor/161097 2025-08-26T19:43:57.8972659Z * [new tag] ciflow/inductor/161098 -> ciflow/inductor/161098 2025-08-26T19:43:57.8973335Z * [new tag] ciflow/inductor/161100 -> ciflow/inductor/161100 2025-08-26T19:43:57.8974327Z * [new tag] ciflow/inductor/161107 -> ciflow/inductor/161107 2025-08-26T19:43:57.8975279Z * [new tag] ciflow/inductor/161110 -> ciflow/inductor/161110 2025-08-26T19:43:57.8975871Z * [new tag] ciflow/inductor/161117 -> ciflow/inductor/161117 2025-08-26T19:43:57.8976572Z * [new tag] ciflow/inductor/161118 -> ciflow/inductor/161118 2025-08-26T19:43:57.8977279Z * [new tag] ciflow/inductor/161123 -> ciflow/inductor/161123 2025-08-26T19:43:57.8977940Z * [new tag] ciflow/inductor/161124 -> ciflow/inductor/161124 2025-08-26T19:43:57.8978685Z * [new tag] ciflow/inductor/161125 -> ciflow/inductor/161125 2025-08-26T19:43:57.8979319Z * [new tag] ciflow/inductor/161126 -> ciflow/inductor/161126 2025-08-26T19:43:57.8980389Z * [new tag] ciflow/inductor/161144 -> ciflow/inductor/161144 2025-08-26T19:43:57.8981084Z * [new tag] ciflow/inductor/161148 -> ciflow/inductor/161148 2025-08-26T19:43:57.8981826Z * [new tag] ciflow/inductor/161158 -> ciflow/inductor/161158 2025-08-26T19:43:57.8982808Z * [new tag] ciflow/inductor/161178 -> ciflow/inductor/161178 2025-08-26T19:43:57.8983556Z * [new tag] ciflow/inductor/161190 -> ciflow/inductor/161190 2025-08-26T19:43:57.8984125Z * [new tag] ciflow/inductor/161208 -> ciflow/inductor/161208 2025-08-26T19:43:57.8984774Z * [new tag] ciflow/inductor/161225 -> ciflow/inductor/161225 2025-08-26T19:43:57.8985576Z * [new tag] ciflow/inductor/161229 -> ciflow/inductor/161229 2025-08-26T19:43:57.8986360Z * [new tag] ciflow/inductor/161237 -> ciflow/inductor/161237 2025-08-26T19:43:57.8987057Z * [new tag] ciflow/inductor/161241 -> ciflow/inductor/161241 2025-08-26T19:43:57.8987816Z * [new tag] ciflow/inductor/161246 -> ciflow/inductor/161246 2025-08-26T19:43:57.8988650Z * [new tag] ciflow/inductor/161274 -> ciflow/inductor/161274 2025-08-26T19:43:57.8989509Z * [new tag] ciflow/inductor/161278 -> ciflow/inductor/161278 2025-08-26T19:43:57.8990234Z * [new tag] ciflow/inductor/161279 -> ciflow/inductor/161279 2025-08-26T19:43:57.8990904Z * [new tag] ciflow/inductor/161288 -> ciflow/inductor/161288 2025-08-26T19:43:57.8991899Z * [new tag] ciflow/inductor/161314 -> ciflow/inductor/161314 2025-08-26T19:43:57.8993491Z * [new tag] ciflow/inductor/161320 -> ciflow/inductor/161320 2025-08-26T19:43:57.8994124Z * [new tag] ciflow/inductor/161336 -> ciflow/inductor/161336 2025-08-26T19:43:57.8994803Z * [new tag] ciflow/inductor/161337 -> ciflow/inductor/161337 2025-08-26T19:43:57.8995485Z * [new tag] ciflow/inductor/161338 -> ciflow/inductor/161338 2025-08-26T19:43:57.8996151Z * [new tag] ciflow/inductor/161339 -> ciflow/inductor/161339 2025-08-26T19:43:57.8996831Z * [new tag] ciflow/inductor/161340 -> ciflow/inductor/161340 2025-08-26T19:43:57.8997526Z * [new tag] ciflow/inductor/161341 -> ciflow/inductor/161341 2025-08-26T19:43:57.8998203Z * [new tag] ciflow/inductor/161342 -> ciflow/inductor/161342 2025-08-26T19:43:57.8998909Z * [new tag] ciflow/inductor/161343 -> ciflow/inductor/161343 2025-08-26T19:43:57.8999679Z * [new tag] ciflow/inductor/161344 -> ciflow/inductor/161344 2025-08-26T19:43:57.9000366Z * [new tag] ciflow/inductor/161345 -> ciflow/inductor/161345 2025-08-26T19:43:57.9001031Z * [new tag] ciflow/inductor/161346 -> ciflow/inductor/161346 2025-08-26T19:43:57.9001700Z * [new tag] ciflow/inductor/161347 -> ciflow/inductor/161347 2025-08-26T19:43:57.9002371Z * [new tag] ciflow/inductor/161348 -> ciflow/inductor/161348 2025-08-26T19:43:57.9003042Z * [new tag] ciflow/inductor/161349 -> ciflow/inductor/161349 2025-08-26T19:43:57.9003801Z * [new tag] ciflow/inductor/161350 -> ciflow/inductor/161350 2025-08-26T19:43:57.9004512Z * [new tag] ciflow/inductor/161351 -> ciflow/inductor/161351 2025-08-26T19:43:57.9005194Z * [new tag] ciflow/inductor/161353 -> ciflow/inductor/161353 2025-08-26T19:43:57.9005863Z * [new tag] ciflow/inductor/161354 -> ciflow/inductor/161354 2025-08-26T19:43:57.9006573Z * [new tag] ciflow/inductor/161355 -> ciflow/inductor/161355 2025-08-26T19:43:57.9007711Z * [new tag] ciflow/inductor/161362 -> ciflow/inductor/161362 2025-08-26T19:43:57.9008256Z * [new tag] ciflow/inductor/161363 -> ciflow/inductor/161363 2025-08-26T19:43:57.9008983Z * [new tag] ciflow/inductor/161382 -> ciflow/inductor/161382 2025-08-26T19:43:57.9009654Z * [new tag] ciflow/inductor/161383 -> ciflow/inductor/161383 2025-08-26T19:43:57.9010325Z * [new tag] ciflow/inductor/161385 -> ciflow/inductor/161385 2025-08-26T19:43:57.9010990Z * [new tag] ciflow/inductor/161396 -> ciflow/inductor/161396 2025-08-26T19:43:57.9011954Z * [new tag] ciflow/inductor/161397 -> ciflow/inductor/161397 2025-08-26T19:43:57.9012587Z * [new tag] ciflow/inductor/161404 -> ciflow/inductor/161404 2025-08-26T19:43:57.9013153Z * [new tag] ciflow/inductor/161405 -> ciflow/inductor/161405 2025-08-26T19:43:57.9013830Z * [new tag] ciflow/inductor/161406 -> ciflow/inductor/161406 2025-08-26T19:43:57.9014892Z * [new tag] ciflow/inductor/161409 -> ciflow/inductor/161409 2025-08-26T19:43:57.9015615Z * [new tag] ciflow/inductor/161410 -> ciflow/inductor/161410 2025-08-26T19:43:57.9016316Z * [new tag] ciflow/inductor/161414 -> ciflow/inductor/161414 2025-08-26T19:43:57.9017118Z * [new tag] ciflow/inductor/161416 -> ciflow/inductor/161416 2025-08-26T19:43:57.9017718Z * [new tag] ciflow/inductor/161420 -> ciflow/inductor/161420 2025-08-26T19:43:57.9018372Z * [new tag] ciflow/inductor/161431 -> ciflow/inductor/161431 2025-08-26T19:43:57.9019054Z * [new tag] ciflow/inductor/161435 -> ciflow/inductor/161435 2025-08-26T19:43:57.9020003Z * [new tag] ciflow/inductor/161440 -> ciflow/inductor/161440 2025-08-26T19:43:57.9020669Z * [new tag] ciflow/inductor/161447 -> ciflow/inductor/161447 2025-08-26T19:43:57.9021396Z * [new tag] ciflow/inductor/161452 -> ciflow/inductor/161452 2025-08-26T19:43:57.9022318Z * [new tag] ciflow/inductor/161453 -> ciflow/inductor/161453 2025-08-26T19:43:57.9022995Z * [new tag] ciflow/inductor/161458 -> ciflow/inductor/161458 2025-08-26T19:43:57.9023673Z * [new tag] ciflow/inductor/161461 -> ciflow/inductor/161461 2025-08-26T19:43:57.9024354Z * [new tag] ciflow/inductor/161464 -> ciflow/inductor/161464 2025-08-26T19:43:57.9025023Z * [new tag] ciflow/inductor/161466 -> ciflow/inductor/161466 2025-08-26T19:43:57.9025711Z * [new tag] ciflow/inductor/161468 -> ciflow/inductor/161468 2025-08-26T19:43:57.9026654Z * [new tag] ciflow/inductor/161469 -> ciflow/inductor/161469 2025-08-26T19:43:57.9027196Z * [new tag] ciflow/inductor/161474 -> ciflow/inductor/161474 2025-08-26T19:43:57.9027890Z * [new tag] ciflow/inductor/161477 -> ciflow/inductor/161477 2025-08-26T19:43:57.9028821Z * [new tag] ciflow/inductor/161485 -> ciflow/inductor/161485 2025-08-26T19:43:57.9029425Z * [new tag] ciflow/inductor/161486 -> ciflow/inductor/161486 2025-08-26T19:43:57.9030108Z * [new tag] ciflow/inductor/161487 -> ciflow/inductor/161487 2025-08-26T19:43:57.9030821Z * [new tag] ciflow/inductor/161495 -> ciflow/inductor/161495 2025-08-26T19:43:57.9031747Z * [new tag] ciflow/inductor/161497 -> ciflow/inductor/161497 2025-08-26T19:43:57.9032342Z * [new tag] ciflow/inductor/161499 -> ciflow/inductor/161499 2025-08-26T19:43:57.9033015Z * [new tag] ciflow/inductor/161512 -> ciflow/inductor/161512 2025-08-26T19:43:57.9033719Z * [new tag] ciflow/inductor/161521 -> ciflow/inductor/161521 2025-08-26T19:43:57.9034431Z * [new tag] ciflow/inductor/161526 -> ciflow/inductor/161526 2025-08-26T19:43:57.9035133Z * [new tag] ciflow/inductor/161530 -> ciflow/inductor/161530 2025-08-26T19:43:57.9036148Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-08-26T19:43:57.9037091Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-08-26T19:43:57.9037759Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-08-26T19:43:57.9038720Z * [new tag] ciflow/linux-aarch64/159737 -> ciflow/linux-aarch64/159737 2025-08-26T19:43:57.9039398Z * [new tag] ciflow/linux-aarch64/160078 -> ciflow/linux-aarch64/160078 2025-08-26T19:43:57.9039981Z * [new tag] ciflow/linux-aarch64/160080 -> ciflow/linux-aarch64/160080 2025-08-26T19:43:57.9040741Z * [new tag] ciflow/mps/155923 -> ciflow/mps/155923 2025-08-26T19:43:57.9041426Z * [new tag] ciflow/mps/157553 -> ciflow/mps/157553 2025-08-26T19:43:57.9042034Z * [new tag] ciflow/mps/157635 -> ciflow/mps/157635 2025-08-26T19:43:57.9042796Z * [new tag] ciflow/mps/160839 -> ciflow/mps/160839 2025-08-26T19:43:57.9043418Z * [new tag] ciflow/mps/161511 -> ciflow/mps/161511 2025-08-26T19:43:57.9044503Z * [new tag] ciflow/nightly/158104 -> ciflow/nightly/158104 2025-08-26T19:43:57.9045268Z * [new tag] ciflow/periodic-rocm-mi300/161180 -> ciflow/periodic-rocm-mi300/161180 2025-08-26T19:43:57.9046325Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-08-26T19:43:57.9047416Z * [new tag] ciflow/periodic/0dea191ff7b844352dc2cd5e3b5ef5ea13a76756 -> ciflow/periodic/0dea191ff7b844352dc2cd5e3b5ef5ea13a76756 2025-08-26T19:43:57.9047843Z * [new tag] ciflow/periodic/156491 -> ciflow/periodic/156491 2025-08-26T19:43:57.9048636Z * [new tag] ciflow/periodic/161013 -> ciflow/periodic/161013 2025-08-26T19:43:57.9049469Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-08-26T19:43:57.9050244Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-08-26T19:43:57.9051049Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-08-26T19:43:57.9051889Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-08-26T19:43:57.9053201Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-08-26T19:43:57.9054626Z * [new tag] ciflow/periodic/bc7eaa0d8a1f5ca8ec0eaac461d1df500dcaea84 -> ciflow/periodic/bc7eaa0d8a1f5ca8ec0eaac461d1df500dcaea84 2025-08-26T19:43:57.9055920Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-08-26T19:43:57.9056719Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-08-26T19:43:57.9057554Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-08-26T19:43:57.9058525Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-08-26T19:43:57.9059664Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-08-26T19:43:57.9060410Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-08-26T19:43:57.9061389Z * [new tag] ciflow/rocm-mi300/159158 -> ciflow/rocm-mi300/159158 2025-08-26T19:43:57.9062013Z * [new tag] ciflow/rocm-mi300/161040 -> ciflow/rocm-mi300/161040 2025-08-26T19:43:57.9062593Z * [new tag] ciflow/rocm-mi300/161180 -> ciflow/rocm-mi300/161180 2025-08-26T19:43:57.9063231Z * [new tag] ciflow/rocm-mi300/161225 -> ciflow/rocm-mi300/161225 2025-08-26T19:43:57.9064114Z * [new tag] ciflow/rocm-mi300/161429 -> ciflow/rocm-mi300/161429 2025-08-26T19:43:57.9065500Z * [new tag] ciflow/rocm-mi355/160215 -> ciflow/rocm-mi355/160215 2025-08-26T19:43:57.9066228Z * [new tag] ciflow/rocm/148492 -> ciflow/rocm/148492 2025-08-26T19:43:57.9066887Z * [new tag] ciflow/rocm/151845 -> ciflow/rocm/151845 2025-08-26T19:43:57.9067808Z * [new tag] ciflow/rocm/152526 -> ciflow/rocm/152526 2025-08-26T19:43:57.9068353Z * [new tag] ciflow/rocm/156491 -> ciflow/rocm/156491 2025-08-26T19:43:57.9069074Z * [new tag] ciflow/rocm/158352 -> ciflow/rocm/158352 2025-08-26T19:43:57.9069741Z * [new tag] ciflow/rocm/159158 -> ciflow/rocm/159158 2025-08-26T19:43:57.9070328Z * [new tag] ciflow/rocm/160215 -> ciflow/rocm/160215 2025-08-26T19:43:57.9071002Z * [new tag] ciflow/rocm/160671 -> ciflow/rocm/160671 2025-08-26T19:43:57.9072094Z * [new tag] ciflow/rocm/160676 -> ciflow/rocm/160676 2025-08-26T19:43:57.9072611Z * [new tag] ciflow/rocm/161180 -> ciflow/rocm/161180 2025-08-26T19:43:57.9073274Z * [new tag] ciflow/rocm/161225 -> ciflow/rocm/161225 2025-08-26T19:43:57.9074540Z * [new tag] ciflow/rocm/161277 -> ciflow/rocm/161277 2025-08-26T19:43:57.9075301Z * [new tag] ciflow/rocm/161429 -> ciflow/rocm/161429 2025-08-26T19:43:57.9076239Z * [new tag] ciflow/rocm/161496 -> ciflow/rocm/161496 2025-08-26T19:43:57.9077188Z * [new tag] ciflow/s390/160893 -> ciflow/s390/160893 2025-08-26T19:43:57.9078104Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-08-26T19:43:57.9078788Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-08-26T19:43:57.9080140Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-08-26T19:43:57.9080540Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-08-26T19:43:57.9081283Z * [new tag] ciflow/slow/161182 -> ciflow/slow/161182 2025-08-26T19:43:57.9081946Z * [new tag] ciflow/slow/161395 -> ciflow/slow/161395 2025-08-26T19:43:57.9082865Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-08-26T19:43:57.9083741Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-08-26T19:43:57.9084810Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-08-26T19:43:57.9085863Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-08-26T19:43:57.9086820Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-08-26T19:43:57.9087536Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-08-26T19:43:57.9088466Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-08-26T19:43:57.9089172Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-08-26T19:43:57.9090620Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-08-26T19:43:57.9090976Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-08-26T19:43:57.9092066Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-08-26T19:43:57.9093145Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-08-26T19:43:57.9094101Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-08-26T19:43:57.9095022Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-08-26T19:43:57.9096281Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-08-26T19:43:57.9096695Z * [new tag] ciflow/torchbench/158137 -> ciflow/torchbench/158137 2025-08-26T19:43:57.9097533Z * [new tag] ciflow/trunk/148492 -> ciflow/trunk/148492 2025-08-26T19:43:57.9098170Z * [new tag] ciflow/trunk/151845 -> ciflow/trunk/151845 2025-08-26T19:43:57.9098864Z * [new tag] ciflow/trunk/153784 -> ciflow/trunk/153784 2025-08-26T19:43:57.9099557Z * [new tag] ciflow/trunk/154694 -> ciflow/trunk/154694 2025-08-26T19:43:57.9100298Z * [new tag] ciflow/trunk/156418 -> ciflow/trunk/156418 2025-08-26T19:43:57.9101442Z * [new tag] ciflow/trunk/157196 -> ciflow/trunk/157196 2025-08-26T19:43:57.9102014Z * [new tag] ciflow/trunk/157537 -> ciflow/trunk/157537 2025-08-26T19:43:57.9102656Z * [new tag] ciflow/trunk/157767 -> ciflow/trunk/157767 2025-08-26T19:43:57.9103334Z * [new tag] ciflow/trunk/157944 -> ciflow/trunk/157944 2025-08-26T19:43:57.9104073Z * [new tag] ciflow/trunk/158104 -> ciflow/trunk/158104 2025-08-26T19:43:57.9104617Z * [new tag] ciflow/trunk/158541 -> ciflow/trunk/158541 2025-08-26T19:43:57.9105600Z * [new tag] ciflow/trunk/158733 -> ciflow/trunk/158733 2025-08-26T19:43:57.9106667Z * [new tag] ciflow/trunk/158747 -> ciflow/trunk/158747 2025-08-26T19:43:57.9107548Z * [new tag] ciflow/trunk/159158 -> ciflow/trunk/159158 2025-08-26T19:43:57.9108287Z * [new tag] ciflow/trunk/159387 -> ciflow/trunk/159387 2025-08-26T19:43:57.9108841Z * [new tag] ciflow/trunk/159562 -> ciflow/trunk/159562 2025-08-26T19:43:57.9109520Z * [new tag] ciflow/trunk/159835 -> ciflow/trunk/159835 2025-08-26T19:43:57.9110221Z * [new tag] ciflow/trunk/159889 -> ciflow/trunk/159889 2025-08-26T19:43:57.9110856Z * [new tag] ciflow/trunk/159923 -> ciflow/trunk/159923 2025-08-26T19:43:57.9111552Z * [new tag] ciflow/trunk/160156 -> ciflow/trunk/160156 2025-08-26T19:43:57.9112242Z * [new tag] ciflow/trunk/160180 -> ciflow/trunk/160180 2025-08-26T19:43:57.9112921Z * [new tag] ciflow/trunk/160198 -> ciflow/trunk/160198 2025-08-26T19:43:57.9113578Z * [new tag] ciflow/trunk/160258 -> ciflow/trunk/160258 2025-08-26T19:43:57.9114259Z * [new tag] ciflow/trunk/160431 -> ciflow/trunk/160431 2025-08-26T19:43:57.9114958Z * [new tag] ciflow/trunk/160448 -> ciflow/trunk/160448 2025-08-26T19:43:57.9115756Z * [new tag] ciflow/trunk/160449 -> ciflow/trunk/160449 2025-08-26T19:43:57.9116397Z * [new tag] ciflow/trunk/160527 -> ciflow/trunk/160527 2025-08-26T19:43:57.9117617Z * [new tag] ciflow/trunk/160532 -> ciflow/trunk/160532 2025-08-26T19:43:57.9118219Z * [new tag] ciflow/trunk/160671 -> ciflow/trunk/160671 2025-08-26T19:43:57.9118931Z * [new tag] ciflow/trunk/160677 -> ciflow/trunk/160677 2025-08-26T19:43:57.9119686Z * [new tag] ciflow/trunk/160692 -> ciflow/trunk/160692 2025-08-26T19:43:57.9120376Z * [new tag] ciflow/trunk/160781 -> ciflow/trunk/160781 2025-08-26T19:43:57.9121048Z * [new tag] ciflow/trunk/160825 -> ciflow/trunk/160825 2025-08-26T19:43:57.9121752Z * [new tag] ciflow/trunk/160836 -> ciflow/trunk/160836 2025-08-26T19:43:57.9122646Z * [new tag] ciflow/trunk/160866 -> ciflow/trunk/160866 2025-08-26T19:43:57.9123446Z * [new tag] ciflow/trunk/160915 -> ciflow/trunk/160915 2025-08-26T19:43:57.9124134Z * [new tag] ciflow/trunk/160991 -> ciflow/trunk/160991 2025-08-26T19:43:57.9124881Z * [new tag] ciflow/trunk/160992 -> ciflow/trunk/160992 2025-08-26T19:43:57.9125569Z * [new tag] ciflow/trunk/161004 -> ciflow/trunk/161004 2025-08-26T19:43:57.9126215Z * [new tag] ciflow/trunk/161016 -> ciflow/trunk/161016 2025-08-26T19:43:57.9127134Z * [new tag] ciflow/trunk/161023 -> ciflow/trunk/161023 2025-08-26T19:43:57.9127667Z * [new tag] ciflow/trunk/161026 -> ciflow/trunk/161026 2025-08-26T19:43:57.9128383Z * [new tag] ciflow/trunk/161032 -> ciflow/trunk/161032 2025-08-26T19:43:57.9129144Z * [new tag] ciflow/trunk/161035 -> ciflow/trunk/161035 2025-08-26T19:43:57.9129815Z * [new tag] ciflow/trunk/161040 -> ciflow/trunk/161040 2025-08-26T19:43:57.9130631Z * [new tag] ciflow/trunk/161094 -> ciflow/trunk/161094 2025-08-26T19:43:57.9131307Z * [new tag] ciflow/trunk/161097 -> ciflow/trunk/161097 2025-08-26T19:43:57.9132049Z * [new tag] ciflow/trunk/161098 -> ciflow/trunk/161098 2025-08-26T19:43:57.9132647Z * [new tag] ciflow/trunk/161100 -> ciflow/trunk/161100 2025-08-26T19:43:57.9133385Z * [new tag] ciflow/trunk/161106 -> ciflow/trunk/161106 2025-08-26T19:43:57.9133910Z * [new tag] ciflow/trunk/161110 -> ciflow/trunk/161110 2025-08-26T19:43:57.9134894Z * [new tag] ciflow/trunk/161114 -> ciflow/trunk/161114 2025-08-26T19:43:57.9135466Z * [new tag] ciflow/trunk/161117 -> ciflow/trunk/161117 2025-08-26T19:43:57.9136157Z * [new tag] ciflow/trunk/161123 -> ciflow/trunk/161123 2025-08-26T19:43:57.9136815Z * [new tag] ciflow/trunk/161124 -> ciflow/trunk/161124 2025-08-26T19:43:57.9137493Z * [new tag] ciflow/trunk/161125 -> ciflow/trunk/161125 2025-08-26T19:43:57.9138230Z * [new tag] ciflow/trunk/161126 -> ciflow/trunk/161126 2025-08-26T19:43:57.9139200Z * [new tag] ciflow/trunk/161131 -> ciflow/trunk/161131 2025-08-26T19:43:57.9139962Z * [new tag] ciflow/trunk/161143 -> ciflow/trunk/161143 2025-08-26T19:43:57.9140704Z * [new tag] ciflow/trunk/161144 -> ciflow/trunk/161144 2025-08-26T19:43:57.9141448Z * [new tag] ciflow/trunk/161164 -> ciflow/trunk/161164 2025-08-26T19:43:57.9142113Z * [new tag] ciflow/trunk/161180 -> ciflow/trunk/161180 2025-08-26T19:43:57.9142827Z * [new tag] ciflow/trunk/161214 -> ciflow/trunk/161214 2025-08-26T19:43:57.9143448Z * [new tag] ciflow/trunk/161217 -> ciflow/trunk/161217 2025-08-26T19:43:57.9144104Z * [new tag] ciflow/trunk/161225 -> ciflow/trunk/161225 2025-08-26T19:43:57.9144799Z * [new tag] ciflow/trunk/161236 -> ciflow/trunk/161236 2025-08-26T19:43:57.9145473Z * [new tag] ciflow/trunk/161237 -> ciflow/trunk/161237 2025-08-26T19:43:57.9146189Z * [new tag] ciflow/trunk/161241 -> ciflow/trunk/161241 2025-08-26T19:43:57.9146964Z * [new tag] ciflow/trunk/161262 -> ciflow/trunk/161262 2025-08-26T19:43:57.9148269Z * [new tag] ciflow/trunk/161263 -> ciflow/trunk/161263 2025-08-26T19:43:57.9148890Z * [new tag] ciflow/trunk/161279 -> ciflow/trunk/161279 2025-08-26T19:43:57.9149699Z * [new tag] ciflow/trunk/161306 -> ciflow/trunk/161306 2025-08-26T19:43:57.9150371Z * [new tag] ciflow/trunk/161311 -> ciflow/trunk/161311 2025-08-26T19:43:57.9151124Z * [new tag] ciflow/trunk/161354 -> ciflow/trunk/161354 2025-08-26T19:43:57.9151765Z * [new tag] ciflow/trunk/161355 -> ciflow/trunk/161355 2025-08-26T19:43:57.9152466Z * [new tag] ciflow/trunk/161362 -> ciflow/trunk/161362 2025-08-26T19:43:57.9153139Z * [new tag] ciflow/trunk/161363 -> ciflow/trunk/161363 2025-08-26T19:43:57.9154035Z * [new tag] ciflow/trunk/161370 -> ciflow/trunk/161370 2025-08-26T19:43:57.9154725Z * [new tag] ciflow/trunk/161383 -> ciflow/trunk/161383 2025-08-26T19:43:57.9155249Z * [new tag] ciflow/trunk/161385 -> ciflow/trunk/161385 2025-08-26T19:43:57.9156134Z * [new tag] ciflow/trunk/161389 -> ciflow/trunk/161389 2025-08-26T19:43:57.9156706Z * [new tag] ciflow/trunk/161392 -> ciflow/trunk/161392 2025-08-26T19:43:57.9157356Z * [new tag] ciflow/trunk/161395 -> ciflow/trunk/161395 2025-08-26T19:43:57.9158054Z * [new tag] ciflow/trunk/161396 -> ciflow/trunk/161396 2025-08-26T19:43:57.9158704Z * [new tag] ciflow/trunk/161409 -> ciflow/trunk/161409 2025-08-26T19:43:57.9159486Z * [new tag] ciflow/trunk/161410 -> ciflow/trunk/161410 2025-08-26T19:43:57.9160091Z * [new tag] ciflow/trunk/161435 -> ciflow/trunk/161435 2025-08-26T19:43:57.9161046Z * [new tag] ciflow/trunk/161437 -> ciflow/trunk/161437 2025-08-26T19:43:57.9161721Z * [new tag] ciflow/trunk/161451 -> ciflow/trunk/161451 2025-08-26T19:43:57.9162626Z * [new tag] ciflow/trunk/161453 -> ciflow/trunk/161453 2025-08-26T19:43:57.9163306Z * [new tag] ciflow/trunk/161454 -> ciflow/trunk/161454 2025-08-26T19:43:57.9164686Z * [new tag] ciflow/trunk/161489 -> ciflow/trunk/161489 2025-08-26T19:43:57.9165516Z * [new tag] ciflow/trunk/161517 -> ciflow/trunk/161517 2025-08-26T19:43:57.9166625Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-08-26T19:43:57.9167367Z * [new tag] ciflow/win-arm64/158104 -> ciflow/win-arm64/158104 2025-08-26T19:43:57.9168024Z * [new tag] ciflow/win-arm64/159562 -> ciflow/win-arm64/159562 2025-08-26T19:43:57.9168683Z * [new tag] ciflow/win-arm64/160258 -> ciflow/win-arm64/160258 2025-08-26T19:43:57.9169596Z * [new tag] ciflow/win-arm64/161504 -> ciflow/win-arm64/161504 2025-08-26T19:43:57.9170544Z * [new tag] ciflow/xpu/143553 -> ciflow/xpu/143553 2025-08-26T19:43:57.9171061Z * [new tag] ciflow/xpu/158733 -> ciflow/xpu/158733 2025-08-26T19:43:57.9171727Z * [new tag] ciflow/xpu/159473 -> ciflow/xpu/159473 2025-08-26T19:43:57.9172351Z * [new tag] ciflow/xpu/159944 -> ciflow/xpu/159944 2025-08-26T19:43:57.9172987Z * [new tag] ciflow/xpu/160067 -> ciflow/xpu/160067 2025-08-26T19:43:57.9173730Z * [new tag] ciflow/xpu/160158 -> ciflow/xpu/160158 2025-08-26T19:43:57.9174473Z * [new tag] ciflow/xpu/160940 -> ciflow/xpu/160940 2025-08-26T19:43:57.9175219Z * [new tag] ciflow/xpu/161041 -> ciflow/xpu/161041 2025-08-26T19:43:57.9175944Z * [new tag] ciflow/xpu/161045 -> ciflow/xpu/161045 2025-08-26T19:43:57.9176629Z * [new tag] ciflow/xpu/161142 -> ciflow/xpu/161142 2025-08-26T19:43:57.9177582Z * [new tag] ciflow/xpu/161152 -> ciflow/xpu/161152 2025-08-26T19:43:57.9178516Z * [new tag] ciflow/xpu/161246 -> ciflow/xpu/161246 2025-08-26T19:43:57.9179447Z * [new tag] ciflow/xpu/161389 -> ciflow/xpu/161389 2025-08-26T19:43:57.9180142Z * [new tag] ciflow/xpu/161392 -> ciflow/xpu/161392 2025-08-26T19:43:57.9180921Z * [new tag] ciflow/xpu/161397 -> ciflow/xpu/161397 2025-08-26T19:43:57.9181614Z * [new tag] ciflow/xpu/161477 -> ciflow/xpu/161477 2025-08-26T19:43:57.9182296Z * [new tag] ciflow/xpu/161489 -> ciflow/xpu/161489 2025-08-26T19:43:57.9183189Z * [new tag] cslpull75 -> cslpull75 2025-08-26T19:43:57.9183917Z * [new tag] cslpull76 -> cslpull76 2025-08-26T19:43:57.9184588Z * [new tag] cslpull77 -> cslpull77 2025-08-26T19:43:57.9185544Z * [new tag] cslpull78 -> cslpull78 2025-08-26T19:43:57.9186502Z * [new tag] cslpull79 -> cslpull79 2025-08-26T19:43:57.9187567Z * [new tag] cslpull80 -> cslpull80 2025-08-26T19:43:57.9188425Z * [new tag] cslpull81 -> cslpull81 2025-08-26T19:43:57.9189110Z * [new tag] cslpull82 -> cslpull82 2025-08-26T19:43:57.9190051Z * [new tag] cslpull83 -> cslpull83 2025-08-26T19:43:57.9190665Z * [new tag] cslpull84 -> cslpull84 2025-08-26T19:43:57.9191499Z * [new tag] cslpull85 -> cslpull85 2025-08-26T19:43:57.9195749Z * [new tag] cslpull86 -> cslpull86 2025-08-26T19:43:57.9196677Z * [new tag] cslpull87 -> cslpull87 2025-08-26T19:43:57.9197481Z * [new tag] cslpull88 -> cslpull88 2025-08-26T19:43:57.9198333Z * [new tag] cslpull89 -> cslpull89 2025-08-26T19:43:57.9199068Z * [new tag] cslpull90 -> cslpull90 2025-08-26T19:43:57.9200208Z * [new tag] cslpull91 -> cslpull91 2025-08-26T19:43:57.9200867Z * [new tag] cslpull92 -> cslpull92 2025-08-26T19:43:57.9201640Z * [new tag] flight_5 -> flight_5 2025-08-26T19:43:57.9202710Z * [new tag] flight_5.1 -> flight_5.1 2025-08-26T19:43:57.9203402Z * [new tag] flight_5.2 -> flight_5.2 2025-08-26T19:43:57.9204113Z * [new tag] flight_5.3 -> flight_5.3 2025-08-26T19:43:57.9205024Z * [new tag] forpull1 -> forpull1 2025-08-26T19:43:57.9206071Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-08-26T19:43:57.9206790Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-08-26T19:43:57.9207559Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-08-26T19:43:57.9208611Z * [new tag] nightly-binary -> nightly-binary 2025-08-26T19:43:57.9208964Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-08-26T19:43:57.9210053Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-08-26T19:43:57.9211268Z * [new tag] trunk/00efeabc295e072fb9d6e68b008a31fb04201fd1 -> trunk/00efeabc295e072fb9d6e68b008a31fb04201fd1 2025-08-26T19:43:57.9211916Z * [new tag] trunk/037c43d3b24d4db733011cb904c385eaa6e11bcf -> trunk/037c43d3b24d4db733011cb904c385eaa6e11bcf 2025-08-26T19:43:57.9213104Z * [new tag] trunk/0533ff2ccba7e77622ac3c6758f1032bdc10feff -> trunk/0533ff2ccba7e77622ac3c6758f1032bdc10feff 2025-08-26T19:43:57.9214119Z * [new tag] trunk/05e8fac4f374c4dbf0cd0e85e925e9112cf234a2 -> trunk/05e8fac4f374c4dbf0cd0e85e925e9112cf234a2 2025-08-26T19:43:57.9215125Z * [new tag] trunk/089ad1d88bf31ddab769a4f87750b474ed1214c8 -> trunk/089ad1d88bf31ddab769a4f87750b474ed1214c8 2025-08-26T19:43:57.9215996Z * [new tag] trunk/0924304e728b9507a54eced28c812fbd5b13c397 -> trunk/0924304e728b9507a54eced28c812fbd5b13c397 2025-08-26T19:43:57.9216864Z * [new tag] trunk/0a5ab612dd2b9fc5bb2e1281ec7ca8730c5c3c89 -> trunk/0a5ab612dd2b9fc5bb2e1281ec7ca8730c5c3c89 2025-08-26T19:43:57.9217621Z * [new tag] trunk/0d19541284c38212235f78db24e3ac3ae4787e45 -> trunk/0d19541284c38212235f78db24e3ac3ae4787e45 2025-08-26T19:43:57.9218426Z * [new tag] trunk/0d9da384ef76e3ce2e7eaf951252ae9edb922863 -> trunk/0d9da384ef76e3ce2e7eaf951252ae9edb922863 2025-08-26T19:43:57.9219090Z * [new tag] trunk/0dea191ff7b844352dc2cd5e3b5ef5ea13a76756 -> trunk/0dea191ff7b844352dc2cd5e3b5ef5ea13a76756 2025-08-26T19:43:57.9219872Z * [new tag] trunk/0f801a510f5f185543388717241adb7237c3d46a -> trunk/0f801a510f5f185543388717241adb7237c3d46a 2025-08-26T19:43:57.9220643Z * [new tag] trunk/10e67f5ec3834da93fc2022caa7ac69cf97c01f0 -> trunk/10e67f5ec3834da93fc2022caa7ac69cf97c01f0 2025-08-26T19:43:57.9221555Z * [new tag] trunk/1113e7de30da95973c1eac7921601f9a0e94f2db -> trunk/1113e7de30da95973c1eac7921601f9a0e94f2db 2025-08-26T19:43:57.9222478Z * [new tag] trunk/117f11adb4b41a5485b570c4337c22ecc8e00aeb -> trunk/117f11adb4b41a5485b570c4337c22ecc8e00aeb 2025-08-26T19:43:57.9223198Z * [new tag] trunk/1471b20cb3fc502931ef12b1420414e32facd5b0 -> trunk/1471b20cb3fc502931ef12b1420414e32facd5b0 2025-08-26T19:43:57.9224402Z * [new tag] trunk/16e811e0b5073c7b42fe76f650ca2b79e339e053 -> trunk/16e811e0b5073c7b42fe76f650ca2b79e339e053 2025-08-26T19:43:57.9225084Z * [new tag] trunk/17b0263e86aec8aed068bb8b6744b129233e8084 -> trunk/17b0263e86aec8aed068bb8b6744b129233e8084 2025-08-26T19:43:57.9225738Z * [new tag] trunk/18271148d32da3d48897e9e7515de45066fce5bc -> trunk/18271148d32da3d48897e9e7515de45066fce5bc 2025-08-26T19:43:57.9226579Z * [new tag] trunk/19c70c2f3dc345a6555318f5f8b46cd55c42d0b4 -> trunk/19c70c2f3dc345a6555318f5f8b46cd55c42d0b4 2025-08-26T19:43:57.9227386Z * [new tag] trunk/1a566c4909ccf16ace1fbf1f65d90c995b362712 -> trunk/1a566c4909ccf16ace1fbf1f65d90c995b362712 2025-08-26T19:43:57.9228191Z * [new tag] trunk/1d458e294755ff2bfa314c67ddc5cb1dacc2aee8 -> trunk/1d458e294755ff2bfa314c67ddc5cb1dacc2aee8 2025-08-26T19:43:57.9228995Z * [new tag] trunk/1d46aa736fc8870dc88015c729a8c64470fa985c -> trunk/1d46aa736fc8870dc88015c729a8c64470fa985c 2025-08-26T19:43:57.9229892Z * [new tag] trunk/1de4540449ad6b9df8f452ab72da30ce8908af60 -> trunk/1de4540449ad6b9df8f452ab72da30ce8908af60 2025-08-26T19:43:57.9231291Z * [new tag] trunk/1e3fe78a104776cd708f150116348540346dae25 -> trunk/1e3fe78a104776cd708f150116348540346dae25 2025-08-26T19:43:57.9231959Z * [new tag] trunk/1ea918caf990c84bcb4e4ee5eee90f1102815b0a -> trunk/1ea918caf990c84bcb4e4ee5eee90f1102815b0a 2025-08-26T19:43:57.9232750Z * [new tag] trunk/1eccfb157ab9855b3f81872a23502fb15f455e0a -> trunk/1eccfb157ab9855b3f81872a23502fb15f455e0a 2025-08-26T19:43:57.9233658Z * [new tag] trunk/1fbe230b0d82251c6de8b5ae86c4da456b1db05c -> trunk/1fbe230b0d82251c6de8b5ae86c4da456b1db05c 2025-08-26T19:43:57.9234485Z * [new tag] trunk/209143ddeb99b0b075d16525088cee4893be7492 -> trunk/209143ddeb99b0b075d16525088cee4893be7492 2025-08-26T19:43:57.9235282Z * [new tag] trunk/22df59efc0a845b3ff37019029efd07c5a25c456 -> trunk/22df59efc0a845b3ff37019029efd07c5a25c456 2025-08-26T19:43:57.9236112Z * [new tag] trunk/23b033452fb1d4b404216279bbf5b6d06d8570c3 -> trunk/23b033452fb1d4b404216279bbf5b6d06d8570c3 2025-08-26T19:43:57.9236933Z * [new tag] trunk/24e7f3c21c9452c81d72bbd4b0c6b1f96f33536a -> trunk/24e7f3c21c9452c81d72bbd4b0c6b1f96f33536a 2025-08-26T19:43:57.9237839Z * [new tag] trunk/25df65afd8b5e2fffbcaf2b7ed63ef7a1e37ecb9 -> trunk/25df65afd8b5e2fffbcaf2b7ed63ef7a1e37ecb9 2025-08-26T19:43:57.9238587Z * [new tag] trunk/262640fd220236042fbf4443cc163c8838c84c3d -> trunk/262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:43:57.9239395Z * [new tag] trunk/266784ec6ae82f823abe406582e7a91f2ebb564a -> trunk/266784ec6ae82f823abe406582e7a91f2ebb564a 2025-08-26T19:43:57.9240064Z * [new tag] trunk/2835cc5e91eda8cbc4ac59de2ca990fa17107409 -> trunk/2835cc5e91eda8cbc4ac59de2ca990fa17107409 2025-08-26T19:43:57.9240863Z * [new tag] trunk/284b7190054686e68d9cc683b6ce43e45dd22338 -> trunk/284b7190054686e68d9cc683b6ce43e45dd22338 2025-08-26T19:43:57.9241802Z * [new tag] trunk/29afde20203ee6773641b4e3552942a37315316f -> trunk/29afde20203ee6773641b4e3552942a37315316f 2025-08-26T19:43:57.9242607Z * [new tag] trunk/2a7a7ad7116d930fde86cda02f668e624d26ec3e -> trunk/2a7a7ad7116d930fde86cda02f668e624d26ec3e 2025-08-26T19:43:57.9243585Z * [new tag] trunk/2b62ef74208792c7c4bf923f872e54b5f384efc8 -> trunk/2b62ef74208792c7c4bf923f872e54b5f384efc8 2025-08-26T19:43:57.9244457Z * [new tag] trunk/2beffb3311a41589021c121dac543994a7cbdff2 -> trunk/2beffb3311a41589021c121dac543994a7cbdff2 2025-08-26T19:43:57.9245192Z * [new tag] trunk/2c0650a00a0a0dd2bbf25ed22780fdd881bcda54 -> trunk/2c0650a00a0a0dd2bbf25ed22780fdd881bcda54 2025-08-26T19:43:57.9246112Z * [new tag] trunk/2cf69fe0e1bdb1413fe9e802c4b84d8958708421 -> trunk/2cf69fe0e1bdb1413fe9e802c4b84d8958708421 2025-08-26T19:43:57.9247017Z * [new tag] trunk/2cf7ac2fb7ab4067e17cc5ca71034b1c61a4fb10 -> trunk/2cf7ac2fb7ab4067e17cc5ca71034b1c61a4fb10 2025-08-26T19:43:57.9247826Z * [new tag] trunk/2f0cba934de7094a66c6ce68f5e937254f23142a -> trunk/2f0cba934de7094a66c6ce68f5e937254f23142a 2025-08-26T19:43:57.9248499Z * [new tag] trunk/2f0de0ff9361ca4f2b1e6f9edbc600b5fb6abcd6 -> trunk/2f0de0ff9361ca4f2b1e6f9edbc600b5fb6abcd6 2025-08-26T19:43:57.9249306Z * [new tag] trunk/2f50ae7d2022cb096c4156f5a207c291e36ddecf -> trunk/2f50ae7d2022cb096c4156f5a207c291e36ddecf 2025-08-26T19:43:57.9250171Z * [new tag] trunk/2fdd4f918cdc5fc8070e4c9c0d87b9045d316c06 -> trunk/2fdd4f918cdc5fc8070e4c9c0d87b9045d316c06 2025-08-26T19:43:57.9251068Z * [new tag] trunk/30384abcb1d181e774c0ac21b580aa34336a96c6 -> trunk/30384abcb1d181e774c0ac21b580aa34336a96c6 2025-08-26T19:43:57.9252317Z * [new tag] trunk/31a41daff49f2cde941d8b9e35cb2eaeeb606c0d -> trunk/31a41daff49f2cde941d8b9e35cb2eaeeb606c0d 2025-08-26T19:43:57.9253037Z * [new tag] trunk/332fa5b388521c05a19217649745c6edfdc2836d -> trunk/332fa5b388521c05a19217649745c6edfdc2836d 2025-08-26T19:43:57.9254066Z * [new tag] trunk/33346b58148c55592994a43385c321ae8c8808f2 -> trunk/33346b58148c55592994a43385c321ae8c8808f2 2025-08-26T19:43:57.9254910Z * [new tag] trunk/3373b074f5ea5277974fa6e945544fdfb16bb446 -> trunk/3373b074f5ea5277974fa6e945544fdfb16bb446 2025-08-26T19:43:57.9255749Z * [new tag] trunk/33c3794533844236a6e30ba377e0a6802b279fc8 -> trunk/33c3794533844236a6e30ba377e0a6802b279fc8 2025-08-26T19:43:57.9256586Z * [new tag] trunk/35e4d97e047bff8b38fee1dcf6ef6503f0fc9208 -> trunk/35e4d97e047bff8b38fee1dcf6ef6503f0fc9208 2025-08-26T19:43:57.9257418Z * [new tag] trunk/36ac916929ca67b533cc45932970297e9824324e -> trunk/36ac916929ca67b533cc45932970297e9824324e 2025-08-26T19:43:57.9258223Z * [new tag] trunk/371909cfd10e0da1bab1e12fb54a2403c37c5f76 -> trunk/371909cfd10e0da1bab1e12fb54a2403c37c5f76 2025-08-26T19:43:57.9259027Z * [new tag] trunk/373e25c2eb9f882356a9c7a2f18020935ff1d78b -> trunk/373e25c2eb9f882356a9c7a2f18020935ff1d78b 2025-08-26T19:43:57.9259832Z * [new tag] trunk/37a34022b59a6ff2757e5cec0fdc72278418f339 -> trunk/37a34022b59a6ff2757e5cec0fdc72278418f339 2025-08-26T19:43:57.9260710Z * [new tag] trunk/38a492d40d7ebb2856cb120df337c6cdac244528 -> trunk/38a492d40d7ebb2856cb120df337c6cdac244528 2025-08-26T19:43:57.9261696Z * [new tag] trunk/394728bab2de21e8002fc6a47aa4d3acb2d7a728 -> trunk/394728bab2de21e8002fc6a47aa4d3acb2d7a728 2025-08-26T19:43:57.9262672Z * [new tag] trunk/39862acb2e320783245d2a03acfd1b14cae28038 -> trunk/39862acb2e320783245d2a03acfd1b14cae28038 2025-08-26T19:43:57.9263562Z * [new tag] trunk/3a4140bf8e783db3f0094d2a2ce1d8534066432f -> trunk/3a4140bf8e783db3f0094d2a2ce1d8534066432f 2025-08-26T19:43:57.9264352Z * [new tag] trunk/3caddd4daa5b1a167663c07219e065e86247ad76 -> trunk/3caddd4daa5b1a167663c07219e065e86247ad76 2025-08-26T19:43:57.9265237Z * [new tag] trunk/3dacaf0e1eb3286e70bf8d572000ecebf2c1f4c9 -> trunk/3dacaf0e1eb3286e70bf8d572000ecebf2c1f4c9 2025-08-26T19:43:57.9266064Z * [new tag] trunk/3e210f90c2cbd5817aa23d430da10cad200a3ffa -> trunk/3e210f90c2cbd5817aa23d430da10cad200a3ffa 2025-08-26T19:43:57.9266883Z * [new tag] trunk/3e3e83418d0f6b1495f79380f3a3dbc8b2d23062 -> trunk/3e3e83418d0f6b1495f79380f3a3dbc8b2d23062 2025-08-26T19:43:57.9267405Z * [new tag] trunk/3e5b021f217a42ae55dc690083f67a28126808ed -> trunk/3e5b021f217a42ae55dc690083f67a28126808ed 2025-08-26T19:43:57.9268206Z * [new tag] trunk/3ea6cc8c2d443d6104159d50e8328c144f6caa39 -> trunk/3ea6cc8c2d443d6104159d50e8328c144f6caa39 2025-08-26T19:43:57.9269240Z * [new tag] trunk/3f1a97a99cad4cc682b20b43c1178ed9e1b81f24 -> trunk/3f1a97a99cad4cc682b20b43c1178ed9e1b81f24 2025-08-26T19:43:57.9270030Z * [new tag] trunk/3f5a8e2003f2234ca8be19fdc307ba7b995f9be3 -> trunk/3f5a8e2003f2234ca8be19fdc307ba7b995f9be3 2025-08-26T19:43:57.9270847Z * [new tag] trunk/40c0e700a488191cd8f541b30d8e3b9f2c0bc759 -> trunk/40c0e700a488191cd8f541b30d8e3b9f2c0bc759 2025-08-26T19:43:57.9271698Z * [new tag] trunk/419a2dbf5f69cee52382090200b532a81da92c69 -> trunk/419a2dbf5f69cee52382090200b532a81da92c69 2025-08-26T19:43:57.9272569Z * [new tag] trunk/431846a6323c6f1d02da49e311ac694324f386f4 -> trunk/431846a6323c6f1d02da49e311ac694324f386f4 2025-08-26T19:43:57.9273394Z * [new tag] trunk/44549c7146bd6c4166f97e856037babe1b7f4f49 -> trunk/44549c7146bd6c4166f97e856037babe1b7f4f49 2025-08-26T19:43:57.9274212Z * [new tag] trunk/447d34b5f80fb7350f79decd855cb599cab39083 -> trunk/447d34b5f80fb7350f79decd855cb599cab39083 2025-08-26T19:43:57.9275067Z * [new tag] trunk/46429be72323c1807a785234164bd91011f68d08 -> trunk/46429be72323c1807a785234164bd91011f68d08 2025-08-26T19:43:57.9275766Z * [new tag] trunk/4651aaac47ff855e08a74e2fdbfa605bc53afba8 -> trunk/4651aaac47ff855e08a74e2fdbfa605bc53afba8 2025-08-26T19:43:57.9276636Z * [new tag] trunk/47d267364cad407b5612bf4a5faa160d2f4a7121 -> trunk/47d267364cad407b5612bf4a5faa160d2f4a7121 2025-08-26T19:43:57.9277467Z * [new tag] trunk/49ff884b1edc3b872eeb2387ec60ef230cae7f24 -> trunk/49ff884b1edc3b872eeb2387ec60ef230cae7f24 2025-08-26T19:43:57.9278305Z * [new tag] trunk/4a1aca11c20cfa29a1513b9f289d75bfe32d05d4 -> trunk/4a1aca11c20cfa29a1513b9f289d75bfe32d05d4 2025-08-26T19:43:57.9279102Z * [new tag] trunk/4acdbb8311f760513556e2e4fdd7bfd88c225e52 -> trunk/4acdbb8311f760513556e2e4fdd7bfd88c225e52 2025-08-26T19:43:57.9279904Z * [new tag] trunk/4c36c8a99463c898190a462300ba7f05b5b3384e -> trunk/4c36c8a99463c898190a462300ba7f05b5b3384e 2025-08-26T19:43:57.9280704Z * [new tag] trunk/4e19c1906a830714c1d9d71361357ce616a034d6 -> trunk/4e19c1906a830714c1d9d71361357ce616a034d6 2025-08-26T19:43:57.9281594Z * [new tag] trunk/4ed3184dee1bf4f775839bfd1448a7a34fe5a898 -> trunk/4ed3184dee1bf4f775839bfd1448a7a34fe5a898 2025-08-26T19:43:57.9282381Z * [new tag] trunk/50cfe76231768ee2c784f68a1eba03369f386019 -> trunk/50cfe76231768ee2c784f68a1eba03369f386019 2025-08-26T19:43:57.9283211Z * [new tag] trunk/510825e5fed8b56eb5e9352c12f0df1feeadb810 -> trunk/510825e5fed8b56eb5e9352c12f0df1feeadb810 2025-08-26T19:43:57.9284027Z * [new tag] trunk/512fc768e94c937df350911aaa4ebce757d1f9df -> trunk/512fc768e94c937df350911aaa4ebce757d1f9df 2025-08-26T19:43:57.9285781Z * [new tag] trunk/517d38d3406abbba35d0694bff259a698cad3ec9 -> trunk/517d38d3406abbba35d0694bff259a698cad3ec9 2025-08-26T19:43:57.9286585Z * [new tag] trunk/5255e65c01bf48bbcd916ecf16ed81cf28d3c6e2 -> trunk/5255e65c01bf48bbcd916ecf16ed81cf28d3c6e2 2025-08-26T19:43:57.9287391Z * [new tag] trunk/543896fcf3312f2053018edf9ee74c0fbb1d28ed -> trunk/543896fcf3312f2053018edf9ee74c0fbb1d28ed 2025-08-26T19:43:57.9288212Z * [new tag] trunk/54c2b66592d168e4a7525f7a58f8ca020517a9cb -> trunk/54c2b66592d168e4a7525f7a58f8ca020517a9cb 2025-08-26T19:43:57.9288931Z * [new tag] trunk/54cc63b467f24242cf0d6538d3e1df39e553daf1 -> trunk/54cc63b467f24242cf0d6538d3e1df39e553daf1 2025-08-26T19:43:57.9289850Z * [new tag] trunk/56ebed627a23eea36190e1ced5024a18ffcedbd7 -> trunk/56ebed627a23eea36190e1ced5024a18ffcedbd7 2025-08-26T19:43:57.9290709Z * [new tag] trunk/576a0e64ed2470abd2c430205d1984a11951ce05 -> trunk/576a0e64ed2470abd2c430205d1984a11951ce05 2025-08-26T19:43:57.9291513Z * [new tag] trunk/5805c4210b477f0a7315d6038078dc4a8be1c8fa -> trunk/5805c4210b477f0a7315d6038078dc4a8be1c8fa 2025-08-26T19:43:57.9292479Z * [new tag] trunk/58f9a3dd6391397e439c5f5075837e8f983735aa -> trunk/58f9a3dd6391397e439c5f5075837e8f983735aa 2025-08-26T19:43:57.9293475Z * [new tag] trunk/595987d28d4c8aee68de83734af919c7710ad58b -> trunk/595987d28d4c8aee68de83734af919c7710ad58b 2025-08-26T19:43:57.9294371Z * [new tag] trunk/599f639ddb8bb45abb2dc305542f38288427183d -> trunk/599f639ddb8bb45abb2dc305542f38288427183d 2025-08-26T19:43:57.9295261Z * [new tag] trunk/5afa4187dfe1e99278f8e372ec09102d5b937572 -> trunk/5afa4187dfe1e99278f8e372ec09102d5b937572 2025-08-26T19:43:57.9296114Z * [new tag] trunk/5d9653d90ee003173dd03f93e09fed236500ef06 -> trunk/5d9653d90ee003173dd03f93e09fed236500ef06 2025-08-26T19:43:57.9297082Z * [new tag] trunk/5dad5b4f57ade4001c0f421dbdad2e418304870e -> trunk/5dad5b4f57ade4001c0f421dbdad2e418304870e 2025-08-26T19:43:57.9297992Z * [new tag] trunk/5ee464db5c4293ac09521f9069fa7d2106680a7f -> trunk/5ee464db5c4293ac09521f9069fa7d2106680a7f 2025-08-26T19:43:57.9298833Z * [new tag] trunk/6096d277c543f5dd40351431ef9a8d556134c74d -> trunk/6096d277c543f5dd40351431ef9a8d556134c74d 2025-08-26T19:43:57.9299651Z * [new tag] trunk/62db8ec39116544ae247f876b3e06753178db49b -> trunk/62db8ec39116544ae247f876b3e06753178db49b 2025-08-26T19:43:57.9300560Z * [new tag] trunk/639b8cc51ddebf10361f3840a6b0a244eb6092a1 -> trunk/639b8cc51ddebf10361f3840a6b0a244eb6092a1 2025-08-26T19:43:57.9301375Z * [new tag] trunk/6443ea337df843681bc558d99efa84a3e5559b7f -> trunk/6443ea337df843681bc558d99efa84a3e5559b7f 2025-08-26T19:43:57.9302304Z * [new tag] trunk/6598f00c18dfcc4fc50427305b6b5724e617246f -> trunk/6598f00c18dfcc4fc50427305b6b5724e617246f 2025-08-26T19:43:57.9303086Z * [new tag] trunk/65d21dae18a34e8bd1b2f0e5aec7144b9dd33611 -> trunk/65d21dae18a34e8bd1b2f0e5aec7144b9dd33611 2025-08-26T19:43:57.9303971Z * [new tag] trunk/660b5656a436dcccb0275ea5421d3eb4f1157b43 -> trunk/660b5656a436dcccb0275ea5421d3eb4f1157b43 2025-08-26T19:43:57.9304858Z * [new tag] trunk/66166cf1e7696bf25f6f7bb815a93df367db48dc -> trunk/66166cf1e7696bf25f6f7bb815a93df367db48dc 2025-08-26T19:43:57.9305728Z * [new tag] trunk/667245dc60242a35ae0a6b0072628eb8e15a6d03 -> trunk/667245dc60242a35ae0a6b0072628eb8e15a6d03 2025-08-26T19:43:57.9306495Z * [new tag] trunk/67b98da1b262317f9c0375d64a4b467c82712548 -> trunk/67b98da1b262317f9c0375d64a4b467c82712548 2025-08-26T19:43:57.9307346Z * [new tag] trunk/67d31f6b281d3b15b205756fc7ebc450cdde1dab -> trunk/67d31f6b281d3b15b205756fc7ebc450cdde1dab 2025-08-26T19:43:57.9308211Z * [new tag] trunk/67fc16c7447f4fc04e7d28bfe201a4a0c78f3ea4 -> trunk/67fc16c7447f4fc04e7d28bfe201a4a0c78f3ea4 2025-08-26T19:43:57.9309056Z * [new tag] trunk/6aef9f3a6906c011a57541c1de7a246222bc9ac9 -> trunk/6aef9f3a6906c011a57541c1de7a246222bc9ac9 2025-08-26T19:43:57.9309779Z * [new tag] trunk/6ea4be1e2eca952ea66090182bd2eede89799a45 -> trunk/6ea4be1e2eca952ea66090182bd2eede89799a45 2025-08-26T19:43:57.9310593Z * [new tag] trunk/7006fd0c8874cb0228d3f2bfd83a989bde4b7021 -> trunk/7006fd0c8874cb0228d3f2bfd83a989bde4b7021 2025-08-26T19:43:57.9311424Z * [new tag] trunk/710514a2a51facaba445d2c188541d778f9fdb59 -> trunk/710514a2a51facaba445d2c188541d778f9fdb59 2025-08-26T19:43:57.9312387Z * [new tag] trunk/7131bfab89c46ffe31b61ea4937a8727e9cf33c1 -> trunk/7131bfab89c46ffe31b61ea4937a8727e9cf33c1 2025-08-26T19:43:57.9313200Z * [new tag] trunk/726dce3c944cbda16e54d3b15cdb4b6ced05af72 -> trunk/726dce3c944cbda16e54d3b15cdb4b6ced05af72 2025-08-26T19:43:57.9313984Z * [new tag] trunk/72e4786d1635681b8d053d0168c7d16b980e5124 -> trunk/72e4786d1635681b8d053d0168c7d16b980e5124 2025-08-26T19:43:57.9314799Z * [new tag] trunk/7376111d59f3170c2814d565c09d09435189692a -> trunk/7376111d59f3170c2814d565c09d09435189692a 2025-08-26T19:43:57.9317719Z * [new tag] trunk/74124d1b46774f2a73aa1aadc2b0874cb523b1c1 -> trunk/74124d1b46774f2a73aa1aadc2b0874cb523b1c1 2025-08-26T19:43:57.9318113Z * [new tag] trunk/74280d091321343b47a2975e17584b973d7c22c4 -> trunk/74280d091321343b47a2975e17584b973d7c22c4 2025-08-26T19:43:57.9318507Z * [new tag] trunk/74c4c758afa8c28162f00a456c185552e1159fd3 -> trunk/74c4c758afa8c28162f00a456c185552e1159fd3 2025-08-26T19:43:57.9319232Z * [new tag] trunk/763053dc536341997641e920d8887b3010901b3b -> trunk/763053dc536341997641e920d8887b3010901b3b 2025-08-26T19:43:57.9320179Z * [new tag] trunk/774b4befa18741b3115802cae71000168a40c384 -> trunk/774b4befa18741b3115802cae71000168a40c384 2025-08-26T19:43:57.9321333Z * [new tag] trunk/77bc959fe122bfd131e339ca36cab445a1860806 -> trunk/77bc959fe122bfd131e339ca36cab445a1860806 2025-08-26T19:43:57.9322456Z * [new tag] trunk/78a8e6a671c5631bc0e89b0e674790a424540547 -> trunk/78a8e6a671c5631bc0e89b0e674790a424540547 2025-08-26T19:43:57.9323662Z * [new tag] trunk/7e4bfa74eafab994b01f8b5501d4d061cbf64808 -> trunk/7e4bfa74eafab994b01f8b5501d4d061cbf64808 2025-08-26T19:43:57.9324922Z * [new tag] trunk/7e6ce41555d595e3fa0d91059491f21cee3eb5ea -> trunk/7e6ce41555d595e3fa0d91059491f21cee3eb5ea 2025-08-26T19:43:57.9326135Z * [new tag] trunk/7f201baf414301b3312576893b7f6f2698acd9ba -> trunk/7f201baf414301b3312576893b7f6f2698acd9ba 2025-08-26T19:43:57.9327408Z * [new tag] trunk/7fcdd8d6afeda6a4c8630816e12bf7cca44b8f8a -> trunk/7fcdd8d6afeda6a4c8630816e12bf7cca44b8f8a 2025-08-26T19:43:57.9328646Z * [new tag] trunk/801851086d09506d081800108c9e214edb3f5b7d -> trunk/801851086d09506d081800108c9e214edb3f5b7d 2025-08-26T19:43:57.9329936Z * [new tag] trunk/8047cde0f3a27f3afa218792b8464d5e0c9d942f -> trunk/8047cde0f3a27f3afa218792b8464d5e0c9d942f 2025-08-26T19:43:57.9331124Z * [new tag] trunk/80df27a612be3433516d7e6dfc8d8be058425d3e -> trunk/80df27a612be3433516d7e6dfc8d8be058425d3e 2025-08-26T19:43:57.9332474Z * [new tag] trunk/818ba434c7de4cd604184b2857d544e0ad95735f -> trunk/818ba434c7de4cd604184b2857d544e0ad95735f 2025-08-26T19:43:57.9333359Z * [new tag] trunk/83283ce7f5a7847b4e561e22be9b0f4530b05527 -> trunk/83283ce7f5a7847b4e561e22be9b0f4530b05527 2025-08-26T19:43:57.9334663Z * [new tag] trunk/85adf80cf15538a7e010fa235036fe8e06f8bede -> trunk/85adf80cf15538a7e010fa235036fe8e06f8bede 2025-08-26T19:43:57.9335857Z * [new tag] trunk/8aad3a60ce16a4acab17a8e46e5df339db2ff740 -> trunk/8aad3a60ce16a4acab17a8e46e5df339db2ff740 2025-08-26T19:43:57.9337368Z * [new tag] trunk/8c442e4fd3310e15f57770944f883ac1d73e77e2 -> trunk/8c442e4fd3310e15f57770944f883ac1d73e77e2 2025-08-26T19:43:57.9338632Z * [new tag] trunk/8c506e6310b9b5295151fb725be479d0f80ce5e8 -> trunk/8c506e6310b9b5295151fb725be479d0f80ce5e8 2025-08-26T19:43:57.9340064Z * [new tag] trunk/8cfc119491f533c4edded4263a78eb0af782a2d5 -> trunk/8cfc119491f533c4edded4263a78eb0af782a2d5 2025-08-26T19:43:57.9341417Z * [new tag] trunk/8dbe7f99bd707ee28ae12ecb9cab54e1785bf13e -> trunk/8dbe7f99bd707ee28ae12ecb9cab54e1785bf13e 2025-08-26T19:43:57.9342627Z * [new tag] trunk/8e1770905565cd67d6c3a91c7afa462f4ef6e6aa -> trunk/8e1770905565cd67d6c3a91c7afa462f4ef6e6aa 2025-08-26T19:43:57.9343859Z * [new tag] trunk/8f31aa97a3e1e17bed29b6cedf9884f0c6b145e9 -> trunk/8f31aa97a3e1e17bed29b6cedf9884f0c6b145e9 2025-08-26T19:43:57.9345080Z * [new tag] trunk/8f766d68397736053883aa281cae0eb46bb233bb -> trunk/8f766d68397736053883aa281cae0eb46bb233bb 2025-08-26T19:43:57.9346292Z * [new tag] trunk/908b0ccb1f70ed2cfa830484e05ee32af13b1836 -> trunk/908b0ccb1f70ed2cfa830484e05ee32af13b1836 2025-08-26T19:43:57.9347511Z * [new tag] trunk/90ea9ccefe3e2d9a9e4840016d1af10c1814d48b -> trunk/90ea9ccefe3e2d9a9e4840016d1af10c1814d48b 2025-08-26T19:43:57.9348652Z * [new tag] trunk/9225c6199412f8a2ee99b7c29f533fb98b9ff62e -> trunk/9225c6199412f8a2ee99b7c29f533fb98b9ff62e 2025-08-26T19:43:57.9349858Z * [new tag] trunk/923bc46122d173a7964c646311a3bea3cd8dd561 -> trunk/923bc46122d173a7964c646311a3bea3cd8dd561 2025-08-26T19:43:57.9351553Z * [new tag] trunk/92ab18482459a63e97f1374e27e8411964da9762 -> trunk/92ab18482459a63e97f1374e27e8411964da9762 2025-08-26T19:43:57.9352767Z * [new tag] trunk/94b9569c4a86e12b944ca66e3125357a14d0eb9e -> trunk/94b9569c4a86e12b944ca66e3125357a14d0eb9e 2025-08-26T19:43:57.9354124Z * [new tag] trunk/957b170d8efe2a51147e0cdb7515acc345ba81da -> trunk/957b170d8efe2a51147e0cdb7515acc345ba81da 2025-08-26T19:43:57.9355333Z * [new tag] trunk/958f9ca88e9a1580de7c94a5a2ca8a750b1335ae -> trunk/958f9ca88e9a1580de7c94a5a2ca8a750b1335ae 2025-08-26T19:43:57.9356237Z * [new tag] trunk/96682103026b5ea27f19e6db9303e17572095b0e -> trunk/96682103026b5ea27f19e6db9303e17572095b0e 2025-08-26T19:43:57.9357575Z * [new tag] trunk/97200c971110d54030feaad999698c7341f8acc7 -> trunk/97200c971110d54030feaad999698c7341f8acc7 2025-08-26T19:43:57.9358793Z * [new tag] trunk/981ac533c6e69a77538aaa7a9747c3d840dfa8be -> trunk/981ac533c6e69a77538aaa7a9747c3d840dfa8be 2025-08-26T19:43:57.9359973Z * [new tag] trunk/995397d47a0e27394ee1010f158e181eb304100a -> trunk/995397d47a0e27394ee1010f158e181eb304100a 2025-08-26T19:43:57.9361391Z * [new tag] trunk/9a41570199155eee92ebd28452a556075e34e1b4 -> trunk/9a41570199155eee92ebd28452a556075e34e1b4 2025-08-26T19:43:57.9362611Z * [new tag] trunk/9b3ebd25acfd2ff4e9b7428079ba364d6f8a14da -> trunk/9b3ebd25acfd2ff4e9b7428079ba364d6f8a14da 2025-08-26T19:43:57.9363809Z * [new tag] trunk/9b4adc4db7494dbc4dbbac5dd85ccbf5babaef44 -> trunk/9b4adc4db7494dbc4dbbac5dd85ccbf5babaef44 2025-08-26T19:43:57.9365379Z * [new tag] trunk/9d18bf01b1661d227f6af41ac07a1e9ef20a9e1a -> trunk/9d18bf01b1661d227f6af41ac07a1e9ef20a9e1a 2025-08-26T19:43:57.9366703Z * [new tag] trunk/9d7cecdd6c44c5421d341bcc359be4097ea9a2f5 -> trunk/9d7cecdd6c44c5421d341bcc359be4097ea9a2f5 2025-08-26T19:43:57.9368051Z * [new tag] trunk/9d882fd9ffc6ad2a292fee548740aabfea745002 -> trunk/9d882fd9ffc6ad2a292fee548740aabfea745002 2025-08-26T19:43:57.9369337Z * [new tag] trunk/9d9cc9897ac44a1a8df38211b03d8342a8af48c3 -> trunk/9d9cc9897ac44a1a8df38211b03d8342a8af48c3 2025-08-26T19:43:57.9370481Z * [new tag] trunk/9e1c9541344b2aa1c946edb779d275072f3b8f4a -> trunk/9e1c9541344b2aa1c946edb779d275072f3b8f4a 2025-08-26T19:43:57.9371704Z * [new tag] trunk/9e491f753ee521a70e6a7e7dbb36f96c9350f5ea -> trunk/9e491f753ee521a70e6a7e7dbb36f96c9350f5ea 2025-08-26T19:43:57.9372857Z * [new tag] trunk/a03cc53e6f6e2fe67316cb8c74c25f5b953f445b -> trunk/a03cc53e6f6e2fe67316cb8c74c25f5b953f445b 2025-08-26T19:43:57.9374020Z * [new tag] trunk/a154c2093c0f2646346f032e1f30012779b3c51d -> trunk/a154c2093c0f2646346f032e1f30012779b3c51d 2025-08-26T19:43:57.9375323Z * [new tag] trunk/a391fa1c42dd32e32a2e5b1cb196bac56daaca88 -> trunk/a391fa1c42dd32e32a2e5b1cb196bac56daaca88 2025-08-26T19:43:57.9376656Z * [new tag] trunk/a3a82e3da85a53afc4bbf3d75bd3d3dcc2e06645 -> trunk/a3a82e3da85a53afc4bbf3d75bd3d3dcc2e06645 2025-08-26T19:43:57.9377763Z * [new tag] trunk/a3fe1ced409d186628ff2975f05ba529a86fae84 -> trunk/a3fe1ced409d186628ff2975f05ba529a86fae84 2025-08-26T19:43:57.9378939Z * [new tag] trunk/a43480d19cdd68e544163b1a07c328a9c54723b8 -> trunk/a43480d19cdd68e544163b1a07c328a9c54723b8 2025-08-26T19:43:57.9380153Z * [new tag] trunk/a445b41e4f11daa82a53a21ec413c15d5079ae77 -> trunk/a445b41e4f11daa82a53a21ec413c15d5079ae77 2025-08-26T19:43:57.9381535Z * [new tag] trunk/a44a0d3671b4ccf2fe915896a8a5204fe79b1e7b -> trunk/a44a0d3671b4ccf2fe915896a8a5204fe79b1e7b 2025-08-26T19:43:57.9383004Z * [new tag] trunk/a6401cb5aa51622045c3f9a03b2cebef236e4182 -> trunk/a6401cb5aa51622045c3f9a03b2cebef236e4182 2025-08-26T19:43:57.9384363Z * [new tag] trunk/a68f63e33161b4665e0f4c399bf8072135a35a57 -> trunk/a68f63e33161b4665e0f4c399bf8072135a35a57 2025-08-26T19:43:57.9385329Z * [new tag] trunk/a72803f1e3c69c780b7d7bcdd9b35360fd98148b -> trunk/a72803f1e3c69c780b7d7bcdd9b35360fd98148b 2025-08-26T19:43:57.9386642Z * [new tag] trunk/a7b5955ea8851d73e35f50a0de5bb0626bae24cb -> trunk/a7b5955ea8851d73e35f50a0de5bb0626bae24cb 2025-08-26T19:43:57.9387813Z * [new tag] trunk/a818fa77e3a72271f144514ef349c5a666313205 -> trunk/a818fa77e3a72271f144514ef349c5a666313205 2025-08-26T19:43:57.9389116Z * [new tag] trunk/a825557ed53507e85ac613862311a81eb88710a4 -> trunk/a825557ed53507e85ac613862311a81eb88710a4 2025-08-26T19:43:57.9390042Z * [new tag] trunk/a85711d565f37b0095af9f7dafa77f392c9aa31e -> trunk/a85711d565f37b0095af9f7dafa77f392c9aa31e 2025-08-26T19:43:57.9391389Z * [new tag] trunk/a941d7ffe54b5f256c1fbd3959ddbf608b7eea88 -> trunk/a941d7ffe54b5f256c1fbd3959ddbf608b7eea88 2025-08-26T19:43:57.9392792Z * [new tag] trunk/a9fabeb012a4b804836a2b8d4b3742b92c9a6b58 -> trunk/a9fabeb012a4b804836a2b8d4b3742b92c9a6b58 2025-08-26T19:43:57.9393925Z * [new tag] trunk/ab7787fb82dd777b2f777ef58bc20dbb7bd8289b -> trunk/ab7787fb82dd777b2f777ef58bc20dbb7bd8289b 2025-08-26T19:43:57.9395150Z * [new tag] trunk/ab8d60f4c86ca19ed00d6e79ae8e6939266f28e6 -> trunk/ab8d60f4c86ca19ed00d6e79ae8e6939266f28e6 2025-08-26T19:43:57.9396113Z * [new tag] trunk/ac8d9418aee4543fa193c86ae0bc3e63707bcd3b -> trunk/ac8d9418aee4543fa193c86ae0bc3e63707bcd3b 2025-08-26T19:43:57.9397408Z * [new tag] trunk/acb00d3ccf5f2d566225f07ed66bd579d5d3e44e -> trunk/acb00d3ccf5f2d566225f07ed66bd579d5d3e44e 2025-08-26T19:43:57.9398539Z * [new tag] trunk/adecb0c9e89e0dfe18d944d292c98c97b686fc83 -> trunk/adecb0c9e89e0dfe18d944d292c98c97b686fc83 2025-08-26T19:43:57.9399827Z * [new tag] trunk/ae8d319fd4a0b0fa7b1372aa07690a36ce823abc -> trunk/ae8d319fd4a0b0fa7b1372aa07690a36ce823abc 2025-08-26T19:43:57.9401116Z * [new tag] trunk/af3265d20f763e5366bfa37e3d4a6307036d0c18 -> trunk/af3265d20f763e5366bfa37e3d4a6307036d0c18 2025-08-26T19:43:57.9402302Z * [new tag] trunk/b0420d24386263f2727fd5714b63cfa6bc89f3e6 -> trunk/b0420d24386263f2727fd5714b63cfa6bc89f3e6 2025-08-26T19:43:57.9403538Z * [new tag] trunk/b1380f434da2fa2de0e5ff6fd70f73082dc08687 -> trunk/b1380f434da2fa2de0e5ff6fd70f73082dc08687 2025-08-26T19:43:57.9404696Z * [new tag] trunk/b2632e79828300302fd11e093d765196c3c0db58 -> trunk/b2632e79828300302fd11e093d765196c3c0db58 2025-08-26T19:43:57.9405905Z * [new tag] trunk/b2e06e0194c3fa8f7578a1b48751cc027394fb67 -> trunk/b2e06e0194c3fa8f7578a1b48751cc027394fb67 2025-08-26T19:43:57.9407184Z * [new tag] trunk/b3e215b864e6ca43b2c4e50ce666673f80feee27 -> trunk/b3e215b864e6ca43b2c4e50ce666673f80feee27 2025-08-26T19:43:57.9408359Z * [new tag] trunk/b708966201811b31ee765ec57715ac21d06ef652 -> trunk/b708966201811b31ee765ec57715ac21d06ef652 2025-08-26T19:43:57.9409361Z * [new tag] trunk/b9e9e92817fd7d1a778f074105603efb07e05004 -> trunk/b9e9e92817fd7d1a778f074105603efb07e05004 2025-08-26T19:43:57.9410349Z * [new tag] trunk/bc7eaa0d8a1f5ca8ec0eaac461d1df500dcaea84 -> trunk/bc7eaa0d8a1f5ca8ec0eaac461d1df500dcaea84 2025-08-26T19:43:57.9411629Z * [new tag] trunk/bcfe1b2d714cbb2716495e09ae010e7c34daf045 -> trunk/bcfe1b2d714cbb2716495e09ae010e7c34daf045 2025-08-26T19:43:57.9412821Z * [new tag] trunk/bd5857a1d6d5455d4f0057c182dff5e8ad2a4c8a -> trunk/bd5857a1d6d5455d4f0057c182dff5e8ad2a4c8a 2025-08-26T19:43:57.9414001Z * [new tag] trunk/be2e6b3158552405acc13ef7829a0217826fb271 -> trunk/be2e6b3158552405acc13ef7829a0217826fb271 2025-08-26T19:43:57.9415180Z * [new tag] trunk/be87f22dfba4488963fcc854699829e2782ee0f2 -> trunk/be87f22dfba4488963fcc854699829e2782ee0f2 2025-08-26T19:43:57.9416365Z * [new tag] trunk/becd6cd744bdf950578519437652a0d1f4b48781 -> trunk/becd6cd744bdf950578519437652a0d1f4b48781 2025-08-26T19:43:57.9417747Z * [new tag] trunk/bf8431ba062efa9ff0cdd5032a3ddf2e007a3216 -> trunk/bf8431ba062efa9ff0cdd5032a3ddf2e007a3216 2025-08-26T19:43:57.9418903Z * [new tag] trunk/c02e26bf31eb3da301158a061aa68527dbfb4d32 -> trunk/c02e26bf31eb3da301158a061aa68527dbfb4d32 2025-08-26T19:43:57.9420138Z * [new tag] trunk/c081481bbebdb568d07ee19cfe2cd3125de6cba7 -> trunk/c081481bbebdb568d07ee19cfe2cd3125de6cba7 2025-08-26T19:43:57.9421428Z * [new tag] trunk/c2390087c34c964ef648addf43efb8c6a34e30c2 -> trunk/c2390087c34c964ef648addf43efb8c6a34e30c2 2025-08-26T19:43:57.9422614Z * [new tag] trunk/c4670e40c9b741d50a79b714e3830149833be908 -> trunk/c4670e40c9b741d50a79b714e3830149833be908 2025-08-26T19:43:57.9423782Z * [new tag] trunk/c5cb255625deb4cdbc5780e6911b73498e17ed5a -> trunk/c5cb255625deb4cdbc5780e6911b73498e17ed5a 2025-08-26T19:43:57.9424981Z * [new tag] trunk/c60dea5261d9648d1da51528a07731966bb6823e -> trunk/c60dea5261d9648d1da51528a07731966bb6823e 2025-08-26T19:43:57.9426132Z * [new tag] trunk/c74e5f60611b7eac4321f53a9e4a15b077fb1bcc -> trunk/c74e5f60611b7eac4321f53a9e4a15b077fb1bcc 2025-08-26T19:43:57.9427082Z * [new tag] trunk/c7a77470c54b28e555319e34048af14d1d66198a -> trunk/c7a77470c54b28e555319e34048af14d1d66198a 2025-08-26T19:43:57.9428422Z * [new tag] trunk/c7fb031706330684fc3a2d8d169bebea874d4e95 -> trunk/c7fb031706330684fc3a2d8d169bebea874d4e95 2025-08-26T19:43:57.9430023Z * [new tag] trunk/c8bb0e4720ddddf3cd1b0b48b336978f763c71ca -> trunk/c8bb0e4720ddddf3cd1b0b48b336978f763c71ca 2025-08-26T19:43:57.9431259Z * [new tag] trunk/ca9fe0107e165a4a4147325ff6d34235ebde447f -> trunk/ca9fe0107e165a4a4147325ff6d34235ebde447f 2025-08-26T19:43:57.9432384Z * [new tag] trunk/cb579532150c9e87e7c143adcb020fb7de7cc6b1 -> trunk/cb579532150c9e87e7c143adcb020fb7de7cc6b1 2025-08-26T19:43:57.9433282Z * [new tag] trunk/cc2b65a91ae7773d4ecf9a600dda48fc3e69aa8f -> trunk/cc2b65a91ae7773d4ecf9a600dda48fc3e69aa8f 2025-08-26T19:43:57.9434556Z * [new tag] trunk/cc791d5857f4aa06b8d4e567b1fb2852e3ae963d -> trunk/cc791d5857f4aa06b8d4e567b1fb2852e3ae963d 2025-08-26T19:43:57.9435847Z * [new tag] trunk/cd31be28ec5cd0c4d9cdb6742efe151eee1406ec -> trunk/cd31be28ec5cd0c4d9cdb6742efe151eee1406ec 2025-08-26T19:43:57.9436989Z * [new tag] trunk/cd87f3029582cedb3b88747a3bd7d200b05c1138 -> trunk/cd87f3029582cedb3b88747a3bd7d200b05c1138 2025-08-26T19:43:57.9438330Z * [new tag] trunk/ce048de608180fa88335e5821070472539968b54 -> trunk/ce048de608180fa88335e5821070472539968b54 2025-08-26T19:43:57.9439519Z * [new tag] trunk/ce467df5d1d763d1648aee51c93ce3e9a4699936 -> trunk/ce467df5d1d763d1648aee51c93ce3e9a4699936 2025-08-26T19:43:57.9440804Z * [new tag] trunk/cee72119b2dec7776bc2550dd39a9b1349772751 -> trunk/cee72119b2dec7776bc2550dd39a9b1349772751 2025-08-26T19:43:57.9442106Z * [new tag] trunk/cf94cadbeee31a4d1d46a57f11bce7c9fd1cebc0 -> trunk/cf94cadbeee31a4d1d46a57f11bce7c9fd1cebc0 2025-08-26T19:43:57.9443340Z * [new tag] trunk/cfdaaaaa26d7f34427ba941569eca46f02f79f3e -> trunk/cfdaaaaa26d7f34427ba941569eca46f02f79f3e 2025-08-26T19:43:57.9444610Z * [new tag] trunk/d1faf2ef0476eb60b42c057baee9af0f48ae849a -> trunk/d1faf2ef0476eb60b42c057baee9af0f48ae849a 2025-08-26T19:43:57.9445969Z * [new tag] trunk/d228a776e90368bb693837ae23285ad8fc33def5 -> trunk/d228a776e90368bb693837ae23285ad8fc33def5 2025-08-26T19:43:57.9447317Z * [new tag] trunk/d2b8c0d431e00ad57354c5247e46c1bea0b8cd31 -> trunk/d2b8c0d431e00ad57354c5247e46c1bea0b8cd31 2025-08-26T19:43:57.9448532Z * [new tag] trunk/d2bd55d8de784df439b38378f161271dc43b744c -> trunk/d2bd55d8de784df439b38378f161271dc43b744c 2025-08-26T19:43:57.9449707Z * [new tag] trunk/d4703fb91c3510460d71f648da113177edf593c8 -> trunk/d4703fb91c3510460d71f648da113177edf593c8 2025-08-26T19:43:57.9450934Z * [new tag] trunk/d875d3ca1e5099636c766c9df70ac5888c25215a -> trunk/d875d3ca1e5099636c766c9df70ac5888c25215a 2025-08-26T19:43:57.9452108Z * [new tag] trunk/d8fcb2a4acb506f9c72a1f44fc8b857158bda892 -> trunk/d8fcb2a4acb506f9c72a1f44fc8b857158bda892 2025-08-26T19:43:57.9453286Z * [new tag] trunk/daeb3a6094c62d1881ea68091fcadb02d1dc687e -> trunk/daeb3a6094c62d1881ea68091fcadb02d1dc687e 2025-08-26T19:43:57.9454475Z * [new tag] trunk/db38c44ad639e7ada3e9df2ba026a2cb5e40feb0 -> trunk/db38c44ad639e7ada3e9df2ba026a2cb5e40feb0 2025-08-26T19:43:57.9456103Z * [new tag] trunk/db44de4c0d3e9f1fe5334ff4cc261fb8fe4390c8 -> trunk/db44de4c0d3e9f1fe5334ff4cc261fb8fe4390c8 2025-08-26T19:43:57.9457332Z * [new tag] trunk/dbef6066311a1ce6e60e1f2b6084249d1ad45769 -> trunk/dbef6066311a1ce6e60e1f2b6084249d1ad45769 2025-08-26T19:43:57.9458526Z * [new tag] trunk/df571ae7ad7dacf77ce42c00189cf369d7993387 -> trunk/df571ae7ad7dacf77ce42c00189cf369d7993387 2025-08-26T19:43:57.9459792Z * [new tag] trunk/df6073641079c781e66a905e4f15ee49ac257eb2 -> trunk/df6073641079c781e66a905e4f15ee49ac257eb2 2025-08-26T19:43:57.9461089Z * [new tag] trunk/e1a64b75ff3dc834774a9174c2e7b1c46dea35ec -> trunk/e1a64b75ff3dc834774a9174c2e7b1c46dea35ec 2025-08-26T19:43:57.9462351Z * [new tag] trunk/e20f6d798606f3245686e950c43635bbe526232d -> trunk/e20f6d798606f3245686e950c43635bbe526232d 2025-08-26T19:43:57.9463102Z * [new tag] trunk/e25ee0290ef16503f178e04890c15717f6e9ea44 -> trunk/e25ee0290ef16503f178e04890c15717f6e9ea44 2025-08-26T19:43:57.9464511Z * [new tag] trunk/e34b6a01039df5d8940acdccd8d8989f3cd827aa -> trunk/e34b6a01039df5d8940acdccd8d8989f3cd827aa 2025-08-26T19:43:57.9465752Z * [new tag] trunk/e3d68dfae2dee15e74d3b95beaed7149b6afb94a -> trunk/e3d68dfae2dee15e74d3b95beaed7149b6afb94a 2025-08-26T19:43:57.9466970Z * [new tag] trunk/e3ebf364e6d2fb8008da113a596d3cc426ba9c79 -> trunk/e3ebf364e6d2fb8008da113a596d3cc426ba9c79 2025-08-26T19:43:57.9467833Z * [new tag] trunk/e4839470470168648dee5997f57347bb8541ea2b -> trunk/e4839470470168648dee5997f57347bb8541ea2b 2025-08-26T19:43:57.9469153Z * [new tag] trunk/e63155751825ba026ced3a1fc89563231bc85ccc -> trunk/e63155751825ba026ced3a1fc89563231bc85ccc 2025-08-26T19:43:57.9470346Z * [new tag] trunk/e6aa7287f8c8cac76d792097f20ba1dae6dc8717 -> trunk/e6aa7287f8c8cac76d792097f20ba1dae6dc8717 2025-08-26T19:43:57.9471658Z * [new tag] trunk/e6e45e6ae8452f0bc5e3e258027c42eb9a1394fb -> trunk/e6e45e6ae8452f0bc5e3e258027c42eb9a1394fb 2025-08-26T19:43:57.9472919Z * [new tag] trunk/e795450a35bca909902e12de99245e1c0e7e2872 -> trunk/e795450a35bca909902e12de99245e1c0e7e2872 2025-08-26T19:43:57.9473964Z * [new tag] trunk/e7e270a33a3f368c3ef0c3339950a47fdbfadd71 -> trunk/e7e270a33a3f368c3ef0c3339950a47fdbfadd71 2025-08-26T19:43:57.9475195Z * [new tag] trunk/e836323a23f5750e800abe04ef8ca386b3066b58 -> trunk/e836323a23f5750e800abe04ef8ca386b3066b58 2025-08-26T19:43:57.9476403Z * [new tag] trunk/e83825f91cb2901567fedbf31ba7cc434a897271 -> trunk/e83825f91cb2901567fedbf31ba7cc434a897271 2025-08-26T19:43:57.9477862Z * [new tag] trunk/e9d42b3880dcdbd823bbdc9370c8b0b3af0ba2e3 -> trunk/e9d42b3880dcdbd823bbdc9370c8b0b3af0ba2e3 2025-08-26T19:43:57.9479061Z * [new tag] trunk/eb5549a43164cdf8689cd7d177c03b2508c699f4 -> trunk/eb5549a43164cdf8689cd7d177c03b2508c699f4 2025-08-26T19:43:57.9480250Z * [new tag] trunk/eba1ad09e47b66478f973e03cece7f314ac3b412 -> trunk/eba1ad09e47b66478f973e03cece7f314ac3b412 2025-08-26T19:43:57.9481409Z * [new tag] trunk/eba20d2d748cb17dce9aa26e5513e4567bfd8282 -> trunk/eba20d2d748cb17dce9aa26e5513e4567bfd8282 2025-08-26T19:43:57.9482558Z * [new tag] trunk/ec21cafd85d491d2d220e4e54080fe340a37c4c2 -> trunk/ec21cafd85d491d2d220e4e54080fe340a37c4c2 2025-08-26T19:43:57.9483740Z * [new tag] trunk/ed8bcccf31e1ba01a35e818a4afbb74c333e8dc3 -> trunk/ed8bcccf31e1ba01a35e818a4afbb74c333e8dc3 2025-08-26T19:43:57.9485128Z * [new tag] trunk/eddaaa6c2a66a84e17b17bf8af5131852067b259 -> trunk/eddaaa6c2a66a84e17b17bf8af5131852067b259 2025-08-26T19:43:57.9486278Z * [new tag] trunk/ef761c43538abae5bccc0c4b6ebaf42ff676db7a -> trunk/ef761c43538abae5bccc0c4b6ebaf42ff676db7a 2025-08-26T19:43:57.9487483Z * [new tag] trunk/f085f299584b06a2a7d8855eda2a411313e782ad -> trunk/f085f299584b06a2a7d8855eda2a411313e782ad 2025-08-26T19:43:57.9488679Z * [new tag] trunk/f09458c2e16b4fe7063d73d80fd3e7e354bad3f8 -> trunk/f09458c2e16b4fe7063d73d80fd3e7e354bad3f8 2025-08-26T19:43:57.9489883Z * [new tag] trunk/f0e0a6897ee5cb31ccee10ee8e2d3c01140ff999 -> trunk/f0e0a6897ee5cb31ccee10ee8e2d3c01140ff999 2025-08-26T19:43:57.9490995Z * [new tag] trunk/f30501937738a2440f90988d1d46920529309ba8 -> trunk/f30501937738a2440f90988d1d46920529309ba8 2025-08-26T19:43:57.9492333Z * [new tag] trunk/f391afe9bf8c542fdbb822423d2a1e454b3d9744 -> trunk/f391afe9bf8c542fdbb822423d2a1e454b3d9744 2025-08-26T19:43:57.9493253Z * [new tag] trunk/f521e82a4e80df502fa57e5852af14d8779dcbd1 -> trunk/f521e82a4e80df502fa57e5852af14d8779dcbd1 2025-08-26T19:43:57.9494012Z * [new tag] trunk/f5bf5147ad18994c9a6e0f565d7831362bf5a18a -> trunk/f5bf5147ad18994c9a6e0f565d7831362bf5a18a 2025-08-26T19:43:57.9494836Z * [new tag] trunk/f795e92802c55608ad4f4f198726d250056d0232 -> trunk/f795e92802c55608ad4f4f198726d250056d0232 2025-08-26T19:43:57.9496080Z * [new tag] trunk/f8bd85827d465a8a2a610c27ed9e62a4c27ac07d -> trunk/f8bd85827d465a8a2a610c27ed9e62a4c27ac07d 2025-08-26T19:43:57.9496847Z * [new tag] trunk/f90ccad1651b5a1698b2232acc3e92e2829b7935 -> trunk/f90ccad1651b5a1698b2232acc3e92e2829b7935 2025-08-26T19:43:57.9497587Z * [new tag] trunk/f912c93344caa74e24c8164a2e25fe84a8203073 -> trunk/f912c93344caa74e24c8164a2e25fe84a8203073 2025-08-26T19:43:57.9498454Z * [new tag] trunk/f9875166a953a51bbd454d963ee03d41818a27e8 -> trunk/f9875166a953a51bbd454d963ee03d41818a27e8 2025-08-26T19:43:57.9499374Z * [new tag] trunk/f9df4ec2af0ac19b42f658ae87acf12067e67b36 -> trunk/f9df4ec2af0ac19b42f658ae87acf12067e67b36 2025-08-26T19:43:57.9500255Z * [new tag] trunk/fab5dac734344105ae107e85c08151758a4a9b4d -> trunk/fab5dac734344105ae107e85c08151758a4a9b4d 2025-08-26T19:43:57.9501275Z * [new tag] trunk/fb241d0a448f1dd88471098ac149418124a7c4aa -> trunk/fb241d0a448f1dd88471098ac149418124a7c4aa 2025-08-26T19:43:57.9502280Z * [new tag] trunk/fc0683b1e75fdf3182e0855b3f79e80fe0124ef1 -> trunk/fc0683b1e75fdf3182e0855b3f79e80fe0124ef1 2025-08-26T19:43:57.9504893Z * [new tag] trunk/fc69c2bc67672c3b2d0c62c1821895f09288f1c0 -> trunk/fc69c2bc67672c3b2d0c62c1821895f09288f1c0 2025-08-26T19:43:57.9506170Z * [new tag] trunk/febfc3ec03004116dfd6d504e6853ff02a1dd6e0 -> trunk/febfc3ec03004116dfd6d504e6853ff02a1dd6e0 2025-08-26T19:43:57.9506986Z * [new tag] trunk/fecc5f600110209aaaedead11770a445b3c879e6 -> trunk/fecc5f600110209aaaedead11770a445b3c879e6 2025-08-26T19:43:57.9507881Z * [new tag] trunk/ff4f5dd8ed8e2aaee903c7d30cd4f8bd04d883c8 -> trunk/ff4f5dd8ed8e2aaee903c7d30cd4f8bd04d883c8 2025-08-26T19:43:57.9508635Z * [new tag] trunk/ffa1ce7650766c2ae6eaa96415dfc29e9eb0b3ec -> trunk/ffa1ce7650766c2ae6eaa96415dfc29e9eb0b3ec 2025-08-26T19:43:57.9509251Z * [new tag] v0.1.1 -> v0.1.1 2025-08-26T19:43:57.9510237Z * [new tag] v0.1.10 -> v0.1.10 2025-08-26T19:43:57.9511082Z * [new tag] v0.1.11 -> v0.1.11 2025-08-26T19:43:57.9511919Z * [new tag] v0.1.12 -> v0.1.12 2025-08-26T19:43:57.9512895Z * [new tag] v0.1.2 -> v0.1.2 2025-08-26T19:43:57.9513425Z * [new tag] v0.1.3 -> v0.1.3 2025-08-26T19:43:57.9514141Z * [new tag] v0.1.4 -> v0.1.4 2025-08-26T19:43:57.9514871Z * [new tag] v0.1.5 -> v0.1.5 2025-08-26T19:43:57.9515639Z * [new tag] v0.1.6 -> v0.1.6 2025-08-26T19:43:57.9516576Z * [new tag] v0.1.7 -> v0.1.7 2025-08-26T19:43:57.9517253Z * [new tag] v0.1.8 -> v0.1.8 2025-08-26T19:43:57.9517909Z * [new tag] v0.1.9 -> v0.1.9 2025-08-26T19:43:57.9518702Z * [new tag] v0.2.0 -> v0.2.0 2025-08-26T19:43:57.9519485Z * [new tag] v0.3.0 -> v0.3.0 2025-08-26T19:43:57.9520471Z * [new tag] v0.3.1 -> v0.3.1 2025-08-26T19:43:57.9521134Z * [new tag] v0.4.0 -> v0.4.0 2025-08-26T19:43:57.9521886Z * [new tag] v0.4.1 -> v0.4.1 2025-08-26T19:43:57.9522615Z * [new tag] v1.0.0 -> v1.0.0 2025-08-26T19:43:57.9523607Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-08-26T19:43:57.9524227Z * [new tag] v1.0.1 -> v1.0.1 2025-08-26T19:43:57.9525573Z * [new tag] v1.0rc0 -> v1.0rc0 2025-08-26T19:43:57.9526108Z * [new tag] v1.0rc1 -> v1.0rc1 2025-08-26T19:43:57.9526955Z * [new tag] v1.1.0 -> v1.1.0 2025-08-26T19:43:57.9527791Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-08-26T19:43:57.9528749Z * [new tag] v1.10.0 -> v1.10.0 2025-08-26T19:43:57.9529665Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-08-26T19:43:57.9530390Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-08-26T19:43:57.9531079Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-08-26T19:43:57.9531915Z * [new tag] v1.10.1 -> v1.10.1 2025-08-26T19:43:57.9532528Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-08-26T19:43:57.9533196Z * [new tag] v1.10.2 -> v1.10.2 2025-08-26T19:43:57.9533790Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-08-26T19:43:57.9534758Z * [new tag] v1.11.0 -> v1.11.0 2025-08-26T19:43:57.9535606Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-08-26T19:43:57.9536386Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-08-26T19:43:57.9537365Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-08-26T19:43:57.9538065Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-08-26T19:43:57.9538881Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-08-26T19:43:57.9539487Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-08-26T19:43:57.9540106Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-08-26T19:43:57.9541250Z * [new tag] v1.12.0 -> v1.12.0 2025-08-26T19:43:57.9542067Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-08-26T19:43:57.9542865Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-08-26T19:43:57.9543841Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-08-26T19:43:57.9544612Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-08-26T19:43:57.9545377Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-08-26T19:43:57.9546361Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-08-26T19:43:57.9546820Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-08-26T19:43:57.9547477Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-08-26T19:43:57.9548107Z * [new tag] v1.12.1 -> v1.12.1 2025-08-26T19:43:57.9549160Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-08-26T19:43:57.9549851Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-08-26T19:43:57.9550832Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-08-26T19:43:57.9551618Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-08-26T19:43:57.9552356Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-08-26T19:43:57.9553031Z * [new tag] v1.13.0 -> v1.13.0 2025-08-26T19:43:57.9553971Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-08-26T19:43:57.9554604Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-08-26T19:43:57.9555535Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-08-26T19:43:57.9556443Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-08-26T19:43:57.9557099Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-08-26T19:43:57.9557704Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-08-26T19:43:57.9558531Z * [new tag] v1.13.1 -> v1.13.1 2025-08-26T19:43:57.9559166Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-08-26T19:43:57.9559921Z * [new tag] v1.2.0 -> v1.2.0 2025-08-26T19:43:57.9561002Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-08-26T19:43:57.9561547Z * [new tag] v1.3.0 -> v1.3.0 2025-08-26T19:43:57.9562324Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-08-26T19:43:57.9562949Z * [new tag] v1.3.1 -> v1.3.1 2025-08-26T19:43:57.9563686Z * [new tag] v1.4.0 -> v1.4.0 2025-08-26T19:43:57.9564686Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-08-26T19:43:57.9565113Z * [new tag] v1.4.1 -> v1.4.1 2025-08-26T19:43:57.9566119Z * [new tag] v1.5.0 -> v1.5.0 2025-08-26T19:43:57.9567133Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-08-26T19:43:57.9567997Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-08-26T19:43:57.9568741Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-08-26T19:43:57.9569496Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-08-26T19:43:57.9570118Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-08-26T19:43:57.9571087Z * [new tag] v1.5.1 -> v1.5.1 2025-08-26T19:43:57.9571659Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-08-26T19:43:57.9572266Z * [new tag] v1.6.0 -> v1.6.0 2025-08-26T19:43:57.9583206Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-08-26T19:43:57.9583582Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-08-26T19:43:57.9583743Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-08-26T19:43:57.9583924Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-08-26T19:43:57.9584072Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-08-26T19:43:57.9584236Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-08-26T19:43:57.9584378Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-08-26T19:43:57.9584551Z * [new tag] v1.7.0 -> v1.7.0 2025-08-26T19:43:57.9584755Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-08-26T19:43:57.9584913Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-08-26T19:43:57.9585070Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-08-26T19:43:57.9585211Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-08-26T19:43:57.9585364Z * [new tag] v1.7.1 -> v1.7.1 2025-08-26T19:43:57.9585521Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-08-26T19:43:57.9585664Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-08-26T19:43:57.9585933Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-08-26T19:43:57.9586995Z * [new tag] v1.8.0 -> v1.8.0 2025-08-26T19:43:57.9587818Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-08-26T19:43:57.9588423Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-08-26T19:43:57.9589471Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-08-26T19:43:57.9590111Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-08-26T19:43:57.9590776Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-08-26T19:43:57.9591351Z * [new tag] v1.8.1 -> v1.8.1 2025-08-26T19:43:57.9595805Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-08-26T19:43:57.9596739Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-08-26T19:43:57.9597310Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-08-26T19:43:57.9598735Z * [new tag] v1.8.2 -> v1.8.2 2025-08-26T19:43:57.9599266Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-08-26T19:43:57.9600184Z * [new tag] v1.9.0 -> v1.9.0 2025-08-26T19:43:57.9601071Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-08-26T19:43:57.9601895Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-08-26T19:43:57.9602767Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-08-26T19:43:57.9603413Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-08-26T19:43:57.9604268Z * [new tag] v1.9.1 -> v1.9.1 2025-08-26T19:43:57.9605581Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-08-26T19:43:57.9605815Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-08-26T19:43:57.9606703Z * [new tag] v2.0.0 -> v2.0.0 2025-08-26T19:43:57.9607570Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-08-26T19:43:57.9608384Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-08-26T19:43:57.9609221Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-08-26T19:43:57.9610049Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-08-26T19:43:57.9610911Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-08-26T19:43:57.9611406Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-08-26T19:43:57.9612355Z * [new tag] v2.0.1 -> v2.0.1 2025-08-26T19:43:57.9613231Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-08-26T19:43:57.9613898Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-08-26T19:43:57.9614610Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-08-26T19:43:57.9615221Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-08-26T19:43:57.9616688Z * [new tag] v2.1.0 -> v2.1.0 2025-08-26T19:43:57.9617450Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-08-26T19:43:57.9618260Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-08-26T19:43:57.9619191Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-08-26T19:43:57.9619997Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-08-26T19:43:57.9621117Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-08-26T19:43:57.9621648Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-08-26T19:43:57.9622366Z * [new tag] v2.1.1 -> v2.1.1 2025-08-26T19:43:57.9623354Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-08-26T19:43:57.9624225Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-08-26T19:43:57.9625124Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-08-26T19:43:57.9625989Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-08-26T19:43:57.9626698Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-08-26T19:43:57.9627327Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-08-26T19:43:57.9628149Z * [new tag] v2.1.2 -> v2.1.2 2025-08-26T19:43:57.9629055Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-08-26T19:43:57.9629943Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-08-26T19:43:57.9630483Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-08-26T19:43:57.9631405Z * [new tag] v2.2.0 -> v2.2.0 2025-08-26T19:43:57.9632658Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-08-26T19:43:57.9633474Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-08-26T19:43:57.9634289Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-08-26T19:43:57.9635014Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-08-26T19:43:57.9635839Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-08-26T19:43:57.9636670Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-08-26T19:43:57.9637240Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-08-26T19:43:57.9637811Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-08-26T19:43:57.9638896Z * [new tag] v2.2.1 -> v2.2.1 2025-08-26T19:43:57.9639710Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-08-26T19:43:57.9640284Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-08-26T19:43:57.9640838Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-08-26T19:43:57.9641527Z * [new tag] v2.2.2 -> v2.2.2 2025-08-26T19:43:57.9642493Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-08-26T19:43:57.9643246Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-08-26T19:43:57.9643789Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-08-26T19:43:57.9644598Z * [new tag] v2.3.0 -> v2.3.0 2025-08-26T19:43:57.9645401Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-08-26T19:43:57.9646584Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-08-26T19:43:57.9647379Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-08-26T19:43:57.9647971Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-08-26T19:43:57.9649056Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-08-26T19:43:57.9649809Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-08-26T19:43:57.9650591Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-08-26T19:43:57.9651538Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-08-26T19:43:57.9652059Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-08-26T19:43:57.9653092Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-08-26T19:43:57.9653793Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-08-26T19:43:57.9654430Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-08-26T19:43:57.9655141Z * [new tag] v2.3.1 -> v2.3.1 2025-08-26T19:43:57.9655933Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-08-26T19:43:57.9656900Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-08-26T19:43:57.9657573Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-08-26T19:43:57.9658515Z * [new tag] v2.4.0 -> v2.4.0 2025-08-26T19:43:57.9659396Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-08-26T19:43:57.9660079Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-08-26T19:43:57.9661101Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-08-26T19:43:57.9661970Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-08-26T19:43:57.9662882Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-08-26T19:43:57.9663693Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-08-26T19:43:57.9664567Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-08-26T19:43:57.9665378Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-08-26T19:43:57.9666315Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-08-26T19:43:57.9666977Z * [new tag] v2.4.1 -> v2.4.1 2025-08-26T19:43:57.9667996Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-08-26T19:43:57.9668762Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-08-26T19:43:57.9669785Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-08-26T19:43:57.9670453Z * [new tag] v2.5.0 -> v2.5.0 2025-08-26T19:43:57.9671346Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-08-26T19:43:57.9672014Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-08-26T19:43:57.9672734Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-08-26T19:43:57.9673589Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-08-26T19:43:57.9674445Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-08-26T19:43:57.9675375Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-08-26T19:43:57.9676367Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-08-26T19:43:57.9677239Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-08-26T19:43:57.9678085Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-08-26T19:43:57.9678954Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-08-26T19:43:57.9679645Z * [new tag] v2.5.1 -> v2.5.1 2025-08-26T19:43:57.9680216Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-08-26T19:43:57.9680886Z * [new tag] v2.6.0 -> v2.6.0 2025-08-26T19:43:57.9681873Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-08-26T19:43:57.9682791Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-08-26T19:43:57.9683593Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-08-26T19:43:57.9684928Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-08-26T19:43:57.9686053Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-08-26T19:43:57.9687075Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-08-26T19:43:57.9687858Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-08-26T19:43:57.9688899Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-08-26T19:43:57.9689716Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-08-26T19:43:57.9690856Z * [new tag] v2.7.0 -> v2.7.0 2025-08-26T19:43:57.9691565Z * [new tag] v2.7.0-rc1 -> v2.7.0-rc1 2025-08-26T19:43:57.9692422Z * [new tag] v2.7.0-rc10 -> v2.7.0-rc10 2025-08-26T19:43:57.9693551Z * [new tag] v2.7.0-rc2 -> v2.7.0-rc2 2025-08-26T19:43:57.9694450Z * [new tag] v2.7.0-rc3 -> v2.7.0-rc3 2025-08-26T19:43:57.9695241Z * [new tag] v2.7.0-rc4 -> v2.7.0-rc4 2025-08-26T19:43:57.9696266Z * [new tag] v2.7.0-rc5 -> v2.7.0-rc5 2025-08-26T19:43:57.9696873Z * [new tag] v2.7.0-rc6 -> v2.7.0-rc6 2025-08-26T19:43:57.9697815Z * [new tag] v2.7.0-rc7 -> v2.7.0-rc7 2025-08-26T19:43:57.9698709Z * [new tag] v2.7.0-rc8 -> v2.7.0-rc8 2025-08-26T19:43:57.9699643Z * [new tag] v2.7.0-rc9 -> v2.7.0-rc9 2025-08-26T19:43:57.9700266Z * [new tag] v2.7.1 -> v2.7.1 2025-08-26T19:43:57.9701428Z * [new tag] v2.7.1-rc1 -> v2.7.1-rc1 2025-08-26T19:43:57.9702182Z * [new tag] v2.7.1-rc2 -> v2.7.1-rc2 2025-08-26T19:43:57.9703234Z * [new tag] v2.7.1-rc3 -> v2.7.1-rc3 2025-08-26T19:43:57.9704225Z * [new tag] v2.7.1-rc4 -> v2.7.1-rc4 2025-08-26T19:43:57.9705029Z * [new tag] v2.7.1-rc5 -> v2.7.1-rc5 2025-08-26T19:43:57.9705647Z * [new tag] v2.8.0 -> v2.8.0 2025-08-26T19:43:57.9706588Z * [new tag] v2.8.0-rc1 -> v2.8.0-rc1 2025-08-26T19:43:57.9707384Z * [new tag] v2.8.0-rc2 -> v2.8.0-rc2 2025-08-26T19:43:57.9708438Z * [new tag] v2.8.0-rc3 -> v2.8.0-rc3 2025-08-26T19:43:57.9709365Z * [new tag] v2.8.0-rc4 -> v2.8.0-rc4 2025-08-26T19:43:57.9710138Z * [new tag] v2.8.0-rc5 -> v2.8.0-rc5 2025-08-26T19:43:57.9711089Z * [new tag] v2.8.0-rc6 -> v2.8.0-rc6 2025-08-26T19:43:57.9712028Z * [new tag] v2.8.0-rc7 -> v2.8.0-rc7 2025-08-26T19:43:57.9712756Z * [new tag] v2.8.0-rc8 -> v2.8.0-rc8 2025-08-26T19:43:57.9713602Z * [new tag] whc_flight_1 -> whc_flight_1 2025-08-26T19:43:57.9714414Z * [new tag] whc_flight_2 -> whc_flight_2 2025-08-26T19:43:57.9715225Z * [new tag] whc_flight_4 -> whc_flight_4 2025-08-26T19:43:58.0287905Z [command]/usr/bin/git rev-parse --verify --quiet 262640fd220236042fbf4443cc163c8838c84c3d^{object} 2025-08-26T19:43:58.0310882Z 262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:43:58.0315163Z ##[endgroup] 2025-08-26T19:43:58.0315666Z ##[group]Determining the checkout info 2025-08-26T19:43:58.0316783Z ##[endgroup] 2025-08-26T19:43:58.0321292Z [command]/usr/bin/git sparse-checkout disable 2025-08-26T19:43:58.0353000Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-08-26T19:43:58.0377110Z ##[group]Checking out the ref 2025-08-26T19:43:58.0381291Z [command]/usr/bin/git checkout --progress --force 262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:43:59.0615857Z Updating files: 87% (17043/19395) 2025-08-26T19:43:59.0724637Z Updating files: 88% (17068/19395) 2025-08-26T19:43:59.0843288Z Updating files: 89% (17262/19395) 2025-08-26T19:43:59.1000621Z Updating files: 90% (17456/19395) 2025-08-26T19:43:59.1118723Z Updating files: 91% (17650/19395) 2025-08-26T19:43:59.1243066Z Updating files: 92% (17844/19395) 2025-08-26T19:43:59.1418850Z Updating files: 93% (18038/19395) 2025-08-26T19:43:59.1603494Z Updating files: 94% (18232/19395) 2025-08-26T19:43:59.1776569Z Updating files: 95% (18426/19395) 2025-08-26T19:43:59.1923439Z Updating files: 96% (18620/19395) 2025-08-26T19:43:59.2096878Z Updating files: 97% (18814/19395) 2025-08-26T19:43:59.2339128Z Updating files: 98% (19008/19395) 2025-08-26T19:43:59.2485021Z Updating files: 99% (19202/19395) 2025-08-26T19:43:59.2485407Z Updating files: 100% (19395/19395) 2025-08-26T19:43:59.2485966Z Updating files: 100% (19395/19395), done. 2025-08-26T19:43:59.2759491Z Note: switching to '262640fd220236042fbf4443cc163c8838c84c3d'. 2025-08-26T19:43:59.2759816Z 2025-08-26T19:43:59.2760055Z You are in 'detached HEAD' state. You can look around, make experimental 2025-08-26T19:43:59.2760629Z changes and commit them, and you can discard any commits you make in this 2025-08-26T19:43:59.2761185Z state without impacting any branches by switching back to a branch. 2025-08-26T19:43:59.2761534Z 2025-08-26T19:43:59.2761748Z If you want to create a new branch to retain commits you create, you may 2025-08-26T19:43:59.2762290Z do so (now or later) by using -c with the switch command. Example: 2025-08-26T19:43:59.2762587Z 2025-08-26T19:43:59.2762716Z git switch -c 2025-08-26T19:43:59.2762921Z 2025-08-26T19:43:59.2763044Z Or undo this operation with: 2025-08-26T19:43:59.2763231Z 2025-08-26T19:43:59.2763321Z git switch - 2025-08-26T19:43:59.2763477Z 2025-08-26T19:43:59.2763717Z Turn off this advice by setting config variable advice.detachedHead to false 2025-08-26T19:43:59.2764084Z 2025-08-26T19:43:59.2764319Z HEAD is now at 262640fd220 [ROCm][CI] restore test_flex_attention tests (#161519) 2025-08-26T19:43:59.2826863Z ##[endgroup] 2025-08-26T19:43:59.2827712Z ##[group]Setting up auth for fetching submodules 2025-08-26T19:43:59.2833409Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-08-26T19:43:59.2880003Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-08-26T19:43:59.2907056Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-08-26T19:43:59.2931491Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-08-26T19:43:59.2953605Z ##[endgroup] 2025-08-26T19:43:59.2954091Z ##[group]Fetching submodules 2025-08-26T19:43:59.2957343Z [command]/usr/bin/git submodule sync --recursive 2025-08-26T19:43:59.3249178Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-08-26T19:43:59.3537352Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-08-26T19:43:59.3538514Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-08-26T19:43:59.3541123Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-08-26T19:43:59.3898878Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-08-26T19:43:59.3900373Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-08-26T19:43:59.3903974Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-08-26T19:43:59.3906804Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-08-26T19:43:59.3910113Z Submodule 'third_party/aiter' (https://github.com/ROCm/aiter.git) registered for path 'third_party/aiter' 2025-08-26T19:43:59.3913577Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-08-26T19:43:59.3917154Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-08-26T19:43:59.3920698Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-08-26T19:43:59.3935743Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-08-26T19:43:59.3939586Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-08-26T19:43:59.3943539Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-08-26T19:43:59.3947605Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-08-26T19:43:59.3952486Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-08-26T19:43:59.3956882Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-08-26T19:43:59.3961007Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-08-26T19:43:59.3965485Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-08-26T19:43:59.3983524Z Submodule 'third_party/gloo' (https://github.com/pytorch/gloo) registered for path 'third_party/gloo' 2025-08-26T19:43:59.3988153Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-08-26T19:43:59.3992993Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-08-26T19:43:59.3997722Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-08-26T19:43:59.4002619Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-08-26T19:43:59.4007676Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-08-26T19:43:59.4012484Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-08-26T19:43:59.4017514Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-08-26T19:43:59.4034236Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-08-26T19:43:59.4039640Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-08-26T19:43:59.4044794Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-08-26T19:43:59.4050292Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-08-26T19:43:59.4055724Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-08-26T19:43:59.4061592Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-08-26T19:43:59.4067206Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-08-26T19:43:59.4074654Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-08-26T19:43:59.4090566Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-08-26T19:43:59.4098341Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-08-26T19:43:59.4129632Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-08-26T19:43:59.6643165Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-08-26T19:43:59.6644482Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-08-26T19:43:59.6674762Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-08-26T19:44:09.2965856Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-08-26T19:44:09.2967641Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-08-26T19:44:09.2969292Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-08-26T19:44:09.3111847Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-08-26T19:44:09.3113359Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-08-26T19:44:09.3114883Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-08-26T19:44:09.3116397Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-08-26T19:44:09.3117998Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-08-26T19:44:09.3119677Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-08-26T19:44:09.3121127Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2025-08-26T19:44:09.3122494Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-08-26T19:44:09.3123918Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-08-26T19:44:09.3125334Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2025-08-26T19:44:09.3127860Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-08-26T19:44:09.3129734Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2025-08-26T19:44:09.3131795Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-08-26T19:44:09.3133590Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2025-08-26T19:44:09.3135036Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-08-26T19:44:09.3137011Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2025-08-26T19:44:09.9675974Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-08-26T19:44:09.9677835Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-08-26T19:44:09.9679542Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter'... 2025-08-26T19:44:09.9681215Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-08-26T19:44:09.9708808Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-08-26T19:44:38.5556452Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-08-26T19:44:38.5558118Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-08-26T19:44:38.5559680Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-08-26T19:44:38.5561144Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-08-26T19:44:38.5562581Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-08-26T19:44:38.5564011Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-08-26T19:44:38.5565455Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-08-26T19:44:38.5566915Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-08-26T19:44:38.6558187Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-08-26T19:44:40.2517398Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-08-26T19:44:40.2635629Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-08-26T19:44:40.2726530Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-08-26T19:44:40.2969619Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-08-26T19:44:40.3733686Z Submodule path 'third_party/NVTX': checked out '2942f167cc30c5e3a44a2aecd5b0d9c07ff61a07' 2025-08-26T19:44:40.4241282Z Submodule path 'third_party/VulkanMemoryAllocator': checked out '1d8f600fd424278486eade7ed3e877c99f0846b1' 2025-08-26T19:44:41.1449332Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-08-26T19:44:41.3000777Z Submodule path 'third_party/aiter': checked out '01aae101b9e5e94d6c16a9514c9fb8df99c93150' 2025-08-26T19:44:41.3021626Z Submodule '3rdparty/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/aiter/3rdparty/composable_kernel' 2025-08-26T19:44:41.3046357Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/aiter/3rdparty/composable_kernel'... 2025-08-26T19:44:45.3332933Z Submodule path 'third_party/aiter/3rdparty/composable_kernel': checked out 'cffe8fa2a442ac8e80dd236a1a5d24fe3d7e0cbf' 2025-08-26T19:44:45.3564648Z Submodule path 'third_party/benchmark': checked out '299e5928955cc62af9968370293b916f5130916f' 2025-08-26T19:44:45.6720083Z Submodule path 'third_party/composable_kernel': checked out '7fe50dc3da2069d6645d9deb8c017a876472a977' 2025-08-26T19:44:45.7266890Z Submodule path 'third_party/cpp-httplib': checked out '3af7f2c16147f3fbc6e4d717032daf505dc1652c' 2025-08-26T19:44:45.8229699Z Submodule path 'third_party/cpuinfo': checked out '5e3d2445e6a84d9599bee2bf78edbb4d80865e1d' 2025-08-26T19:44:45.8657524Z Submodule path 'third_party/cudnn_frontend': checked out 'f937055efc6d414d11f4c6577e3977fe74f35fb6' 2025-08-26T19:44:46.4819193Z Submodule path 'third_party/cutlass': checked out 'e51efbfe18fe4f4cbb66ab814c55bf4aa0185491' 2025-08-26T19:44:46.6206210Z Submodule path 'third_party/fbgemm': checked out '21c7d30c526c0f1ad873ecc632dca6cfa8a69067' 2025-08-26T19:44:46.6225754Z Submodule 'external/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/external/asmjit' 2025-08-26T19:44:46.6228024Z Submodule 'external/composable_kernel' (https://github.com/jwfromm/composable_kernel.git) registered for path 'third_party/fbgemm/external/composable_kernel' 2025-08-26T19:44:46.6230260Z Submodule 'external/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/external/cpuinfo' 2025-08-26T19:44:46.6232952Z Submodule 'external/cutlass' (https://github.com/jwfromm/cutlass) registered for path 'third_party/fbgemm/external/cutlass' 2025-08-26T19:44:46.6235570Z Submodule 'external/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/external/googletest' 2025-08-26T19:44:46.6238397Z Submodule 'external/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/external/hipify_torch' 2025-08-26T19:44:46.6241026Z Submodule 'external/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/fbgemm/external/json' 2025-08-26T19:44:46.6269183Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/asmjit'... 2025-08-26T19:44:48.5829181Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/hipify_torch'... 2025-08-26T19:44:48.5831495Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cpuinfo'... 2025-08-26T19:44:48.6196270Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/composable_kernel'... 2025-08-26T19:44:48.8526771Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/googletest'... 2025-08-26T19:44:48.9527859Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/cutlass'... 2025-08-26T19:44:49.8513055Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/external/json'... 2025-08-26T19:44:55.6528158Z Submodule path 'third_party/fbgemm/external/asmjit': checked out 'a3199e8857792cd10b7589ff5d58343d2c9008ea' 2025-08-26T19:44:55.9084332Z Submodule path 'third_party/fbgemm/external/composable_kernel': checked out 'b1281b8b08d973a7064f864f47eeb30f3e2596e9' 2025-08-26T19:44:56.0076464Z Submodule path 'third_party/fbgemm/external/cpuinfo': checked out '6543fec09b2f04ac4a666882998b534afc9c1349' 2025-08-26T19:44:56.6158096Z Submodule path 'third_party/fbgemm/external/cutlass': checked out 'b40777404c174b9694a870bff5c13ce6b7f656ad' 2025-08-26T19:44:56.6620148Z Submodule path 'third_party/fbgemm/external/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-08-26T19:44:56.6734709Z Submodule path 'third_party/fbgemm/external/hipify_torch': checked out 'a4337c69fe0e2552a7b7b0669178926beeed828c' 2025-08-26T19:44:56.7747049Z Submodule path 'third_party/fbgemm/external/json': checked out '9cca280a4d0ccf0c08f47a99aa71d1b0e52f8d03' 2025-08-26T19:44:56.8428935Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-08-26T19:44:56.8449778Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-08-26T19:44:56.8451162Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-08-26T19:44:56.8476139Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-08-26T19:45:00.6638574Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-08-26T19:45:00.8995956Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-08-26T19:45:01.4532779Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-08-26T19:45:01.5904154Z Submodule path 'third_party/flatbuffers': checked out 'a2cd1ea3b6d3fee220106b5fed3f7ce8da9eb757' 2025-08-26T19:45:01.6242884Z Submodule path 'third_party/fmt': checked out '40626af88bd7df9a5fb80be7b25ac85b122d6c21' 2025-08-26T19:45:01.6648873Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-08-26T19:45:01.6896153Z Submodule path 'third_party/gloo': checked out 'c7b7b022c124d9643957d9bd55f57ac59fce8fa2' 2025-08-26T19:45:01.7350488Z Submodule path 'third_party/googletest': checked out '52eb8108c5bdec04579160ae17225d66034bd723' 2025-08-26T19:45:01.7481171Z Submodule path 'third_party/ideep': checked out '719d8e6cd7f7a0e01b155657526d693acf97c2b3' 2025-08-26T19:45:01.7497941Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-08-26T19:45:01.7522096Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-08-26T19:45:15.0945884Z Submodule path 'third_party/ideep/mkl-dnn': checked out '8d263e693366ef8db40acc569cc7d8edf644556d' 2025-08-26T19:45:15.1144799Z Submodule path 'third_party/ittapi': checked out 'dec1d23ca65ab069d225dfe40dea14f455170959' 2025-08-26T19:45:15.2079084Z Submodule path 'third_party/kineto': checked out '5e7501833f1021ce6f618572d3baf657b6319658' 2025-08-26T19:45:15.2098152Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-08-26T19:45:15.2100562Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-08-26T19:45:15.2103630Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-08-26T19:45:15.2131840Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-08-26T19:45:16.4839336Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-08-26T19:45:17.3266322Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-08-26T19:45:17.4107363Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-08-26T19:45:17.4123560Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-08-26T19:45:17.4125985Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-08-26T19:45:17.4128903Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-08-26T19:45:17.4132211Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-08-26T19:45:17.4135273Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-08-26T19:45:17.4138996Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-08-26T19:45:17.4141955Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-08-26T19:45:17.4145094Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-08-26T19:45:17.4174177Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-08-26T19:45:19.4128162Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-08-26T19:45:19.4130861Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-08-26T19:45:19.4140395Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-08-26T19:45:19.4142620Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-08-26T19:45:19.4750135Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-08-26T19:45:19.8535356Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'... 2025-08-26T19:45:19.9536816Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'... 2025-08-26T19:45:26.3853089Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9' 2025-08-26T19:45:26.4032552Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400' 2025-08-26T19:45:26.4393947Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05' 2025-08-26T19:45:26.4532288Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067' 2025-08-26T19:45:26.4548150Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-08-26T19:45:26.4574051Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'... 2025-08-26T19:45:26.7517338Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4' 2025-08-26T19:45:26.7703093Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446' 2025-08-26T19:45:26.8106537Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850' 2025-08-26T19:45:26.9094664Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5' 2025-08-26T19:45:26.9260821Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150' 2025-08-26T19:45:26.9650081Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164' 2025-08-26T19:45:27.0246673Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347' 2025-08-26T19:45:27.0674131Z Submodule path 'third_party/kleidiai': checked out 'cca02c2f69dd18e1f12647c1c0bdc8cf90e680c7' 2025-08-26T19:45:27.1052440Z Submodule path 'third_party/mimalloc': 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'third_party/opentelemetry-cpp/third_party/benchmark' 2025-08-26T19:45:28.6686045Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest' 2025-08-26T19:45:28.6688511Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-08-26T19:45:28.6691226Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-08-26T19:45:28.6694428Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-08-26T19:45:28.6697147Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-26T19:45:28.6699971Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-26T19:45:28.6703006Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-26T19:45:28.6730669Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'... 2025-08-26T19:45:29.2102985Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'... 2025-08-26T19:45:29.2105075Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'... 2025-08-26T19:45:29.2106302Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'... 2025-08-26T19:45:29.2107539Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'... 2025-08-26T19:45:29.3104154Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'... 2025-08-26T19:45:30.5185491Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'... 2025-08-26T19:45:39.8757102Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'... 2025-08-26T19:45:40.1931441Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2' 2025-08-26T19:45:40.2332540Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1' 2025-08-26T19:45:40.2501407Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out 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2025-08-26T19:45:48.1449555Z [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" 2025-08-26T19:45:48.1735152Z Entering 'android/libs/fbjni' 2025-08-26T19:45:48.1785223Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config remote.origin.url 2025-08-26T19:45:48.1801607Z Entering 'third_party/FP16' 2025-08-26T19:45:48.1850865Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config remote.origin.url 2025-08-26T19:45:48.1865080Z Entering 'third_party/FXdiv' 2025-08-26T19:45:48.1913810Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config remote.origin.url 2025-08-26T19:45:48.1929151Z Entering 'third_party/NNPACK' 2025-08-26T19:45:48.1976957Z 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2025-08-26T19:45:48.9723300Z Entering 'third_party/mimalloc' 2025-08-26T19:45:48.9763754Z Entering 'third_party/nlohmann' 2025-08-26T19:45:48.9806798Z Entering 'third_party/onnx' 2025-08-26T19:45:48.9866890Z Entering 'third_party/onnx/third_party/pybind11' 2025-08-26T19:45:48.9911422Z Entering 'third_party/opentelemetry-cpp' 2025-08-26T19:45:48.9955256Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-08-26T19:45:48.9994729Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-08-26T19:45:49.0034522Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-08-26T19:45:49.0073424Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-08-26T19:45:49.0114381Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-08-26T19:45:49.0152470Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-26T19:45:49.0190053Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-26T19:45:49.0228809Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-08-26T19:45:49.0269815Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-08-26T19:45:49.0311593Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-26T19:45:49.0371063Z Entering 'third_party/pocketfft' 2025-08-26T19:45:49.0413833Z Entering 'third_party/protobuf' 2025-08-26T19:45:49.0457201Z Entering 'third_party/protobuf/third_party/benchmark' 2025-08-26T19:45:49.0497122Z Entering 'third_party/protobuf/third_party/googletest' 2025-08-26T19:45:49.0538461Z Entering 'third_party/psimd' 2025-08-26T19:45:49.0579367Z Entering 'third_party/pthreadpool' 2025-08-26T19:45:49.0618895Z Entering 'third_party/pybind11' 2025-08-26T19:45:49.0658971Z Entering 'third_party/python-peachpy' 2025-08-26T19:45:49.0701085Z Entering 'third_party/sleef' 2025-08-26T19:45:49.0739901Z Entering 'third_party/tensorpipe' 2025-08-26T19:45:49.0780086Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-08-26T19:45:49.0818623Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-08-26T19:45:49.0857738Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-08-26T19:45:49.0897940Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-08-26T19:45:49.0935425Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-08-26T19:45:49.0991475Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-08-26T19:45:49.1276303Z Entering 'android/libs/fbjni' 2025-08-26T19:45:49.1318912Z Entering 'third_party/FP16' 2025-08-26T19:45:49.1360698Z Entering 'third_party/FXdiv' 2025-08-26T19:45:49.1401163Z Entering 'third_party/NNPACK' 2025-08-26T19:45:49.1440581Z Entering 'third_party/NVTX' 2025-08-26T19:45:49.1481844Z Entering 'third_party/VulkanMemoryAllocator' 2025-08-26T19:45:49.1525559Z Entering 'third_party/XNNPACK' 2025-08-26T19:45:49.1581399Z Entering 'third_party/aiter' 2025-08-26T19:45:49.1624297Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-08-26T19:45:49.1673037Z Entering 'third_party/benchmark' 2025-08-26T19:45:49.1713859Z Entering 'third_party/composable_kernel' 2025-08-26T19:45:49.1763019Z Entering 'third_party/cpp-httplib' 2025-08-26T19:45:49.1803675Z Entering 'third_party/cpuinfo' 2025-08-26T19:45:49.1843501Z Entering 'third_party/cudnn_frontend' 2025-08-26T19:45:49.1883614Z Entering 'third_party/cutlass' 2025-08-26T19:45:49.1933790Z Entering 'third_party/fbgemm' 2025-08-26T19:45:49.1975983Z Entering 'third_party/fbgemm/external/asmjit' 2025-08-26T19:45:49.2015300Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-08-26T19:45:49.2061498Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-08-26T19:45:49.2100067Z Entering 'third_party/fbgemm/external/cutlass' 2025-08-26T19:45:49.2146938Z Entering 'third_party/fbgemm/external/googletest' 2025-08-26T19:45:49.2185468Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-08-26T19:45:49.2223863Z Entering 'third_party/fbgemm/external/json' 2025-08-26T19:45:49.2265274Z Entering 'third_party/flash-attention' 2025-08-26T19:45:49.2307023Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-08-26T19:45:49.2352372Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-08-26T19:45:49.2401518Z Entering 'third_party/flatbuffers' 2025-08-26T19:45:49.2444750Z Entering 'third_party/fmt' 2025-08-26T19:45:49.2484949Z Entering 'third_party/gemmlowp/gemmlowp' 2025-08-26T19:45:49.2527161Z Entering 'third_party/gloo' 2025-08-26T19:45:49.2567270Z Entering 'third_party/googletest' 2025-08-26T19:45:49.2607405Z Entering 'third_party/ideep' 2025-08-26T19:45:49.2645982Z Entering 'third_party/ideep/mkl-dnn' 2025-08-26T19:45:49.2693157Z Entering 'third_party/ittapi' 2025-08-26T19:45:49.2733943Z Entering 'third_party/kineto' 2025-08-26T19:45:49.2774284Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-08-26T19:45:49.2814548Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-08-26T19:45:49.2856185Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-08-26T19:45:49.2896484Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-08-26T19:45:49.2935844Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-08-26T19:45:49.2975155Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-08-26T19:45:49.3017456Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-08-26T19:45:49.3057933Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-08-26T19:45:49.3098142Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-08-26T19:45:49.3138259Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-08-26T19:45:49.3178042Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-08-26T19:45:49.3217545Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-08-26T19:45:49.3258488Z Entering 'third_party/kleidiai' 2025-08-26T19:45:49.3299641Z Entering 'third_party/mimalloc' 2025-08-26T19:45:49.3339343Z Entering 'third_party/nlohmann' 2025-08-26T19:45:49.3380257Z Entering 'third_party/onnx' 2025-08-26T19:45:49.3439299Z Entering 'third_party/onnx/third_party/pybind11' 2025-08-26T19:45:49.3482035Z Entering 'third_party/opentelemetry-cpp' 2025-08-26T19:45:49.3525214Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-08-26T19:45:49.3563885Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-08-26T19:45:49.3602148Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-08-26T19:45:49.3639466Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-08-26T19:45:49.3678536Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-08-26T19:45:49.3716581Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-26T19:45:49.3754289Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-26T19:45:49.3792667Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-08-26T19:45:49.3832136Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-08-26T19:45:49.3871872Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-26T19:45:49.3930611Z Entering 'third_party/pocketfft' 2025-08-26T19:45:49.3972132Z Entering 'third_party/protobuf' 2025-08-26T19:45:49.4016459Z Entering 'third_party/protobuf/third_party/benchmark' 2025-08-26T19:45:49.4056865Z Entering 'third_party/protobuf/third_party/googletest' 2025-08-26T19:45:49.4099428Z Entering 'third_party/psimd' 2025-08-26T19:45:49.4139000Z Entering 'third_party/pthreadpool' 2025-08-26T19:45:49.4177801Z Entering 'third_party/pybind11' 2025-08-26T19:45:49.4217975Z Entering 'third_party/python-peachpy' 2025-08-26T19:45:49.4258104Z Entering 'third_party/sleef' 2025-08-26T19:45:49.4302111Z Entering 'third_party/tensorpipe' 2025-08-26T19:45:49.4339712Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-08-26T19:45:49.4378574Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-08-26T19:45:49.4416888Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-08-26T19:45:49.4454632Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-08-26T19:45:49.4493236Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-08-26T19:45:49.4545223Z ##[endgroup] 2025-08-26T19:45:49.4580142Z [command]/usr/bin/git log -1 --format=%H 2025-08-26T19:45:49.4602184Z 262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:45:49.4802392Z Prepare all required actions 2025-08-26T19:45:49.4803057Z Getting action download info 2025-08-26T19:45:49.6338228Z ##[group]Run ./.github/actions/setup-linux 2025-08-26T19:45:49.6338577Z env: 2025-08-26T19:45:49.6338808Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:49.6339064Z ##[endgroup] 2025-08-26T19:45:49.6383891Z ##[group]Run set -euo pipefail 2025-08-26T19:45:49.6384278Z set -euo pipefail 2025-08-26T19:45:49.6384587Z function get_ec2_metadata() { 2025-08-26T19:45:49.6384972Z  # Pulled from instance metadata endpoint for EC2 2025-08-26T19:45:49.6385628Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-08-26T19:45:49.6386206Z  category=$1 2025-08-26T19:45:49.6386578Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-08-26T19:45:49.6387035Z  runner_name_str=i-0d10cabc7fe6d3867 2025-08-26T19:45:49.6387669Z  if [[ -f /.inarc ]]; then 2025-08-26T19:45:49.6388154Z  echo "ARC Runner, no info on ec2 metadata" 2025-08-26T19:45:49.6388668Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-08-26T19:45:49.6389304Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-08-26T19:45:49.6389891Z  else 2025-08-26T19:45:49.6391094Z  curl -H "X-aws-ec2-metadata-token: $(curl -s -X PUT "http://169.254.169.254/latest/api/token" -H "X-aws-ec2-metadata-token-ttl-seconds: 30")" -fsSL "http://169.254.169.254/latest/meta-data/${category}" 2025-08-26T19:45:49.6392987Z  fi 2025-08-26T19:45:49.6393280Z } 2025-08-26T19:45:49.6393643Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-08-26T19:45:49.6394230Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-08-26T19:45:49.6394925Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-08-26T19:45:49.6395521Z echo "system info $(uname -a)" 2025-08-26T19:45:49.6404680Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:49.6405077Z env: 2025-08-26T19:45:49.6405305Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:49.6405568Z ##[endgroup] 2025-08-26T19:45:49.6546389Z ami-id: ami-05ffe3c48a9991133 2025-08-26T19:45:49.6652997Z instance-id: i-0d10cabc7fe6d3867 2025-08-26T19:45:49.6755152Z instance-type: c5.2xlarge 2025-08-26T19:45:49.6765405Z system info Linux ip-10-1-64-236.ec2.internal 6.1.141-155.222.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Tue Jun 17 10:29:47 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-08-26T19:45:49.6790780Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-26T19:45:49.6792042Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-26T19:45:49.6798555Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:49.6798974Z env: 2025-08-26T19:45:49.6799191Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:49.6799469Z ##[endgroup] 2025-08-26T19:45:49.6855618Z ##[group]Run if systemctl is-active --quiet docker; then 2025-08-26T19:45:49.6856106Z if systemctl is-active --quiet docker; then 2025-08-26T19:45:49.6856510Z  echo "Docker daemon is running..."; 2025-08-26T19:45:49.6856854Z else 2025-08-26T19:45:49.6857210Z  echo "Starting docker daemon..." && sudo systemctl start docker; 2025-08-26T19:45:49.6857653Z fi 2025-08-26T19:45:49.6863699Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:49.6864095Z env: 2025-08-26T19:45:49.6864314Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:49.6864581Z ##[endgroup] 2025-08-26T19:45:49.6946865Z Docker daemon is running... 2025-08-26T19:45:49.6992491Z ##[group]Run nick-fields/retry@v3.0.0 2025-08-26T19:45:49.6992819Z with: 2025-08-26T19:45:49.6993044Z shell: bash 2025-08-26T19:45:49.6993459Z timeout_minutes: 5 2025-08-26T19:45:49.6993724Z max_attempts: 3 2025-08-26T19:45:49.6993972Z retry_wait_seconds: 30 2025-08-26T19:45:49.6996357Z command: AWS_ACCOUNT_ID=$(aws sts get-caller-identity|grep Account|cut -f4 -d\") aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" # For LF Runners we need to make sure we also login to Meta's ECR docker registry too. META_AWS_ACCOUNT_ID=308535385114 if [ "$AWS_ACCOUNT_ID" != "$META_AWS_ACCOUNT_ID" ] ; then aws ecr get-login-password --region "$AWS_DEFAULT_REGION" | docker login --username AWS \ --password-stdin "$META_AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com" fi 2025-08-26T19:45:49.6998737Z polling_interval_seconds: 1 2025-08-26T19:45:49.6999032Z warning_on_retry: true 2025-08-26T19:45:49.6999294Z continue_on_error: false 2025-08-26T19:45:49.6999672Z env: 2025-08-26T19:45:49.6999896Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:49.7000179Z AWS_RETRY_MODE: standard 2025-08-26T19:45:49.7000442Z AWS_MAX_ATTEMPTS: 5 2025-08-26T19:45:49.7000709Z AWS_DEFAULT_REGION: us-east-1 2025-08-26T19:45:49.7000989Z ##[endgroup] 2025-08-26T19:45:51.0425080Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-26T19:45:51.0457959Z Configure a credential helper to remove this warning. See 2025-08-26T19:45:51.0458946Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-26T19:45:51.0459573Z 2025-08-26T19:45:51.0459709Z Login Succeeded 2025-08-26T19:45:51.6029893Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-26T19:45:51.6030685Z Configure a credential helper to remove this warning. See 2025-08-26T19:45:51.6031612Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-26T19:45:51.6032224Z 2025-08-26T19:45:51.6032376Z Login Succeeded 2025-08-26T19:45:51.9238190Z Command completed after 1 attempt(s). 2025-08-26T19:45:51.9308606Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-26T19:45:51.9309166Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-26T19:45:51.9309641Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-26T19:45:51.9316671Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:51.9317066Z env: 2025-08-26T19:45:51.9317294Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:51.9317569Z ##[endgroup] 2025-08-26T19:45:51.9426983Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-08-26T19:45:51.9427588Z # ignore expansion of "docker ps -q" since it could be empty 2025-08-26T19:45:51.9428021Z # shellcheck disable=SC2046 2025-08-26T19:45:51.9428371Z docker stop $(docker ps -q) || true 2025-08-26T19:45:51.9428730Z # Prune all of the docker images 2025-08-26T19:45:51.9429103Z docker system prune -af 2025-08-26T19:45:51.9434866Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:51.9435257Z env: 2025-08-26T19:45:51.9435485Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:51.9435758Z ##[endgroup] 2025-08-26T19:45:51.9676530Z "docker stop" requires at least 1 argument. 2025-08-26T19:45:51.9676940Z See 'docker stop --help'. 2025-08-26T19:45:51.9677132Z 2025-08-26T19:45:51.9677301Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-08-26T19:45:51.9677595Z 2025-08-26T19:45:51.9677706Z Stop one or more running containers 2025-08-26T19:45:51.9863299Z Total reclaimed space: 0B 2025-08-26T19:45:51.9906674Z ##[group]Run set +e 2025-08-26T19:45:51.9906966Z set +e 2025-08-26T19:45:51.9907207Z set -x 2025-08-26T19:45:51.9907425Z  2025-08-26T19:45:51.9907680Z PT_DOMAIN=download.pytorch.org 2025-08-26T19:45:51.9908287Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-08-26T19:45:51.9909264Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-08-26T19:45:51.9909819Z # one is returned at random 2025-08-26T19:45:51.9910214Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-08-26T19:45:51.9910613Z  2025-08-26T19:45:51.9910854Z if [ -z "${RESOLVED_IP}" ]; then 2025-08-26T19:45:51.9911300Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-08-26T19:45:51.9911827Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-08-26T19:45:51.9912236Z  2025-08-26T19:45:51.9912481Z  if [ -z "${RESOLVED_IP}" ]; then 2025-08-26T19:45:51.9912880Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-08-26T19:45:51.9913244Z  exit 1 2025-08-26T19:45:51.9913483Z  fi 2025-08-26T19:45:51.9913703Z fi 2025-08-26T19:45:51.9914016Z  2025-08-26T19:45:51.9914274Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-08-26T19:45:51.9914713Z  # Clean up any old records first 2025-08-26T19:45:51.9915085Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-08-26T19:45:51.9915526Z fi 2025-08-26T19:45:51.9915734Z  2025-08-26T19:45:51.9916246Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-08-26T19:45:51.9916661Z cat /etc/hosts 2025-08-26T19:45:51.9922780Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:51.9923173Z env: 2025-08-26T19:45:51.9923398Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:51.9923672Z ##[endgroup] 2025-08-26T19:45:51.9948374Z + PT_DOMAIN=download.pytorch.org 2025-08-26T19:45:51.9953735Z ++ dig -4 +short download.pytorch.org 2025-08-26T19:45:51.9954642Z ++ tail -n1 2025-08-26T19:45:52.0364879Z + RESOLVED_IP=18.160.10.76 2025-08-26T19:45:52.0365234Z + '[' -z 18.160.10.76 ']' 2025-08-26T19:45:52.0365598Z + grep -r download.pytorch.org /etc/hosts 2025-08-26T19:45:52.0380524Z + echo '18.160.10.76 download.pytorch.org' 2025-08-26T19:45:52.0381166Z + sudo tee -a /etc/hosts 2025-08-26T19:45:52.4081866Z 18.160.10.76 download.pytorch.org 2025-08-26T19:45:52.4098188Z + cat /etc/hosts 2025-08-26T19:45:52.4107073Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-08-26T19:45:52.4114122Z ::1 localhost6 localhost6.localdomain6 2025-08-26T19:45:52.4114510Z 18.160.10.76 download.pytorch.org 2025-08-26T19:45:52.4295278Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-08-26T19:45:52.4295775Z with: 2025-08-26T19:45:52.4296535Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:52.4297401Z use-custom-docker-registry: true 2025-08-26T19:45:52.4297719Z docker-build-dir: .ci/docker 2025-08-26T19:45:52.4298050Z docker-build-script: ./build.sh 2025-08-26T19:45:52.4298363Z working-directory: . 2025-08-26T19:45:52.4298725Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:52.4299250Z force-push: false 2025-08-26T19:45:52.4299501Z env: 2025-08-26T19:45:52.4299725Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:52.4299987Z ##[endgroup] 2025-08-26T19:45:52.4328774Z ##[group]Run set -ex 2025-08-26T19:45:52.4329103Z set -ex 2025-08-26T19:45:52.4329343Z  2025-08-26T19:45:52.4329794Z # If the docker build directory or the build script doesn't exist, the action will 2025-08-26T19:45:52.4330487Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-08-26T19:45:52.4331064Z # job could then download the pre-built image as usual 2025-08-26T19:45:52.4331774Z if [[ -d "${DOCKER_BUILD_DIR}" ]] && [[ -f "${DOCKER_BUILD_DIR}/${DOCKER_BUILD_SCRIPT}" ]] && [[ "${USE_CUSTOM_DOCKER_REGISTRY}" == "true" ]]; then 2025-08-26T19:45:52.4332456Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4332795Z else 2025-08-26T19:45:52.4333065Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4333556Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4333976Z  2025-08-26T19:45:52.4334551Z  echo "Not using custom ECR registry. Either it was not requested or there is no Docker build script in the ${REPO_NAME} repo..." 2025-08-26T19:45:52.4335217Z  exit 0 2025-08-26T19:45:52.4335435Z fi 2025-08-26T19:45:52.4335655Z  2025-08-26T19:45:52.4336004Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-08-26T19:45:52.4336634Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-08-26T19:45:52.4337187Z  # use it as it is, but first let's extract the tag 2025-08-26T19:45:52.4337836Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-08-26T19:45:52.4338366Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4338871Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4339293Z else 2025-08-26T19:45:52.4339569Z  if [[ "${DOCKER_IMAGE_NAME}" == *:* ]]; then 2025-08-26T19:45:52.4339955Z  CUSTOM_TAG_PREFIX=${DOCKER_IMAGE_NAME#*:} 2025-08-26T19:45:52.4340478Z  DOCKER_IMAGE_NAME=${DOCKER_IMAGE_NAME%%:*} 2025-08-26T19:45:52.4340839Z  fi 2025-08-26T19:45:52.4341305Z  DOCKER_TAG=${CUSTOM_TAG_PREFIX:+${CUSTOM_TAG_PREFIX}-}$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-08-26T19:45:52.4341925Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4342587Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4343316Z  echo "custom-tag-prefix=${CUSTOM_TAG_PREFIX}" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4343760Z fi 2025-08-26T19:45:52.4351976Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:52.4352355Z env: 2025-08-26T19:45:52.4352576Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:52.4352854Z REPO_NAME: pytorch 2025-08-26T19:45:52.4353771Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:52.4354619Z DOCKER_BUILD_DIR: .ci/docker 2025-08-26T19:45:52.4354906Z DOCKER_BUILD_SCRIPT: ./build.sh 2025-08-26T19:45:52.4355306Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:52.4355730Z USE_CUSTOM_DOCKER_REGISTRY: true 2025-08-26T19:45:52.4356037Z CUSTOM_TAG_PREFIX: 2025-08-26T19:45:52.4356280Z ##[endgroup] 2025-08-26T19:45:52.4382762Z + [[ -d .ci/docker ]] 2025-08-26T19:45:52.4383087Z + [[ -f .ci/docker/./build.sh ]] 2025-08-26T19:45:52.4383396Z + [[ true == \t\r\u\e ]] 2025-08-26T19:45:52.4383665Z + echo skip=false 2025-08-26T19:45:52.4384876Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 == *\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* ]] 2025-08-26T19:45:52.4391311Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:52.4392365Z ++ awk -F '[:,]' '{print $2}' 2025-08-26T19:45:52.4412819Z + DOCKER_TAG=pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:52.4413647Z + echo docker-tag=pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:52.4414873Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:52.4448149Z ##[group]Run set +e 2025-08-26T19:45:52.4448469Z set +e 2025-08-26T19:45:52.4448715Z set -x 2025-08-26T19:45:52.4448952Z  2025-08-26T19:45:52.4449157Z login() { 2025-08-26T19:45:52.4449646Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-08-26T19:45:52.4450179Z } 2025-08-26T19:45:52.4450392Z  2025-08-26T19:45:52.4450608Z retry () { 2025-08-26T19:45:52.4450890Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-08-26T19:45:52.4451213Z } 2025-08-26T19:45:52.4451427Z  2025-08-26T19:45:52.4451653Z retry login "${DOCKER_REGISTRY}" 2025-08-26T19:45:52.4451972Z  2025-08-26T19:45:52.4452196Z START_TIME=$(date +%s) 2025-08-26T19:45:52.4452499Z # Wait up to 120 minutes 2025-08-26T19:45:52.4452873Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-08-26T19:45:52.4453512Z  # Check if image already exists, if it does then skip building it 2025-08-26T19:45:52.4454020Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-08-26T19:45:52.4454398Z  exit 0 2025-08-26T19:45:52.4454638Z  fi 2025-08-26T19:45:52.4454845Z  2025-08-26T19:45:52.4455239Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-08-26T19:45:52.4455940Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-08-26T19:45:52.4456634Z  # latter, it will wait for the Docker images to become available before continuing 2025-08-26T19:45:52.4457176Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-08-26T19:45:52.4457581Z  # It's a Docker build job, let's build the image 2025-08-26T19:45:52.4457946Z  break 2025-08-26T19:45:52.4458199Z  else 2025-08-26T19:45:52.4458555Z  # It's a regular build job, wait for the image to become available 2025-08-26T19:45:52.4459097Z  sleep 300 2025-08-26T19:45:52.4459354Z  fi 2025-08-26T19:45:52.4459580Z done 2025-08-26T19:45:52.4459804Z  2025-08-26T19:45:52.4460151Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-08-26T19:45:52.4460827Z # be empty. The default action would be to continue rebuild the image 2025-08-26T19:45:52.4461491Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-08-26T19:45:52.4462066Z  # if we're on the base branch then use the parent commit 2025-08-26T19:45:52.4462490Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-08-26T19:45:52.4462816Z else 2025-08-26T19:45:52.4463159Z  # otherwise we're on a PR, so use the most recent base commit 2025-08-26T19:45:52.4463659Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-08-26T19:45:52.4464046Z fi 2025-08-26T19:45:52.4464252Z  2025-08-26T19:45:52.4464498Z if [[ -z "${MERGE_BASE}" ]]; then 2025-08-26T19:45:52.4464867Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4465207Z  2025-08-26T19:45:52.4465680Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-08-26T19:45:52.4466249Z  exit 0 2025-08-26T19:45:52.4466483Z fi 2025-08-26T19:45:52.4466701Z  2025-08-26T19:45:52.4467016Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-08-26T19:45:52.4467699Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-08-26T19:45:52.4468299Z  exit 1 2025-08-26T19:45:52.4468533Z fi 2025-08-26T19:45:52.4468747Z  2025-08-26T19:45:52.4469107Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-08-26T19:45:52.4469795Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-08-26T19:45:52.4470409Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-08-26T19:45:52.4471114Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-08-26T19:45:52.4471911Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-08-26T19:45:52.4472369Z fi 2025-08-26T19:45:52.4472656Z  2025-08-26T19:45:52.4472924Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-08-26T19:45:52.4478934Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:52.4479323Z env: 2025-08-26T19:45:52.4479535Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:52.4479824Z DOCKER_BUILD_DIR: .ci/docker 2025-08-26T19:45:52.4480170Z BASE_REVISION: 262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:45:52.4481140Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:52.4482166Z DOCKER_TAG: pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:52.4482799Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:52.4483224Z DOCKER_PUSH: 2025-08-26T19:45:52.4483540Z ##[endgroup] 2025-08-26T19:45:52.4508556Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:52.4509348Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:52.4511266Z + aws ecr get-login-password --region us-east-1 2025-08-26T19:45:52.4513036Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:53.0073565Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-26T19:45:53.0074248Z Configure a credential helper to remove this warning. See 2025-08-26T19:45:53.0074852Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-26T19:45:53.0075249Z 2025-08-26T19:45:53.0075629Z Login Succeeded 2025-08-26T19:45:53.0092051Z ++ date +%s 2025-08-26T19:45:53.0101048Z + START_TIME=1756237553 2025-08-26T19:45:53.0104546Z ++ date +%s 2025-08-26T19:45:53.0112931Z + [[ 1756230353 -lt 1756237553 ]] 2025-08-26T19:45:53.0114812Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:53.2098393Z { 2025-08-26T19:45:53.2098867Z "schemaVersion": 2, 2025-08-26T19:45:53.2099548Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-08-26T19:45:53.2100228Z "config": { 2025-08-26T19:45:53.2100650Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-08-26T19:45:53.2101325Z "size": 29883, 2025-08-26T19:45:53.2102008Z "digest": "sha256:bcfee952306a7e44650613e1b9a4dc8a749cc45f82748f17141360b84d5c4b94" 2025-08-26T19:45:53.2102768Z }, 2025-08-26T19:45:53.2102976Z "layers": [ 2025-08-26T19:45:53.2103236Z { 2025-08-26T19:45:53.2103843Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2104599Z "size": 30448173, 2025-08-26T19:45:53.2105196Z "digest": "sha256:660ffc76f83b006444a5731b215acc2e35138d8be5cac8ed1ffd40f947117495" 2025-08-26T19:45:53.2105951Z }, 2025-08-26T19:45:53.2106317Z { 2025-08-26T19:45:53.2106954Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2107392Z "size": 1554, 2025-08-26T19:45:53.2107818Z "digest": "sha256:bc6828dc3a67bd1a0f7dc77dd5b628363201b20ed5e62b20b10984b773009991" 2025-08-26T19:45:53.2108292Z }, 2025-08-26T19:45:53.2108493Z { 2025-08-26T19:45:53.2108829Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2109265Z "size": 344953099, 2025-08-26T19:45:53.2109700Z "digest": "sha256:077c5ccf8634c7f0028234842674c21eb81df6eefca3bc64667b986f7b2d5bf6" 2025-08-26T19:45:53.2110184Z }, 2025-08-26T19:45:53.2110381Z { 2025-08-26T19:45:53.2110713Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2111125Z "size": 791, 2025-08-26T19:45:53.2111540Z "digest": "sha256:c4b66cb3e1458a047d25f65267cd75101a856b69bf7f0e4cf3ab0f5170b80d21" 2025-08-26T19:45:53.2112022Z }, 2025-08-26T19:45:53.2112218Z { 2025-08-26T19:45:53.2112537Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2112965Z "size": 63270193, 2025-08-26T19:45:53.2113395Z "digest": "sha256:be2351a8a9e1090a6c189ac5ab7f9129fcd11333aae23d65624537890b2c5759" 2025-08-26T19:45:53.2113877Z }, 2025-08-26T19:45:53.2114076Z { 2025-08-26T19:45:53.2114409Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2114889Z "size": 704, 2025-08-26T19:45:53.2115408Z "digest": "sha256:ac0275b0c50249be92f01cc4af6e523f5d9836efbd51a5c46e35e6456bbe6aca" 2025-08-26T19:45:53.2116131Z }, 2025-08-26T19:45:53.2116319Z { 2025-08-26T19:45:53.2116662Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2117092Z "size": 1216, 2025-08-26T19:45:53.2117525Z "digest": "sha256:12a376ecda848fc1fe7dfeb7f5d35e5e9091bf1e384ed9f80970b282404a05d8" 2025-08-26T19:45:53.2118010Z }, 2025-08-26T19:45:53.2118213Z { 2025-08-26T19:45:53.2118555Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2118986Z "size": 485, 2025-08-26T19:45:53.2119399Z "digest": "sha256:ab2a2f72897faf2d13f1cbae8106c9f855813fbe992743f91e07f038b0756a48" 2025-08-26T19:45:53.2119890Z }, 2025-08-26T19:45:53.2120092Z { 2025-08-26T19:45:53.2120471Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2120886Z "size": 110, 2025-08-26T19:45:53.2121299Z "digest": 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"sha256:13792f6142bb7a1ef0a78094cc19143b3302ac9aec9b5b76d8714461dbaec2f7" 2025-08-26T19:45:53.2127828Z }, 2025-08-26T19:45:53.2128026Z { 2025-08-26T19:45:53.2128346Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2128767Z "size": 543, 2025-08-26T19:45:53.2129167Z "digest": "sha256:3926b9c5c75770c2130307a795639924b8f840d9db2c4241e6998a0e61b90cc8" 2025-08-26T19:45:53.2129639Z }, 2025-08-26T19:45:53.2129820Z { 2025-08-26T19:45:53.2130150Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2130587Z "size": 3406041060, 2025-08-26T19:45:53.2131026Z "digest": "sha256:d6ab61cb07e7224b31187c48d8349900f6065a2b9f9cba19bfd0e2cdc24de159" 2025-08-26T19:45:53.2131501Z }, 2025-08-26T19:45:53.2131699Z { 2025-08-26T19:45:53.2132034Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-08-26T19:45:53.2132460Z "size": 32, 2025-08-26T19:45:53.2132869Z "digest": 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pipefail {0} 2025-08-26T19:45:53.2309271Z env: 2025-08-26T19:45:53.2309495Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:53.2309775Z ##[endgroup] 2025-08-26T19:45:53.2338199Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-08-26T19:45:53.2339607Z + jq --raw-output .SecretString 2025-08-26T19:45:53.2340203Z + jq -r .docker_hub_readonly_token 2025-08-26T19:45:53.2341639Z + docker login --username pytorchbot --password-stdin 2025-08-26T19:45:53.8459349Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-26T19:45:53.8460570Z Configure a credential helper to remove this warning. See 2025-08-26T19:45:53.8461547Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-26T19:45:53.8462189Z 2025-08-26T19:45:53.8462328Z Login Succeeded 2025-08-26T19:45:53.8550066Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*:} 2025-08-26T19:45:53.8550469Z tag=${ECR_DOCKER_IMAGE##*:} 2025-08-26T19:45:53.8551013Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-08-26T19:45:53.8557502Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:53.8557900Z env: 2025-08-26T19:45:53.8558129Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:53.8558932Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:53.8559748Z ##[endgroup] 2025-08-26T19:45:53.8585427Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:53.8664287Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-08-26T19:45:53.8664749Z with: 2025-08-26T19:45:53.8665485Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:53.8666414Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:53.8666832Z env: 2025-08-26T19:45:53.8667058Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:53.8667331Z ##[endgroup] 2025-08-26T19:45:53.8690567Z ##[group]Run set -x 2025-08-26T19:45:53.8690862Z set -x 2025-08-26T19:45:53.8691102Z set +e 2025-08-26T19:45:53.8691331Z  2025-08-26T19:45:53.8691536Z login() { 2025-08-26T19:45:53.8692331Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-08-26T19:45:53.8692873Z } 2025-08-26T19:45:53.8693094Z  2025-08-26T19:45:53.8693354Z retry () { 2025-08-26T19:45:53.8693635Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-08-26T19:45:53.8693951Z } 2025-08-26T19:45:53.8694167Z  2025-08-26T19:45:53.8694411Z retry login "${DOCKER_REGISTRY}" 2025-08-26T19:45:53.8694727Z  2025-08-26T19:45:53.8695220Z IMAGE_SIZE=$(docker manifest inspect "${DOCKER_IMAGE}" | jq '[.layers[].size, .config.size] | add / 1024 / 1024') 2025-08-26T19:45:53.8695926Z echo "Compressed size of image in MB: ${IMAGE_SIZE}" 2025-08-26T19:45:53.8696317Z  2025-08-26T19:45:53.8696534Z set -e 2025-08-26T19:45:53.8696874Z # ignore output since only exit code is used for conditional 2025-08-26T19:45:53.8697383Z # only pull docker image if it's not available locally 2025-08-26T19:45:53.8697942Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-08-26T19:45:53.8698464Z  retry docker pull "${DOCKER_IMAGE}" 2025-08-26T19:45:53.8698793Z fi 2025-08-26T19:45:53.8704891Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:45:53.8705301Z env: 2025-08-26T19:45:53.8705532Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:45:53.8706321Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:53.8707252Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:53.8707639Z ##[endgroup] 2025-08-26T19:45:53.8730729Z + set +e 2025-08-26T19:45:53.8731268Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:53.8731963Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:53.8734528Z + aws ecr get-login-password --region us-east-1 2025-08-26T19:45:53.8735210Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-08-26T19:45:54.4606184Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-08-26T19:45:54.4606837Z Configure a credential helper to remove this warning. See 2025-08-26T19:45:54.4607557Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-08-26T19:45:54.4608190Z 2025-08-26T19:45:54.4608720Z Login Succeeded 2025-08-26T19:45:54.4625925Z ++ docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:54.4627137Z ++ jq '[.layers[].size, .config.size] | add / 1024 / 1024' 2025-08-26T19:45:54.6701099Z + IMAGE_SIZE=4404.510594367981 2025-08-26T19:45:54.6701671Z Compressed size of image in MB: 4404.510594367981 2025-08-26T19:45:54.6702113Z + echo 'Compressed size of image in MB: 4404.510594367981' 2025-08-26T19:45:54.6702496Z + set -e 2025-08-26T19:45:54.6703612Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:54.6821699Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:54.6823074Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:45:54.9276340Z pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6: Pulling from pytorch/ci-image 2025-08-26T19:45:54.9281969Z 660ffc76f83b: Pulling fs layer 2025-08-26T19:45:54.9282464Z bc6828dc3a67: Pulling fs layer 2025-08-26T19:45:54.9282938Z 077c5ccf8634: Pulling fs layer 2025-08-26T19:45:54.9283385Z c4b66cb3e145: Pulling fs layer 2025-08-26T19:45:54.9283796Z be2351a8a9e1: Pulling fs layer 2025-08-26T19:45:54.9284090Z ac0275b0c502: Pulling fs layer 2025-08-26T19:45:54.9284521Z 12a376ecda84: Pulling fs layer 2025-08-26T19:45:54.9284867Z ab2a2f72897f: Pulling fs layer 2025-08-26T19:45:54.9285237Z 98d248f35952: Pulling fs layer 2025-08-26T19:45:54.9285517Z c4b66cb3e145: Waiting 2025-08-26T19:45:54.9285778Z 03d1105a106a: Pulling fs layer 2025-08-26T19:45:54.9286056Z be2351a8a9e1: Waiting 2025-08-26T19:45:54.9286403Z c6de97442ffc: Pulling fs layer 2025-08-26T19:45:54.9286742Z ac0275b0c502: Waiting 2025-08-26T19:45:54.9286989Z 12a376ecda84: Waiting 2025-08-26T19:45:54.9287350Z 13792f6142bb: Pulling fs layer 2025-08-26T19:45:54.9287621Z ab2a2f72897f: Waiting 2025-08-26T19:45:54.9287881Z 3926b9c5c757: Pulling fs layer 2025-08-26T19:45:54.9288176Z d6ab61cb07e7: Pulling fs layer 2025-08-26T19:45:54.9288537Z 4f4fb700ef54: Pulling fs layer 2025-08-26T19:45:54.9288806Z 13792f6142bb: Waiting 2025-08-26T19:45:54.9289102Z 98d248f35952: Waiting 2025-08-26T19:45:54.9289360Z 949734882de5: Pulling fs layer 2025-08-26T19:45:54.9289647Z ee3cb7b4e161: Pulling fs layer 2025-08-26T19:45:54.9289915Z c6de97442ffc: Waiting 2025-08-26T19:45:54.9290159Z 03d1105a106a: Waiting 2025-08-26T19:45:54.9290419Z 8e2e9e025db0: Pulling fs layer 2025-08-26T19:45:54.9290693Z 3926b9c5c757: Waiting 2025-08-26T19:45:54.9290935Z f7974c6e740c: Pulling fs layer 2025-08-26T19:45:54.9291228Z f69c3f35c384: Pulling fs layer 2025-08-26T19:45:54.9291510Z d6ab61cb07e7: Waiting 2025-08-26T19:45:54.9291987Z 89159c061439: Pulling fs layer 2025-08-26T19:45:54.9292267Z 7543369e8f46: Pulling fs layer 2025-08-26T19:45:54.9292558Z 4f4fb700ef54: Waiting 2025-08-26T19:45:54.9292821Z f7fc941934c6: Pulling fs layer 2025-08-26T19:45:54.9293114Z 949734882de5: Waiting 2025-08-26T19:45:54.9293359Z f655cd3e2552: Pulling fs layer 2025-08-26T19:45:54.9293649Z 0780d907ae80: Pulling fs layer 2025-08-26T19:45:54.9293932Z ee3cb7b4e161: Waiting 2025-08-26T19:45:54.9294195Z 7712da47fe11: Pulling fs layer 2025-08-26T19:45:54.9294463Z 8e2e9e025db0: Waiting 2025-08-26T19:45:54.9294752Z 7e9e15046c34: Pulling fs layer 2025-08-26T19:45:54.9295051Z 0daf7355ddc9: Pulling fs layer 2025-08-26T19:45:54.9295333Z 89159c061439: Waiting 2025-08-26T19:45:54.9295596Z 0435a6eaf460: Pulling fs layer 2025-08-26T19:45:54.9295864Z f7974c6e740c: Waiting 2025-08-26T19:45:54.9296113Z 7543369e8f46: Waiting 2025-08-26T19:45:54.9296374Z e7c2dba69e4c: Pulling fs layer 2025-08-26T19:45:54.9296660Z f69c3f35c384: Waiting 2025-08-26T19:45:54.9296907Z 2b73a19dab76: Pulling fs layer 2025-08-26T19:45:54.9297227Z ed5131773436: Pulling fs layer 2025-08-26T19:45:54.9297812Z 0780d907ae80: Waiting 2025-08-26T19:45:54.9298195Z d7e9765898f9: Pulling fs layer 2025-08-26T19:45:54.9298528Z f655cd3e2552: Waiting 2025-08-26T19:45:54.9298876Z 13c02d1fa95a: Pulling fs layer 2025-08-26T19:45:54.9299200Z 0435a6eaf460: Waiting 2025-08-26T19:45:54.9299459Z c4ac9ebc03ab: Pulling fs layer 2025-08-26T19:45:54.9299797Z 7712da47fe11: Waiting 2025-08-26T19:45:54.9300051Z e7c2dba69e4c: Waiting 2025-08-26T19:45:54.9300294Z 7e9e15046c34: Waiting 2025-08-26T19:45:54.9300692Z 0f80df9ec0a8: Pulling fs layer 2025-08-26T19:45:54.9300959Z 2b73a19dab76: Waiting 2025-08-26T19:45:54.9301215Z d773426c510c: Pulling fs layer 2025-08-26T19:45:54.9301718Z 0daf7355ddc9: Waiting 2025-08-26T19:45:54.9301974Z ed5131773436: Waiting 2025-08-26T19:45:54.9302304Z bc0360305afd: Pulling fs layer 2025-08-26T19:45:54.9302744Z 13c02d1fa95a: Waiting 2025-08-26T19:45:54.9303006Z b1b61de995ce: Pulling fs layer 2025-08-26T19:45:54.9303364Z d7e9765898f9: Waiting 2025-08-26T19:45:54.9303636Z 69cf1c80c0c4: Pulling fs layer 2025-08-26T19:45:54.9303940Z c4ac9ebc03ab: Waiting 2025-08-26T19:45:54.9304256Z bc0360305afd: Waiting 2025-08-26T19:45:54.9304490Z d773426c510c: Waiting 2025-08-26T19:45:54.9304743Z 494516fc2864: Pulling fs layer 2025-08-26T19:45:54.9305022Z 0f80df9ec0a8: Waiting 2025-08-26T19:45:54.9305276Z 73a0967e5609: Pulling fs layer 2025-08-26T19:45:54.9305551Z ec659429b335: Pulling fs layer 2025-08-26T19:45:54.9305825Z 494516fc2864: Waiting 2025-08-26T19:45:54.9306066Z 69cf1c80c0c4: Waiting 2025-08-26T19:45:54.9306326Z 52e8b9613cef: Pulling fs layer 2025-08-26T19:45:54.9306588Z 73a0967e5609: Waiting 2025-08-26T19:45:54.9306840Z a1435d9de553: Pulling fs layer 2025-08-26T19:45:54.9307128Z b1b61de995ce: Waiting 2025-08-26T19:45:54.9307386Z 6a8d17e39fba: Pulling fs layer 2025-08-26T19:45:54.9307667Z f82de2f9fbbd: Pulling fs layer 2025-08-26T19:45:54.9307995Z a1435d9de553: Waiting 2025-08-26T19:45:54.9308284Z 6a8d17e39fba: Waiting 2025-08-26T19:45:54.9308545Z c4a1946e885f: Pulling fs layer 2025-08-26T19:45:54.9308889Z f82de2f9fbbd: Waiting 2025-08-26T19:45:54.9309153Z 8150c2b87518: Pulling fs layer 2025-08-26T19:45:54.9309439Z b260b8a04e14: Pulling fs layer 2025-08-26T19:45:54.9309730Z 2ba586ae34b9: Pulling fs layer 2025-08-26T19:45:54.9310005Z 1a18b687e3b3: Pulling fs layer 2025-08-26T19:45:54.9310282Z 2ba586ae34b9: Waiting 2025-08-26T19:45:54.9310538Z b627c22edb90: Pulling fs layer 2025-08-26T19:45:54.9310948Z c4a1946e885f: Waiting 2025-08-26T19:45:54.9311181Z 8150c2b87518: Waiting 2025-08-26T19:45:54.9311435Z 434bc8db9244: Pulling fs layer 2025-08-26T19:45:54.9311777Z b260b8a04e14: Waiting 2025-08-26T19:45:54.9312020Z 1a18b687e3b3: Waiting 2025-08-26T19:45:54.9312272Z 19597bcec455: Pulling fs layer 2025-08-26T19:45:54.9312549Z 434bc8db9244: Waiting 2025-08-26T19:45:54.9312807Z 288706c7e50f: Pulling fs layer 2025-08-26T19:45:54.9313082Z b627c22edb90: Waiting 2025-08-26T19:45:54.9313411Z fa5087659588: Pulling fs layer 2025-08-26T19:45:54.9313710Z 5c615319e208: Pulling fs layer 2025-08-26T19:45:54.9314086Z 424f9ce476c8: Pulling fs layer 2025-08-26T19:45:54.9314456Z d221e08864cf: Pulling fs layer 2025-08-26T19:45:54.9314744Z 288706c7e50f: Waiting 2025-08-26T19:45:54.9315074Z 68effbf08d78: Pulling fs layer 2025-08-26T19:45:54.9315355Z 424f9ce476c8: Waiting 2025-08-26T19:45:54.9315603Z fa5087659588: Waiting 2025-08-26T19:45:54.9315830Z d221e08864cf: Waiting 2025-08-26T19:45:54.9316069Z 5c615319e208: Waiting 2025-08-26T19:45:54.9316324Z 3bf7eaa748f0: Pulling fs layer 2025-08-26T19:45:54.9316607Z 68effbf08d78: Waiting 2025-08-26T19:45:54.9316843Z 3bf7eaa748f0: Waiting 2025-08-26T19:45:55.0029038Z bc6828dc3a67: Verifying Checksum 2025-08-26T19:45:55.0029473Z bc6828dc3a67: Download complete 2025-08-26T19:45:55.0664254Z c4b66cb3e145: Verifying Checksum 2025-08-26T19:45:55.0664637Z c4b66cb3e145: Download complete 2025-08-26T19:45:55.2965430Z 660ffc76f83b: Verifying Checksum 2025-08-26T19:45:55.2966018Z 660ffc76f83b: Download complete 2025-08-26T19:45:55.3853632Z ac0275b0c502: Verifying Checksum 2025-08-26T19:45:55.3854063Z ac0275b0c502: Download complete 2025-08-26T19:45:55.4919883Z 12a376ecda84: Verifying Checksum 2025-08-26T19:45:55.4920301Z 12a376ecda84: Download complete 2025-08-26T19:45:55.5785527Z ab2a2f72897f: Download complete 2025-08-26T19:45:55.6521423Z 98d248f35952: Download complete 2025-08-26T19:45:55.7427615Z 03d1105a106a: Download complete 2025-08-26T19:45:55.7808831Z be2351a8a9e1: Verifying Checksum 2025-08-26T19:45:55.7809502Z be2351a8a9e1: Download complete 2025-08-26T19:45:55.8290015Z c6de97442ffc: Verifying Checksum 2025-08-26T19:45:55.8290457Z c6de97442ffc: Download complete 2025-08-26T19:45:55.8457770Z 13792f6142bb: Verifying Checksum 2025-08-26T19:45:55.8458465Z 13792f6142bb: Download complete 2025-08-26T19:45:55.9166738Z 3926b9c5c757: Verifying Checksum 2025-08-26T19:45:55.9167312Z 3926b9c5c757: Download complete 2025-08-26T19:45:55.9235827Z 4f4fb700ef54: Verifying Checksum 2025-08-26T19:45:55.9236443Z 4f4fb700ef54: Download complete 2025-08-26T19:45:56.0131826Z 949734882de5: Download complete 2025-08-26T19:45:56.0977826Z ee3cb7b4e161: Verifying Checksum 2025-08-26T19:45:56.0978470Z ee3cb7b4e161: Download complete 2025-08-26T19:45:56.1683739Z 8e2e9e025db0: Verifying Checksum 2025-08-26T19:45:56.1684298Z 8e2e9e025db0: Download complete 2025-08-26T19:45:56.2304830Z 660ffc76f83b: Pull complete 2025-08-26T19:45:56.2481745Z f7974c6e740c: Verifying Checksum 2025-08-26T19:45:56.2482172Z f7974c6e740c: Download complete 2025-08-26T19:45:56.2525082Z bc6828dc3a67: Pull complete 2025-08-26T19:45:56.3512559Z f69c3f35c384: Verifying Checksum 2025-08-26T19:45:56.3512940Z f69c3f35c384: Download complete 2025-08-26T19:45:56.4352976Z 89159c061439: Verifying Checksum 2025-08-26T19:45:56.4353431Z 89159c061439: Download complete 2025-08-26T19:45:56.4936524Z 7543369e8f46: Verifying Checksum 2025-08-26T19:45:56.4936924Z 7543369e8f46: Download complete 2025-08-26T19:45:56.5746382Z f7fc941934c6: Verifying Checksum 2025-08-26T19:45:56.5746816Z f7fc941934c6: Download complete 2025-08-26T19:45:56.6506319Z f655cd3e2552: Verifying Checksum 2025-08-26T19:45:56.6506912Z f655cd3e2552: Download complete 2025-08-26T19:45:56.7153163Z 0780d907ae80: Download complete 2025-08-26T19:45:56.8009212Z 7712da47fe11: Download complete 2025-08-26T19:45:56.8787139Z 7e9e15046c34: Verifying Checksum 2025-08-26T19:45:56.8787624Z 7e9e15046c34: Download complete 2025-08-26T19:45:58.4374654Z 077c5ccf8634: Verifying Checksum 2025-08-26T19:45:58.4375044Z 077c5ccf8634: Download complete 2025-08-26T19:45:58.5299660Z 0435a6eaf460: Download complete 2025-08-26T19:45:58.6093949Z e7c2dba69e4c: Verifying Checksum 2025-08-26T19:45:58.6094636Z e7c2dba69e4c: Download complete 2025-08-26T19:45:58.6703757Z 2b73a19dab76: Verifying Checksum 2025-08-26T19:45:58.6704431Z 2b73a19dab76: Download complete 2025-08-26T19:45:58.7445803Z ed5131773436: Download complete 2025-08-26T19:45:58.9997640Z d7e9765898f9: Verifying Checksum 2025-08-26T19:45:58.9998284Z d7e9765898f9: Download complete 2025-08-26T19:45:59.0871772Z 13c02d1fa95a: Verifying Checksum 2025-08-26T19:45:59.0872417Z 13c02d1fa95a: Download complete 2025-08-26T19:45:59.1785491Z c4ac9ebc03ab: Verifying Checksum 2025-08-26T19:45:59.1785997Z c4ac9ebc03ab: Download complete 2025-08-26T19:45:59.2847934Z 0f80df9ec0a8: Verifying Checksum 2025-08-26T19:45:59.2848568Z 0f80df9ec0a8: Download complete 2025-08-26T19:45:59.3553775Z d773426c510c: Verifying Checksum 2025-08-26T19:45:59.3554413Z d773426c510c: Download complete 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2025-08-26T19:47:45.7557228Z 19597bcec455: Pull complete 2025-08-26T19:47:45.7793448Z 288706c7e50f: Pull complete 2025-08-26T19:47:45.8005757Z fa5087659588: Pull complete 2025-08-26T19:47:45.8220418Z 5c615319e208: Pull complete 2025-08-26T19:47:45.8443661Z 424f9ce476c8: Pull complete 2025-08-26T19:47:45.8659610Z d221e08864cf: Pull complete 2025-08-26T19:47:45.9103751Z 68effbf08d78: Pull complete 2025-08-26T19:47:47.7800068Z 3bf7eaa748f0: Pull complete 2025-08-26T19:47:47.9130461Z Digest: sha256:16e8df60cdede606d7a1939b8ee6c9786a6466d17602dc7150706f3392d20293 2025-08-26T19:47:47.9311889Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:47:47.9419571Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:47:47.9466262Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-26T19:47:47.9467250Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-08-26T19:47:47.9476200Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:47:47.9476596Z env: 2025-08-26T19:47:47.9476832Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:47:47.9477110Z ##[endgroup] 2025-08-26T19:47:47.9603546Z Prepare all required actions 2025-08-26T19:47:47.9675528Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-08-26T19:47:47.9675884Z with: 2025-08-26T19:47:47.9676486Z github-token: *** 2025-08-26T19:47:47.9676712Z env: 2025-08-26T19:47:47.9676933Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:47:47.9677209Z ##[endgroup] 2025-08-26T19:47:47.9727949Z ##[group]Run set -eux 2025-08-26T19:47:47.9728253Z set -eux 2025-08-26T19:47:47.9728719Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-08-26T19:47:47.9734606Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:47:47.9734982Z env: 2025-08-26T19:47:47.9735205Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:47:47.9735683Z GITHUB_TOKEN: *** 2025-08-26T19:47:47.9735930Z ##[endgroup] 2025-08-26T19:47:47.9760741Z + python3 .github/scripts/get_workflow_job_id.py 17248463620 i-0d10cabc7fe6d3867 2025-08-26T19:47:49.6905438Z Setting output job-id=48944862621 2025-08-26T19:47:49.6906231Z Setting output job-name=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:47:49.7022743Z ##[group]Run python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-08-26T19:47:49.7023528Z python3 -m pip install psutil==5.9.8 dataclasses_json==0.6.7 nvidia-ml-py==11.525.84 2025-08-26T19:47:49.7024523Z python3 -m tools.stats.monitor --log-interval "$MONITOR_LOG_INTERVAL" --data-collect-interval "$MONITOR_DATA_COLLECT_INTERVAL" > usage_log.txt 2>&1 & 2025-08-26T19:47:49.7025547Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-08-26T19:47:49.7031416Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:47:49.7031808Z env: 2025-08-26T19:47:49.7032035Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:47:49.7032310Z JOB_ID: 48944862621 2025-08-26T19:47:49.7032734Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:47:49.7033238Z WORKFLOW_NAME: pull 2025-08-26T19:47:49.7033506Z WORKFLOW_RUN_ID: 17248463620 2025-08-26T19:47:49.7033826Z MONITOR_LOG_INTERVAL: 5 2025-08-26T19:47:49.7034117Z MONITOR_DATA_COLLECT_INTERVAL: 1 2025-08-26T19:47:49.7034419Z ##[endgroup] 2025-08-26T19:47:50.1487692Z Defaulting to user installation because normal site-packages is not writeable 2025-08-26T19:47:50.5065388Z Collecting psutil==5.9.8 2025-08-26T19:47:50.5219577Z Downloading psutil-5.9.8-cp36-abi3-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (288 kB) 2025-08-26T19:47:50.5928166Z Collecting dataclasses_json==0.6.7 2025-08-26T19:47:50.5964626Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-08-26T19:47:50.6234902Z Collecting nvidia-ml-py==11.525.84 2025-08-26T19:47:50.6267596Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2025-08-26T19:47:50.7432255Z Collecting marshmallow<4.0.0,>=3.18.0 2025-08-26T19:47:50.7470079Z Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB) 2025-08-26T19:47:50.7702155Z Collecting typing-inspect<1,>=0.4.0 2025-08-26T19:47:50.7733912Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-08-26T19:47:50.8275167Z Collecting packaging>=17.0 2025-08-26T19:47:50.8307247Z Downloading packaging-25.0-py3-none-any.whl (66 kB) 2025-08-26T19:47:50.8825713Z Collecting typing-extensions>=3.7.4 2025-08-26T19:47:50.8859116Z Downloading typing_extensions-4.15.0-py3-none-any.whl (44 kB) 2025-08-26T19:47:50.9055946Z Collecting mypy-extensions>=0.3.0 2025-08-26T19:47:50.9087928Z Downloading mypy_extensions-1.1.0-py3-none-any.whl (5.0 kB) 2025-08-26T19:47:51.0025630Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json 2025-08-26T19:47:51.2824392Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.1.0 nvidia-ml-py-11.525.84 packaging-25.0 psutil-5.9.8 typing-extensions-4.15.0 typing-inspect-0.9.0 2025-08-26T19:47:51.4588865Z Prepare all required actions 2025-08-26T19:47:51.4589294Z Getting action download info 2025-08-26T19:47:51.6154425Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-08-26T19:47:51.9180577Z Download action repository 'actions/download-artifact@v4' (SHA:d3f86a106a0bac45b974a628896c90dbdf5c8093) 2025-08-26T19:47:52.2380157Z ##[group]Run ./.github/actions/download-build-artifacts 2025-08-26T19:47:52.2380646Z with: 2025-08-26T19:47:52.2380889Z name: linux-jammy-py3.13-clang12 2025-08-26T19:47:52.2381207Z s3-bucket: gha-artifacts 2025-08-26T19:47:52.2381463Z env: 2025-08-26T19:47:52.2381685Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:47:52.2381959Z ##[endgroup] 2025-08-26T19:47:52.2414580Z ##[group]Run seemethere/download-artifact-s3@v4 2025-08-26T19:47:52.2414934Z with: 2025-08-26T19:47:52.2415173Z name: linux-jammy-py3.13-clang12 2025-08-26T19:47:52.2415489Z s3-bucket: gha-artifacts 2025-08-26T19:47:52.2415800Z region: us-east-1 2025-08-26T19:47:52.2416024Z env: 2025-08-26T19:47:52.2416242Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:47:52.2416510Z ##[endgroup] 2025-08-26T19:47:52.7509821Z (node:41535) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-08-26T19:47:52.7510357Z 2025-08-26T19:47:52.7510555Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-08-26T19:47:52.7511451Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-08-26T19:47:52.7512038Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-08-26T19:47:53.0237078Z Found 1 objects with prefix pytorch/pytorch/17248463620/linux-jammy-py3.13-clang12/ 2025-08-26T19:47:53.0237834Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-08-26T19:47:57.3125906Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-08-26T19:47:57.3131699Z Artifact download has finished successfully 2025-08-26T19:47:57.3313349Z ##[group]Run unzip -o artifacts.zip 2025-08-26T19:47:57.3313710Z unzip -o artifacts.zip 2025-08-26T19:47:57.3319466Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:47:57.3319844Z env: 2025-08-26T19:47:57.3320067Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:47:57.3320345Z ##[endgroup] 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2025-08-26T19:48:04.3151741Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:48:04.3152123Z env: 2025-08-26T19:48:04.3152355Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:04.3152623Z ##[endgroup] 2025-08-26T19:48:04.3562736Z ##[group]Run df -H 2025-08-26T19:48:04.3563006Z df -H 2025-08-26T19:48:04.3568790Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:48:04.3569171Z env: 2025-08-26T19:48:04.3569400Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:04.3569675Z ##[endgroup] 2025-08-26T19:48:04.3861236Z Filesystem Size Used Avail Use% Mounted on 2025-08-26T19:48:04.3861869Z devtmpfs 4.2M 0 4.2M 0% /dev 2025-08-26T19:48:04.3862210Z tmpfs 8.2G 0 8.2G 0% /dev/shm 2025-08-26T19:48:04.3862560Z tmpfs 3.3G 484k 3.3G 1% /run 2025-08-26T19:48:04.3862897Z /dev/nvme0n1p1 161G 27G 135G 17% / 2025-08-26T19:48:04.3863423Z tmpfs 8.2G 13k 8.2G 1% /tmp 2025-08-26T19:48:04.3863771Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2025-08-26T19:48:04.3924625Z Prepare all required actions 2025-08-26T19:48:04.3925436Z Getting action download info 2025-08-26T19:48:04.5519311Z ##[group]Run ./.github/actions/download-td-artifacts 2025-08-26T19:48:04.5519708Z with: 2025-08-26T19:48:04.5519937Z env: 2025-08-26T19:48:04.5520196Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:04.5520456Z ##[endgroup] 2025-08-26T19:48:04.5579831Z ##[group]Run seemethere/download-artifact-s3@v4 2025-08-26T19:48:04.5580194Z with: 2025-08-26T19:48:04.5580490Z name: td_results 2025-08-26T19:48:04.5580751Z s3-bucket: gha-artifacts 2025-08-26T19:48:04.5581032Z region: us-east-1 2025-08-26T19:48:04.5581268Z env: 2025-08-26T19:48:04.5581474Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:04.5581741Z ##[endgroup] 2025-08-26T19:48:05.0148977Z (node:41554) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-08-26T19:48:05.0149486Z 2025-08-26T19:48:05.0149749Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-08-26T19:48:05.0150313Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-08-26T19:48:05.0150892Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-08-26T19:48:05.1244573Z Found 1 objects with prefix pytorch/pytorch/17248463620/td_results/ 2025-08-26T19:48:05.1245317Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-08-26T19:48:05.1961117Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/td_results.json 2025-08-26T19:48:05.1966109Z Artifact download has finished successfully 2025-08-26T19:48:05.2146821Z ##[group]Run mkdir -p .additional_ci_files 2025-08-26T19:48:05.2147213Z mkdir -p .additional_ci_files 2025-08-26T19:48:05.2147657Z mv td_results.json .additional_ci_files/td_results.json || true 2025-08-26T19:48:05.2153512Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:48:05.2153908Z env: 2025-08-26T19:48:05.2154154Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:05.2154414Z ##[endgroup] 2025-08-26T19:48:05.2706744Z ##[group]Run .github/scripts/parse_ref.py 2025-08-26T19:48:05.2707192Z .github/scripts/parse_ref.py 2025-08-26T19:48:05.2712919Z shell: /usr/bin/bash -e {0} 2025-08-26T19:48:05.2713358Z env: 2025-08-26T19:48:05.2713583Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:05.2713842Z ##[endgroup] 2025-08-26T19:48:05.2927373Z Setting output branch=main 2025-08-26T19:48:05.3070769Z Prepare all required actions 2025-08-26T19:48:05.3071190Z Getting action download info 2025-08-26T19:48:05.4474658Z ##[group]Run ./.github/actions/filter-test-configs 2025-08-26T19:48:05.4475026Z with: 2025-08-26T19:48:05.4475535Z github-token: *** 2025-08-26T19:48:05.4478533Z test-matrix: {"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]} 2025-08-26T19:48:05.4481767Z job-name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:48:05.4482253Z env: 2025-08-26T19:48:05.4482474Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:05.4482743Z ##[endgroup] 2025-08-26T19:48:05.4526716Z ##[group]Run nick-fields/retry@v3.0.0 2025-08-26T19:48:05.4527037Z with: 2025-08-26T19:48:05.4527236Z shell: bash 2025-08-26T19:48:05.4527469Z timeout_minutes: 10 2025-08-26T19:48:05.4527730Z max_attempts: 5 2025-08-26T19:48:05.4527972Z retry_wait_seconds: 30 2025-08-26T19:48:05.4528774Z 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 2025-08-26T19:48:05.4529663Z polling_interval_seconds: 1 2025-08-26T19:48:05.4529957Z warning_on_retry: true 2025-08-26T19:48:05.4530222Z continue_on_error: false 2025-08-26T19:48:05.4530471Z env: 2025-08-26T19:48:05.4530683Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:05.4531130Z GITHUB_TOKEN: *** 2025-08-26T19:48:05.4531373Z ##[endgroup] 2025-08-26T19:48:05.5470809Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.2 2025-08-26T19:48:05.7824034Z Defaulting to user installation because normal site-packages is not writeable 2025-08-26T19:48:05.8968147Z Collecting requests==2.27.1 2025-08-26T19:48:05.9120935Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-08-26T19:48:06.0746605Z Collecting pyyaml==6.0.2 2025-08-26T19:48:06.0788179Z Downloading PyYAML-6.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (737 kB) 2025-08-26T19:48:06.4526396Z Collecting charset-normalizer~=2.0.0 2025-08-26T19:48:06.4562597Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-08-26T19:48:06.4611520Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-08-26T19:48:06.5129583Z Collecting certifi>=2017.4.17 2025-08-26T19:48:06.5347555Z Downloading certifi-2025.8.3-py3-none-any.whl (161 kB) 2025-08-26T19:48:06.5425899Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-08-26T19:48:06.6255212Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2025-08-26T19:48:06.7445381Z Successfully installed certifi-2025.8.3 charset-normalizer-2.0.12 pyyaml-6.0.2 requests-2.27.1 2025-08-26T19:48:07.5300945Z Command completed after 1 attempt(s). 2025-08-26T19:48:07.5364545Z ##[group]Run set -x 2025-08-26T19:48:07.5365022Z set -x 2025-08-26T19:48:07.5365432Z  2025-08-26T19:48:07.5366360Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-08-26T19:48:07.5367500Z # in runner workspace 2025-08-26T19:48:07.5368255Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-08-26T19:48:07.5376879Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:48:07.5377552Z env: 2025-08-26T19:48:07.5377870Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:07.5378284Z ##[endgroup] 2025-08-26T19:48:07.5409564Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-08-26T19:48:07.5585656Z Setting output branch=main 2025-08-26T19:48:07.5646260Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-08-26T19:48:07.5646694Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-08-26T19:48:07.5647050Z echo "Job name: ${JOB_NAME}" 2025-08-26T19:48:07.5647393Z  2025-08-26T19:48:07.5647785Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-08-26T19:48:07.5648284Z # in runner workspace 2025-08-26T19:48:07.5648734Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-08-26T19:48:07.5649229Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-08-26T19:48:07.5649574Z  --job-name "${JOB_NAME}" \ 2025-08-26T19:48:07.5652601Z  --test-matrix "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]}" \ 2025-08-26T19:48:07.5655656Z  --selected-test-configs "" \ 2025-08-26T19:48:07.5656003Z  --pr-number "${PR_NUMBER}" \ 2025-08-26T19:48:07.5656328Z  --tag "${TAG}" \ 2025-08-26T19:48:07.5656618Z  --event-name "${EVENT_NAME}" \ 2025-08-26T19:48:07.5656955Z  --schedule "${SCHEDULE}" \ 2025-08-26T19:48:07.5657277Z  --branch "${HEAD_BRANCH}" 2025-08-26T19:48:07.5662999Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:48:07.5663390Z env: 2025-08-26T19:48:07.5663601Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:07.5664242Z GITHUB_TOKEN: *** 2025-08-26T19:48:07.5664687Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:48:07.5665177Z PR_NUMBER: 2025-08-26T19:48:07.5665414Z TAG: 2025-08-26T19:48:07.5665616Z EVENT_NAME: push 2025-08-26T19:48:07.5665852Z SCHEDULE: 2025-08-26T19:48:07.5666081Z HEAD_BRANCH: main 2025-08-26T19:48:07.5666312Z ##[endgroup] 2025-08-26T19:48:07.5690313Z Workflow: pull 2025-08-26T19:48:07.5691132Z Job name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:48:07.7539865Z Setting output keep-going=True 2025-08-26T19:48:07.7540290Z Setting output ci-verbose-test-logs=False 2025-08-26T19:48:07.7540714Z Setting output ci-test-showlocals=False 2025-08-26T19:48:07.7541082Z Setting output ci-no-test-timeout=False 2025-08-26T19:48:07.7541420Z Setting output ci-no-td=False 2025-08-26T19:48:07.7541725Z Setting output ci-td-distributed=False 2025-08-26T19:48:07.7542064Z Setting output is-unstable=False 2025-08-26T19:48:07.7542380Z Setting output reenabled-issues= 2025-08-26T19:48:07.7545849Z Setting output test-matrix={"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]} 2025-08-26T19:48:07.7548989Z Setting output is-test-matrix-empty=False 2025-08-26T19:48:07.7703998Z ##[group]Run echo "Filtered matrix:" 2025-08-26T19:48:07.7704384Z echo "Filtered matrix:" 2025-08-26T19:48:07.7707367Z echo "{"include": [{"config": "default", "shard": 1, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 2, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 3, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 4, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "default", "shard": 5, "num_shards": 5, "runner": "lf.linux.4xlarge"}, {"config": "crossref", "shard": 1, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "crossref", "shard": 2, "num_shards": 2, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 1, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 2, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "dynamo_wrapped", "shard": 3, "num_shards": 3, "runner": "lf.linux.2xlarge"}, {"config": "einops", "shard": 1, "num_shards": 1, "runner": "lf.linux.2xlarge"}]}" 2025-08-26T19:48:07.7710378Z  2025-08-26T19:48:07.7710583Z echo 2025-08-26T19:48:07.7710858Z echo "Is the current job unstable? False" 2025-08-26T19:48:07.7711188Z  2025-08-26T19:48:07.7711390Z echo 2025-08-26T19:48:07.7711651Z echo "Is keep-going label set? True" 2025-08-26T19:48:07.7711996Z  2025-08-26T19:48:07.7712189Z echo 2025-08-26T19:48:07.7712434Z echo "Reenabled issues? " 2025-08-26T19:48:07.7718378Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:48:07.7718769Z env: 2025-08-26T19:48:07.7718982Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:07.7719249Z ##[endgroup] 2025-08-26T19:48:07.7742563Z Filtered matrix: 2025-08-26T19:48:07.7746893Z {include: [{config: default, shard: 1, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 2, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 3, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 4, num_shards: 5, runner: lf.linux.4xlarge}, {config: default, shard: 5, num_shards: 5, runner: lf.linux.4xlarge}, {config: crossref, shard: 1, num_shards: 2, runner: lf.linux.2xlarge}, {config: crossref, shard: 2, num_shards: 2, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 1, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 2, num_shards: 3, runner: lf.linux.2xlarge}, {config: dynamo_wrapped, shard: 3, num_shards: 3, runner: lf.linux.2xlarge}, {config: einops, shard: 1, num_shards: 1, runner: lf.linux.2xlarge}]} 2025-08-26T19:48:07.7749748Z 2025-08-26T19:48:07.7749876Z Is the current job unstable? False 2025-08-26T19:48:07.7750100Z 2025-08-26T19:48:07.7750246Z Is keep-going label set? True 2025-08-26T19:48:07.7750448Z 2025-08-26T19:48:07.7750541Z Reenabled issues? 2025-08-26T19:48:07.7797651Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-08-26T19:48:07.7798329Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-08-26T19:48:07.7803830Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:48:07.7804251Z env: 2025-08-26T19:48:07.7804476Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:07.7804750Z JOB_TIMEOUT: 240 2025-08-26T19:48:07.7804992Z ##[endgroup] 2025-08-26T19:48:07.7859105Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-26T19:48:07.7859648Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-26T19:48:07.7860108Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-08-26T19:48:07.7865442Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T19:48:07.7865840Z env: 2025-08-26T19:48:07.7866063Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:07.7866333Z ##[endgroup] 2025-08-26T19:48:07.7964161Z ##[group]Run set -x 2025-08-26T19:48:07.7964519Z set -x 2025-08-26T19:48:07.7964752Z  2025-08-26T19:48:07.7965012Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-08-26T19:48:07.7965423Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-08-26T19:48:07.7965835Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-08-26T19:48:07.7966198Z  TEST_COMMAND=.ci/onnx/test.sh 2025-08-26T19:48:07.7966510Z else 2025-08-26T19:48:07.7966770Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-08-26T19:48:07.7967087Z fi 2025-08-26T19:48:07.7967291Z  2025-08-26T19:48:07.7967564Z # Leaving 1GB for the runner and other things 2025-08-26T19:48:07.7968163Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-08-26T19:48:07.7969078Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-08-26T19:48:07.7969801Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-08-26T19:48:07.7970397Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-08-26T19:48:07.7970852Z  2025-08-26T19:48:07.7971128Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-08-26T19:48:07.7971477Z  SHM_OPTS= 2025-08-26T19:48:07.7971734Z  JENKINS_USER= 2025-08-26T19:48:07.7972097Z  # ensure that docker container cleanly exits in 12 hours 2025-08-26T19:48:07.7972592Z  # if for some reason cleanup action doesn't stop container 2025-08-26T19:48:07.7973010Z  # when job is cancelled 2025-08-26T19:48:07.7973322Z  DOCKER_SHELL_CMD="sleep 12h" 2025-08-26T19:48:07.7973629Z else 2025-08-26T19:48:07.7973885Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-08-26T19:48:07.7974236Z  JENKINS_USER="--user jenkins" 2025-08-26T19:48:07.7974553Z  DOCKER_SHELL_CMD= 2025-08-26T19:48:07.7974827Z fi 2025-08-26T19:48:07.7975037Z  2025-08-26T19:48:07.7975387Z # detached container should get cleaned up by teardown_ec2_linux 2025-08-26T19:48:07.7975927Z # TODO: Stop building test binaries as part of the build phase 2025-08-26T19:48:07.7976556Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-08-26T19:48:07.7977111Z # shellcheck disable=SC2086,SC2090 2025-08-26T19:48:07.7977458Z container_name=$(docker run \ 2025-08-26T19:48:07.7977784Z  ${GPU_FLAG:-} \ 2025-08-26T19:48:07.7978085Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-08-26T19:48:07.7978443Z  -e BUILD_ENVIRONMENT \ 2025-08-26T19:48:07.7978749Z  -e PR_NUMBER \ 2025-08-26T19:48:07.7979028Z  -e GITHUB_ACTIONS \ 2025-08-26T19:48:07.7979315Z  -e GITHUB_REPOSITORY \ 2025-08-26T19:48:07.7979624Z  -e GITHUB_WORKFLOW \ 2025-08-26T19:48:07.7979915Z  -e GITHUB_JOB \ 2025-08-26T19:48:07.7980333Z  -e GITHUB_RUN_ID \ 2025-08-26T19:48:07.7980728Z  -e GITHUB_RUN_NUMBER \ 2025-08-26T19:48:07.7981043Z  -e GITHUB_RUN_ATTEMPT \ 2025-08-26T19:48:07.7981344Z  -e JOB_ID \ 2025-08-26T19:48:07.7981608Z  -e JOB_NAME \ 2025-08-26T19:48:07.7981868Z  -e BASE_SHA \ 2025-08-26T19:48:07.7982140Z  -e BRANCH \ 2025-08-26T19:48:07.7982401Z  -e SHA1 \ 2025-08-26T19:48:07.7982665Z  -e AWS_DEFAULT_REGION \ 2025-08-26T19:48:07.7982966Z  -e IN_WHEEL_TEST \ 2025-08-26T19:48:07.7983258Z  -e SHARD_NUMBER \ 2025-08-26T19:48:07.7983549Z  -e TEST_CONFIG \ 2025-08-26T19:48:07.7983838Z  -e NUM_TEST_SHARDS \ 2025-08-26T19:48:07.7984126Z  -e REENABLED_ISSUES \ 2025-08-26T19:48:07.7984443Z  -e CONTINUE_THROUGH_ERROR \ 2025-08-26T19:48:07.7984873Z  -e VERBOSE_TEST_LOGS \ 2025-08-26T19:48:07.7985186Z  -e TEST_SHOWLOCALS \ 2025-08-26T19:48:07.7985477Z  -e NO_TEST_TIMEOUT \ 2025-08-26T19:48:07.7985768Z  -e NO_TD \ 2025-08-26T19:48:07.7986034Z  -e TD_DISTRIBUTED \ 2025-08-26T19:48:07.7986326Z  -e PR_LABELS \ 2025-08-26T19:48:07.7986635Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-08-26T19:48:07.7986968Z  -e SCCACHE_BUCKET \ 2025-08-26T19:48:07.7987266Z  -e SCCACHE_REGION \ 2025-08-26T19:48:07.7987557Z  -e XLA_CUDA \ 2025-08-26T19:48:07.7987859Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-08-26T19:48:07.7988223Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-08-26T19:48:07.7988609Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-08-26T19:48:07.7988992Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-08-26T19:48:07.7989354Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-08-26T19:48:07.7989688Z  -e VLLM_TEST_HUGGING_FACE_TOKEN \ 2025-08-26T19:48:07.7990048Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-08-26T19:48:07.7990386Z  -e DASHBOARD_TAG \ 2025-08-26T19:48:07.7990687Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-08-26T19:48:07.7991054Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-08-26T19:48:07.7991490Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-08-26T19:48:07.7992157Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-08-26T19:48:07.7992577Z  --security-opt seccomp=unconfined \ 2025-08-26T19:48:07.7992940Z  --cap-add=SYS_PTRACE \ 2025-08-26T19:48:07.7993240Z  --ipc=host \ 2025-08-26T19:48:07.7993511Z  ${SHM_OPTS} \ 2025-08-26T19:48:07.7993780Z  --tty \ 2025-08-26T19:48:07.7994029Z  --detach \ 2025-08-26T19:48:07.7994292Z  --name="${container_name}" \ 2025-08-26T19:48:07.7994617Z  ${JENKINS_USER} \ 2025-08-26T19:48:07.7994981Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-08-26T19:48:07.7995393Z  -w /var/lib/jenkins/workspace \ 2025-08-26T19:48:07.7995715Z  "${DOCKER_IMAGE}" \ 2025-08-26T19:48:07.7996006Z  ${DOCKER_SHELL_CMD} 2025-08-26T19:48:07.7996280Z ) 2025-08-26T19:48:07.7996579Z # Propagate download.pytorch.org IP to container 2025-08-26T19:48:07.7997256Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-08-26T19:48:07.7997992Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-08-26T19:48:07.7998419Z  2025-08-26T19:48:07.7998696Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-08-26T19:48:07.7999293Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-08-26T19:48:07.7999816Z fi 2025-08-26T19:48:07.8000032Z  2025-08-26T19:48:07.8000541Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-08-26T19:48:07.8006142Z shell: /usr/bin/bash -e {0} 2025-08-26T19:48:07.8006426Z env: 2025-08-26T19:48:07.8006636Z GIT_DEFAULT_BRANCH: main 2025-08-26T19:48:07.8006964Z BUILD_ENVIRONMENT: linux-jammy-py3.13-clang12 2025-08-26T19:48:07.8007319Z PR_NUMBER: 2025-08-26T19:48:07.8007570Z GITHUB_REPOSITORY: pytorch/pytorch 2025-08-26T19:48:07.8007878Z GITHUB_WORKFLOW: pull 2025-08-26T19:48:07.8008144Z GITHUB_JOB: test 2025-08-26T19:48:07.8008392Z GITHUB_RUN_ID: 17248463620 2025-08-26T19:48:07.8008672Z GITHUB_RUN_NUMBER: 350026 2025-08-26T19:48:07.8008940Z GITHUB_RUN_ATTEMPT: 1 2025-08-26T19:48:07.8009197Z JOB_ID: 48944862621 2025-08-26T19:48:07.8009629Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:48:07.8010123Z BRANCH: main 2025-08-26T19:48:07.8010372Z SHA1: 262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:07.8010865Z BASE_SHA: 262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:07.8011227Z TEST_CONFIG: dynamo_wrapped 2025-08-26T19:48:07.8011517Z SHARD_NUMBER: 1 2025-08-26T19:48:07.8011744Z NUM_TEST_SHARDS: 3 2025-08-26T19:48:07.8012000Z REENABLED_ISSUES: 2025-08-26T19:48:07.8012264Z CONTINUE_THROUGH_ERROR: True 2025-08-26T19:48:07.8012561Z VERBOSE_TEST_LOGS: False 2025-08-26T19:48:07.8012830Z TEST_SHOWLOCALS: False 2025-08-26T19:48:07.8013101Z NO_TEST_TIMEOUT: False 2025-08-26T19:48:07.8013361Z NO_TD: False 2025-08-26T19:48:07.8013598Z TD_DISTRIBUTED: False 2025-08-26T19:48:07.8013910Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-08-26T19:48:07.8014286Z SCCACHE_REGION: us-east-1 2025-08-26T19:48:07.8014558Z SHM_SIZE: 1g 2025-08-26T19:48:07.8015295Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:48:07.8016087Z XLA_CUDA: 2025-08-26T19:48:07.8016454Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-08-26T19:48:07.8016933Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-08-26T19:48:07.8017263Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-08-26T19:48:07.8017578Z DASHBOARD_TAG: 2025-08-26T19:48:07.8018045Z VLLM_TEST_HUGGING_FACE_TOKEN: *** 2025-08-26T19:48:07.8018457Z HUGGING_FACE_HUB_TOKEN: *** 2025-08-26T19:48:07.8018886Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-08-26T19:48:07.8019349Z ARTIFACTS_FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621 2025-08-26T19:48:07.8019820Z ##[endgroup] 2025-08-26T19:48:07.8042059Z + [[ dynamo_wrapped == \m\u\l\t\i\g\p\u ]] 2025-08-26T19:48:07.8042438Z + [[ linux-jammy-py3.13-clang12 == *onnx* ]] 2025-08-26T19:48:07.8042777Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-08-26T19:48:07.8045197Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-08-26T19:48:07.8076310Z + TOTAL_AVAILABLE_MEMORY_IN_GB='14.244 ' 2025-08-26T19:48:07.8076820Z + TOTAL_MEMORY_WITH_SWAP=17 2025-08-26T19:48:07.8077136Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-08-26T19:48:07.8077502Z + SHM_OPTS=--shm-size=1g 2025-08-26T19:48:07.8077835Z + JENKINS_USER='--user jenkins' 2025-08-26T19:48:07.8078119Z + DOCKER_SHELL_CMD= 2025-08-26T19:48:07.8084676Z +++ nproc --ignore=2 2025-08-26T19:48:07.8112221Z ++ docker run -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=6 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e VLLM_TEST_HUGGING_FACE_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e ARTIFACTS_FILE_SUFFIX --memory=14g --memory-swap=17g --env-file=/tmp/github_env_17248463620 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=1g --tty --detach --name= --user jenkins -v /home/ec2-user/actions-runner/_work/pytorch/pytorch:/var/lib/jenkins/workspace -w /var/lib/jenkins/workspace 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 2025-08-26T19:48:16.1141156Z + container_name=77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T19:48:16.1143848Z + grep download.pytorch.org /etc/hosts 2025-08-26T19:48:16.1145382Z + docker exec -i 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db sudo bash -c '/bin/cat >> /etc/hosts' 2025-08-26T19:48:16.2707649Z + echo DOCKER_CONTAINER_ID=77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T19:48:16.2708928Z + [[ linux-jammy-py3.13-clang12 == *\s\3\9\0\x* ]] 2025-08-26T19:48:16.2710846Z ++ echo dist/torch-2.9.0a0+git262640f-cp313-cp313-linux_x86_64.whl 2025-08-26T19:48:16.2713961Z + docker exec -t 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db sh -c 'python3 -m pip install dist/torch-2.9.0a0+git262640f-cp313-cp313-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-08-26T19:48:16.8394622Z Processing ./dist/torch-2.9.0a0+git262640f-cp313-cp313-linux_x86_64.whl (from torch==2.9.0a0+git262640f) 2025-08-26T19:48:17.3725640Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (3.19.1) 2025-08-26T19:48:17.3728274Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (4.15.0) 2025-08-26T19:48:17.3733370Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (80.9.0) 2025-08-26T19:48:17.3737845Z Requirement already satisfied: sympy>=1.13.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (1.13.3) 2025-08-26T19:48:17.3742857Z Requirement already satisfied: networkx>=2.5.1 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (2.8.8) 2025-08-26T19:48:17.3746341Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (3.1.6) 2025-08-26T19:48:17.3751567Z Requirement already satisfied: fsspec>=0.8.5 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (2025.7.0) 2025-08-26T19:48:17.3765843Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (3.3.0) 2025-08-26T19:48:17.3872366Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from opt-einsum>=3.3->torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (2.1.2) 2025-08-26T19:48:17.3919595Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from sympy>=1.13.3->torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (1.3.0) 2025-08-26T19:48:17.3953207Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.13/lib/python3.13/site-packages (from jinja2->torch==2.9.0a0+git262640f->torch==2.9.0a0+git262640f) (3.0.2) 2025-08-26T19:48:17.6164456Z Installing collected packages: torch 2025-08-26T19:48:29.4241583Z Successfully installed torch-2.9.0a0+git262640f 2025-08-26T19:48:29.4984110Z + export TERM=vt100 2025-08-26T19:48:29.4984422Z + TERM=vt100 2025-08-26T19:48:29.4986099Z ++ dirname .ci/pytorch/test.sh 2025-08-26T19:48:29.5005957Z + source .ci/pytorch/common.sh 2025-08-26T19:48:29.5015515Z +++ dirname .ci/pytorch/common.sh 2025-08-26T19:48:29.5022615Z ++ source .ci/pytorch/common_utils.sh 2025-08-26T19:48:29.5030503Z +++ declare -f -t trap_add 2025-08-26T19:48:29.5035613Z ++ set -ex -o pipefail 2025-08-26T19:48:29.5035977Z ++ [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-08-26T19:48:29.5036324Z ++ BUILD_TEST_LIBTORCH=0 2025-08-26T19:48:29.5038796Z ++ dirname .ci/pytorch/test.sh 2025-08-26T19:48:29.5045228Z + source .ci/pytorch/common-build.sh 2025-08-26T19:48:29.5053244Z ++ [[ linux-jammy-py3.13-clang12 != *win-* ]] 2025-08-26T19:48:29.5059204Z ++++ dirname .ci/pytorch/common-build.sh 2025-08-26T19:48:29.5066017Z +++ cd .ci/pytorch 2025-08-26T19:48:29.5066320Z +++ pwd -P 2025-08-26T19:48:29.5068172Z ++ script_dir=/var/lib/jenkins/workspace/.ci/pytorch 2025-08-26T19:48:29.5068677Z ++ [[ linux-jammy-py3.13-clang12 == *-pch* ]] 2025-08-26T19:48:29.5069015Z ++ which sccache 2025-08-26T19:48:29.5090842Z ++ [[ -z ossci-compiler-cache-circleci-v2 ]] 2025-08-26T19:48:29.5091212Z ++ sccache --stop-server 2025-08-26T19:48:29.5147753Z ++ true 2025-08-26T19:48:29.5148052Z ++ rm -f /var/lib/jenkins/sccache_error.log 2025-08-26T19:48:29.5169187Z ++ trap_add sccache_epilogue EXIT 2025-08-26T19:48:29.5169525Z ++ trap_add_cmd=sccache_epilogue 2025-08-26T19:48:29.5169818Z ++ shift 2025-08-26T19:48:29.5170054Z ++ for trap_add_name in "$@" 2025-08-26T19:48:29.5175688Z ++++ trap -p EXIT 2025-08-26T19:48:29.5178254Z +++ eval 'extract_trap_cmd ' 2025-08-26T19:48:29.5178839Z ++++ extract_trap_cmd 2025-08-26T19:48:29.5179191Z ++++ printf '%s\n' '' 2025-08-26T19:48:29.5179446Z +++ printf '%s\n' sccache_epilogue 2025-08-26T19:48:29.5181235Z ++ trap -- ' 2025-08-26T19:48:29.5181676Z sccache_epilogue' EXIT 2025-08-26T19:48:29.5182042Z ++ [[ -n 1 ]] 2025-08-26T19:48:29.5182530Z ++ echo 'Skipping sccache server initialization, setting environment variables' 2025-08-26T19:48:29.5183584Z Skipping sccache server initialization, setting environment variables 2025-08-26T19:48:29.5184156Z ++ export SCCACHE_IDLE_TIMEOUT=0 2025-08-26T19:48:29.5184780Z ++ SCCACHE_IDLE_TIMEOUT=0 2025-08-26T19:48:29.5185300Z ++ export SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-08-26T19:48:29.5185749Z ++ SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-08-26T19:48:29.5186170Z ++ export RUST_LOG=sccache::server=error 2025-08-26T19:48:29.5186511Z ++ RUST_LOG=sccache::server=error 2025-08-26T19:48:29.5186815Z ++ sccache --zero-stats 2025-08-26T19:48:29.6206086Z Statistics zeroed. 2025-08-26T19:48:29.6209606Z ++ which ccache 2025-08-26T19:48:29.6222041Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-08-26T19:48:29.6222764Z + [[ linux-jammy-py3.13-clang12 != *s390x* ]] 2025-08-26T19:48:29.6223138Z + [[ -d /var/lib/jenkins/workspace ]] 2025-08-26T19:48:29.6224754Z ++ stat -c %u /var/lib/jenkins/workspace 2025-08-26T19:48:29.6281890Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-08-26T19:48:29.6282529Z + trap_add cleanup_workspace EXIT 2025-08-26T19:48:29.6282965Z + trap_add_cmd=cleanup_workspace 2025-08-26T19:48:29.6283241Z + shift 2025-08-26T19:48:29.6283464Z + for trap_add_name in "$@" 2025-08-26T19:48:29.6289087Z +++ trap -p EXIT 2025-08-26T19:48:29.6291327Z ++ eval 'extract_trap_cmd trap -- '\'' 2025-08-26T19:48:29.6292612Z sccache_epilogue'\'' EXIT' 2025-08-26T19:48:29.6293172Z +++ extract_trap_cmd trap -- ' 2025-08-26T19:48:29.6293622Z sccache_epilogue' EXIT 2025-08-26T19:48:29.6293989Z +++ printf '%s\n' ' 2025-08-26T19:48:29.6294322Z sccache_epilogue' 2025-08-26T19:48:29.6294786Z ++ printf '%s\n' cleanup_workspace 2025-08-26T19:48:29.6295296Z + trap -- ' 2025-08-26T19:48:29.6295522Z sccache_epilogue 2025-08-26T19:48:29.6295758Z cleanup_workspace' EXIT 2025-08-26T19:48:29.6296071Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-08-26T19:48:30.2438192Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-08-26T19:48:30.2634573Z + echo 'Environment variables:' 2025-08-26T19:48:30.2634957Z Environment variables: 2025-08-26T19:48:30.2635238Z + env 2025-08-26T19:48:30.2656103Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-26T19:48:30.2656962Z CONTINUE_THROUGH_ERROR=True 2025-08-26T19:48:30.2657303Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-08-26T19:48:30.2657907Z VLLM_TEST_HUGGING_FACE_TOKEN=*** 2025-08-26T19:48:30.2658210Z HOSTNAME=77c509ac59ad 2025-08-26T19:48:30.2658823Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2659476Z GITHUB_ACTION=__run_2 2025-08-26T19:48:30.2659755Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-08-26T19:48:30.2660073Z GITHUB_RUN_NUMBER=350026 2025-08-26T19:48:30.2660409Z TEST_CONFIG=dynamo_wrapped 2025-08-26T19:48:30.2660741Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-08-26T19:48:30.2661087Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-08-26T19:48:30.2661460Z SCCACHE_IDLE_TIMEOUT=0 2025-08-26T19:48:30.2661914Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-08-26T19:48:30.2662440Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-08-26T19:48:30.2662763Z GITHUB_REF_TYPE=branch 2025-08-26T19:48:30.2663065Z BASE_SHA=262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:30.2663409Z XLA_CUDA= 2025-08-26T19:48:30.2663633Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-08-26T19:48:30.2664053Z HUGGING_FACE_HUB_TOKEN=*** 2025-08-26T19:48:30.2665984Z *** 2025-08-26T19:48:30.2666224Z GITHUB_REPOSITORY_ID=65600975 2025-08-26T19:48:30.2666518Z GITHUB_ACTIONS=true 2025-08-26T19:48:30.2666812Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-08-26T19:48:30.2667213Z SHA1=262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:30.2667589Z GITHUB_SHA=262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:30.2668108Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-08-26T19:48:30.2668577Z UCC_HOME=/usr 2025-08-26T19:48:30.2668865Z VERBOSE_TEST_LOGS=False 2025-08-26T19:48:30.2669128Z GITHUB_REF=refs/heads/main 2025-08-26T19:48:30.2669405Z SHARD_NUMBER=1 2025-08-26T19:48:30.2669661Z GITHUB_REF_PROTECTED=true 2025-08-26T19:48:30.2669922Z HOME=/var/lib/jenkins 2025-08-26T19:48:30.2670217Z GITHUB_API_URL=https://api.github.com 2025-08-26T19:48:30.2670569Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-08-26T19:48:30.2670870Z UCX_COMMIT= 2025-08-26T19:48:30.2671075Z USE_SYSTEM_NCCL=1 2025-08-26T19:48:30.2671314Z NUM_TEST_SHARDS=3 2025-08-26T19:48:30.2671548Z UCX_HOME=/usr 2025-08-26T19:48:30.2672142Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2672985Z JOB_NAME=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:48:30.2673803Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2674649Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-08-26T19:48:30.2675171Z GITHUB_EVENT_NAME=push 2025-08-26T19:48:30.2675432Z DASHBOARD_TAG= 2025-08-26T19:48:30.2675665Z GITHUB_RUN_ID=17248463620 2025-08-26T19:48:30.2675936Z INSTALLED_OPENBLAS= 2025-08-26T19:48:30.2676591Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2677320Z GITHUB_ACTOR=pytorchmergebot 2025-08-26T19:48:30.2677591Z PR_NUMBER= 2025-08-26T19:48:30.2677808Z DESIRED_CUDA= 2025-08-26T19:48:30.2678040Z GITHUB_RUN_ATTEMPT=1 2025-08-26T19:48:30.2678303Z ANACONDA_PYTHON_VERSION=3.13 2025-08-26T19:48:30.2678636Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-08-26T19:48:30.2679057Z TERM=vt100 2025-08-26T19:48:30.2679278Z INSTALLED_VISION=yes 2025-08-26T19:48:30.2679523Z BRANCH=main 2025-08-26T19:48:30.2679736Z SCCACHE_REGION=us-east-1 2025-08-26T19:48:30.2680014Z OPENSSL_ROOT_DIR=/opt/openssl 2025-08-26T19:48:30.2680305Z CUDA_PATH=/usr/local/cuda 2025-08-26T19:48:30.2680840Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-08-26T19:48:30.2681434Z GITHUB_SERVER_URL=https://github.com 2025-08-26T19:48:30.2681743Z UCC_COMMIT= 2025-08-26T19:48:30.2682086Z REENABLED_ISSUES= 2025-08-26T19:48:30.2682318Z DOCS= 2025-08-26T19:48:30.2682512Z SHLVL=1 2025-08-26T19:48:30.2682719Z MAX_JOBS=6 2025-08-26T19:48:30.2682944Z GITHUB_ACTOR_ID=97764156 2025-08-26T19:48:30.2683292Z GITHUB_WORKFLOW_SHA=262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:30.2683668Z GITHUB_REF_NAME=main 2025-08-26T19:48:30.2684057Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-08-26T19:48:30.2684499Z GITHUB_JOB=test 2025-08-26T19:48:30.2684737Z NO_TEST_TIMEOUT=False 2025-08-26T19:48:30.2684981Z TD_DISTRIBUTED=False 2025-08-26T19:48:30.2685251Z GITHUB_REPOSITORY=pytorch/pytorch 2025-08-26T19:48:30.2685562Z GITHUB_RETENTION_DAYS=90 2025-08-26T19:48:30.2685836Z OPENSSL_DIR=/opt/openssl 2025-08-26T19:48:30.2686097Z GITHUB_ACTION_REPOSITORY= 2025-08-26T19:48:30.2686976Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-08-26T19:48:30.2687820Z GITHUB_BASE_REF= 2025-08-26T19:48:30.2688059Z INSTALLED_ACL= 2025-08-26T19:48:30.2688439Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621 2025-08-26T19:48:30.2688894Z CI=true 2025-08-26T19:48:30.2689127Z GITHUB_REPOSITORY_OWNER=pytorch 2025-08-26T19:48:30.2689464Z RUST_LOG=sccache::server=error 2025-08-26T19:48:30.2689736Z JOB_ID=48944862621 2025-08-26T19:48:30.2689969Z GITHUB_HEAD_REF= 2025-08-26T19:48:30.2690212Z GITHUB_ACTION_REF= 2025-08-26T19:48:30.2690515Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-08-26T19:48:30.2690870Z TEST_SHOWLOCALS=False 2025-08-26T19:48:30.2691135Z GITHUB_WORKFLOW=pull 2025-08-26T19:48:30.2691407Z DEBIAN_FRONTEND=noninteractive 2025-08-26T19:48:30.2694882Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2695552Z NO_TD=False 2025-08-26T19:48:30.2695803Z SKIP_SCCACHE_INITIALIZATION=1 2025-08-26T19:48:30.2696130Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-08-26T19:48:30.2696474Z _=/usr/bin/env 2025-08-26T19:48:30.2696796Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-08-26T19:48:30.2798827Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch 2025-08-26T19:48:30.2799822Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/bin 2025-08-26T19:48:30.2800743Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib 2025-08-26T19:48:30.2801424Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/test 2025-08-26T19:48:30.2801948Z + BUILD_DIR=build 2025-08-26T19:48:30.2802212Z + BUILD_RENAMED_DIR=build_renamed 2025-08-26T19:48:30.2802574Z + BUILD_BIN_DIR=build/bin 2025-08-26T19:48:30.2802855Z + SHARD_NUMBER=1 2025-08-26T19:48:30.2803081Z + NUM_TEST_SHARDS=3 2025-08-26T19:48:30.2803401Z + export TORCH_SERIALIZATION_DEBUG=1 2025-08-26T19:48:30.2803738Z + TORCH_SERIALIZATION_DEBUG=1 2025-08-26T19:48:30.2804064Z + export VALGRIND=ON 2025-08-26T19:48:30.2804330Z + VALGRIND=ON 2025-08-26T19:48:30.2804637Z + [[ linux-jammy-py3.13-clang12 == *clang9* ]] 2025-08-26T19:48:30.2805153Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-08-26T19:48:30.2805476Z + detect_cuda_arch 2025-08-26T19:48:30.2805818Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-08-26T19:48:30.2806176Z + [[ linux-jammy-py3.13-clang12 == *s390x* ]] 2025-08-26T19:48:30.2806557Z + [[ 0 == \1 ]] 2025-08-26T19:48:30.2806834Z + [[ True == \1 ]] 2025-08-26T19:48:30.2807134Z + [[ linux-jammy-py3.13-clang12 != *bazel* ]] 2025-08-26T19:48:30.2807503Z ++ realpath build/custom_test_artifacts 2025-08-26T19:48:30.2827269Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-08-26T19:48:30.2827804Z + [[ -n '' ]] 2025-08-26T19:48:30.2828038Z + echo 'Environment variables' 2025-08-26T19:48:30.2828340Z Environment variables 2025-08-26T19:48:30.2828599Z + env 2025-08-26T19:48:30.2834939Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-08-26T19:48:30.2836048Z CONTINUE_THROUGH_ERROR=True 2025-08-26T19:48:30.2836537Z BUILD_ENVIRONMENT=linux-jammy-py3.13-clang12 2025-08-26T19:48:30.2837158Z VLLM_TEST_HUGGING_FACE_TOKEN=*** 2025-08-26T19:48:30.2837497Z HOSTNAME=77c509ac59ad 2025-08-26T19:48:30.2838150Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2838914Z GITHUB_ACTION=__run_2 2025-08-26T19:48:30.2839180Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-08-26T19:48:30.2839491Z GITHUB_RUN_NUMBER=350026 2025-08-26T19:48:30.2839768Z TEST_CONFIG=dynamo_wrapped 2025-08-26T19:48:30.2840059Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-08-26T19:48:30.2840388Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-08-26T19:48:30.2840716Z SCCACHE_IDLE_TIMEOUT=0 2025-08-26T19:48:30.2841129Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-08-26T19:48:30.2841598Z GITHUB_TRIGGERING_ACTOR=pytorchmergebot 2025-08-26T19:48:30.2841932Z GITHUB_REF_TYPE=branch 2025-08-26T19:48:30.2842219Z BASE_SHA=262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:30.2842564Z XLA_CUDA= 2025-08-26T19:48:30.2842803Z NCCL_LIB_DIR=/usr/local/cuda/lib64/ 2025-08-26T19:48:30.2843400Z HUGGING_FACE_HUB_TOKEN=*** 2025-08-26T19:48:30.2843823Z *** 2025-08-26T19:48:30.2844142Z GITHUB_REPOSITORY_ID=65600975 2025-08-26T19:48:30.2844564Z GITHUB_ACTIONS=true 2025-08-26T19:48:30.2844981Z SCCACHE_ERROR_LOG=/var/lib/jenkins/sccache_error.log 2025-08-26T19:48:30.2845368Z SHA1=262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:30.2845740Z GITHUB_SHA=262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:30.2846300Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/pull.yml@refs/heads/main 2025-08-26T19:48:30.2846780Z UCC_HOME=/usr 2025-08-26T19:48:30.2847011Z TORCH_SERIALIZATION_DEBUG=1 2025-08-26T19:48:30.2847298Z VERBOSE_TEST_LOGS=False 2025-08-26T19:48:30.2847562Z GITHUB_REF=refs/heads/main 2025-08-26T19:48:30.2847839Z SHARD_NUMBER=1 2025-08-26T19:48:30.2848066Z GITHUB_REF_PROTECTED=true 2025-08-26T19:48:30.2848342Z HOME=/var/lib/jenkins 2025-08-26T19:48:30.2848628Z GITHUB_API_URL=https://api.github.com 2025-08-26T19:48:30.2848968Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-08-26T19:48:30.2849255Z UCX_COMMIT= 2025-08-26T19:48:30.2849472Z USE_SYSTEM_NCCL=1 2025-08-26T19:48:30.2849711Z NUM_TEST_SHARDS=3 2025-08-26T19:48:30.2849943Z UCX_HOME=/usr 2025-08-26T19:48:30.2850526Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2851365Z JOB_NAME=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T19:48:30.2852187Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2853026Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-08-26T19:48:30.2853551Z GITHUB_EVENT_NAME=push 2025-08-26T19:48:30.2853835Z DASHBOARD_TAG= 2025-08-26T19:48:30.2854082Z GITHUB_RUN_ID=17248463620 2025-08-26T19:48:30.2854357Z INSTALLED_OPENBLAS= 2025-08-26T19:48:30.2854994Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2855702Z GITHUB_ACTOR=pytorchmergebot 2025-08-26T19:48:30.2855983Z PR_NUMBER= 2025-08-26T19:48:30.2856197Z DESIRED_CUDA= 2025-08-26T19:48:30.2856427Z GITHUB_RUN_ATTEMPT=1 2025-08-26T19:48:30.2856664Z VALGRIND=ON 2025-08-26T19:48:30.2856898Z ANACONDA_PYTHON_VERSION=3.13 2025-08-26T19:48:30.2857263Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-08-26T19:48:30.2857620Z TERM=vt100 2025-08-26T19:48:30.2857830Z INSTALLED_VISION=yes 2025-08-26T19:48:30.2858081Z BRANCH=main 2025-08-26T19:48:30.2858318Z SCCACHE_REGION=us-east-1 2025-08-26T19:48:30.2858601Z OPENSSL_ROOT_DIR=/opt/openssl 2025-08-26T19:48:30.2858883Z CUDA_PATH=/usr/local/cuda 2025-08-26T19:48:30.2859429Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-08-26T19:48:30.2860179Z GITHUB_SERVER_URL=https://github.com 2025-08-26T19:48:30.2860588Z UCC_COMMIT= 2025-08-26T19:48:30.2860801Z REENABLED_ISSUES= 2025-08-26T19:48:30.2861039Z DOCS= 2025-08-26T19:48:30.2861245Z SHLVL=1 2025-08-26T19:48:30.2861447Z MAX_JOBS=6 2025-08-26T19:48:30.2861694Z GITHUB_ACTOR_ID=97764156 2025-08-26T19:48:30.2862050Z GITHUB_WORKFLOW_SHA=262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T19:48:30.2862445Z GITHUB_REF_NAME=main 2025-08-26T19:48:30.2862836Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-08-26T19:48:30.2863266Z GITHUB_JOB=test 2025-08-26T19:48:30.2863509Z NO_TEST_TIMEOUT=False 2025-08-26T19:48:30.2863767Z TD_DISTRIBUTED=False 2025-08-26T19:48:30.2864040Z GITHUB_REPOSITORY=pytorch/pytorch 2025-08-26T19:48:30.2864338Z GITHUB_RETENTION_DAYS=90 2025-08-26T19:48:30.2864613Z OPENSSL_DIR=/opt/openssl 2025-08-26T19:48:30.2864884Z GITHUB_ACTION_REPOSITORY= 2025-08-26T19:48:30.2865758Z PATH=/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-08-26T19:48:30.2866585Z GITHUB_BASE_REF= 2025-08-26T19:48:30.2866827Z INSTALLED_ACL= 2025-08-26T19:48:30.2867213Z ARTIFACTS_FILE_SUFFIX=test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621 2025-08-26T19:48:30.2867672Z CI=true 2025-08-26T19:48:30.2867932Z GITHUB_REPOSITORY_OWNER=pytorch 2025-08-26T19:48:30.2868272Z RUST_LOG=sccache::server=error 2025-08-26T19:48:30.2868553Z JOB_ID=48944862621 2025-08-26T19:48:30.2868794Z GITHUB_HEAD_REF= 2025-08-26T19:48:30.2869020Z GITHUB_ACTION_REF= 2025-08-26T19:48:30.2869320Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-08-26T19:48:30.2869689Z TEST_SHOWLOCALS=False 2025-08-26T19:48:30.2869948Z GITHUB_WORKFLOW=pull 2025-08-26T19:48:30.2870203Z DEBIAN_FRONTEND=noninteractive 2025-08-26T19:48:30.2870859Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_f9b9cb9b-ef35-47b4-a7ed-c383f17f984b 2025-08-26T19:48:30.2871522Z NO_TD=False 2025-08-26T19:48:30.2871765Z SKIP_SCCACHE_INITIALIZATION=1 2025-08-26T19:48:30.2872067Z NCCL_INCLUDE_DIR=/usr/local/cuda/include/ 2025-08-26T19:48:30.2872385Z _=/usr/bin/env 2025-08-26T19:48:30.2872617Z + echo 'Testing pytorch' 2025-08-26T19:48:30.2872878Z Testing pytorch 2025-08-26T19:48:30.2873114Z + export LANG=C.UTF-8 2025-08-26T19:48:30.2873365Z + LANG=C.UTF-8 2025-08-26T19:48:30.2913065Z + PR_NUMBER= 2025-08-26T19:48:30.2913473Z + [[ dynamo_wrapped == \d\e\f\a\u\l\t ]] 2025-08-26T19:48:30.2914127Z + [[ dynamo_wrapped == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-08-26T19:48:30.2914787Z + [[ dynamo_wrapped == \s\l\o\w ]] 2025-08-26T19:48:30.2915502Z + [[ linux-jammy-py3.13-clang12 == *slow-gradcheck* ]] 2025-08-26T19:48:30.2915991Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-08-26T19:48:30.2916347Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-08-26T19:48:30.2916684Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-08-26T19:48:30.2917038Z + [[ dynamo_wrapped == *crossref* ]] 2025-08-26T19:48:30.2917373Z + [[ linux-jammy-py3.13-clang12 == *rocm* ]] 2025-08-26T19:48:30.2917729Z + [[ linux-jammy-py3.13-clang12 == *xpu* ]] 2025-08-26T19:48:30.2918100Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-08-26T19:48:30.2918449Z + pip_install ninja==1.10.2 2025-08-26T19:48:30.2918827Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-08-26T19:48:30.2919310Z + python3 -m pip install --progress-bar off ninja==1.10.2 2025-08-26T19:48:30.7456660Z Collecting ninja==1.10.2 2025-08-26T19:48:30.7619271Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-08-26T19:48:30.7731210Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-08-26T19:48:30.9479465Z Installing collected packages: ninja 2025-08-26T19:48:30.9480137Z Attempting uninstall: ninja 2025-08-26T19:48:30.9514925Z Found existing installation: ninja 1.11.1.3 2025-08-26T19:48:30.9535567Z Uninstalling ninja-1.11.1.3: 2025-08-26T19:48:30.9587227Z Successfully uninstalled ninja-1.11.1.3 2025-08-26T19:48:30.9820948Z Successfully installed ninja-1.10.2 2025-08-26T19:48:31.0478025Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-08-26T19:48:31.0479696Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/opt/conda/envs/py_3.13/bin:/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-08-26T19:48:31.0480986Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-08-26T19:48:31.0481580Z + [[ linux-jammy-py3.13-clang12 == *asan* ]] 2025-08-26T19:48:31.0481997Z + [[ linux-jammy-py3.13-clang12 == *-debug* ]] 2025-08-26T19:48:31.0482373Z + [[ linux-jammy-py3.13-clang12 != *-bazel-* ]] 2025-08-26T19:48:31.0483606Z + echo 'We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass' 2025-08-26T19:48:31.0484293Z We are not in debug mode: linux-jammy-py3.13-clang12. Expect the assertion to pass 2025-08-26T19:48:31.0484777Z + cd test 2025-08-26T19:48:31.0496061Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-08-26T19:48:32.4475150Z + [[ dynamo_wrapped == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-08-26T19:48:32.4475623Z + [[ dynamo_wrapped == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-08-26T19:48:32.4476062Z + [[ dynamo_wrapped == \l\e\g\a\c\y\_\n\v\i\d\i\a\_\d\r\i\v\e\r ]] 2025-08-26T19:48:32.4479171Z + DYNAMO_BENCHMARK_FLAGS=() 2025-08-26T19:48:32.4479833Z + [[ dynamo_wrapped == *pr_time_benchmarks* ]] 2025-08-26T19:48:32.4480543Z + [[ dynamo_wrapped == *dynamo_eager* ]] 2025-08-26T19:48:32.4481011Z + [[ dynamo_wrapped == *aot_eager* ]] 2025-08-26T19:48:32.4481332Z + [[ dynamo_wrapped == *aot_inductor* ]] 2025-08-26T19:48:32.4481700Z + [[ dynamo_wrapped == *max_autotune_inductor* ]] 2025-08-26T19:48:32.4482088Z + [[ dynamo_wrapped == *inductor* ]] 2025-08-26T19:48:32.4482414Z + [[ dynamo_wrapped == *dynamic* ]] 2025-08-26T19:48:32.4482729Z + [[ dynamo_wrapped == *cpu* ]] 2025-08-26T19:48:32.4483059Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-08-26T19:48:32.4503310Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-08-26T19:48:32.4503991Z + [[ linux-jammy-py3.13-clang12 == *-bazel-* ]] 2025-08-26T19:48:32.4506734Z + cd test 2025-08-26T19:48:32.4507091Z + python -c 'import torch; print(torch.__config__.show())' 2025-08-26T19:48:33.5409207Z PyTorch built with: 2025-08-26T19:48:33.5409512Z - GCC 4.2 2025-08-26T19:48:33.5409744Z - C++ Version: 201703 2025-08-26T19:48:33.5410018Z - clang 12.0.1 2025-08-26T19:48:33.5410582Z - Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-08-26T19:48:33.5411320Z - Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-08-26T19:48:33.5411773Z - OpenMP 201811 2025-08-26T19:48:33.5412085Z - LAPACK is enabled (usually provided by MKL) 2025-08-26T19:48:33.5412439Z - NNPACK is enabled 2025-08-26T19:48:33.5412718Z - CPU capability usage: AVX512 2025-08-26T19:48:33.5418112Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=a03cc53e6f6e2fe67316cb8c74c25f5b953f445b, CXX_COMPILER=/opt/cache/bin/clang++, CXX_FLAGS= -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOCUPTI -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -DC10_NODEPRECATED -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=braced-scalar-init -Werror=range-loop-construct -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wvla-extension -Wsuggest-override -Wnewline-eof -Winconsistent-missing-override -Winconsistent-missing-destructor-override -Wno-pass-failed -Wno-error=old-style-cast -Wconstant-conversion -Qunused-arguments -faligned-new -Werror -fno-math-errno -fno-trapping-math -Werror=format, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.9.0, USE_CUDA=OFF, USE_CUDNN=OFF, USE_CUSPARSELT=OFF, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=OFF, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, USE_XCCL=OFF, USE_XPU=OFF, 2025-08-26T19:48:33.5423988Z 2025-08-26T19:48:33.8261345Z + cd test 2025-08-26T19:48:33.8261977Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-08-26T19:48:34.9207806Z ATen/Parallel: 2025-08-26T19:48:34.9208288Z at::get_num_threads() : 4 2025-08-26T19:48:34.9208720Z at::get_num_interop_threads() : 4 2025-08-26T19:48:34.9209138Z OpenMP 201811 2025-08-26T19:48:34.9209483Z omp_get_max_threads() : 4 2025-08-26T19:48:34.9210339Z Intel(R) oneAPI Math Kernel Library Version 2024.2-Product Build 20240605 for Intel(R) 64 architecture applications 2025-08-26T19:48:34.9211632Z mkl_get_max_threads() : 4 2025-08-26T19:48:34.9212207Z Intel(R) MKL-DNN v3.7.1 (Git Hash 8d263e693366ef8db40acc569cc7d8edf644556d) 2025-08-26T19:48:34.9212899Z std::thread::hardware_concurrency() : 8 2025-08-26T19:48:34.9213378Z Environment variables: 2025-08-26T19:48:34.9213765Z OMP_NUM_THREADS : [not set] 2025-08-26T19:48:34.9214159Z MKL_NUM_THREADS : [not set] 2025-08-26T19:48:34.9214571Z ATen parallel backend: OpenMP 2025-08-26T19:48:34.9214880Z 2025-08-26T19:48:35.1865378Z + [[ dynamo_wrapped == *numpy_2* ]] 2025-08-26T19:48:35.1866069Z + [[ linux-jammy-py3.13-clang12 == *aarch64* ]] 2025-08-26T19:48:35.1866443Z + [[ dynamo_wrapped == *backward* ]] 2025-08-26T19:48:35.1866764Z + [[ dynamo_wrapped == *xla* ]] 2025-08-26T19:48:35.1867059Z + [[ dynamo_wrapped == *vllm* ]] 2025-08-26T19:48:35.1867359Z + [[ dynamo_wrapped == *executorch* ]] 2025-08-26T19:48:35.1867705Z + [[ dynamo_wrapped == \j\i\t\_\l\e\g\a\c\y ]] 2025-08-26T19:48:35.1868102Z + [[ linux-jammy-py3.13-clang12 == *libtorch* ]] 2025-08-26T19:48:35.1868465Z + [[ dynamo_wrapped == distributed ]] 2025-08-26T19:48:35.1868814Z + [[ dynamo_wrapped == *operator_benchmark* ]] 2025-08-26T19:48:35.1869216Z + [[ dynamo_wrapped == *inductor_distributed* ]] 2025-08-26T19:48:35.1869721Z + [[ dynamo_wrapped == *inductor-halide* ]] 2025-08-26T19:48:35.1870402Z + [[ dynamo_wrapped == *inductor-triton-cpu* ]] 2025-08-26T19:48:35.1870886Z + [[ dynamo_wrapped == *inductor-micro-benchmark* ]] 2025-08-26T19:48:35.1871264Z + [[ dynamo_wrapped == *huggingface* ]] 2025-08-26T19:48:35.1871588Z + [[ dynamo_wrapped == *timm* ]] 2025-08-26T19:48:35.1871890Z + [[ dynamo_wrapped == cachebench ]] 2025-08-26T19:48:35.1872209Z + [[ dynamo_wrapped == verify_cachebench ]] 2025-08-26T19:48:35.1872552Z + [[ dynamo_wrapped == *torchbench* ]] 2025-08-26T19:48:35.1872901Z + [[ dynamo_wrapped == *inductor_cpp_wrapper* ]] 2025-08-26T19:48:35.1873251Z + [[ dynamo_wrapped == *inductor* ]] 2025-08-26T19:48:35.1873554Z + [[ dynamo_wrapped == *einops* ]] 2025-08-26T19:48:35.1873881Z + [[ dynamo_wrapped == *dynamo_wrapped* ]] 2025-08-26T19:48:35.1874204Z + install_torchvision 2025-08-26T19:48:35.1874479Z + local orig_preload 2025-08-26T19:48:35.1874715Z + local commit 2025-08-26T19:48:35.1874956Z ++ get_pinned_commit vision 2025-08-26T19:48:35.1875259Z ++ cat .github/ci_commit_pins/vision.txt 2025-08-26T19:48:35.1894471Z + commit=966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-26T19:48:35.1895020Z + orig_preload= 2025-08-26T19:48:35.1895385Z + '[' -n '' ']' 2025-08-26T19:48:35.1895721Z + [[ linux-jammy-py3.13-clang12 == *cuda* ]] 2025-08-26T19:48:35.1896529Z + pip_build_and_install git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 dist/vision 2025-08-26T19:48:35.1897447Z + local build_target=git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-26T19:48:35.1898024Z + local wheel_dir=dist/vision 2025-08-26T19:48:35.1898308Z + local found_whl=0 2025-08-26T19:48:35.1898578Z + for file in "${wheel_dir}"/*.whl 2025-08-26T19:48:35.1898891Z + [[ -f dist/vision/*.whl ]] 2025-08-26T19:48:35.1899150Z + '[' 0 == 0 ']' 2025-08-26T19:48:35.1900177Z + python3 -m pip wheel --no-build-isolation --no-deps --no-use-pep517 -w dist/vision git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-26T19:48:35.5014727Z Collecting git+https://github.com/pytorch/vision.git@966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-26T19:48:35.5019457Z Cloning https://github.com/pytorch/vision.git (to revision 966da7e46f65d6d49df3e31214470a4fe5cc8e66) to /tmp/pip-req-build-n3nncrjq 2025-08-26T19:48:35.5038870Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-n3nncrjq 2025-08-26T19:48:37.3181427Z Running command git rev-parse -q --verify 'sha^966da7e46f65d6d49df3e31214470a4fe5cc8e66' 2025-08-26T19:48:37.3199148Z Running command git fetch -q https://github.com/pytorch/vision.git 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-26T19:48:37.4497857Z Running command git checkout -q 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-26T19:48:37.7670892Z Resolved https://github.com/pytorch/vision.git to commit 966da7e46f65d6d49df3e31214470a4fe5cc8e66 2025-08-26T19:48:39.7684254Z Preparing metadata (setup.py) ... [?25l- \ | done 2025-08-26T19:48:39.7717086Z [?25hBuilding wheels for collected packages: torchvision 2025-08-26T19:48:39.7762655Z  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 2025-08-26T19:50:18.3988252Z  Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done 2025-08-26T19:50:18.3990632Z [?25h Created wheel for torchvision: filename=torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl size=1200298 sha256=be5e780265ff3712a660ee6106b8c491a3ea7d0970e6418a8e5ef3b2e4fd9ea3 2025-08-26T19:50:18.3993373Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/89/53/07/16d05bff0990e585bfa232438657da6e5e4f6d0cea71bd3e09 2025-08-26T19:50:18.3994617Z Successfully built torchvision 2025-08-26T19:50:18.4880448Z + for file in "${wheel_dir}"/*.whl 2025-08-26T19:50:18.4881255Z + pip_install_whl dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-08-26T19:50:18.4881960Z + args=('dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl') 2025-08-26T19:50:18.4882441Z + local args 2025-08-26T19:50:18.4882853Z + [[ dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl == *\ * ]] 2025-08-26T19:50:18.4883374Z + for path in "${args[@]}" 2025-08-26T19:50:18.4883861Z + echo 'Installing dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl' 2025-08-26T19:50:18.4884590Z Installing dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-08-26T19:50:18.4885406Z + python3 -mpip install --no-index --no-deps dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-08-26T19:50:18.8344979Z Processing ./dist/vision/torchvision-0.22.0a0+966da7e-cp313-cp313-linux_x86_64.whl 2025-08-26T19:50:18.8447473Z Installing collected packages: torchvision 2025-08-26T19:50:19.3050045Z Successfully installed torchvision-0.22.0a0+966da7e 2025-08-26T19:50:19.3551733Z + '[' -n '' ']' 2025-08-26T19:50:19.3552039Z + test_dynamo_wrapped_shard 1 2025-08-26T19:50:19.3552341Z + [[ -z 3 ]] 2025-08-26T19:50:19.3552602Z + python tools/dynamo/verify_dynamo.py 2025-08-26T19:50:20.4649920Z Python version: 3.13.5 2025-08-26T19:50:20.4650282Z `torch` version: 2.9.0a0+git262640f 2025-08-26T19:50:20.4650609Z CUDA version: None 2025-08-26T19:50:20.4650857Z ROCM version: None 2025-08-26T19:50:20.4651333Z 2025-08-26T19:50:20.4651890Z /var/lib/jenkins/workspace/tools/dynamo/verify_dynamo.py:220: UserWarning: Dynamo not yet supported in Python 3.13. Skipping check. 2025-08-26T19:50:20.4652780Z warnings.warn("Dynamo not yet supported in Python 3.13. Skipping check.") 2025-08-26T19:50:20.4653238Z All required checks passed 2025-08-26T19:50:20.7382613Z + python test/run_test.py --dynamo --exclude-inductor-tests --exclude-jit-executor --exclude-distributed-tests --exclude-torch-export-tests --exclude-aot-dispatch-tests --shard 1 3 --verbose --upload-artifacts-while-running 2025-08-26T19:50:23.6974538Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T19:50:23.6976127Z import pkg_resources 2025-08-26T19:50:25.3781122Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-08-26T19:50:25.4319952Z Ignoring disabled issues: [''] 2025-08-26T19:50:25.4449683Z Found test times from artifacts 2025-08-26T19:50:25.4957342Z Found test times from artifacts 2025-08-26T19:50:25.4974325Z Running all tests 2025-08-26T19:50:25.5154390Z Running parallel tests on 3 processes 2025-08-26T19:50:25.5158736Z Name: tests to run (est. time: 120.75min) 2025-08-26T19:50:25.5159148Z Serial tests (46): 2025-08-26T19:50:25.5159427Z test_ci_sanity_check_fail 1/1 2025-08-26T19:50:25.5159736Z test_overrides 1/1 2025-08-26T19:50:25.5159986Z test_reductions 2/2 2025-08-26T19:50:25.5160274Z test_cpp_extensions_mtia_backend 1/1 2025-08-26T19:50:25.5160656Z test_nn 1/4 2025-08-26T19:50:25.5160886Z test_nn 3/4 2025-08-26T19:50:25.5161155Z test_cpp_extensions_stream_and_event 1/1 2025-08-26T19:50:25.5161561Z doctests 1/1 2025-08-26T19:50:25.5161828Z dynamo/test_fake_distributed 1/1 2025-08-26T19:50:25.5162191Z test_utils 1/1 2025-08-26T19:50:25.5162414Z test_fx 1/1 2025-08-26T19:50:25.5162671Z test_transformers_privateuse1 1/1 2025-08-26T19:50:25.5163058Z test_cpp_api_parity 1/1 2025-08-26T19:50:25.5163345Z test_extension_utils 1/1 2025-08-26T19:50:25.5163655Z test_openreg 1/1 2025-08-26T19:50:25.5163930Z test_show_pickle 1/1 2025-08-26T19:50:25.5164192Z test_torch 1/2 2025-08-26T19:50:25.5164495Z test_torch 2/2 2025-08-26T19:50:25.5164735Z test_tensorexpr 1/1 2025-08-26T19:50:25.5165019Z test_namedtuple_return_api 1/1 2025-08-26T19:50:25.5165399Z test_multiprocessing 1/1 2025-08-26T19:50:25.5165692Z test_autograd_fallback 1/1 2025-08-26T19:50:25.5166040Z test_fake_tensor 1/1 2025-08-26T19:50:25.5166309Z test_python_dispatch 1/1 2025-08-26T19:50:25.5166599Z test_autocast 1/1 2025-08-26T19:50:25.5166916Z test_jit_disabled 1/1 2025-08-26T19:50:25.5167185Z test_dispatch 1/1 2025-08-26T19:50:25.5167434Z test_native_mha 1/1 2025-08-26T19:50:25.5167764Z test_sort_and_select 1/1 2025-08-26T19:50:25.5168055Z test_cpp_extensions_jit 1/1 2025-08-26T19:50:25.5168408Z nn/test_pooling 1/1 2025-08-26T19:50:25.5168708Z nn/test_convolution 1/2 2025-08-26T19:50:25.5169034Z nn/test_convolution 2/2 2025-08-26T19:50:25.5169445Z test_multiprocessing_spawn 1/1 2025-08-26T19:50:25.5169761Z test_cuda_primary_ctx 1/1 2025-08-26T19:50:25.5170040Z test_mobile_optimizer 1/1 2025-08-26T19:50:25.5170324Z test_cuda_trace 1/1 2025-08-26T19:50:25.5170590Z test_cuda_nvml_based_avail 1/1 2025-08-26T19:50:25.5170897Z test_spectral_ops 1/1 2025-08-26T19:50:25.5171180Z distributions/test_distributions 1/3 2025-08-26T19:50:25.5171532Z distributions/test_distributions 2/3 2025-08-26T19:50:25.5171963Z distributions/test_distributions 3/3 2025-08-26T19:50:25.5172501Z test_cpp_extensions_aot_no_ninja 1/1 2025-08-26T19:50:25.5172836Z test_autoload_disable 1/1 2025-08-26T19:50:25.5173400Z test_autoload_enable 1/1 2025-08-26T19:50:25.5173702Z test_cpp_extensions_aot_ninja 1/1 2025-08-26T19:50:25.5174089Z Parallel tests (35): 2025-08-26T19:50:25.5174350Z dynamo/test_utils 1/1 2025-08-26T19:50:25.5174624Z dynamo/test_modes 1/1 2025-08-26T19:50:25.5174954Z dynamo/test_logging 1/1 2025-08-26T19:50:25.5175242Z dynamo/test_higher_order_ops 1/1 2025-08-26T19:50:25.5175634Z dynamo/test_aot_autograd_cache 1/1 2025-08-26T19:50:25.5175953Z dynamo/test_recompile_ux 1/1 2025-08-26T19:50:25.5176328Z dynamo/test_deque_reconstruct 1/1 2025-08-26T19:50:25.5176648Z dynamo/test_base_output 1/1 2025-08-26T19:50:25.5176952Z dynamo/test_recompiles 1/1 2025-08-26T19:50:25.5177297Z dynamo/test_interop 1/1 2025-08-26T19:50:25.5177580Z dynamo/test_sdpa 1/1 2025-08-26T19:50:25.5177912Z dynamo/test_nops 1/1 2025-08-26T19:50:25.5178322Z dynamo/test_metrics_context 1/1 2025-08-26T19:50:25.5178684Z dynamo/test_modules 1/1 2025-08-26T19:50:25.5178969Z dynamo/test_resume 1/1 2025-08-26T19:50:25.5179248Z dynamo/test_unittest 1/1 2025-08-26T19:50:25.5179613Z dynamo/test_autograd_function 1/1 2025-08-26T19:50:25.5179916Z dynamo/test_list 1/1 2025-08-26T19:50:25.5180247Z dynamo/test_profiler 1/1 2025-08-26T19:50:25.5180620Z dynamo/test_deviceguard 1/1 2025-08-26T19:50:25.5180949Z dynamo/test_flat_apply 1/1 2025-08-26T19:50:25.5181262Z dynamo/test_sets 1/1 2025-08-26T19:50:25.5181544Z dynamo/test_aot_autograd 1/1 2025-08-26T19:50:25.5181921Z dynamo/test_compiler_bisector 1/1 2025-08-26T19:50:25.5182234Z dynamo/test_bytecode_utils 1/1 2025-08-26T19:50:25.5182607Z dynamo/test_torchrec 1/1 2025-08-26T19:50:25.5182925Z dynamo/test_activation_checkpointing 1/1 2025-08-26T19:50:25.5183312Z dynamo/test_hooks 1/1 2025-08-26T19:50:25.5183593Z dynamo/test_comptime 1/1 2025-08-26T19:50:25.5183886Z test_matmul_cuda 1/1 2025-08-26T19:50:25.5184207Z test_jiterator 1/1 2025-08-26T19:50:25.5184483Z functorch/test_ac 1/1 2025-08-26T19:50:25.5184745Z test_cuda_sanitizer 1/1 2025-08-26T19:50:25.5185112Z dynamo/test_nested_graph_breaks 1/1 2025-08-26T19:50:25.5185434Z test_quantization 9/9 2025-08-26T19:50:25.5185778Z Name: excluded (est. time: 0.0min) 2025-08-26T19:50:25.5186063Z Serial tests (0): 2025-08-26T19:50:25.5186312Z Parallel tests (0): 2025-08-26T19:50:25.5186746Z Running test_ci_sanity_check_fail 1/1 ... [2025-08-26 19:50:25.516796] 2025-08-26T19:50:25.5187239Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T19:50:25.5188401Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_ci_sanity_check_fail.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 19:50:25.517119] 2025-08-26T19:50:44.4632228Z Uploading artifacts took 0.26 seconds 2025-08-26T19:50:44.4634890Z Running test_overrides 1/1 ... [2025-08-26 19:50:44.463318] 2025-08-26T19:50:44.4635323Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T19:50:44.4639065Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_overrides.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 19:50:44.463678] 2025-08-26T19:53:22.2479389Z 2025-08-26T19:53:22.2480626Z test_overrides 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_overrides_1.1_b528260c2a0ae71c_.log 2025-08-26T19:53:22.2975702Z Running 1470 items in this shard: test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_H___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_T___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__backward_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__base___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__cdata___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__post_accumulate_grad_hooks___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase__version___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_data___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_device___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_dtype___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_grad_fn___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_imag___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cpu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_cuda___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_ipu___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_leaf___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_is_maia___get__, 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test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_itemsize___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_layout___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mH___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_mT___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_name___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_names___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_nbytes___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_ndim___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_output_nr___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_real___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_requires_grad___get__, test/test_overrides.py::TestTorchFunctionOverride::test_TensorBase_retains_grad___get__, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_igammac_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_copy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inner, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ipu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_coalesced, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_contiguous, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_pinned, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_set_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_shared, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_istft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_item, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kron, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lcm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ldexp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_le_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lerp_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_less_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lgamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log10_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log1p_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_and_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_not_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_or_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logical_xor_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logit_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_long, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map2_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_map_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_fill_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_max, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_median, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_min, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_mode, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nan_to_num_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ndimension, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ne_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_neg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_negative_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nelement, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nextafter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_norm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_not_equal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_numpy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_outer, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_permute, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pin_memory, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_polygamma_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_positive, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_pow_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_prod, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_put_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qr, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_qscheme, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rad2deg_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_random_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reciprocal_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_record_stream, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_refine_names, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_register_post_accumulate_grad_hook, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_relu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_remainder_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rename_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_renorm_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_requires_grad_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_reshape_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resize_as_sparse_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_retain_grad, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_roll, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_round_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_row_indices, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_rsqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_add_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_scatter_reduce_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_set_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sgn_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_share_memory_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_short, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sigmoid_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sign_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sin_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinc_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sinh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_smm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sort, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_mask, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sparse_resize_and_clear_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sqrt_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_square_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_squeeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sspaddmm, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_std, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_stft, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_offset, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_storage_type, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sub_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_subtract_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_sum_to_size, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_svd, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapaxes_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_swapdims_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_t_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tan_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tanh_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tile, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_dense, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_mkldnn, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_to_sparse, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tolist, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_topk, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_trace, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_transpose_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_tril_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_triu_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_true_divide, 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test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_unsqueeze_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_untyped_storage, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_values, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_var, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_view_as, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_where, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xlogy_, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_xpu, test/test_overrides.py::TestTorchFunctionOverride::test_Tensor_zero_, 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test/test_overrides.py::TestTorchFunctionOverride::test_torch_acosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcdiv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addcmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addmv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_addr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_adjoint, test/test_overrides.py::TestTorchFunctionOverride::test_torch_affine_grid_generator, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alias_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_all, test/test_overrides.py::TestTorchFunctionOverride::test_torch_allclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_amin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_aminmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_angle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_any, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arccosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arcsinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_arctanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argsort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_argwhere, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_as_strided_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_asinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atan2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_atanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_avg_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_baddbmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_backward_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_elemt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_gather_stats_with_counts, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_batch_norm_update_stats, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bernoulli, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bilinear, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bincount, test/test_overrides.py::TestTorchFunctionOverride::test_torch_binomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_left_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_right_shift, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bitwise_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_broadcast_to, test/test_overrides.py::TestTorchFunctionOverride::test_torch_bucketize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ccol_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ceil, test/test_overrides.py::TestTorchFunctionOverride::test_torch_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cholesky_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_choose_qparams_optimized, test/test_overrides.py::TestTorchFunctionOverride::test_torch_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clamp_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_clone, test/test_overrides.py::TestTorchFunctionOverride::test_torch_col_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_column_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_combinations, test/test_overrides.py::TestTorchFunctionOverride::test_torch_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_concatenate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conj_physical, test/test_overrides.py::TestTorchFunctionOverride::test_torch_constant_pad_nd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_tbc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_conv_transpose3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_copysign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_corrcoef, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cos, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cosine_similarity, test/test_overrides.py::TestTorchFunctionOverride::test_torch_count_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cov, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cross, test/test_overrides.py::TestTorchFunctionOverride::test_torch_crow_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cummin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumprod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_cumulative_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_deg2rad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dequantize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_det, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach, test/test_overrides.py::TestTorchFunctionOverride::test_torch_detach_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diag_embed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagflat, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diagonal_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_diff, test/test_overrides.py::TestTorchFunctionOverride::test_torch_digamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_dstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_empty_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_eq, test/test_overrides.py::TestTorchFunctionOverride::test_torch_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_erfinv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_exp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expand_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_expm1, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_channel_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fake_quantize_per_tensor_affine, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_fp16_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_int8_weight_fp32_activation, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_linear_quantize_weight, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_gemm_matrix_fp16, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fbgemm_pack_quantized_matrix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_feature_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fix, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flip, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fliplr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_flipud, test/test_overrides.py::TestTorchFunctionOverride::test_torch_float_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_floor_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fmod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_frobenius_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_full_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_atleast_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_block_diag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_broadcast_tensors, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cartesian_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_cdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_chain_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_einsum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_lu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_meshgrid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_stft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_tensordot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unique_consecutive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_functional_unravel_index, test/test_overrides.py::TestTorchFunctionOverride::test_torch_fused_moving_avg_obs_fake_quant, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gather, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gcd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ge, test/test_overrides.py::TestTorchFunctionOverride::test_torch_geqrf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ger, test/test_overrides.py::TestTorchFunctionOverride::test_torch_get_device, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gradient, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater, test/test_overrides.py::TestTorchFunctionOverride::test_torch_greater_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_grid_sampler_3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_gt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hardshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hash_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_heaviside, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogram, test/test_overrides.py::TestTorchFunctionOverride::test_torch_histogramdd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_hypot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_i0, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_igammac, test/test_overrides.py::TestTorchFunctionOverride::test_torch_imag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_index_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inner, test/test_overrides.py::TestTorchFunctionOverride::test_torch_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_int_repr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_distributed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_floating_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_inference, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_same_size, test/test_overrides.py::TestTorchFunctionOverride::test_torch_is_signed, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isclose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isfinite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isnan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isneginf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isposinf, test/test_overrides.py::TestTorchFunctionOverride::test_torch_isreal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_istft, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kron, test/test_overrides.py::TestTorchFunctionOverride::test_torch_kthvalue, test/test_overrides.py::TestTorchFunctionOverride::test_torch_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lcm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ldexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_le, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lerp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less, test/test_overrides.py::TestTorchFunctionOverride::test_torch_less_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log10, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log1p, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logaddexp2, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logcumsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_and, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_or, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logical_xor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_logsumexp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_lu_unpack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_fill, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_masked_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matmul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_exp, test/test_overrides.py::TestTorchFunctionOverride::test_torch_matrix_power, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_maximum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_median, test/test_overrides.py::TestTorchFunctionOverride::test_torch_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_minimum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_add_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_convolution_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_depthwise_convolution, test/test_overrides.py::TestTorchFunctionOverride::test_torch_miopen_rnn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mode, test/test_overrides.py::TestTorchFunctionOverride::test_torch_moveaxis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_movedim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_msort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mul, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multinomial, test/test_overrides.py::TestTorchFunctionOverride::test_torch_multiply, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mv, test/test_overrides.py::TestTorchFunctionOverride::test_torch_mvlgamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nan_to_num, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanmedian, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nanquantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nansum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_narrow_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_channel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_native_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ne, test/test_overrides.py::TestTorchFunctionOverride::test_torch_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_negative, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nextafter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional__threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_avg_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_adaptive_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_affine_grid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_binary_cross_entropy_with_logits, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_celu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cosine_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_cross_entropy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_ctc_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_dropout3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_elu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_embedding_bag, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_feature_alpha_dropout, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_fractional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gaussian_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_glu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_grid_sample, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_group_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_gumbel_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hardtanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_hinge_embedding_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_huber_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_instance_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_interpolate, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_kl_div, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_layer_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_leaky_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_local_response_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_log_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_lp_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_margin_ranking_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool1d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool2d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_pool3d_with_indices, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_max_unpool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mish, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_mse_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_head_attention_forward, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multi_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_multilabel_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_normalize, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_pad, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_relu6, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_silu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_smooth_l1_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_soft_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softmin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_softsign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_tanhshrink, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_triplet_margin_with_distance_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_functional_unfold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_constant_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_kaiming_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_normal_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nn_init_uniform_, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nonzero_static, test/test_overrides.py::TestTorchFunctionOverride::test_torch_norm_except_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_not_equal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_nuclear_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_numel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ones_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_orgqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ormqr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_outer, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pairwise_distance, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pdist, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute, test/test_overrides.py::TestTorchFunctionOverride::test_torch_permute_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pinverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_shuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pixel_unshuffle, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson, test/test_overrides.py::TestTorchFunctionOverride::test_torch_poisson_nll_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polar, test/test_overrides.py::TestTorchFunctionOverride::test_torch_polygamma, test/test_overrides.py::TestTorchFunctionOverride::test_torch_positive, test/test_overrides.py::TestTorchFunctionOverride::test_torch_pow, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_prod, test/test_overrides.py::TestTorchFunctionOverride::test_torch_put, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_axis, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_scales, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_per_channel_zero_points, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_scale, test/test_overrides.py::TestTorchFunctionOverride::test_torch_q_zero_point, test/test_overrides.py::TestTorchFunctionOverride::test_torch_qr, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_channel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantize_per_tensor_dynamic, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_batch_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_gru_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_lstm_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool1d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool2d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_max_pool3d, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_quantized_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rad2deg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rand_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randint_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_randn_like, test/test_overrides.py::TestTorchFunctionOverride::test_torch_ravel, test/test_overrides.py::TestTorchFunctionOverride::test_torch_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reciprocal, test/test_overrides.py::TestTorchFunctionOverride::test_torch_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_remainder, test/test_overrides.py::TestTorchFunctionOverride::test_torch_renorm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_repeat_interleave, test/test_overrides.py::TestTorchFunctionOverride::test_torch_reshape, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_conj, test/test_overrides.py::TestTorchFunctionOverride::test_torch_resolve_neg, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rms_norm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_relu_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rnn_tanh_cell, test/test_overrides.py::TestTorchFunctionOverride::test_torch_roll, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rot90, test/test_overrides.py::TestTorchFunctionOverride::test_torch_round, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_indices_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_row_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rrelu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_rsub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_saddmm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_add, test/test_overrides.py::TestTorchFunctionOverride::test_torch_scatter_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_searchsorted, test/test_overrides.py::TestTorchFunctionOverride::test_torch_segment_reduce, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_select_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_selu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sgn, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sigmoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sign, test/test_overrides.py::TestTorchFunctionOverride::test_torch_signbit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sin, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sinh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_inverse, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slice_scatter, test/test_overrides.py::TestTorchFunctionOverride::test_torch_slogdet, test/test_overrides.py::TestTorchFunctionOverride::test_torch_smm, test/test_overrides.py::TestTorchFunctionOverride::test_torch_softmax, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sort, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_split_with_sizes_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sqrt, test/test_overrides.py::TestTorchFunctionOverride::test_torch_square, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_squeeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_stack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std, test/test_overrides.py::TestTorchFunctionOverride::test_torch_std_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sub, test/test_overrides.py::TestTorchFunctionOverride::test_torch_subtract, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_svd, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapaxes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_swapdims, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_float, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_int, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_ite, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_max, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_min, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_not, test/test_overrides.py::TestTorchFunctionOverride::test_torch_sym_sum, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t, test/test_overrides.py::TestTorchFunctionOverride::test_torch_t_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take, test/test_overrides.py::TestTorchFunctionOverride::test_torch_take_along_dim, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tan, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tanh, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tensor_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_threshold, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tile, test/test_overrides.py::TestTorchFunctionOverride::test_torch_topk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trace, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose, test/test_overrides.py::TestTorchFunctionOverride::test_torch_transpose_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapezoid, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trapz, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triangular_solve, test/test_overrides.py::TestTorchFunctionOverride::test_torch_tril, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triplet_margin_loss, test/test_overrides.py::TestTorchFunctionOverride::test_torch_triu, test/test_overrides.py::TestTorchFunctionOverride::test_torch_true_divide, test/test_overrides.py::TestTorchFunctionOverride::test_torch_trunc, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unbind_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unflatten, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unfold_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_chunk, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsafe_split_with_sizes, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze, test/test_overrides.py::TestTorchFunctionOverride::test_torch_unsqueeze_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_values_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var, test/test_overrides.py::TestTorchFunctionOverride::test_torch_var_mean, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vdot, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_complex_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_as_real_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_view_copy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vsplit, test/test_overrides.py::TestTorchFunctionOverride::test_torch_vstack, test/test_overrides.py::TestTorchFunctionOverride::test_torch_where, test/test_overrides.py::TestTorchFunctionOverride::test_torch_xlogy, test/test_overrides.py::TestTorchFunctionOverride::test_torch_zeros_like, test/test_overrides.py::TestTorchFunctionOverride::test_user_implementation_raises, test/test_overrides.py::TestEinsumOverride::test_wrapper, test/test_overrides.py::TestGradCheckOverride::test_gradcheck, test/test_overrides.py::TestNamedTuple::test_max, test/test_overrides.py::TestGradNewOnesOverride::test_newones, test/test_overrides.py::TestPickle::test_pickle, test/test_overrides.py::TestBroadcastAllOverride::test_broadcast_all, test/test_overrides.py::TestWrapTorchFunction::test_wrap_torch_function, test/test_overrides.py::TestIndexing::test_getitem, test/test_overrides.py::TestIndexing::test_getitem_subclass, test/test_overrides.py::TestIndexing::test_setitem, test/test_overrides.py::TestIndexing::test_setitem_subclass, test/test_overrides.py::TestIndexing::test_setitem_val, test/test_overrides.py::TestIterator::test_iterator, test/test_overrides.py::TestRNN::test_rnn, test/test_overrides.py::TestDisabledTorchFunction::test_parameter_does_not_prevent_dispatch, test/test_overrides.py::TestResolveName::test_resolve_name, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_standalone_class, test/test_overrides.py::TestTorchFunctionWarning::test_warn_on_invalid_torch_function_tensor_subclass, test/test_overrides.py::TestDisabledUserWarnings::test_no_implicit_user_warning_for_deprecated_functions, test/test_overrides.py::TestTorchFunctionMode::test_all_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_basic, test/test_overrides.py::TestTorchFunctionMode::test_custom_device_type, test/test_overrides.py::TestTorchFunctionMode::test_device_context_semantics, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_subclass, test/test_overrides.py::TestTorchFunctionMode::test_disable_enable_torch_function_ctx, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_mode, test/test_overrides.py::TestTorchFunctionMode::test_disable_subclass_not_mode, test/test_overrides.py::TestTorchFunctionMode::test_distributions_bernoulli, test/test_overrides.py::TestTorchFunctionMode::test_error_using_class_method_on_mode, test/test_overrides.py::TestTorchFunctionMode::test_factory_override, test/test_overrides.py::TestTorchFunctionMode::test_get_cur_mode, test/test_overrides.py::TestTorchFunctionMode::test_get_mode_stack, test/test_overrides.py::TestTorchFunctionMode::test_getitem_call, test/test_overrides.py::TestTorchFunctionMode::test_mode_notimplemented_loop, test/test_overrides.py::TestTorchFunctionMode::test_modes_handle_first, test/test_overrides.py::TestTorchFunctionMode::test_modes_return_notimplemented, test/test_overrides.py::TestTorchFunctionMode::test_nested_modes_with_python_has_torch_function, test/test_overrides.py::TestTorchFunctionMode::test_nested_same_mode, test/test_overrides.py::TestTorchFunctionMode::test_nn_parse_to, test/test_overrides.py::TestTorchFunctionMode::test_reentrant_mode_idiom, test/test_overrides.py::TestTorchFunctionMode::test_restacking_with_ancestor, test/test_overrides.py::TestTorchFunctionMode::test_subclass_hash, test/test_overrides.py::TestTorchFunctionMode::test_torch_function_all_disabled_api, test/test_overrides.py::TestTorchFunctionMode::test_with_mode, test/test_overrides.py::TestTorchFunctionMode::test_with_mode_created_separately, test/test_overrides.py::TestTorchFunctionMode::test_with_nested_modes 2025-08-26T19:53:22.3458147Z 2025-08-26T19:53:22.3458355Z Running test_reductions 2/2 ... [2025-08-26 19:53:22.250332] 2025-08-26T19:53:22.3458831Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T19:53:22.3459879Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_reductions.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 19:53:22.250698] 2025-08-26T20:00:51.4698847Z 2025-08-26T20:00:51.4700040Z test_reductions 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_reductions_2.2_8daaa91ad0d3a4a0_.log 2025-08-26T20:00:51.5573035Z Running 2272 items in this shard: test/test_reductions.py::TestReductionsCPU::test_accreal_type_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_all_any_vs_numpy_cpu_uint8, 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test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmax_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_argmin_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_logsumexp_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_mean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_norm_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_prod_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_std_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_sum_cpu_uint8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_masked_var_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_mean_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nanmean_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_nansum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_prod_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_std_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_sum_cpu_int8, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_bfloat16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_result_dtype_var_unbiased_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_dim_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_std_mean_correction_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_std_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_std_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_sum_all_cpu_bool, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_noncontig_lowp_cpu_float16, test/test_reductions.py::TestReductionsCPU::test_sum_parallel_cpu, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int16, test/test_reductions.py::TestReductionsCPU::test_sum_vs_numpy_cpu_int32, test/test_reductions.py::TestReductionsCPU::test_tensor_compare_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_tensor_reduce_ops_empty_cpu, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_correction_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_var_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_all_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_mean_correction_cpu_float32, test/test_reductions.py::TestReductionsCPU::test_var_mean_some_dims_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability2_cpu, test/test_reductions.py::TestReductionsCPU::test_var_stability_cpu, test/test_reductions.py::TestReductionsCPU::test_var_unbiased_cpu, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_var_vs_numpy_cpu_float64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex128, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_complex64, test/test_reductions.py::TestReductionsCPU::test_warn_invalid_degrees_of_freedom_cpu_float32 2025-08-26T20:00:51.6424609Z 2025-08-26T20:00:51.6424893Z Running test_cpp_extensions_mtia_backend 1/1 ... [2025-08-26 20:00:51.474137] 2025-08-26T20:00:51.6425403Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:00:51.6426552Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_mtia_backend.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:00:51.474596] 2025-08-26T20:00:56.5461850Z 2025-08-26T20:00:56.5463447Z 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_2c219f766ae2a8cd_.log 2025-08-26T20:00:56.5468804Z 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 2025-08-26T20:00:56.5472665Z 2025-08-26T20:00:56.5472873Z Running test_nn 1/4 ... [2025-08-26 20:00:56.546487] 2025-08-26T20:00:56.5473276Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:00:56.5474449Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=1', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:00:56.546897] 2025-08-26T20:10:22.8524700Z 2025-08-26T20:10:22.8526059Z test_nn 1/4 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_1.4_63881287367c1c3c_.log 2025-08-26T20:10:22.8820781Z Running 534 items in this shard: test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_CELU_no_batch_dim, test/test_nn.py::TestNN::test_CTCLoss_zero_lengths, test/test_nn.py::TestNN::test_Conv1d_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1size1_cuda, test/test_nn.py::TestNN::test_Conv1d_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated_cuda, test/test_nn.py::TestNN::test_Conv1d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_stride, test/test_nn.py::TestNN::test_Conv2d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_dilated, test/test_nn.py::TestNN::test_Conv2d_depthwise_padded_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_with_multiplier_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated, test/test_nn.py::TestNN::test_Conv2d_dilated_cuda, test/test_nn.py::TestNN::test_Conv2d_groups, test/test_nn.py::TestNN::test_Conv2d_groups_thnn, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias, test/test_nn.py::TestNN::test_Conv2d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv2d_reflect_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_1x1x1_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2, test/test_nn.py::TestNN::test_Conv3d_dilated_strided_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_cuda, test/test_nn.py::TestNN::test_Conv3d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_cuda, test/test_nn.py::TestNN::test_Conv3d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_pad_same, test/test_nn.py::TestNN::test_Conv3d_replicate_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_stride, test/test_nn.py::TestNN::test_Conv3d_stride_padding, test/test_nn.py::TestNN::test_Conv3d_with_long_tensor, test/test_nn.py::TestNN::test_Conv3d_zero_batch_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated, test/test_nn.py::TestNN::test_ConvTranspose1d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose1d_no_bias_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias, test/test_nn.py::TestNN::test_ConvTranspose2d_no_bias_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_cuda, test/test_nn.py::TestNN::test_ConvTranspose3d_dilated, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_CrossMapLRN2d, test/test_nn.py::TestNN::test_EmbeddingBag_discontiguous_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean, test/test_nn.py::TestNN::test_EmbeddingBag_mean_padding_idx, test/test_nn.py::TestNN::test_EmbeddingBag_sparse, test/test_nn.py::TestNN::test_EmbeddingBag_sum, test/test_nn.py::TestNN::test_Embedding_discontiguous, test/test_nn.py::TestNN::test_Embedding_discontiguous_cuda, test/test_nn.py::TestNN::test_Embedding_sparse, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input, test/test_nn.py::TestNN::test_Fold_no_batch_dim_int_input_cuda, test/test_nn.py::TestNN::test_GELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Hardswish_no_batch_dim, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean_log_target, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target, test/test_nn.py::TestNN::test_KLDivLoss_with_log_target_no_reduce, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum, test/test_nn.py::TestNN::test_L1Loss_no_reduce, test/test_nn.py::TestNN::test_L1Loss_no_reduce_scalar, test/test_nn.py::TestNN::test_LSTM_cell, test/test_nn.py::TestNN::test_LSTM_cell_forward_input_size, test/test_nn.py::TestNN::test_LayerNorm_3d_no_affine_large_feature_cuda, test/test_nn.py::TestNN::test_LeakyReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_MSELoss_no_reduce, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool3d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ModuleList, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_index_neg_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiMarginLoss_margin_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index, test/test_nn.py::TestNN::test_PReLU_no_batch_dim, test/test_nn.py::TestNN::test_PReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_PairwiseDistance, test/test_nn.py::TestNN::test_ParameterDict, test/test_nn.py::TestNN::test_ParameterList_meta, test/test_nn.py::TestNN::test_PixelUnshuffle, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_reduce, test/test_nn.py::TestNN::test_RNN_cell_forward_zero_hidden_size, test/test_nn.py::TestNN::test_RReLU_no_batch_dim, test/test_nn.py::TestNN::test_RReLU_with_up_down_cuda, test/test_nn.py::TestNN::test_RReLU_with_up_down_scalar, test/test_nn.py::TestNN::test_ReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_complex_cuda, test/test_nn.py::TestNN::test_Sequential_rmul, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_SmoothL1Loss_zero_beta_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SoftMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_Softshrink_no_batch_dim, test/test_nn.py::TestNN::test_Softsign_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Tanhshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerDecoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_gelu_activation, test/test_nn.py::TestNN::test_Transformer_multilayer_coder, test/test_nn.py::TestNN::test_Transformer_multilayer_coder_cuda, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_TripletMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_Unfold, test/test_nn.py::TestNN::test_batch_norm_update_stats, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_2D_train_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_cudnn_nhwc, test/test_nn.py::TestNN::test_batchnorm_load_state_dict, test/test_nn.py::TestNN::test_batchnorm_nhwc_cpu, test/test_nn.py::TestNN::test_batchnorm_nonaffine_cuda_half_input, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_bias_is_not_same_size_as_input, test/test_nn.py::TestNN::test_bce_with_logits_broadcasts_weights, test/test_nn.py::TestNN::test_bce_with_logits_has_correct_forward_grad, test/test_nn.py::TestNN::test_buffer_bad_module_subclass, test/test_nn.py::TestNN::test_buffer_not_persistent_load, test/test_nn.py::TestNN::test_buffers_and_named_buffers, test/test_nn.py::TestNN::test_cosine_embedding_loss_margin_no_reduce, test/test_nn.py::TestNN::test_cudnn_rnn_dropout_states_device, test/test_nn.py::TestNN::test_fb_fc_packed, test/test_nn.py::TestNN::test_grid_sample_error_checking, test/test_nn.py::TestNN::test_hardtanh_backward, test/test_nn.py::TestNN::test_hardtanh_inplace_gradgrad, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners, test/test_nn.py::TestNN::test_interpolate_linear_scale_1d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_scale_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_2d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d, test/test_nn.py::TestNN::test_interpolate_nearest_scale_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_nn.py::TestNN::test_l1_loss_correct, test/test_nn.py::TestNN::test_layer_norm_grads_with_create_graph_flag, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightStrided, test/test_nn.py::TestNN::test_log_softmax_spatial_special_cuda, test/test_nn.py::TestNN::test_margin_ranking_loss_no_reduce, test/test_nn.py::TestNN::test_module_backcompat, test/test_nn.py::TestNN::test_named_parameters_remove_duplicate, test/test_nn.py::TestNN::test_nested_tensor_from_mask, test/test_nn.py::TestNN::test_parameterlistdict_setting_attributes, test/test_nn.py::TestNN::test_pdist_empty_col, test/test_nn.py::TestNN::test_pixel_shuffle_unshuffle, test/test_nn.py::TestNN::test_projections_errors_on_gru_and_rnn, test/test_nn.py::TestNN::test_projections_lstm_args_check, test/test_nn.py::TestNN::test_register_buffer_allows_tensor_like_object, test/test_nn.py::TestNN::test_register_parameter_raises_error_if_name_is_not_string, test/test_nn.py::TestNN::test_relu_inplace_on_view, test/test_nn.py::TestNN::test_rnn_check_device, test/test_nn.py::TestNN::test_rnn_weight_norm, test/test_nn.py::TestNN::test_softmax_functional_dim0, test/test_nn.py::TestNN::test_softmax_functional_dim3, test/test_nn.py::TestNN::test_softmax_lastdim, test/test_nn.py::TestNN::test_softmax_lastdim_dtype, test/test_nn.py::TestNN::test_softmax_spatial_dtype_cuda, test/test_nn.py::TestNN::test_spectral_norm_load_state_dict, test/test_nn.py::TestNN::test_spectral_norm_pickle, test/test_nn.py::TestNN::test_swap_module_params_poisons_acc_grad, test/test_nn.py::TestNN::test_transformer_args_check, test/test_nn.py::TestNN::test_transformerdecoderlayer_gelu, test/test_nn.py::TestNN::test_triplet_margin_loss_no_reduce, test/test_nn.py::TestNN::test_triplet_margin_loss_swap, test/test_nn.py::TestNN::test_unflatten, test/test_nn.py::TestNN::test_upsamplingBilinear2d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_not_recompute_scale_factor, test/test_nn.py::TestNN::test_weighted_huber_loss, test/test_nn.py::TestNN::test_weighted_l1_loss_with_weights, test/test_nn.py::TestFusionEval::test_fuse_module_eval_numerics, test/test_nn.py::TestConstantPadNd::test_constant_pad_nd, test/test_nn.py::TestAddRelu::test_add_relu, test/test_nn.py::TestFunctionalPickle::test_pickle_softsign, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_mean_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_none_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_memory_format_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_numeric_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm2d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_grad_and_gradgrad_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_race_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReflectionPad_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_TransformerEncoder_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_3d_rotateRandom_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_simple_average_mixed_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_1_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_inf_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_multi_device_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_64bit_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_errors_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_label_smoothing_weight_ignore_indices_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_large_tensor_reduction_sum_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_mean_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_none_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_elu_inplace_with_neg_alpha_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_glu_bfloat16_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_gumbel_softmax_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm3d_no_batch_dim_True_affine_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_less_than_one_value_per_channel_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_neg_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_with_zero_slope_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_byte_target_matches_long_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_mean_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_out_of_bounds_ignore_index_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_total_weight_is_zero_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nonlinearity_propagate_nan_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_overwrite_module_params_on_conversion_cpu_device_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_prelu_backward_32bit_indexing_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_epsilon_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_rmsnorm_numeric_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_skip_init_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_double_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_results_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_threshold_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_fast_path_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_triplet_margin_with_distance_loss_default_parity_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format0_align_corners_False_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_403_output_size_377_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_False_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_fail_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_launch_rocm_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsampling_64bit_indexing_channels_last_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingnearest2d_backward_64bit_indexing_cpu_float16 2025-08-26T20:10:22.9103942Z 2025-08-26T20:10:22.9104126Z Running test_nn 3/4 ... [2025-08-26 20:10:22.853892] 2025-08-26T20:10:22.9104526Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:10:22.9105534Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_nn.py', '--shard-id=3', '--num-shards=4', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:10:22.854317] 2025-08-26T20:21:48.1675413Z 2025-08-26T20:21:48.1676165Z test_nn 3/4 was successful, full logs can be found in artifacts with path test/test-reports/test_nn_3.4_c766b8b73c5eb90b_.log 2025-08-26T20:21:48.1959293Z Running 542 items in this shard: test/test_nn.py::TestNN::test_AdaptiveLogSoftmax, test/test_nn.py::TestNN::test_AdaptiveLogSoftmax_cuda, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_none, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_BCELoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_cuda, test/test_nn.py::TestNN::test_BCELoss_weights_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_legacy_enum_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_BCEWithLogitsLoss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_CELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Conv1d, test/test_nn.py::TestNN::test_Conv1d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv1d_dilated, test/test_nn.py::TestNN::test_Conv1d_groups_cuda, test/test_nn.py::TestNN::test_Conv1d_pad1, test/test_nn.py::TestNN::test_Conv1d_pad_same2, test/test_nn.py::TestNN::test_Conv1d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv1d_pad_same_dilated, test/test_nn.py::TestNN::test_Conv1d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv1d_zero_batch_cuda, test/test_nn.py::TestNN::test_Conv1d_zeros_stride2_pad2, test/test_nn.py::TestNN::test_Conv2d_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise, test/test_nn.py::TestNN::test_Conv2d_depthwise_cuda, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided, test/test_nn.py::TestNN::test_Conv2d_depthwise_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_dilated_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_groups_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_thnn_cuda, test/test_nn.py::TestNN::test_Conv2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_no_bias_with_long_tensor, test/test_nn.py::TestNN::test_Conv2d_pad_same, test/test_nn.py::TestNN::test_Conv2d_strided_cuda, test/test_nn.py::TestNN::test_Conv2d_zero_batch_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv2d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_circular_stride2_pad2_cuda, test/test_nn.py::TestNN::test_Conv3d_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_same_cuda, test/test_nn.py::TestNN::test_Conv3d_pad_valid, test/test_nn.py::TestNN::test_Conv3d_pad_valid_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_cuda, test/test_nn.py::TestNN::test_Conv3d_stride_padding_with_long_tensor_cuda, test/test_nn.py::TestNN::test_Conv3d_zero_batch, test/test_nn.py::TestNN::test_Conv3d_zeros_stride2_pad2_cuda, test/test_nn.py::TestNN::test_ConvTranspose1d_groups, test/test_nn.py::TestNN::test_ConvTranspose2d_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_dilated_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_cuda, test/test_nn.py::TestNN::test_ConvTranspose2d_groups_with_long_tensor, test/test_nn.py::TestNN::test_ConvTranspose2d_with_long_tensor, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_CosineEmbeddingLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_ELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_max_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_mean_cuda, test/test_nn.py::TestNN::test_EmbeddingBag_sparse_cuda, test/test_nn.py::TestNN::test_Flatten_cuda, test/test_nn.py::TestNN::test_Fold_cuda, test/test_nn.py::TestNN::test_Fold_no_batch_dim_input, test/test_nn.py::TestNN::test_Hardshrink_no_batch_dim_cuda, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_HingeEmbeddingLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_none_cuda_float, test/test_nn.py::TestNN::test_HuberLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_KLDivLoss_batch_mean, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_KLDivLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar, test/test_nn.py::TestNN::test_KLDivLoss_no_reduce_scalar_log_target_cuda, test/test_nn.py::TestNN::test_KLDivLoss_with_target_no_reduce_cuda, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_L1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_L1Loss_no_reduce_complex_cuda, test/test_nn.py::TestNN::test_Linear_no_bias_cuda, test/test_nn.py::TestNN::test_LogSigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MSELoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MarginRankingLoss_no_batch_dim_sum_cuda_half, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool1d_net_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_cuda, test/test_nn.py::TestNN::test_MaxUnpool2d_net_no_batch_dim, test/test_nn.py::TestNN::test_MaxUnpool3d_net, test/test_nn.py::TestNN::test_MaxUnpool3d_net_cuda, test/test_nn.py::TestNN::test_Mish_no_batch_dim_cuda, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_MultiLabelMarginLoss_no_reduce_cuda, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_none_cuda_half, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_MultiLabelSoftMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_1d_no_reduce, test/test_nn.py::TestNN::test_MultiMarginLoss_weights_no_reduce, test/test_nn.py::TestNN::test_NLLLoss2d_no_reduce_cuda, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_ignore_index, test/test_nn.py::TestNN::test_NLLLossNd_no_reduce_weights, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_mean_cuda_half, test/test_nn.py::TestNN::test_NLLLoss_no_batch_dim_none_cuda_double, test/test_nn.py::TestNN::test_NLLLoss_no_reduce, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_cuda, test/test_nn.py::TestNN::test_NLLLoss_no_reduce_weights_ignore_index_neg_cuda, test/test_nn.py::TestNN::test_PairwiseDistance_with_non_default_args_cuda, test/test_nn.py::TestNN::test_ParameterList, test/test_nn.py::TestNN::test_PixelShuffle_cuda, test/test_nn.py::TestNN::test_PixelUnshuffle_cuda, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_PoissonNLLLoss_no_batch_dim_sum, test/test_nn.py::TestNN::test_RNN_cell_no_broadcasting, test/test_nn.py::TestNN::test_RNN_dropout_state, test/test_nn.py::TestNN::test_RReLU_cuda, test/test_nn.py::TestNN::test_RReLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_cuda, test/test_nn.py::TestNN::test_ReplicationPad3d_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SELU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sequential_extend, test/test_nn.py::TestNN::test_Sequential_getitem, test/test_nn.py::TestNN::test_Sequential_iadd, test/test_nn.py::TestNN::test_SiLU_no_batch_dim_cuda, test/test_nn.py::TestNN::test_Sigmoid_no_batch_dim_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_beta, test/test_nn.py::TestNN::test_SmoothL1Loss_beta_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_none, test/test_nn.py::TestNN::test_SmoothL1Loss_no_batch_dim_sum_cuda_double, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_cuda, test/test_nn.py::TestNN::test_SmoothL1Loss_no_reduce_scalar_cuda, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_mean_cuda_float, test/test_nn.py::TestNN::test_SoftMarginLoss_no_batch_dim_sum_cuda_float, test/test_nn.py::TestNN::test_Softplus_no_batch_dim_cuda, test/test_nn.py::TestNN::test_TransformerEncoderLayer_relu_activation_cuda, test/test_nn.py::TestNN::test_Transformer_cell, test/test_nn.py::TestNN::test_Unfold_cuda, test/test_nn.py::TestNN::test_add_module_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_affine_grid_3d, test/test_nn.py::TestNN::test_affine_grid_backward_cl_cf_consistency_device_cpu_nd_2, test/test_nn.py::TestNN::test_affine_grid_error_checking, test/test_nn.py::TestNN::test_assignment, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_2D_inference_NCHW_vs_native_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_cpu_mixed_float16, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_3D_inference_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_float32, test/test_nn.py::TestNN::test_batchnorm_3D_train_NCHW_vs_native_mixed_bfloat16, test/test_nn.py::TestNN::test_batchnorm_buffer_update_when_stats_are_not_tracked, test/test_nn.py::TestNN::test_batchnorm_cudnn_half, test/test_nn.py::TestNN::test_batchnorm_nhwc_cuda, test/test_nn.py::TestNN::test_batchnorm_non_contig_cpu_BatchNorm2d, test/test_nn.py::TestNN::test_batchnorm_raises_error_if_less_than_one_value_per_channel, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss, test/test_nn.py::TestNN::test_bce_with_logits_gives_same_result_as_sigmoid_and_bce_loss_large_tensors_with_grad, test/test_nn.py::TestNN::test_bce_with_logits_ones_in_pos_weights_are_the_same_as_none, test/test_nn.py::TestNN::test_bce_with_logits_with_pos_weight_has_correct_grad_at_zero, test/test_nn.py::TestNN::test_bilinear, test/test_nn.py::TestNN::test_broadcast_double_backwards_gpu, test/test_nn.py::TestNN::test_broadcast_no_grad, test/test_nn.py::TestNN::test_cosine_embedding_loss_no_reduce, test/test_nn.py::TestNN::test_cosine_embedding_loss_with_diff_type, test/test_nn.py::TestNN::test_cross_entropy_loss_precision, test/test_nn.py::TestNN::test_cudnn_weight_tying, test/test_nn.py::TestNN::test_extra_state, test/test_nn.py::TestNN::test_extra_state_missing_set_extra_state, test/test_nn.py::TestNN::test_flatten, test/test_nn.py::TestNN::test_fractional_max_pool2d_invalid_output_ratio, test/test_nn.py::TestNN::test_gaussian_nll_loss_args, test/test_nn.py::TestNN::test_gaussian_nll_loss_broadcasting, test/test_nn.py::TestNN::test_gaussian_nll_loss_scalar_var, test/test_nn.py::TestNN::test_get_buffer, test/test_nn.py::TestNN::test_grid_sample, test/test_nn.py::TestNN::test_grid_sample_3d, test/test_nn.py::TestNN::test_huber_loss_invalid_delta, test/test_nn.py::TestNN::test_inplace_thnn, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_2d_zero_dim, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_shared_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d, test/test_nn.py::TestNN::test_interpolate_bicubic_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_2d, test/test_nn.py::TestNN::test_interpolate_bilinear_scale_tuple_skewed_2d_cuda, test/test_nn.py::TestNN::test_interpolate_bilinear_tuple_2d_align_corners_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_cuda, test/test_nn.py::TestNN::test_interpolate_linear_1d_zero_dim, test/test_nn.py::TestNN::test_interpolate_linear_tuple_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_1d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_2d_launch_configs, test/test_nn.py::TestNN::test_interpolate_nearest_3d, test/test_nn.py::TestNN::test_interpolate_nearest_3d_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim, test/test_nn.py::TestNN::test_interpolate_nearest_3d_zero_dim_cuda, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_1d, test/test_nn.py::TestNN::test_interpolate_nearest_tuple_2d, test/test_nn.py::TestNN::test_interpolate_trilinear_3d, test/test_nn.py::TestNN::test_interpolate_trilinear_scale_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_align_corners, test/test_nn.py::TestNN::test_interpolate_trilinear_tuple_3d_cuda, test/test_nn.py::TestNN::test_interpolate_undefined_behavior_casting, test/test_nn.py::TestNN::test_layer_norm_large_tensor, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_bias_weightCSR, test/test_nn.py::TestNN::test_linear_autograd_device_cpu_nobias_weightCOO, test/test_nn.py::TestNN::test_log_softmax_scalar, test/test_nn.py::TestNN::test_loss_equal_input_target_shape, test/test_nn.py::TestNN::test_module_super_init, test/test_nn.py::TestNN::test_modules, test/test_nn.py::TestNN::test_multimarginloss_1d_input_0d_target_no_reduce, test/test_nn.py::TestNN::test_named_modules, test/test_nn.py::TestNN::test_overwrite_module_params_on_conversion, test/test_nn.py::TestNN::test_pack_sequence_batch_sizes_throw, test/test_nn.py::TestNN::test_padding_list, test/test_nn.py::TestNN::test_pickle_module_no_weights_only_warning, test/test_nn.py::TestNN::test_pixel_shuffle_nhwc_cpu, test/test_nn.py::TestNN::test_pointwise_loss_broadcast, test/test_nn.py::TestNN::test_projections_lstm_initial_hidden_state, test/test_nn.py::TestNN::test_register_buffer_allows_overwriting_with_same_name, test/test_nn.py::TestNN::test_register_buffer_raises_error_if_attr_exists, test/test_nn.py::TestNN::test_rnn_initial_hidden_state, test/test_nn.py::TestNN::test_set_submodule, test/test_nn.py::TestNN::test_smoothl1loss_intergral_target, test/test_nn.py::TestNN::test_softmax_functional_dim0_cuda, test/test_nn.py::TestNN::test_softmax_spatial, test/test_nn.py::TestNN::test_softmax_spatial_special, test/test_nn.py::TestNN::test_softmin, test/test_nn.py::TestNN::test_spectral_norm, test/test_nn.py::TestNN::test_spectral_norm_dim, test/test_nn.py::TestNN::test_spectral_norm_forward, test/test_nn.py::TestNN::test_state_dict, test/test_nn.py::TestNN::test_to, test/test_nn.py::TestNN::test_type, test/test_nn.py::TestNN::test_unfold_invalid_arg, test/test_nn.py::TestNN::test_upsamplingLinear1d_spatial_invariance, test/test_nn.py::TestNN::test_upsampling_bfloat16, test/test_nn.py::TestNN::test_upsampling_small_scale, test/test_nn.py::TestNN::test_weighted_mse_loss, test/test_nn.py::TestFusionUtils::test_fuse_linear_bn_requires_grad, test/test_nn.py::TestUtils::test_consume_prefix_in_state_dict_if_present, test/test_nn.py::TestNNDeviceTypeCPU::test_CTCLoss_no_batch_dim_reduction_sum_use_module_form_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_GroupNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm1d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_InstanceNorm3d_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_LSTM_differentiable_backward_using_oneDNN_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_LayerNorm_general_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_race_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_MarginLoss_warnings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad2d_large_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ReplicationPad_empty_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_Unfold_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_activations_bfloat16_half_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_adaptiveavg_pool1d_shmem_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_affine_2d_rotate0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_avg_pool_large_tensor_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_affine_mixed_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_eval_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_batchnorm_grad_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_False_norm_type_0_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_norm_foreach_True_norm_type_2_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_clip_grad_value_foreach_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_conv_empty_input_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_one_hot_target_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_False_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_cross_entropy_loss_prob_target_no_batch_dim_reduction_sum_weighted_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_ctc_loss_cudnn_tensor_cpu_length_cuda_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_grid_sample_nan_inf_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_groupnorm_nhwc_cpu_float16, test/test_nn.py::TestNNDeviceTypeCPU::test_hardswish_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_for_single_spatial_element_during_training_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_instancenorm_raises_error_if_input_channels_is_not_num_features_InstanceNorm2d_no_batch_dim_False_affine_True_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_invalid_reduction_strings_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_leaky_relu_inplace_overlap_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_linear_empty_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_big_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_log_softmax_cpu_cpu_bfloat16, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_forward_with_nans_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_masked_softmax_transformer_layout_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_module_to_empty_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_invalid_weights_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nll_loss_large_tensor_reduction_none_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_nn_scalars_reductions_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_one_hot_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_pad_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_rnn_fused_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_smooth_l1_loss_vs_huber_loss_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_backward_unaligned_grad_output_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_softmax_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex128, test/test_nn.py::TestNNDeviceTypeCPU::test_to_complex_cpu_complex64, test/test_nn.py::TestNNDeviceTypeCPU::test_transformerencoderlayer_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_False_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiLinear2d_consistency_interp_size_bug_memory_format1_align_corners_True_input_size_399_output_size_437_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_False_mode_bicubic_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_False_align_corners_True_mode_bilinear_memory_format0_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bicubic_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_antialias_True_align_corners_True_mode_bilinear_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format0_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bicubic_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_False_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_False_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_False_non_contig_False_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_3_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_False_non_contig_sliced_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_32_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_5_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_False_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_consistency_memory_format1_mode_bilinear_antialias_True_align_corners_True_num_channels_5_output_size_600_check_as_unsqueezed_3d_tensor_True_non_contig_restrided_batch_size_1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_bilinear_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest-exact_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_3_mode_nearest_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bicubic_int8_cpu_int8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest-exact_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_False_num_channels_5_mode_nearest_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bicubic_int32_cpu_int32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int16_cpu_int16, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_int64_cpu_int64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_bilinear_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_3_mode_nearest_float64_cpu_float64, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_bicubic_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_float32_cpu_float32, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBiMode2d_nonsupported_dtypes_antialias_True_num_channels_5_mode_nearest-exact_uint8_cpu_uint8, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBicubic2d_correctness_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingBilinear2d_aa_correctness_memory_format1_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_correctness_memory_format1_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format0_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest-exact_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest2d_memory_format1_mode_nearest_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearest3d_launch_config_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact1d_correctness_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact2d_correctness_memory_format0_isize_20_osize_11_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format0_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_upsamplingNearestExact3d_correctness_memory_format1_isize_10_osize_15_cpu, test/test_nn.py::TestNNDeviceTypeCPU::test_variable_sequence_cpu_float32 2025-08-26T20:21:48.2230826Z 2025-08-26T20:21:48.5828757Z Uploading artifacts took 0.41 seconds 2025-08-26T20:21:48.5831957Z Running test_cpp_extensions_stream_and_event 1/1 ... [2025-08-26 20:21:48.583004] 2025-08-26T20:21:48.5832469Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:21:48.5835928Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_stream_and_event.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:21:48.583356] 2025-08-26T20:21:53.5045537Z 2025-08-26T20:21:53.5046559Z test_cpp_extensions_stream_and_event 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_stream_and_event_1.1_c6dbaef626af19a2_.log 2025-08-26T20:21:53.5047869Z Running 1 items in this shard: test/test_cpp_extensions_stream_and_event.py::TestCppExtensionStreamAndEvent::test_stream_event 2025-08-26T20:21:53.5048656Z 2025-08-26T20:21:53.5048819Z Running doctests 1/1 ... [2025-08-26 20:21:53.504709] 2025-08-26T20:21:53.6045368Z Start doctest_module('/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch') 2025-08-26T20:21:53.6045902Z Listing tests 2025-08-26T20:21:53.8178218Z msg = Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1493. 2025-08-26T20:21:53.8179183Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.8179737Z 2025-08-26T20:21:53.8179933Z dim_order(ambiguity_check=False) -> tuple 2025-08-26T20:21:53.8180258Z 2025-08-26T20:21:53.8180668Z Returns the uniquely determined tuple of int describing the dim order or 2025-08-26T20:21:53.8181387Z physical layout of :attr:`self`. 2025-08-26T20:21:53.8181690Z 2025-08-26T20:21:53.8182102Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-08-26T20:21:53.8182947Z starting from the outermost to the innermost dimension. 2025-08-26T20:21:53.8183368Z 2025-08-26T20:21:53.8183645Z Note that the dim order may not always be uniquely determined. 2025-08-26T20:21:53.8184687Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-08-26T20:21:53.8185612Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-08-26T20:21:53.8186388Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-08-26T20:21:53.8187096Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-08-26T20:21:53.8187647Z Otherwise, it will raise TypeError. 2025-08-26T20:21:53.8187870Z 2025-08-26T20:21:53.8187978Z Args: 2025-08-26T20:21:53.8188423Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-08-26T20:21:53.8188867Z 2025-08-26T20:21:53.8188998Z Examples:: 2025-08-26T20:21:53.8189123Z 2025-08-26T20:21:53.8189250Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-08-26T20:21:53.8189549Z (0, 1, 2, 3) 2025-08-26T20:21:53.8189850Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-08-26T20:21:53.8190208Z (0, 2, 1, 3) 2025-08-26T20:21:53.8190560Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-08-26T20:21:53.8190959Z (0, 2, 3, 1) 2025-08-26T20:21:53.8191211Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-08-26T20:21:53.8191519Z (0, 1, 2, 3) 2025-08-26T20:21:53.8191969Z >>> try: 2025-08-26T20:21:53.8192269Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-08-26T20:21:53.8192676Z ... except RuntimeError as e: 2025-08-26T20:21:53.8193000Z ... print(e) 2025-08-26T20:21:53.8193470Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-08-26T20:21:53.8194018Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-08-26T20:21:53.8194446Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-08-26T20:21:53.8194915Z ... ) # It can be mapped to contiguous format 2025-08-26T20:21:53.8195244Z (0, 1, 2, 3) 2025-08-26T20:21:53.8195467Z >>> try: 2025-08-26T20:21:53.8195774Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-08-26T20:21:53.8196182Z ... except TypeError as e: 2025-08-26T20:21:53.8196463Z ... print(e) 2025-08-26T20:21:53.8196847Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-08-26T20:21:53.8197191Z 2025-08-26T20:21:53.8197288Z .. warning:: 2025-08-26T20:21:53.8197618Z The dim_order tensor API is experimental and subject to change. 2025-08-26T20:21:53.8197936Z 2025-08-26T20:21:53.8198188Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.8198555Z 2025-08-26T20:21:53.8733238Z msg = Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=397. 2025-08-26T20:21:53.8734160Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.8734792Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-08-26T20:21:53.8735396Z 2025-08-26T20:21:53.8735592Z This is helpful when you want to visualize data over some 2025-08-26T20:21:53.8736029Z range of inputs. See below for a plotting example. 2025-08-26T20:21:53.8736309Z 2025-08-26T20:21:53.8736477Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-08-26T20:21:53.8736947Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-08-26T20:21:53.8737447Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-08-26T20:21:53.8737900Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-08-26T20:21:53.8738362Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-08-26T20:21:53.8738771Z to the result shape. 2025-08-26T20:21:53.8738949Z 2025-08-26T20:21:53.8739067Z .. note:: 2025-08-26T20:21:53.8739486Z 0D inputs are treated equivalently to 1D inputs of a 2025-08-26T20:21:53.8739854Z single element. 2025-08-26T20:21:53.8740046Z 2025-08-26T20:21:53.8740138Z .. warning:: 2025-08-26T20:21:53.8740578Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-08-26T20:21:53.8741047Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-08-26T20:21:53.8741319Z 2025-08-26T20:21:53.8741472Z In the future `torch.meshgrid` will transition to 2025-08-26T20:21:53.8741859Z `indexing='xy'` as the default. 2025-08-26T20:21:53.8742091Z 2025-08-26T20:21:53.8742290Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-08-26T20:21:53.8742764Z this issue with the goal of migrating to NumPy's behavior. 2025-08-26T20:21:53.8743057Z 2025-08-26T20:21:53.8743161Z .. seealso:: 2025-08-26T20:21:53.8743309Z 2025-08-26T20:21:53.8743480Z :func:`torch.cartesian_prod` has the same effect but it 2025-08-26T20:21:53.8743905Z collects the data in a tensor of vectors. 2025-08-26T20:21:53.8744160Z 2025-08-26T20:21:53.8744250Z Args: 2025-08-26T20:21:53.8744647Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-08-26T20:21:53.8745196Z treated as tensors of size :math:`(1,)` automatically 2025-08-26T20:21:53.8745476Z 2025-08-26T20:21:53.8745653Z indexing: (str, optional): the indexing mode, either "xy" 2025-08-26T20:21:53.8746118Z or "ij", defaults to "ij". See warning for future changes. 2025-08-26T20:21:53.8746412Z 2025-08-26T20:21:53.8746567Z If "xy" is selected, the first dimension corresponds 2025-08-26T20:21:53.8747005Z to the cardinality of the second input and the second 2025-08-26T20:21:53.8747452Z dimension corresponds to the cardinality of the first 2025-08-26T20:21:53.8747862Z input. 2025-08-26T20:21:53.8748028Z 2025-08-26T20:21:53.8748183Z If "ij" is selected, the dimensions are in the same 2025-08-26T20:21:53.8748583Z order as the cardinality of the inputs. 2025-08-26T20:21:53.8748829Z 2025-08-26T20:21:53.8748927Z Returns: 2025-08-26T20:21:53.8749236Z seq (sequence of Tensors): If the input has :math:`N` 2025-08-26T20:21:53.8749658Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-08-26T20:21:53.8750343Z output will also have :math:`N` tensors, where each tensor 2025-08-26T20:21:53.8750828Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-08-26T20:21:53.8751198Z 2025-08-26T20:21:53.8751325Z Example:: 2025-08-26T20:21:53.8751484Z 2025-08-26T20:21:53.8751658Z >>> x = torch.tensor([1, 2, 3]) 2025-08-26T20:21:53.8752149Z >>> y = torch.tensor([4, 5, 6]) 2025-08-26T20:21:53.8752486Z 2025-08-26T20:21:53.8752776Z Observe the element-wise pairings across the grid, (1, 4), 2025-08-26T20:21:53.8753364Z (1, 5), ..., (3, 6). This is the same thing as the 2025-08-26T20:21:53.8753775Z cartesian product. 2025-08-26T20:21:53.8754662Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-08-26T20:21:53.8755106Z >>> grid_x 2025-08-26T20:21:53.8755364Z tensor([[1, 1, 1], 2025-08-26T20:21:53.8755646Z [2, 2, 2], 2025-08-26T20:21:53.8755958Z [3, 3, 3]]) 2025-08-26T20:21:53.8756227Z >>> grid_y 2025-08-26T20:21:53.8756478Z tensor([[4, 5, 6], 2025-08-26T20:21:53.8756752Z [4, 5, 6], 2025-08-26T20:21:53.8757010Z [4, 5, 6]]) 2025-08-26T20:21:53.8757201Z 2025-08-26T20:21:53.8757371Z This correspondence can be seen when these grids are 2025-08-26T20:21:53.8757759Z stacked properly. 2025-08-26T20:21:53.8758151Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-08-26T20:21:53.8758670Z ... torch.cartesian_prod(x, y)) 2025-08-26T20:21:53.8759003Z True 2025-08-26T20:21:53.8759153Z 2025-08-26T20:21:53.8759333Z `torch.meshgrid` is commonly used to produce a grid for 2025-08-26T20:21:53.8759719Z plotting. 2025-08-26T20:21:53.8760001Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-08-26T20:21:53.8760377Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-08-26T20:21:53.8760743Z >>> import matplotlib.pyplot as plt 2025-08-26T20:21:53.8761111Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-08-26T20:21:53.8761479Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-08-26T20:21:53.8761844Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-08-26T20:21:53.8762227Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-08-26T20:21:53.8762584Z >>> ax = plt.axes(projection='3d') 2025-08-26T20:21:53.8762971Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-08-26T20:21:53.8763334Z >>> plt.show() 2025-08-26T20:21:53.8763514Z 2025-08-26T20:21:53.8763649Z .. image:: ../_static/img/meshgrid.png 2025-08-26T20:21:53.8763978Z :width: 512 2025-08-26T20:21:53.8764134Z 2025-08-26T20:21:53.8764231Z 2025-08-26T20:21:53.8764597Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.8765006Z 2025-08-26T20:21:53.8765561Z msg = Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=793. 2025-08-26T20:21:53.8766429Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.8767180Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-08-26T20:21:53.8767685Z 2025-08-26T20:21:53.8767837Z Returns the unique elements of the input tensor. 2025-08-26T20:21:53.8768106Z 2025-08-26T20:21:53.8768402Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-08-26T20:21:53.8769027Z this function also eliminates non-consecutive duplicate values. 2025-08-26T20:21:53.8769357Z 2025-08-26T20:21:53.8769599Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-08-26T20:21:53.8770228Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-08-26T20:21:53.8770924Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-08-26T20:21:53.8771508Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-08-26T20:21:53.8771809Z 2025-08-26T20:21:53.8771893Z Args: 2025-08-26T20:21:53.8772132Z input (Tensor): the input tensor 2025-08-26T20:21:53.8772558Z sorted (bool): Whether to sort the unique elements in ascending order 2025-08-26T20:21:53.8773007Z before returning as output. 2025-08-26T20:21:53.8773444Z return_inverse (bool): Whether to also return the indices for where 2025-08-26T20:21:53.8774003Z elements in the original input ended up in the returned unique list. 2025-08-26T20:21:53.8774632Z return_counts (bool): Whether to also return the counts for each unique 2025-08-26T20:21:53.8775073Z element. 2025-08-26T20:21:53.8775438Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-08-26T20:21:53.8775974Z unique of the flattened input is returned. Otherwise, each of the 2025-08-26T20:21:53.8776508Z tensors indexed by the given dimension is treated as one of the 2025-08-26T20:21:53.8777031Z elements to apply the unique operation upon. See examples for more 2025-08-26T20:21:53.8777474Z details. Default: ``None`` 2025-08-26T20:21:53.8777693Z 2025-08-26T20:21:53.8777777Z Returns: 2025-08-26T20:21:53.8778191Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-08-26T20:21:53.8778697Z 2025-08-26T20:21:53.8778910Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-08-26T20:21:53.8779372Z - **inverse_indices** (*Tensor*): (optional) if 2025-08-26T20:21:53.8779817Z :attr:`return_inverse` is True, there will be an additional 2025-08-26T20:21:53.8780326Z returned tensor (same shape as input) representing the indices 2025-08-26T20:21:53.8780965Z for where elements in the original input map to in the output; 2025-08-26T20:21:53.8781462Z otherwise, this function will only return a single tensor. 2025-08-26T20:21:53.8781897Z - **counts** (*Tensor*): (optional) if 2025-08-26T20:21:53.8782313Z :attr:`return_counts` is True, there will be an additional 2025-08-26T20:21:53.8782799Z returned tensor (same shape as output or output.size(dim), 2025-08-26T20:21:53.8783285Z if dim was specified) representing the number of occurrences 2025-08-26T20:21:53.8783717Z for each unique value or tensor. 2025-08-26T20:21:53.8783952Z 2025-08-26T20:21:53.8784048Z Example:: 2025-08-26T20:21:53.8784178Z 2025-08-26T20:21:53.8784404Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-08-26T20:21:53.8784828Z >>> output 2025-08-26T20:21:53.8785057Z tensor([1, 2, 3]) 2025-08-26T20:21:53.8785234Z 2025-08-26T20:21:53.8785387Z >>> output, inverse_indices = torch.unique( 2025-08-26T20:21:53.8785865Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-08-26T20:21:53.8786310Z >>> output 2025-08-26T20:21:53.8786536Z tensor([1, 2, 3]) 2025-08-26T20:21:53.8786800Z >>> inverse_indices 2025-08-26T20:21:53.8787075Z tensor([0, 2, 1, 2]) 2025-08-26T20:21:53.8787246Z 2025-08-26T20:21:53.8787392Z >>> output, inverse_indices = torch.unique( 2025-08-26T20:21:53.8787876Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-08-26T20:21:53.8788311Z >>> output 2025-08-26T20:21:53.8788557Z tensor([1, 2, 3]) 2025-08-26T20:21:53.8788821Z >>> inverse_indices 2025-08-26T20:21:53.8789087Z tensor([[0, 2], 2025-08-26T20:21:53.8789321Z [1, 2]]) 2025-08-26T20:21:53.8789492Z 2025-08-26T20:21:53.8789589Z >>> a = torch.tensor([ 2025-08-26T20:21:53.8789858Z ... [ 2025-08-26T20:21:53.8790097Z ... [1, 1, 0, 0], 2025-08-26T20:21:53.8790369Z ... [1, 1, 0, 0], 2025-08-26T20:21:53.8790646Z ... [0, 0, 1, 1], 2025-08-26T20:21:53.8790920Z ... ], 2025-08-26T20:21:53.8791148Z ... [ 2025-08-26T20:21:53.8791366Z ... [0, 0, 1, 1], 2025-08-26T20:21:53.8791645Z ... [0, 0, 1, 1], 2025-08-26T20:21:53.8792145Z ... [1, 1, 1, 1], 2025-08-26T20:21:53.8792427Z ... ], 2025-08-26T20:21:53.8792647Z ... [ 2025-08-26T20:21:53.8792890Z ... [1, 1, 0, 0], 2025-08-26T20:21:53.8793173Z ... [1, 1, 0, 0], 2025-08-26T20:21:53.8793628Z ... [0, 0, 1, 1], 2025-08-26T20:21:53.8793890Z ... ], 2025-08-26T20:21:53.8794123Z ... ]) 2025-08-26T20:21:53.8794255Z 2025-08-26T20:21:53.8794485Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-08-26T20:21:53.8795033Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-08-26T20:21:53.8795484Z >>> # each other, so one of them will be removed. 2025-08-26T20:21:53.8795836Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-08-26T20:21:53.8796138Z tensor(True) 2025-08-26T20:21:53.8796417Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-08-26T20:21:53.8796742Z >>> a_unique_dim0 2025-08-26T20:21:53.8797007Z tensor([[[0, 0, 1, 1], 2025-08-26T20:21:53.8797278Z [0, 0, 1, 1], 2025-08-26T20:21:53.8797637Z [1, 1, 1, 1]], 2025-08-26T20:21:53.8797899Z [[1, 1, 0, 0], 2025-08-26T20:21:53.8798167Z [1, 1, 0, 0], 2025-08-26T20:21:53.8798445Z [0, 0, 1, 1]]]) 2025-08-26T20:21:53.8798624Z 2025-08-26T20:21:53.8798850Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-08-26T20:21:53.8799270Z >>> # `a_unique_dim0`: 2025-08-26T20:21:53.8799577Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-08-26T20:21:53.8799904Z tensor(True) 2025-08-26T20:21:53.8800176Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-08-26T20:21:53.8800500Z tensor(True) 2025-08-26T20:21:53.8800646Z 2025-08-26T20:21:53.8800847Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-08-26T20:21:53.8801358Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-08-26T20:21:53.8801761Z >>> # them will be removed. 2025-08-26T20:21:53.8802072Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-08-26T20:21:53.8802363Z tensor(True) 2025-08-26T20:21:53.8802623Z >>> torch.unique(a, dim=1) 2025-08-26T20:21:53.8802922Z tensor([[[0, 0, 1, 1], 2025-08-26T20:21:53.8803199Z [1, 1, 0, 0]], 2025-08-26T20:21:53.8803458Z [[1, 1, 1, 1], 2025-08-26T20:21:53.8803729Z [0, 0, 1, 1]], 2025-08-26T20:21:53.8803997Z [[0, 0, 1, 1], 2025-08-26T20:21:53.8804311Z [1, 1, 0, 0]]]) 2025-08-26T20:21:53.8804490Z 2025-08-26T20:21:53.8804699Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-08-26T20:21:53.8805196Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-08-26T20:21:53.8805653Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-08-26T20:21:53.8806057Z >>> # sub-tensors will be removed. 2025-08-26T20:21:53.8806386Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-08-26T20:21:53.8806678Z tensor(True) 2025-08-26T20:21:53.8806943Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-08-26T20:21:53.8807244Z tensor(True) 2025-08-26T20:21:53.8807501Z >>> torch.unique(a, dim=2) 2025-08-26T20:21:53.8807784Z tensor([[[0, 1], 2025-08-26T20:21:53.8808036Z [0, 1], 2025-08-26T20:21:53.8808286Z [1, 0]], 2025-08-26T20:21:53.8808524Z [[1, 0], 2025-08-26T20:21:53.8808776Z [1, 0], 2025-08-26T20:21:53.8809025Z [1, 1]], 2025-08-26T20:21:53.8809277Z [[0, 1], 2025-08-26T20:21:53.8809513Z [0, 1], 2025-08-26T20:21:53.8809766Z [1, 0]]]) 2025-08-26T20:21:53.8810013Z 2025-08-26T20:21:53.8810382Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.8810750Z 2025-08-26T20:21:53.8922633Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=565. 2025-08-26T20:21:53.8923510Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.8924081Z 2025-08-26T20:21:53.8924260Z Load a model from a github repo or a local directory. 2025-08-26T20:21:53.8924538Z 2025-08-26T20:21:53.8924806Z Note: Loading a model is the typical use case, but this can also be used to 2025-08-26T20:21:53.8925443Z for loading other objects such as tokenizers, loss functions, etc. 2025-08-26T20:21:53.8925764Z 2025-08-26T20:21:53.8925943Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-08-26T20:21:53.8926376Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-08-26T20:21:53.8926758Z ref (a tag or a branch). 2025-08-26T20:21:53.8926935Z 2025-08-26T20:21:53.8927102Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-08-26T20:21:53.8927489Z path to a local directory. 2025-08-26T20:21:53.8927664Z 2025-08-26T20:21:53.8927789Z Args: 2025-08-26T20:21:53.8928137Z repo_or_dir (str): If ``source`` is 'github', 2025-08-26T20:21:53.8928840Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-08-26T20:21:53.8929636Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-08-26T20:21:53.8930319Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-08-26T20:21:53.8930891Z If ``source`` is 'local' then it should be a path to a local directory. 2025-08-26T20:21:53.8931407Z model (str): the name of a callable (entrypoint) defined in the 2025-08-26T20:21:53.8931824Z repo/dir's ``hubconf.py``. 2025-08-26T20:21:53.8932231Z *args (optional): the corresponding args for callable ``model``. 2025-08-26T20:21:53.8932750Z source (str, optional): 'github' or 'local'. Specifies how 2025-08-26T20:21:53.8933287Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-08-26T20:21:53.8933954Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-08-26T20:21:53.8934567Z This parameter was introduced in v1.12 and helps ensuring that users 2025-08-26T20:21:53.8935081Z only run code from repos that they trust. 2025-08-26T20:21:53.8935332Z 2025-08-26T20:21:53.8935526Z - If ``False``, a prompt will ask the user whether the repo should 2025-08-26T20:21:53.8935930Z be trusted. 2025-08-26T20:21:53.8936265Z - If ``True``, the repo will be added to the trusted list and loaded 2025-08-26T20:21:53.8936708Z without requiring explicit confirmation. 2025-08-26T20:21:53.8937128Z - If ``"check"``, the repo will be checked against the list of 2025-08-26T20:21:53.8937625Z trusted repos in the cache. If it is not present in that list, the 2025-08-26T20:21:53.8938157Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-08-26T20:21:53.8938649Z - If ``None``: this will raise a warning, inviting the user to set 2025-08-26T20:21:53.8939170Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-08-26T20:21:53.8939691Z is only present for backward compatibility and will be removed in 2025-08-26T20:21:53.8940105Z v2.0. 2025-08-26T20:21:53.8940240Z 2025-08-26T20:21:53.8940520Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-08-26T20:21:53.8941065Z force_reload (bool, optional): whether to force a fresh download of 2025-08-26T20:21:53.8941599Z the github repo unconditionally. Does not have any effect if 2025-08-26T20:21:53.8942035Z ``source = 'local'``. Default is ``False``. 2025-08-26T20:21:53.8942486Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-08-26T20:21:53.8943007Z local caches. Note that the message about first download cannot be 2025-08-26T20:21:53.8943507Z muted. Does not have any effect if ``source = 'local'``. 2025-08-26T20:21:53.8943899Z Default is ``True``. 2025-08-26T20:21:53.8944374Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-08-26T20:21:53.8945163Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-08-26T20:21:53.8945841Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-08-26T20:21:53.8946424Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-08-26T20:21:53.8946941Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-08-26T20:21:53.8947274Z 2025-08-26T20:21:53.8947373Z Returns: 2025-08-26T20:21:53.8947674Z The output of the ``model`` callable when called with the given 2025-08-26T20:21:53.8948077Z ``*args`` and ``**kwargs``. 2025-08-26T20:21:53.8948272Z 2025-08-26T20:21:53.8948359Z Example: 2025-08-26T20:21:53.8948616Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-26T20:21:53.8949014Z >>> # from a github repo 2025-08-26T20:21:53.8949300Z >>> repo = "pytorch/vision" 2025-08-26T20:21:53.8949599Z >>> model = torch.hub.load( 2025-08-26T20:21:53.8949982Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-08-26T20:21:53.8950357Z ... ) 2025-08-26T20:21:53.8950586Z >>> # from a local directory 2025-08-26T20:21:53.8950911Z >>> path = "/some/local/path/pytorch/vision" 2025-08-26T20:21:53.8951249Z >>> # xdoctest: +SKIP 2025-08-26T20:21:53.8951649Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-08-26T20:21:53.8952023Z 2025-08-26T20:21:53.8952274Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.8952651Z 2025-08-26T20:21:53.8953098Z msg = Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=657. 2025-08-26T20:21:53.8953934Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.8954309Z 2025-08-26T20:21:53.8954495Z Load a model from a local directory with a ``hubconf.py``. 2025-08-26T20:21:53.8954782Z 2025-08-26T20:21:53.8954880Z Args: 2025-08-26T20:21:53.8955177Z hubconf_dir (str): path to a local directory that contains a 2025-08-26T20:21:53.8955574Z ``hubconf.py``. 2025-08-26T20:21:53.8955922Z model (str): name of an entrypoint defined in the directory's 2025-08-26T20:21:53.8956318Z ``hubconf.py``. 2025-08-26T20:21:53.8956671Z *args (optional): the corresponding args for callable ``model``. 2025-08-26T20:21:53.8957206Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-08-26T20:21:53.8957548Z 2025-08-26T20:21:53.8957633Z Returns: 2025-08-26T20:21:53.8957925Z a single model with corresponding pretrained weights. 2025-08-26T20:21:53.8958198Z 2025-08-26T20:21:53.8958282Z Example: 2025-08-26T20:21:53.8958527Z >>> # xdoctest: +SKIP("stub local path") 2025-08-26T20:21:53.8958890Z >>> path = "/some/local/path/pytorch/vision" 2025-08-26T20:21:53.8959228Z >>> model = _load_local( 2025-08-26T20:21:53.8959493Z ... path, 2025-08-26T20:21:53.8959781Z ... "resnet50", 2025-08-26T20:21:53.8960084Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-08-26T20:21:53.8960420Z ... ) 2025-08-26T20:21:53.8960539Z 2025-08-26T20:21:53.8960786Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.8961167Z 2025-08-26T20:21:53.8961645Z msg = Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=696. 2025-08-26T20:21:53.8962507Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.8963038Z Download object at the given URL to a local path. 2025-08-26T20:21:53.8963297Z 2025-08-26T20:21:53.8963394Z Args: 2025-08-26T20:21:53.8963629Z url (str): URL of the object to download 2025-08-26T20:21:53.8964103Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-08-26T20:21:53.8964859Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-08-26T20:21:53.8965409Z Default: None 2025-08-26T20:21:53.8965834Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-08-26T20:21:53.8966294Z Default: True 2025-08-26T20:21:53.8966471Z 2025-08-26T20:21:53.8966557Z Example: 2025-08-26T20:21:53.8966828Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-26T20:21:53.8967188Z >>> # xdoctest: +REQUIRES(POSIX) 2025-08-26T20:21:53.8967513Z >>> torch.hub.download_url_to_file( 2025-08-26T20:21:53.8967973Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-08-26T20:21:53.8968418Z ... "/tmp/temporary_file", 2025-08-26T20:21:53.8968766Z ... ) 2025-08-26T20:21:53.8968894Z 2025-08-26T20:21:53.8969058Z 2025-08-26T20:21:53.8969433Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.8969815Z 2025-08-26T20:21:53.8970304Z msg = Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=821. 2025-08-26T20:21:53.8971189Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.8971712Z Loads the Torch serialized object at the given URL. 2025-08-26T20:21:53.8972037Z 2025-08-26T20:21:53.8972218Z If downloaded file is a zip file, it will be automatically 2025-08-26T20:21:53.8972609Z decompressed. 2025-08-26T20:21:53.8972752Z 2025-08-26T20:21:53.8972983Z If the object is already present in `model_dir`, it's deserialized and 2025-08-26T20:21:53.8973395Z returned. 2025-08-26T20:21:53.8973749Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-08-26T20:21:53.8974289Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-08-26T20:21:53.8974603Z 2025-08-26T20:21:53.8974700Z Args: 2025-08-26T20:21:53.8974956Z url (str): URL of the object to download 2025-08-26T20:21:53.8975383Z model_dir (str, optional): directory in which to save the object 2025-08-26T20:21:53.8976059Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-08-26T20:21:53.8976785Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-08-26T20:21:53.8977260Z Default: True 2025-08-26T20:21:53.8977747Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-08-26T20:21:53.8978413Z ``filename-.ext`` where ```` is the first eight or more 2025-08-26T20:21:53.8978989Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-08-26T20:21:53.8979533Z ensure unique names and to verify the contents of the file. 2025-08-26T20:21:53.8979941Z Default: False 2025-08-26T20:21:53.8980524Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-08-26T20:21:53.8981310Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-08-26T20:21:53.8982013Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-08-26T20:21:53.8982391Z 2025-08-26T20:21:53.8982491Z Example: 2025-08-26T20:21:53.8982761Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-26T20:21:53.8983149Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-08-26T20:21:53.8983634Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-08-26T20:21:53.8984049Z ... ) 2025-08-26T20:21:53.8984176Z 2025-08-26T20:21:53.8984268Z 2025-08-26T20:21:53.8984629Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.8985008Z 2025-08-26T20:21:53.9018877Z msg = Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=375. 2025-08-26T20:21:53.9019922Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:21:53.9020598Z Registers the function implementation as the fallback for the given key. 2025-08-26T20:21:53.9020954Z 2025-08-26T20:21:53.9021181Z This function only works for a library with global namespace ("_"). 2025-08-26T20:21:53.9021512Z 2025-08-26T20:21:53.9021600Z Args: 2025-08-26T20:21:53.9022015Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-08-26T20:21:53.9022530Z to register a fallthrough. 2025-08-26T20:21:53.9023081Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-08-26T20:21:53.9023788Z the dispatch key that the library was created with. 2025-08-26T20:21:53.9024435Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-08-26T20:21:53.9025255Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-08-26T20:21:53.9025709Z 2025-08-26T20:21:53.9025826Z Example:: 2025-08-26T20:21:53.9025967Z 2025-08-26T20:21:53.9026100Z >>> my_lib = Library("_", "IMPL") 2025-08-26T20:21:53.9026460Z >>> def fallback_kernel(op, *args, **kwargs): 2025-08-26T20:21:53.9026826Z >>> # Handle all autocast ops generically 2025-08-26T20:21:53.9027163Z >>> # ... 2025-08-26T20:21:53.9027472Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-08-26T20:21:53.9027816Z 2025-08-26T20:21:53.9028563Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-08-26T20:21:53.9029321Z 2025-08-26T20:21:53.9029452Z my_lib.fallback(fallback_kernel, "Autocast") 2025-08-26T20:21:53.9029781Z ^ 2025-08-26T20:21:53.9098849Z msg = Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=948. 2025-08-26T20:21:53.9099902Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:21:53.9100570Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-08-26T20:21:53.9100914Z 2025-08-26T20:21:53.9101093Z Also sometimes known as a "meta kernel", "abstract impl". 2025-08-26T20:21:53.9101398Z 2025-08-26T20:21:53.9101641Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-08-26T20:21:53.9102231Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-08-26T20:21:53.9102827Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-08-26T20:21:53.9103325Z what the properties of the output Tensors are. 2025-08-26T20:21:53.9103583Z 2025-08-26T20:21:53.9103813Z The FakeTensor implementation has the same signature as the operator. 2025-08-26T20:21:53.9104373Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-08-26T20:21:53.9104917Z implementation, assume that all Tensor inputs to the operator are 2025-08-26T20:21:53.9105458Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-08-26T20:21:53.9105975Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-08-26T20:21:53.9106531Z The FakeTensor implementation must consist of only PyTorch operations 2025-08-26T20:21:53.9107078Z (and may not directly access the storage or data of any input or 2025-08-26T20:21:53.9107489Z intermediate Tensors). 2025-08-26T20:21:53.9107663Z 2025-08-26T20:21:53.9107831Z This API may be used as a decorator (see examples). 2025-08-26T20:21:53.9108214Z 2025-08-26T20:21:53.9108380Z For a detailed guide on custom ops, please see 2025-08-26T20:21:53.9109926Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-08-26T20:21:53.9110287Z 2025-08-26T20:21:53.9110371Z Args: 2025-08-26T20:21:53.9110718Z op_name: Operator name (along with the overload) or OpOverload object. 2025-08-26T20:21:53.9111168Z func: Fake tensor implementation. 2025-08-26T20:21:53.9111594Z lib (Optional[Library]): Library to register the fake tensor to. 2025-08-26T20:21:53.9112097Z allow_override: Flag controlling if we want to override an 2025-08-26T20:21:53.9112571Z existing registered fake impl. This is by default off, 2025-08-26T20:21:53.9113041Z and will error you're trying to register a fake impl to 2025-08-26T20:21:53.9113500Z an operator that already has a fake impl. This also only 2025-08-26T20:21:53.9114042Z applies if the custom operator was not created via 2025-08-26T20:21:53.9114517Z torch.library.custom_op, as overriding and existing fake 2025-08-26T20:21:53.9114951Z impl is already allowed. 2025-08-26T20:21:53.9115176Z 2025-08-26T20:21:53.9115281Z Examples: 2025-08-26T20:21:53.9115501Z >>> import torch 2025-08-26T20:21:53.9115777Z >>> import numpy as np 2025-08-26T20:21:53.9116108Z >>> from torch import Tensor 2025-08-26T20:21:53.9116399Z >>> 2025-08-26T20:21:53.9116713Z >>> # Example 1: an operator without data-dependent output shape 2025-08-26T20:21:53.9117252Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-08-26T20:21:53.9117798Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-08-26T20:21:53.9118322Z >>> raise NotImplementedError("Implementation goes here") 2025-08-26T20:21:53.9118703Z >>> 2025-08-26T20:21:53.9119009Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-08-26T20:21:53.9119389Z >>> def _(x, weight, bias): 2025-08-26T20:21:53.9119698Z >>> assert x.dim() == 2 2025-08-26T20:21:53.9120009Z >>> assert weight.dim() == 2 2025-08-26T20:21:53.9120339Z >>> assert bias.dim() == 1 2025-08-26T20:21:53.9120668Z >>> assert x.shape[1] == weight.shape[1] 2025-08-26T20:21:53.9121046Z >>> assert weight.shape[0] == bias.shape[0] 2025-08-26T20:21:53.9121414Z >>> assert x.device == weight.device 2025-08-26T20:21:53.9121737Z >>> 2025-08-26T20:21:53.9121965Z >>> return (x @ weight.t()) + bias 2025-08-26T20:21:53.9122283Z >>> 2025-08-26T20:21:53.9122586Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-08-26T20:21:53.9122987Z >>> x = torch.randn(2, 3) 2025-08-26T20:21:53.9123288Z >>> w = torch.randn(3, 3) 2025-08-26T20:21:53.9123606Z >>> b = torch.randn(3) 2025-08-26T20:21:53.9123947Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-08-26T20:21:53.9124286Z >>> 2025-08-26T20:21:53.9124504Z >>> assert y.shape == (2, 3) 2025-08-26T20:21:53.9124793Z >>> 2025-08-26T20:21:53.9125095Z >>> # Example 2: an operator with data-dependent output shape 2025-08-26T20:21:53.9125606Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-08-26T20:21:53.9126073Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-08-26T20:21:53.9126414Z >>> x_np = x.numpy(force=True) 2025-08-26T20:21:53.9126765Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-08-26T20:21:53.9127146Z >>> return torch.tensor(res, device=x.device) 2025-08-26T20:21:53.9127482Z >>> 2025-08-26T20:21:53.9127769Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-08-26T20:21:53.9128144Z >>> def _(x): 2025-08-26T20:21:53.9128455Z >>> # Number of nonzero-elements is data-dependent. 2025-08-26T20:21:53.9128873Z >>> # Since we cannot peek at the data in an fake impl, 2025-08-26T20:21:53.9129358Z >>> # we use the ctx object to construct a new symint that 2025-08-26T20:21:53.9129765Z >>> # represents the data-dependent size. 2025-08-26T20:21:53.9130124Z >>> ctx = torch.library.get_ctx() 2025-08-26T20:21:53.9130470Z >>> nnz = ctx.new_dynamic_size() 2025-08-26T20:21:53.9130790Z >>> shape = [nnz, x.dim()] 2025-08-26T20:21:53.9131153Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-08-26T20:21:53.9131520Z >>> return result 2025-08-26T20:21:53.9131787Z >>> 2025-08-26T20:21:53.9132080Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-08-26T20:21:53.9132459Z >>> 2025-08-26T20:21:53.9132697Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-08-26T20:21:53.9133220Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-08-26T20:21:53.9133699Z >>> trace.print_readable() 2025-08-26T20:21:53.9133981Z >>> 2025-08-26T20:21:53.9134328Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-08-26T20:21:53.9134671Z 2025-08-26T20:21:53.9134767Z 2025-08-26T20:21:53.9135415Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-08-26T20:21:53.9136059Z 2025-08-26T20:21:53.9136145Z _._ = None 2025-08-26T20:21:53.9136362Z ^ 2025-08-26T20:21:53.9136981Z msg = Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1083. 2025-08-26T20:21:53.9137865Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.9138393Z Register a backward formula for this custom op. 2025-08-26T20:21:53.9138644Z 2025-08-26T20:21:53.9138850Z In order for an operator to work with autograd, you need to register 2025-08-26T20:21:53.9139271Z a backward formula: 2025-08-26T20:21:53.9139649Z 1. You must tell us how to compute gradients during the backward pass 2025-08-26T20:21:53.9140147Z by providing us a "backward" function. 2025-08-26T20:21:53.9140686Z 2. If you need any values from the forward to compute gradients, you can 2025-08-26T20:21:53.9141165Z use `setup_context` to save values for backward. 2025-08-26T20:21:53.9141430Z 2025-08-26T20:21:53.9141661Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-08-26T20:21:53.9142201Z - ``grads`` is one or more gradients. The number of gradients matches 2025-08-26T20:21:53.9142639Z the number of outputs of the operator. 2025-08-26T20:21:53.9143068Z The ``ctx`` object is `the same ctx object `_ used by 2025-08-26T20:21:53.9143645Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-08-26T20:21:53.9144165Z same as :meth:`torch.autograd.Function.backward`. 2025-08-26T20:21:53.9144429Z 2025-08-26T20:21:53.9144657Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-08-26T20:21:53.9145204Z Please save quantities needed for backward onto the ``ctx`` object via 2025-08-26T20:21:53.9145787Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-08-26T20:21:53.9146335Z or assigning them as attributes of ``ctx``. If your custom op has 2025-08-26T20:21:53.9146864Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-08-26T20:21:53.9147387Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-08-26T20:21:53.9147691Z 2025-08-26T20:21:53.9147910Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-08-26T20:21:53.9148474Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-08-26T20:21:53.9149056Z not depend on or mutate global state. If you need a non-traceable backward, 2025-08-26T20:21:53.9149631Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-08-26T20:21:53.9150043Z 2025-08-26T20:21:53.9150271Z If you need different autograd behavior on different devices, then we 2025-08-26T20:21:53.9150828Z recommend creating two different custom operators, one for each device 2025-08-26T20:21:53.9151404Z that needs different behavior, and switching between them at runtime. 2025-08-26T20:21:53.9151755Z 2025-08-26T20:21:53.9151841Z Examples: 2025-08-26T20:21:53.9152074Z >>> import torch 2025-08-26T20:21:53.9152333Z >>> import numpy as np 2025-08-26T20:21:53.9152634Z >>> from torch import Tensor 2025-08-26T20:21:53.9152931Z >>> 2025-08-26T20:21:53.9153272Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-08-26T20:21:53.9153705Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-08-26T20:21:53.9154047Z >>> x_np = x.cpu().numpy() 2025-08-26T20:21:53.9154435Z >>> y_np = np.sin(x_np) 2025-08-26T20:21:53.9154800Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-08-26T20:21:53.9155151Z >>> 2025-08-26T20:21:53.9155431Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-08-26T20:21:53.9155793Z >>> x, = inputs 2025-08-26T20:21:53.9156078Z >>> ctx.save_for_backward(x) 2025-08-26T20:21:53.9156369Z >>> 2025-08-26T20:21:53.9156629Z >>> def backward(ctx, grad): 2025-08-26T20:21:53.9156942Z >>> x, = ctx.saved_tensors 2025-08-26T20:21:53.9157255Z >>> return grad * x.cos() 2025-08-26T20:21:53.9157534Z >>> 2025-08-26T20:21:53.9157787Z >>> torch.library.register_autograd( 2025-08-26T20:21:53.9158374Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-08-26T20:21:53.9158764Z ... ) 2025-08-26T20:21:53.9158971Z >>> 2025-08-26T20:21:53.9159228Z >>> x = torch.randn(3, requires_grad=True) 2025-08-26T20:21:53.9159581Z >>> y = numpy_sin(x) 2025-08-26T20:21:53.9159938Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-08-26T20:21:53.9160409Z >>> assert torch.allclose(grad_x, x.cos()) 2025-08-26T20:21:53.9160806Z >>> 2025-08-26T20:21:53.9161113Z >>> # Example with a keyword-only arg 2025-08-26T20:21:53.9161593Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-08-26T20:21:53.9162224Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-08-26T20:21:53.9162588Z >>> x_np = x.cpu().numpy() 2025-08-26T20:21:53.9162901Z >>> y_np = x_np * val 2025-08-26T20:21:53.9163277Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-08-26T20:21:53.9163635Z >>> 2025-08-26T20:21:53.9163974Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-08-26T20:21:53.9164449Z >>> ctx.val = keyword_only_inputs["val"] 2025-08-26T20:21:53.9164777Z >>> 2025-08-26T20:21:53.9165011Z >>> def backward(ctx, grad): 2025-08-26T20:21:53.9165313Z >>> return grad * ctx.val 2025-08-26T20:21:53.9165600Z >>> 2025-08-26T20:21:53.9165845Z >>> torch.library.register_autograd( 2025-08-26T20:21:53.9166316Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-08-26T20:21:53.9166685Z ... ) 2025-08-26T20:21:53.9166902Z >>> 2025-08-26T20:21:53.9167151Z >>> x = torch.randn(3, requires_grad=True) 2025-08-26T20:21:53.9167498Z >>> y = numpy_mul(x, val=3.14) 2025-08-26T20:21:53.9167866Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-08-26T20:21:53.9168331Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-08-26T20:21:53.9168630Z 2025-08-26T20:21:53.9168712Z 2025-08-26T20:21:53.9169082Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.9169451Z 2025-08-26T20:21:53.9169967Z msg = Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1482. 2025-08-26T20:21:53.9170890Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:21:53.9171457Z Returns the computed kernel for a given operator and dispatch key. 2025-08-26T20:21:53.9171791Z 2025-08-26T20:21:53.9172016Z This function retrieves the kernel that would be executed for a given 2025-08-26T20:21:53.9172591Z operator and dispatch key combination. The returned SafeKernelFunction 2025-08-26T20:21:53.9173139Z can be used to call the kernel in a boxed fashion. The intended use 2025-08-26T20:21:53.9173760Z case for this function is to retrieve the original kernel for a given 2025-08-26T20:21:53.9174582Z dispatch key and then register another kernel to the same dispatch key 2025-08-26T20:21:53.9175260Z that calls into the original kernel for certain cases. 2025-08-26T20:21:53.9175705Z 2025-08-26T20:21:53.9176000Z Args: 2025-08-26T20:21:53.9176524Z op: Operator name (along with the overload) or OpOverload object 2025-08-26T20:21:53.9177076Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-08-26T20:21:53.9177681Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-08-26T20:21:53.9178252Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-08-26T20:21:53.9178556Z 2025-08-26T20:21:53.9178657Z Returns: 2025-08-26T20:21:53.9179037Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-08-26T20:21:53.9179567Z call the kernel. 2025-08-26T20:21:53.9179756Z 2025-08-26T20:21:53.9179840Z Raises: 2025-08-26T20:21:53.9180112Z RuntimeError: If the operator does not exist. 2025-08-26T20:21:53.9180432Z 2025-08-26T20:21:53.9180537Z Example: 2025-08-26T20:21:53.9180776Z >>> # Get the CPU kernel for torch.add 2025-08-26T20:21:53.9181217Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-08-26T20:21:53.9181622Z >>> 2025-08-26T20:21:53.9181900Z >>> # You can also use DispatchKey enum 2025-08-26T20:21:53.9182380Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-08-26T20:21:53.9182840Z >>> 2025-08-26T20:21:53.9183081Z >>> # Or use an OpOverload directly 2025-08-26T20:21:53.9183527Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-08-26T20:21:53.9183942Z >>> 2025-08-26T20:21:53.9184321Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-08-26T20:21:53.9184779Z >>> # Get the original kernel for torch.sin 2025-08-26T20:21:53.9185234Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-08-26T20:21:53.9185651Z >>> 2025-08-26T20:21:53.9185991Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-08-26T20:21:53.9186478Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-08-26T20:21:53.9186830Z >>> if (x < 0).any(): 2025-08-26T20:21:53.9187206Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-08-26T20:21:53.9187583Z >>> else: 2025-08-26T20:21:53.9187857Z >>> return torch.zeros_like(x) 2025-08-26T20:21:53.9188169Z >>> 2025-08-26T20:21:53.9188431Z >>> lib = torch.library.Library("aten", "IMPL") 2025-08-26T20:21:53.9188930Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-08-26T20:21:53.9189499Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-08-26T20:21:53.9190009Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-08-26T20:21:53.9190408Z >>> 2025-08-26T20:21:53.9190641Z >>> # Test the conditional behavior 2025-08-26T20:21:53.9190993Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-08-26T20:21:53.9191345Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-08-26T20:21:53.9191948Z >>> torch.sin(x_positive) 2025-08-26T20:21:53.9192250Z tensor([0., 0.]) 2025-08-26T20:21:53.9192506Z >>> torch.sin(x_mixed) 2025-08-26T20:21:53.9192792Z tensor([-0.8415, 0.9093]) 2025-08-26T20:21:53.9193069Z 2025-08-26T20:21:53.9193664Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-08-26T20:21:53.9194255Z 2025-08-26T20:21:53.9194433Z which needs to be the first argument to ``kernel.call_boxed`` 2025-08-26T20:21:53.9194821Z ^ 2025-08-26T20:21:53.9195437Z msg = Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1571. 2025-08-26T20:21:53.9196277Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.9197001Z Given an operator and some sample arguments, tests if the operator is 2025-08-26T20:21:53.9197449Z registered correctly. 2025-08-26T20:21:53.9197627Z 2025-08-26T20:21:53.9197836Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-08-26T20:21:53.9198416Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-08-26T20:21:53.9199009Z and these APIs require that the functions you pass them satisfy certain 2025-08-26T20:21:53.9199592Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-08-26T20:21:53.9200080Z ``opcheck`` tests these metadata and properties. 2025-08-26T20:21:53.9200351Z 2025-08-26T20:21:53.9200468Z Concretely, we test the following: 2025-08-26T20:21:53.9200700Z 2025-08-26T20:21:53.9200881Z - test_schema: If the schema matches the implementation of 2025-08-26T20:21:53.9201407Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-08-26T20:21:53.9201968Z then we check the implementation mutates the Tensor. If the schema 2025-08-26T20:21:53.9202478Z specifies that we return a new Tensor, then we check that the 2025-08-26T20:21:53.9203016Z implementation returns a new Tensor (instead of an existing one or 2025-08-26T20:21:53.9203465Z a view of an existing one). 2025-08-26T20:21:53.9203868Z - test_autograd_registration: If the operator supports training 2025-08-26T20:21:53.9204374Z (autograd): we check that its autograd formula is registered via 2025-08-26T20:21:53.9204904Z torch.library.register_autograd or a manual registration to one 2025-08-26T20:21:53.9205443Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-08-26T20:21:53.9205897Z registrations may lead to undefined behavior. 2025-08-26T20:21:53.9206339Z - test_faketensor: If the operator has a FakeTensor kernel 2025-08-26T20:21:53.9206796Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-08-26T20:21:53.9207310Z but not sufficient) for the operator to work with PyTorch compilation 2025-08-26T20:21:53.9207863Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-08-26T20:21:53.9208558Z (also sometimes known as a meta kernel) was registered for the 2025-08-26T20:21:53.9209124Z operator and that it is correct. This test takes the result of 2025-08-26T20:21:53.9209630Z running the operator on real tensors and the result of running 2025-08-26T20:21:53.9210135Z the operator on FakeTensors and checks that they have the same 2025-08-26T20:21:53.9210600Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-08-26T20:21:53.9211068Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-08-26T20:21:53.9211553Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-08-26T20:21:53.9212053Z This checks that the outputs (and gradients, if applicable) are the 2025-08-26T20:21:53.9212533Z same under eager-mode PyTorch and torch.compile. 2025-08-26T20:21:53.9212995Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-08-26T20:21:53.9213559Z other things it tests are that the operator supports 2025-08-26T20:21:53.9214055Z functionalization and that the backward pass (if it exists) also 2025-08-26T20:21:53.9214529Z supports FakeTensor and functionalization. 2025-08-26T20:21:53.9214772Z 2025-08-26T20:21:53.9214981Z For best results, please call ``opcheck`` multiple times with a 2025-08-26T20:21:53.9215477Z representative set of inputs. If your operator supports 2025-08-26T20:21:53.9215994Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-08-26T20:21:53.9216573Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-08-26T20:21:53.9217073Z use ``opcheck`` with inputs on all supported devices. 2025-08-26T20:21:53.9217339Z 2025-08-26T20:21:53.9217433Z Args: 2025-08-26T20:21:53.9217782Z op: The operator. Must either be a function decorated with 2025-08-26T20:21:53.9218293Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-08-26T20:21:53.9218848Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-08-26T20:21:53.9219296Z args: The args to the operator 2025-08-26T20:21:53.9219629Z kwargs: The kwargs to the operator 2025-08-26T20:21:53.9220027Z test_utils: Tests that we should run. Default: all of them. 2025-08-26T20:21:53.9220533Z Example: ("test_schema", "test_faketensor") 2025-08-26T20:21:53.9220980Z raise_exception: If we should raise an exception on the first 2025-08-26T20:21:53.9221466Z error. If False, we will return a dict with information 2025-08-26T20:21:53.9221855Z on if each test passed or not. 2025-08-26T20:21:53.9222320Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-08-26T20:21:53.9222823Z If specified ``atol`` must also be specified. 2025-08-26T20:21:53.9223275Z If omitted, default values based on the ``dtype`` are selected 2025-08-26T20:21:53.9223742Z (see the table in :func:`torch.testing.assert_close`). 2025-08-26T20:21:53.9224270Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-08-26T20:21:53.9224767Z If specified ``rtol`` must also be specified. 2025-08-26T20:21:53.9225208Z If omitted, default values based on the ``dtype`` are selected 2025-08-26T20:21:53.9225684Z (see the table in :func:`torch.testing.assert_close`). 2025-08-26T20:21:53.9225959Z 2025-08-26T20:21:53.9226053Z .. warning:: 2025-08-26T20:21:53.9226203Z 2025-08-26T20:21:53.9226420Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-08-26T20:21:53.9226963Z opcheck tests if your usage of torch.library APIs is correct while 2025-08-26T20:21:53.9227504Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-08-26T20:21:53.9228058Z mathematically correct. Use both to test custom ops that support 2025-08-26T20:21:53.9228489Z gradient computation. 2025-08-26T20:21:53.9228689Z 2025-08-26T20:21:53.9228774Z Example: 2025-08-26T20:21:53.9228899Z 2025-08-26T20:21:53.9229048Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:21:53.9229502Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-08-26T20:21:53.9229952Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-08-26T20:21:53.9230317Z >>> x_np = x.numpy(force=True) 2025-08-26T20:21:53.9230633Z >>> z_np = x_np * y 2025-08-26T20:21:53.9230956Z >>> return torch.from_numpy(z_np).to(x.device) 2025-08-26T20:21:53.9231280Z >>> 2025-08-26T20:21:53.9231511Z >>> @numpy_mul.register_fake 2025-08-26T20:21:53.9231813Z >>> def _(x, y): 2025-08-26T20:21:53.9232091Z >>> return torch.empty_like(x) 2025-08-26T20:21:53.9232388Z >>> 2025-08-26T20:21:53.9232647Z >>> def setup_context(ctx, inputs, output): 2025-08-26T20:21:53.9232991Z >>> y, = inputs 2025-08-26T20:21:53.9233352Z >>> ctx.y = y 2025-08-26T20:21:53.9233590Z >>> 2025-08-26T20:21:53.9233820Z >>> def backward(ctx, grad): 2025-08-26T20:21:53.9234136Z >>> return grad * ctx.y, None 2025-08-26T20:21:53.9234436Z >>> 2025-08-26T20:21:53.9234770Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-08-26T20:21:53.9235200Z >>> 2025-08-26T20:21:53.9235424Z >>> sample_inputs = [ 2025-08-26T20:21:53.9235714Z >>> (torch.randn(3), 3.14), 2025-08-26T20:21:53.9236058Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-08-26T20:21:53.9236449Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-08-26T20:21:53.9236905Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-08-26T20:21:53.9237303Z >>> ] 2025-08-26T20:21:53.9237567Z >>> 2025-08-26T20:21:53.9237808Z >>> for args in sample_inputs: 2025-08-26T20:21:53.9238168Z >>> torch.library.opcheck(numpy_mul, args) 2025-08-26T20:21:53.9238412Z 2025-08-26T20:21:53.9238509Z 2025-08-26T20:21:53.9238884Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.9239253Z 2025-08-26T20:21:53.9619031Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1285. 2025-08-26T20:21:53.9620008Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:53.9620847Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-08-26T20:21:53.9621297Z 2025-08-26T20:21:53.9621546Z Loads an object saved with :func:`torch.save` from a file. 2025-08-26T20:21:53.9621839Z 2025-08-26T20:21:53.9622109Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-08-26T20:21:53.9622727Z which underlie tensors, specially. They are first deserialized on the 2025-08-26T20:21:53.9623358Z CPU and are then moved to the device they were saved from. If this fails 2025-08-26T20:21:53.9623983Z (e.g. because the run time system doesn't have certain devices), an exception 2025-08-26T20:21:53.9624632Z is raised. However, storages can be dynamically remapped to an alternative 2025-08-26T20:21:53.9625174Z set of devices using the :attr:`map_location` argument. 2025-08-26T20:21:53.9625491Z 2025-08-26T20:21:53.9625729Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-08-26T20:21:53.9626376Z storage with two arguments: storage and location. The storage argument 2025-08-26T20:21:53.9627001Z will be the initial deserialization of the storage, residing on the CPU. 2025-08-26T20:21:53.9627651Z Each serialized storage has a location tag associated with it which 2025-08-26T20:21:53.9628211Z identifies the device it was saved from, and this tag is the second 2025-08-26T20:21:53.9628843Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-08-26T20:21:53.9629492Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-08-26T20:21:53.9630097Z :attr:`map_location` should return either ``None`` or a storage. If 2025-08-26T20:21:53.9630727Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-08-26T20:21:53.9631402Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-08-26T20:21:53.9632019Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-08-26T20:21:53.9632435Z 2025-08-26T20:21:53.9632668Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-08-26T20:21:53.9633320Z a device tag, it indicates the location where all tensors should be loaded. 2025-08-26T20:21:53.9633736Z 2025-08-26T20:21:53.9633999Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-08-26T20:21:53.9634864Z appearing in the file (keys), to ones that specify where to put the 2025-08-26T20:21:53.9635333Z storages (values). 2025-08-26T20:21:53.9635513Z 2025-08-26T20:21:53.9635735Z User extensions can register their own location tags and tagging and 2025-08-26T20:21:53.9636407Z deserialization methods using :func:`torch.serialization.register_package`. 2025-08-26T20:21:53.9636860Z 2025-08-26T20:21:53.9637104Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-08-26T20:21:53.9637519Z 2025-08-26T20:21:53.9637617Z Args: 2025-08-26T20:21:53.9638063Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-08-26T20:21:53.9638742Z or a string or os.PathLike object containing a file name 2025-08-26T20:21:53.9639512Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-08-26T20:21:53.9640118Z locations 2025-08-26T20:21:53.9640565Z pickle_module: module used for unpickling metadata and objects (has to 2025-08-26T20:21:53.9641079Z match the :attr:`pickle_module` used to serialize file) 2025-08-26T20:21:53.9641644Z weights_only: Indicates whether unpickler should be restricted to 2025-08-26T20:21:53.9642199Z loading only tensors, primitive types, dictionaries 2025-08-26T20:21:53.9642708Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-08-26T20:21:53.9643228Z See :ref:`weights-only` for more details. 2025-08-26T20:21:53.9643870Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-08-26T20:21:53.9644717Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-08-26T20:21:53.9645578Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-08-26T20:21:53.9646436Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-08-26T20:21:53.9647261Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-08-26T20:21:53.9647924Z will be lazily loaded when their data is accessed. 2025-08-26T20:21:53.9648492Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-08-26T20:21:53.9649132Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-08-26T20:21:53.9649581Z :attr:`errors=...`. 2025-08-26T20:21:53.9649818Z 2025-08-26T20:21:53.9649938Z .. warning:: 2025-08-26T20:21:53.9650293Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-08-26T20:21:53.9650890Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-08-26T20:21:53.9651538Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-08-26T20:21:53.9652229Z during unpickling. Never load data that could have come from an untrusted 2025-08-26T20:21:53.9652932Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-08-26T20:21:53.9653355Z 2025-08-26T20:21:53.9653476Z .. note:: 2025-08-26T20:21:53.9653893Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-08-26T20:21:53.9654581Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-08-26T20:21:53.9655271Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-08-26T20:21:53.9655674Z 2025-08-26T20:21:53.9655760Z .. note:: 2025-08-26T20:21:53.9656149Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-08-26T20:21:53.9656788Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-08-26T20:21:53.9657409Z when loading files saved by Python 2 in Python 3. If this default 2025-08-26T20:21:53.9657980Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-08-26T20:21:53.9658600Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-08-26T20:21:53.9659263Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-08-26T20:21:53.9659854Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-08-26T20:21:53.9660200Z 2025-08-26T20:21:53.9660299Z Example: 2025-08-26T20:21:53.9660672Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-08-26T20:21:53.9661063Z >>> torch.load("tensors.pt", weights_only=True) 2025-08-26T20:21:53.9661435Z # Load all tensors onto the CPU 2025-08-26T20:21:53.9661842Z >>> torch.load( 2025-08-26T20:21:53.9662096Z ... "tensors.pt", 2025-08-26T20:21:53.9662414Z ... map_location=torch.device("cpu"), 2025-08-26T20:21:53.9662826Z ... weights_only=True, 2025-08-26T20:21:53.9663117Z ... ) 2025-08-26T20:21:53.9674243Z # Load all tensors onto the CPU, using a function 2025-08-26T20:21:53.9674664Z >>> torch.load( 2025-08-26T20:21:53.9674943Z ... "tensors.pt", 2025-08-26T20:21:53.9675258Z ... map_location=lambda storage, loc: storage, 2025-08-26T20:21:53.9675615Z ... weights_only=True, 2025-08-26T20:21:53.9675904Z ... ) 2025-08-26T20:21:53.9676148Z # Load all tensors onto GPU 1 2025-08-26T20:21:53.9676488Z >>> torch.load( 2025-08-26T20:21:53.9676747Z ... "tensors.pt", 2025-08-26T20:21:53.9677090Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-08-26T20:21:53.9677479Z ... weights_only=True, 2025-08-26T20:21:53.9677792Z ... ) # type: ignore[attr-defined] 2025-08-26T20:21:53.9678126Z # Map tensors from GPU 1 to GPU 0 2025-08-26T20:21:53.9678450Z >>> torch.load( 2025-08-26T20:21:53.9678710Z ... "tensors.pt", 2025-08-26T20:21:53.9679001Z ... map_location={"cuda:1": "cuda:0"}, 2025-08-26T20:21:53.9679344Z ... weights_only=True, 2025-08-26T20:21:53.9679625Z ... ) 2025-08-26T20:21:53.9679880Z # Load tensor from io.BytesIO object 2025-08-26T20:21:53.9680348Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-08-26T20:21:53.9680847Z >>> with open("tensor.pt", "rb") as f: 2025-08-26T20:21:53.9681183Z ... buffer = io.BytesIO(f.read()) 2025-08-26T20:21:53.9681538Z >>> torch.load(buffer, weights_only=False) 2025-08-26T20:21:53.9681936Z # Load a module with 'ascii' encoding for unpickling 2025-08-26T20:21:53.9682452Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-08-26T20:21:53.9683065Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-08-26T20:21:53.9683500Z 2025-08-26T20:21:53.9683871Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:53.9684241Z 2025-08-26T20:21:54.0154263Z msg = Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py line=1877. 2025-08-26T20:21:54.0155276Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:54.0155880Z Computes the minimum storage size to hold the given tensor geometry. 2025-08-26T20:21:54.0156211Z 2025-08-26T20:21:54.0156297Z Example 2025-08-26T20:21:54.0156510Z ======= 2025-08-26T20:21:54.0156631Z 2025-08-26T20:21:54.0156872Z This is the size of a newly allocated tensor's storage, in units of elements 2025-08-26T20:21:54.0157217Z 2025-08-26T20:21:54.0157342Z >>> t = torch.empty((10, 20)) 2025-08-26T20:21:54.0157772Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-08-26T20:21:54.0158474Z 200 2025-08-26T20:21:54.0158602Z 2025-08-26T20:21:54.0158704Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:21:54.0159041Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-08-26T20:21:54.0159401Z >>> size = compute_required_storage_length( 2025-08-26T20:21:54.0159780Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-08-26T20:21:54.0160119Z ... ) 2025-08-26T20:21:54.0160344Z >>> size == t.storage().size() 2025-08-26T20:21:54.0160617Z True 2025-08-26T20:21:54.0160745Z 2025-08-26T20:21:54.0160941Z A valid tensor may have a larger storage size, but never smaller 2025-08-26T20:21:54.0161262Z 2025-08-26T20:21:54.0161370Z >>> slice = torch.empty(100)[20:40] 2025-08-26T20:21:54.0161691Z >>> slice.storage().size() 2025-08-26T20:21:54.0161963Z 100 2025-08-26T20:21:54.0162075Z 2025-08-26T20:21:54.0162285Z >>> compute_required_storage_length( 2025-08-26T20:21:54.0162676Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-08-26T20:21:54.0163053Z ... ) 2025-08-26T20:21:54.0163265Z 40 2025-08-26T20:21:54.0163378Z 2025-08-26T20:21:54.0163457Z 2025-08-26T20:21:54.0163825Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:54.0164201Z 2025-08-26T20:21:54.0748305Z msg = Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=66. 2025-08-26T20:21:54.0749237Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:21:54.0749853Z Check if the current accelerator is available at runtime: it was build, all the 2025-08-26T20:21:54.0750441Z required drivers are available and at least one device is visible. 2025-08-26T20:21:54.0750923Z See :ref:`accelerator` for details. 2025-08-26T20:21:54.0751198Z 2025-08-26T20:21:54.0751297Z Returns: 2025-08-26T20:21:54.0751698Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-08-26T20:21:54.0752113Z 2025-08-26T20:21:54.0752384Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-08-26T20:21:54.0753008Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-08-26T20:21:54.0753645Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-08-26T20:21:54.0754158Z :ref:`multiprocessing-poison-fork-note`. 2025-08-26T20:21:54.0754408Z 2025-08-26T20:21:54.0754498Z Example:: 2025-08-26T20:21:54.0754646Z 2025-08-26T20:21:54.0754914Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-26T20:21:54.0755385Z 2025-08-26T20:21:54.0756067Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-08-26T20:21:54.0756741Z 2025-08-26T20:21:54.0757013Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-26T20:21:54.0757480Z ^ 2025-08-26T20:21:54.0765102Z msg = Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=212. 2025-08-26T20:21:54.0766087Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:21:54.0766736Z Wait for all kernels in all streams on the given device to complete. 2025-08-26T20:21:54.0767078Z 2025-08-26T20:21:54.0767213Z Args: 2025-08-26T20:21:54.0767645Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-08-26T20:21:54.0768385Z the current :ref:`accelerator` device type. If not given, 2025-08-26T20:21:54.0769007Z use :func:`torch.accelerator.current_device_index` by default. 2025-08-26T20:21:54.0769317Z 2025-08-26T20:21:54.0769721Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-08-26T20:21:54.0770442Z 2025-08-26T20:21:54.0770552Z Example:: 2025-08-26T20:21:54.0770686Z 2025-08-26T20:21:54.0770840Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:21:54.0771423Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-26T20:21:54.0772025Z >>> start_event = torch.Event(enable_timing=True) 2025-08-26T20:21:54.0772417Z >>> end_event = torch.Event(enable_timing=True) 2025-08-26T20:21:54.0772847Z >>> start_event.record() 2025-08-26T20:21:54.0773271Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-08-26T20:21:54.0773808Z >>> sum = torch.sum(tensor) 2025-08-26T20:21:54.0774168Z >>> end_event.record() 2025-08-26T20:21:54.0774607Z >>> torch.accelerator.synchronize() 2025-08-26T20:21:54.0775073Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-08-26T20:21:54.0775450Z 2025-08-26T20:21:54.0776199Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-08-26T20:21:54.0776939Z 2025-08-26T20:21:54.0777222Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-26T20:21:54.0777799Z ^ 2025-08-26T20:21:54.1033585Z msg = Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=434. 2025-08-26T20:21:54.1034462Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:21:54.1034962Z Retrieves the CUDA runtime API module. 2025-08-26T20:21:54.1035219Z 2025-08-26T20:21:54.1035224Z 2025-08-26T20:21:54.1035495Z This function initializes the CUDA runtime environment if it is not already 2025-08-26T20:21:54.1036102Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-08-26T20:21:54.1036688Z runtime API module provides access to various CUDA runtime functions. 2025-08-26T20:21:54.1037026Z 2025-08-26T20:21:54.1037109Z Args: 2025-08-26T20:21:54.1037322Z ``None`` 2025-08-26T20:21:54.1037470Z 2025-08-26T20:21:54.1037556Z Returns: 2025-08-26T20:21:54.1037832Z module: The CUDA runtime API module (_cudart). 2025-08-26T20:21:54.1038085Z 2025-08-26T20:21:54.1038168Z Raises: 2025-08-26T20:21:54.1038524Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-08-26T20:21:54.1039235Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-08-26T20:21:54.1039711Z 2025-08-26T20:21:54.1039856Z Example of CUDA operations with profiling: 2025-08-26T20:21:54.1040199Z >>> import torch 2025-08-26T20:21:54.1040498Z >>> from torch.cuda import cudart, check_error 2025-08-26T20:21:54.1040848Z >>> import os 2025-08-26T20:21:54.1041097Z >>> 2025-08-26T20:21:54.1041336Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-08-26T20:21:54.1041636Z >>> 2025-08-26T20:21:54.1041902Z >>> def perform_cuda_operations_with_streams(): 2025-08-26T20:21:54.1042271Z >>> stream = torch.cuda.Stream() 2025-08-26T20:21:54.1042622Z >>> with torch.cuda.stream(stream): 2025-08-26T20:21:54.1042973Z >>> x = torch.randn(100, 100, device='cuda') 2025-08-26T20:21:54.1043364Z >>> y = torch.randn(100, 100, device='cuda') 2025-08-26T20:21:54.1043712Z >>> z = torch.mul(x, y) 2025-08-26T20:21:54.1044018Z >>> return z 2025-08-26T20:21:54.1044253Z >>> 2025-08-26T20:21:54.1044490Z >>> torch.cuda.synchronize() 2025-08-26T20:21:54.1044841Z >>> print("====== Start nsys profiling ======") 2025-08-26T20:21:54.1045234Z >>> check_error(cudart().cudaProfilerStart()) 2025-08-26T20:21:54.1045616Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-08-26T20:21:54.1046282Z >>> result = perform_cuda_operations_with_streams() 2025-08-26T20:21:54.1046690Z >>> print("CUDA operations completed.") 2025-08-26T20:21:54.1047144Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-08-26T20:21:54.1047543Z >>> print("====== End nsys profiling ======") 2025-08-26T20:21:54.1047795Z 2025-08-26T20:21:54.1047991Z To run this example and save the profiling information, execute: 2025-08-26T20:21:54.1048680Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-08-26T20:21:54.1049162Z 2025-08-26T20:21:54.1049406Z This command profiles the CUDA operations in the provided script and saves 2025-08-26T20:21:54.1049976Z the profiling information to a file named `trace_name.prof`. 2025-08-26T20:21:54.1050606Z The `--profile-from-start off` option ensures that profiling starts only 2025-08-26T20:21:54.1051114Z after the `cudaProfilerStart` call in the script. 2025-08-26T20:21:54.1051605Z The `--csv` and `--print-summary` options format the profiling output as a 2025-08-26T20:21:54.1052091Z CSV file and print a summary, respectively. 2025-08-26T20:21:54.1052580Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-08-26T20:21:54.1053084Z overwrite of the output file if it already exists. 2025-08-26T20:21:54.1053432Z 2025-08-26T20:21:54.1054193Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2025-08-26T20:21:54.1054951Z 2025-08-26T20:21:54.1055306Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-08-26T20:21:54.1055854Z ^ 2025-08-26T20:21:54.1206515Z msg = Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2025-08-26T20:21:54.1207434Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:54.1207826Z 2025-08-26T20:21:54.1208054Z Append the given callback function to this ``Future``, which will be run 2025-08-26T20:21:54.1208608Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-08-26T20:21:54.1209140Z the same ``Future``, but the order in which they will be executed cannot 2025-08-26T20:21:54.1209643Z be guaranteed (to enforce a certain order consider chaining: 2025-08-26T20:21:54.1210147Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-08-26T20:21:54.1210680Z is the reference to this ``Future``. The callback function can use the 2025-08-26T20:21:54.1211216Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-08-26T20:21:54.1211758Z already completed, the given callback will be run immediately inline. 2025-08-26T20:21:54.1212108Z 2025-08-26T20:21:54.1212301Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-08-26T20:21:54.1212836Z callback might be invoked while the async kernels that are populating 2025-08-26T20:21:54.1213402Z those tensors haven't yet finished executing on the device. However, the 2025-08-26T20:21:54.1213956Z callback will be invoked with some dedicated streams set as current 2025-08-26T20:21:54.1214474Z (fetched from a global pool) which will be synchronized with those 2025-08-26T20:21:54.1215019Z kernels. Hence any operation performed by the callback on these tensors 2025-08-26T20:21:54.1215572Z will be scheduled on the device after the kernels complete. In other 2025-08-26T20:21:54.1216097Z words, as long as the callback doesn't switch streams, it can safely 2025-08-26T20:21:54.1216726Z manipulate the result without any additional synchronization. This is 2025-08-26T20:21:54.1217315Z similar to the non-blocking behavior of :meth:`wait`. 2025-08-26T20:21:54.1217597Z 2025-08-26T20:21:54.1217809Z Similarly, if the callback returns a value that contains tensors that 2025-08-26T20:21:54.1218553Z reside on a GPU, it can do so even if the kernels that are producing 2025-08-26T20:21:54.1219090Z these tensors are still running on the device, as long as the callback 2025-08-26T20:21:54.1219628Z didn't change streams during its execution. If one wants to change 2025-08-26T20:21:54.1220199Z streams, one must be careful to re-synchronize them with the original 2025-08-26T20:21:54.1220822Z streams, that is, those that were current when the callback was invoked. 2025-08-26T20:21:54.1221166Z 2025-08-26T20:21:54.1221250Z Args: 2025-08-26T20:21:54.1221580Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-08-26T20:21:54.1222021Z the only argument. 2025-08-26T20:21:54.1222238Z 2025-08-26T20:21:54.1222324Z Returns: 2025-08-26T20:21:54.1222713Z A new ``Future`` object that holds the return value of the 2025-08-26T20:21:54.1223188Z ``callback`` and will be marked as completed when the given 2025-08-26T20:21:54.1223593Z ``callback`` finishes. 2025-08-26T20:21:54.1223764Z 2025-08-26T20:21:54.1223963Z .. note:: Note that if the callback function throws, either 2025-08-26T20:21:54.1224457Z through the original future being completed with an exception and 2025-08-26T20:21:54.1224980Z calling ``fut.wait()``, or through other code in the callback, the 2025-08-26T20:21:54.1225493Z future returned by ``then`` will be marked appropriately with the 2025-08-26T20:21:54.1226013Z encountered error. However, if this callback later completes 2025-08-26T20:21:54.1226548Z additional futures, those futures are not marked as completed with 2025-08-26T20:21:54.1227080Z an error and the user is responsible for handling completion/waiting 2025-08-26T20:21:54.1227516Z on those futures independently. 2025-08-26T20:21:54.1227723Z 2025-08-26T20:21:54.1227826Z Example:: 2025-08-26T20:21:54.1227952Z 2025-08-26T20:21:54.1228109Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-08-26T20:21:54.1228462Z >>> def callback(fut): 2025-08-26T20:21:54.1228781Z ... print(f"RPC return value is {fut.wait()}.") 2025-08-26T20:21:54.1229147Z >>> fut = torch.futures.Future() 2025-08-26T20:21:54.1229533Z >>> # The inserted callback will print the return value when 2025-08-26T20:21:54.1229931Z >>> # receiving the response from "worker1" 2025-08-26T20:21:54.1230272Z >>> cb_fut = fut.then(callback) 2025-08-26T20:21:54.1230580Z >>> chain_cb_fut = cb_fut.then( 2025-08-26T20:21:54.1230932Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-08-26T20:21:54.1231267Z ... ) 2025-08-26T20:21:54.1231486Z >>> fut.set_result(5) 2025-08-26T20:21:54.1231755Z RPC return value is 5. 2025-08-26T20:21:54.1232028Z Chained cb done. None 2025-08-26T20:21:54.1232195Z 2025-08-26T20:21:54.1232452Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:54.1232829Z 2025-08-26T20:21:54.1233356Z msg = Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=211. 2025-08-26T20:21:54.1234264Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:54.1234652Z 2025-08-26T20:21:54.1234856Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-08-26T20:21:54.1235393Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-08-26T20:21:54.1235827Z cannot be marked completed twice. 2025-08-26T20:21:54.1236042Z 2025-08-26T20:21:54.1236259Z If the result contains tensors that reside on GPUs, this method can be 2025-08-26T20:21:54.1236799Z called even if the asynchronous kernels that are populating those 2025-08-26T20:21:54.1237333Z tensors haven't yet completed running on the device, provided that the 2025-08-26T20:21:54.1237960Z streams on which those kernels were enqueued are set as the current ones 2025-08-26T20:21:54.1238504Z when this method is called. Put simply, it's safe to call this method 2025-08-26T20:21:54.1239130Z immediately after launching those kernels, without any additional 2025-08-26T20:21:54.1239690Z synchronization, as long as one doesn't change streams in between. This 2025-08-26T20:21:54.1240254Z method will record events on all the relevant current streams and will 2025-08-26T20:21:54.1240793Z use them to ensure proper scheduling for all the consumers of this 2025-08-26T20:21:54.1241186Z ``Future``. 2025-08-26T20:21:54.1241324Z 2025-08-26T20:21:54.1241406Z Args: 2025-08-26T20:21:54.1241684Z result (object): the result object of this ``Future``. 2025-08-26T20:21:54.1241953Z 2025-08-26T20:21:54.1242060Z Example:: 2025-08-26T20:21:54.1242182Z 2025-08-26T20:21:54.1242327Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-08-26T20:21:54.1242687Z >>> import threading 2025-08-26T20:21:54.1242951Z >>> import time 2025-08-26T20:21:54.1243276Z >>> def slow_set_future(fut, value): 2025-08-26T20:21:54.1243585Z ... time.sleep(0.5) 2025-08-26T20:21:54.1243871Z ... fut.set_result(value) 2025-08-26T20:21:54.1244182Z >>> fut = torch.futures.Future() 2025-08-26T20:21:54.1244495Z >>> t = threading.Thread( 2025-08-26T20:21:54.1244774Z ... target=slow_set_future, 2025-08-26T20:21:54.1245087Z ... args=(fut, torch.ones(2) * 3) 2025-08-26T20:21:54.1245390Z ... ) 2025-08-26T20:21:54.1245598Z >>> t.start() 2025-08-26T20:21:54.1245824Z >>> print(fut.wait()) 2025-08-26T20:21:54.1246089Z tensor([3., 3.]) 2025-08-26T20:21:54.1246330Z >>> t.join() 2025-08-26T20:21:54.1246465Z 2025-08-26T20:21:54.1246731Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:54.1247096Z 2025-08-26T20:21:54.1330261Z msg = Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=145. 2025-08-26T20:21:54.1331172Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:54.1331784Z Compiles compute shader from source and allows one to invoke kernels 2025-08-26T20:21:54.1332273Z defined there from the comfort of Python runtime 2025-08-26T20:21:54.1332649Z Example:: 2025-08-26T20:21:54.1332781Z 2025-08-26T20:21:54.1332918Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-08-26T20:21:54.1333285Z >>> lib = torch.mps.compile_shader( 2025-08-26T20:21:54.1333913Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-08-26T20:21:54.1334500Z ... ) 2025-08-26T20:21:54.1334739Z >>> x = torch.zeros(16, device="mps") 2025-08-26T20:21:54.1335126Z >>> lib.full(x, 3.14) 2025-08-26T20:21:54.1335392Z 2025-08-26T20:21:54.1335757Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:54.1336126Z 2025-08-26T20:21:54.1508835Z msg = Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-08-26T20:21:54.1509704Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:54.1510253Z Return the sum of each row of the given sparse tensor. 2025-08-26T20:21:54.1510535Z 2025-08-26T20:21:54.1510760Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-08-26T20:21:54.1511303Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-08-26T20:21:54.1511826Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-08-26T20:21:54.1512292Z returns a dense tensor instead of a sparse tensor. 2025-08-26T20:21:54.1512559Z 2025-08-26T20:21:54.1512813Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-08-26T20:21:54.1513386Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-08-26T20:21:54.1513695Z 2025-08-26T20:21:54.1513925Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-08-26T20:21:54.1514777Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-08-26T20:21:54.1515132Z 2025-08-26T20:21:54.1515216Z Args: 2025-08-26T20:21:54.1515466Z input (Tensor): the input sparse tensor 2025-08-26T20:21:54.1515979Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-08-26T20:21:54.1516469Z over all dims. 2025-08-26T20:21:54.1516883Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-08-26T20:21:54.1517478Z Default: dtype of :attr:`input`. 2025-08-26T20:21:54.1517800Z 2025-08-26T20:21:54.1517929Z Example:: 2025-08-26T20:21:54.1518060Z 2025-08-26T20:21:54.1518159Z >>> nnz = 3 2025-08-26T20:21:54.1518452Z >>> dims = [5, 5, 2, 3] 2025-08-26T20:21:54.1518901Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-08-26T20:21:54.1519367Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-08-26T20:21:54.1519805Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-08-26T20:21:54.1520148Z >>> size = torch.Size(dims) 2025-08-26T20:21:54.1520533Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:21:54.1520914Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-08-26T20:21:54.1521268Z >>> S 2025-08-26T20:21:54.1521515Z tensor(indices=tensor([[2, 0, 3], 2025-08-26T20:21:54.1521842Z [2, 4, 1]]), 2025-08-26T20:21:54.1522188Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-08-26T20:21:54.1522551Z [ 0.3411, 0.0918, -0.2312]], 2025-08-26T20:21:54.1522787Z 2025-08-26T20:21:54.1522899Z [[ 0.5348, 0.0634, -2.0494], 2025-08-26T20:21:54.1523245Z [-0.7125, -1.0646, 2.1844]], 2025-08-26T20:21:54.1523469Z 2025-08-26T20:21:54.1523597Z [[ 0.1276, 0.1874, -0.6334], 2025-08-26T20:21:54.1523938Z [-1.9682, -0.5340, 0.7483]]]), 2025-08-26T20:21:54.1524315Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-08-26T20:21:54.1524569Z 2025-08-26T20:21:54.1524772Z # when sum over only part of sparse_dims, return a sparse tensor 2025-08-26T20:21:54.1525196Z >>> torch.sparse.sum(S, [1, 3]) 2025-08-26T20:21:54.1525515Z tensor(indices=tensor([[0, 2, 3]]), 2025-08-26T20:21:54.1525848Z values=tensor([[-1.4512, 0.4073], 2025-08-26T20:21:54.1526176Z [-0.8901, 0.2017], 2025-08-26T20:21:54.1526496Z [-0.3183, -1.7539]]), 2025-08-26T20:21:54.1526839Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-08-26T20:21:54.1527096Z 2025-08-26T20:21:54.1527250Z # when sum over all sparse dim, return a dense tensor 2025-08-26T20:21:54.1527622Z # with summed dims squeezed 2025-08-26T20:21:54.1527939Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-08-26T20:21:54.1528264Z tensor([-2.6596, -1.1450]) 2025-08-26T20:21:54.1528528Z 2025-08-26T20:21:54.1528895Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:54.1529260Z 2025-08-26T20:21:54.1529800Z msg = Cannot scrape callname=as_sparse_gradcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=550. 2025-08-26T20:21:54.1530707Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:54.1531291Z Decorate function, to extend gradcheck for sparse tensors. 2025-08-26T20:21:54.1531595Z 2025-08-26T20:21:54.1531805Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-08-26T20:21:54.1532376Z variants that extends the gradcheck function with support to input 2025-08-26T20:21:54.1532888Z functions that operate on or/and return sparse tensors. 2025-08-26T20:21:54.1533172Z 2025-08-26T20:21:54.1533393Z The specified gradcheck function itself is guaranteed to operate 2025-08-26T20:21:54.1533893Z on strided tensors only. 2025-08-26T20:21:54.1534089Z 2025-08-26T20:21:54.1534177Z For example: 2025-08-26T20:21:54.1534329Z 2025-08-26T20:21:54.1534565Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-08-26T20:21:54.1535010Z >>> x = ( 2025-08-26T20:21:54.1535289Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-08-26T20:21:54.1535661Z ... .to_sparse_coo() 2025-08-26T20:21:54.1535951Z ... .requires_grad_(True) 2025-08-26T20:21:54.1536238Z ... ) 2025-08-26T20:21:54.1536480Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-08-26T20:21:54.1536809Z True 2025-08-26T20:21:54.1537016Z 2025-08-26T20:21:54.1537380Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:54.1537809Z 2025-08-26T20:21:55.0307295Z msg = Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-08-26T20:21:55.0308288Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:55.0308669Z 2025-08-26T20:21:55.0308963Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-08-26T20:21:55.0309494Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-08-26T20:21:55.0310090Z pushes the map into PyTorch operations called by ``func``, effectively 2025-08-26T20:21:55.0310600Z vectorizing those operations. 2025-08-26T20:21:55.0310792Z 2025-08-26T20:21:55.0311018Z vmap is useful for handling batch dimensions: one can write a function 2025-08-26T20:21:55.0311609Z ``func`` that runs on examples and then lift it to a function that can 2025-08-26T20:21:55.0312186Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-08-26T20:21:55.0312750Z compute batched gradients when composed with autograd. 2025-08-26T20:21:55.0313044Z 2025-08-26T20:21:55.0313160Z .. note:: 2025-08-26T20:21:55.0313532Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-08-26T20:21:55.0313951Z convenience. Use whichever one you'd like. 2025-08-26T20:21:55.0314257Z 2025-08-26T20:21:55.0314342Z Args: 2025-08-26T20:21:55.0314674Z func (function): A Python function that takes one or more arguments. 2025-08-26T20:21:55.0315177Z Must return one or more Tensors. 2025-08-26T20:21:55.0315588Z in_dims (int or nested structure): Specifies which dimension of the 2025-08-26T20:21:55.0316168Z inputs should be mapped over. ``in_dims`` should have a 2025-08-26T20:21:55.0316696Z structure like the inputs. If the ``in_dim`` for a particular 2025-08-26T20:21:55.0317450Z input is None, then that indicates there is no map dimension. 2025-08-26T20:21:55.0318031Z Default: 0. 2025-08-26T20:21:55.0318515Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-08-26T20:21:55.0319395Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-08-26T20:21:55.0319939Z it should have one element per output. Default: 0. 2025-08-26T20:21:55.0320382Z randomness (str): Specifies whether the randomness in this 2025-08-26T20:21:55.0320898Z vmap should be the same or different across batches. If 'different', 2025-08-26T20:21:55.0321437Z the randomness for each batch will be different. If 'same', the 2025-08-26T20:21:55.0321975Z randomness will be the same across batches. If 'error', any calls to 2025-08-26T20:21:55.0322531Z random functions will error. Default: 'error'. WARNING: this flag 2025-08-26T20:21:55.0323056Z only applies to random PyTorch operations and does not apply to 2025-08-26T20:21:55.0323516Z Python's random module or numpy randomness. 2025-08-26T20:21:55.0324000Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-08-26T20:21:55.0324565Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-08-26T20:21:55.0325752Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-08-26T20:21:55.0326407Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-08-26T20:21:55.0326812Z 2025-08-26T20:21:55.0326902Z Returns: 2025-08-26T20:21:55.0327237Z Returns a new "batched" function. It takes the same inputs as 2025-08-26T20:21:55.0327724Z ``func``, except each input has an extra dimension at the index 2025-08-26T20:21:55.0328204Z specified by ``in_dims``. It takes returns the same outputs as 2025-08-26T20:21:55.0328693Z ``func``, except each output has an extra dimension at the index 2025-08-26T20:21:55.0329100Z specified by ``out_dims``. 2025-08-26T20:21:55.0329285Z 2025-08-26T20:21:55.0329382Z .. warning: 2025-08-26T20:21:55.0329800Z :func:`vmap` works best with functional-style code. Please do not 2025-08-26T20:21:55.0330305Z perform any side-effects in ``func``, with the exception of 2025-08-26T20:21:55.0330851Z in-place PyTorch operations. Examples of side-effects include mutating 2025-08-26T20:21:55.0331433Z Python data structures and assigning values to variables not captured 2025-08-26T20:21:55.0331871Z in ``func``. 2025-08-26T20:21:55.0332011Z 2025-08-26T20:21:55.0332243Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-08-26T20:21:55.0332817Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-08-26T20:21:55.0333375Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-08-26T20:21:55.0333705Z 2025-08-26T20:21:55.0333839Z >>> torch.dot # [D], [D] -> [] 2025-08-26T20:21:55.0334238Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-08-26T20:21:55.0334689Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-08-26T20:21:55.0335036Z >>> batched_dot(x, y) 2025-08-26T20:21:55.0335204Z 2025-08-26T20:21:55.0335441Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-08-26T20:21:55.0335896Z model authoring experience. 2025-08-26T20:21:55.0336081Z 2025-08-26T20:21:55.0336189Z >>> batch_size, feature_size = 3, 5 2025-08-26T20:21:55.0336581Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-08-26T20:21:55.0336957Z >>> 2025-08-26T20:21:55.0337227Z >>> def model(feature_vec): 2025-08-26T20:21:55.0337536Z >>> # Very simple linear model with activation 2025-08-26T20:21:55.0337907Z >>> return feature_vec.dot(weights).relu() 2025-08-26T20:21:55.0338230Z >>> 2025-08-26T20:21:55.0338501Z >>> examples = torch.randn(batch_size, feature_size) 2025-08-26T20:21:55.0338874Z >>> result = torch.vmap(model)(examples) 2025-08-26T20:21:55.0339112Z 2025-08-26T20:21:55.0339357Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-08-26T20:21:55.0339967Z or impossible to batch. One example is higher-order gradient computation. 2025-08-26T20:21:55.0340663Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-08-26T20:21:55.0341238Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-08-26T20:21:55.0341838Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-08-26T20:21:55.0342449Z we can vectorize the whole computation, computing the Jacobian in a single 2025-08-26T20:21:55.0342918Z call to ``autograd.grad``. 2025-08-26T20:21:55.0343100Z 2025-08-26T20:21:55.0343199Z >>> # Setup 2025-08-26T20:21:55.0343408Z >>> N = 5 2025-08-26T20:21:55.0343634Z >>> f = lambda x: x**2 2025-08-26T20:21:55.0343926Z >>> x = torch.randn(N, requires_grad=True) 2025-08-26T20:21:55.0344249Z >>> y = f(x) 2025-08-26T20:21:55.0344473Z >>> I_N = torch.eye(N) 2025-08-26T20:21:55.0344727Z >>> 2025-08-26T20:21:55.0344952Z >>> # Sequential approach 2025-08-26T20:21:55.0345364Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-08-26T20:21:55.0345802Z >>> for v in I_N.unbind()] 2025-08-26T20:21:55.0346249Z >>> jacobian = torch.stack(jacobian_rows) 2025-08-26T20:21:55.0346568Z >>> 2025-08-26T20:21:55.0346806Z >>> # vectorized gradient computation 2025-08-26T20:21:55.0347111Z >>> def get_vjp(v): 2025-08-26T20:21:55.0347396Z >>> return torch.autograd.grad(y, x, v) 2025-08-26T20:21:55.0347750Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-08-26T20:21:55.0347972Z 2025-08-26T20:21:55.0348245Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-08-26T20:21:55.0348624Z 2025-08-26T20:21:55.0348727Z >>> torch.dot # [D], [D] -> [] 2025-08-26T20:21:55.0349040Z >>> batched_dot = torch.vmap( 2025-08-26T20:21:55.0349347Z ... torch.vmap(torch.dot) 2025-08-26T20:21:55.0349665Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-08-26T20:21:55.0350090Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-08-26T20:21:55.0350470Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-08-26T20:21:55.0350722Z 2025-08-26T20:21:55.0350960Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-08-26T20:21:55.0351479Z the dimension that each inputs are batched along as 2025-08-26T20:21:55.0351740Z 2025-08-26T20:21:55.0351857Z >>> torch.dot # [N], [N] -> [] 2025-08-26T20:21:55.0352269Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-08-26T20:21:55.0352737Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-08-26T20:21:55.0353069Z >>> batched_dot( 2025-08-26T20:21:55.0353306Z ... x, y 2025-08-26T20:21:55.0353624Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-08-26T20:21:55.0353940Z 2025-08-26T20:21:55.0354196Z If there are multiple inputs each of which is batched along different dimensions, 2025-08-26T20:21:55.0354781Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-08-26T20:21:55.0355094Z 2025-08-26T20:21:55.0355208Z >>> torch.dot # [D], [D] -> [] 2025-08-26T20:21:55.0355653Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-08-26T20:21:55.0356116Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-08-26T20:21:55.0356444Z >>> batched_dot( 2025-08-26T20:21:55.0356688Z ... x, y 2025-08-26T20:21:55.0357029Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-08-26T20:21:55.0357335Z 2025-08-26T20:21:55.0357570Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-08-26T20:21:55.0358044Z matching the shape of the input: 2025-08-26T20:21:55.0358259Z 2025-08-26T20:21:55.0358400Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-08-26T20:21:55.0358779Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-08-26T20:21:55.0359121Z >>> input = {"x": x, "y": y} 2025-08-26T20:21:55.0359486Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-08-26T20:21:55.0359885Z >>> batched_dot(input) 2025-08-26T20:21:55.0360063Z 2025-08-26T20:21:55.0360349Z By default, the output is batched along the first dimension. However, it can be batched 2025-08-26T20:21:55.0360873Z along any dimension by using ``out_dims`` 2025-08-26T20:21:55.0361100Z 2025-08-26T20:21:55.0361196Z >>> f = lambda x: x**2 2025-08-26T20:21:55.0361472Z >>> x = torch.randn(2, 5) 2025-08-26T20:21:55.0361788Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-08-26T20:21:55.0362128Z >>> batched_pow(x) # [5, 2] 2025-08-26T20:21:55.0362314Z 2025-08-26T20:21:55.0362613Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-08-26T20:21:55.0363102Z accept kwargs 2025-08-26T20:21:55.0363248Z 2025-08-26T20:21:55.0363350Z >>> x = torch.randn([2, 5]) 2025-08-26T20:21:55.0363640Z >>> def fn(x, scale=4.): 2025-08-26T20:21:55.0363918Z >>> return x * scale 2025-08-26T20:21:55.0364163Z >>> 2025-08-26T20:21:55.0364392Z >>> batched_pow = torch.vmap(fn) 2025-08-26T20:21:55.0364824Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-08-26T20:21:55.0365302Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-08-26T20:21:55.0365640Z 2025-08-26T20:21:55.0365750Z .. note:: 2025-08-26T20:21:55.0366097Z vmap does not provide general autobatching or handle variable-length 2025-08-26T20:21:55.0366537Z sequences out of the box. 2025-08-26T20:21:55.0366721Z 2025-08-26T20:21:55.0366984Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:55.0367351Z 2025-08-26T20:21:55.0367836Z msg = Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-08-26T20:21:55.0368678Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:55.0369360Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-08-26T20:21:55.0369920Z input(s) specified by ``argnums``. This operator can be nested to 2025-08-26T20:21:55.0370359Z compute higher-order gradients. 2025-08-26T20:21:55.0370570Z 2025-08-26T20:21:55.0370652Z Args: 2025-08-26T20:21:55.0370989Z func (Callable): A Python function that takes one or more arguments. 2025-08-26T20:21:55.0371582Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-08-26T20:21:55.0372249Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-08-26T20:21:55.0372736Z ``(output, aux)``. 2025-08-26T20:21:55.0373187Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-08-26T20:21:55.0373788Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-08-26T20:21:55.0374330Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-08-26T20:21:55.0374837Z auxiliary objects: ``(output, aux)``. Default: False. 2025-08-26T20:21:55.0375114Z 2025-08-26T20:21:55.0375199Z Returns: 2025-08-26T20:21:55.0375615Z Function to compute gradients with respect to its inputs. By default, the output of 2025-08-26T20:21:55.0376255Z the function is the gradient tensor(s) with respect to the first argument. 2025-08-26T20:21:55.0376881Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-08-26T20:21:55.0377528Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-08-26T20:21:55.0378034Z respect to each ``argnums`` value is returned. 2025-08-26T20:21:55.0378296Z 2025-08-26T20:21:55.0378398Z Example of using ``grad``: 2025-08-26T20:21:55.0378594Z 2025-08-26T20:21:55.0378692Z >>> # xdoctest: +SKIP 2025-08-26T20:21:55.0378994Z >>> from torch.func import grad 2025-08-26T20:21:55.0379304Z >>> x = torch.randn([]) 2025-08-26T20:21:55.0379621Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-08-26T20:21:55.0379988Z >>> assert torch.allclose(cos_x, x.cos()) 2025-08-26T20:21:55.0380312Z >>> 2025-08-26T20:21:55.0380617Z >>> # Second-order gradients 2025-08-26T20:21:55.0380980Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-08-26T20:21:55.0381393Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-08-26T20:21:55.0381694Z 2025-08-26T20:21:55.0381957Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-08-26T20:21:55.0382325Z 2025-08-26T20:21:55.0382436Z >>> # xdoctest: +SKIP 2025-08-26T20:21:55.0382729Z >>> from torch.func import grad, vmap 2025-08-26T20:21:55.0383070Z >>> batch_size, feature_size = 3, 5 2025-08-26T20:21:55.0383375Z >>> 2025-08-26T20:21:55.0383614Z >>> def model(weights, feature_vec): 2025-08-26T20:21:55.0383966Z >>> # Very simple linear model with activation 2025-08-26T20:21:55.0384329Z >>> assert feature_vec.dim() == 1 2025-08-26T20:21:55.0384791Z >>> return feature_vec.dot(weights).relu() 2025-08-26T20:21:55.0385118Z >>> 2025-08-26T20:21:55.0385366Z >>> def compute_loss(weights, example, target): 2025-08-26T20:21:55.0385727Z >>> y = model(weights, example) 2025-08-26T20:21:55.0386084Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-08-26T20:21:55.0386418Z >>> 2025-08-26T20:21:55.0386707Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-08-26T20:21:55.0387149Z >>> examples = torch.randn(batch_size, feature_size) 2025-08-26T20:21:55.0387534Z >>> targets = torch.randn(batch_size) 2025-08-26T20:21:55.0387890Z >>> inputs = (weights, examples, targets) 2025-08-26T20:21:55.0388359Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-08-26T20:21:55.0388793Z ... *inputs 2025-08-26T20:21:55.0389095Z ... ) 2025-08-26T20:21:55.0389261Z 2025-08-26T20:21:55.0389452Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-08-26T20:21:55.0389749Z 2025-08-26T20:21:55.0389861Z >>> # xdoctest: +SKIP 2025-08-26T20:21:55.0390148Z >>> from torch.func import grad 2025-08-26T20:21:55.0390479Z >>> def my_loss_func(y, y_pred): 2025-08-26T20:21:55.0390830Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-08-26T20:21:55.0391190Z >>> loss = loss_per_sample.mean() 2025-08-26T20:21:55.0391532Z >>> return loss, (y_pred, loss_per_sample) 2025-08-26T20:21:55.0392132Z >>> 2025-08-26T20:21:55.0392419Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-08-26T20:21:55.0392790Z >>> y_true = torch.rand(4) 2025-08-26T20:21:55.0393116Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-08-26T20:21:55.0393479Z >>> out = fn(y_true, y_preds) 2025-08-26T20:21:55.0393953Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-08-26T20:21:55.0394319Z 2025-08-26T20:21:55.0394429Z .. note:: 2025-08-26T20:21:55.0394740Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-08-26T20:21:55.0395027Z 2025-08-26T20:21:55.0395175Z Case 1: Using ``torch.no_grad`` inside a function: 2025-08-26T20:21:55.0395445Z 2025-08-26T20:21:55.0395548Z >>> # xdoctest: +SKIP 2025-08-26T20:21:55.0395842Z >>> def f(x): 2025-08-26T20:21:55.0396130Z >>> with torch.no_grad(): 2025-08-26T20:21:55.0396435Z >>> c = x ** 2 2025-08-26T20:21:55.0396732Z >>> return x - c 2025-08-26T20:21:55.0396933Z 2025-08-26T20:21:55.0397130Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-08-26T20:21:55.0397440Z 2025-08-26T20:21:55.0397639Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-08-26T20:21:55.0397939Z 2025-08-26T20:21:55.0398057Z >>> # xdoctest: +SKIP 2025-08-26T20:21:55.0398354Z >>> with torch.no_grad(): 2025-08-26T20:21:55.0398661Z >>> grad(f)(x) 2025-08-26T20:21:55.0398853Z 2025-08-26T20:21:55.0399074Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-08-26T20:21:55.0399638Z outer one. This is because ``grad`` is a "function transform": its result 2025-08-26T20:21:55.0400187Z should not depend on the result of a context manager outside of ``f``. 2025-08-26T20:21:55.0400534Z 2025-08-26T20:21:55.0400613Z 2025-08-26T20:21:55.0400981Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:55.0401344Z 2025-08-26T20:21:56.7702158Z msg = Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py line=397. 2025-08-26T20:21:56.7703185Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:21:56.7703730Z Register a FakeTensor implementation for this custom op. 2025-08-26T20:21:56.7704303Z 2025-08-26T20:21:56.7704549Z This is necessary to get the operator to work efficiently with torch.compile. 2025-08-26T20:21:56.7704929Z 2025-08-26T20:21:56.7705146Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-08-26T20:21:56.7705713Z specifies the behavior of this operator on Tensors that carry no data. 2025-08-26T20:21:56.7706217Z Given some input Tensors with certain properties 2025-08-26T20:21:56.7706728Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-08-26T20:21:56.7707210Z the output Tensors are. 2025-08-26T20:21:56.7707415Z 2025-08-26T20:21:56.7707621Z Please see :func:`torch.library.register_fake` for more details. 2025-08-26T20:21:56.7707937Z 2025-08-26T20:21:56.7708033Z Args: 2025-08-26T20:21:56.7708443Z fn (Callable): The function to register as the FakeTensor 2025-08-26T20:21:56.7708836Z implementation. 2025-08-26T20:21:56.7709036Z 2025-08-26T20:21:56.7709129Z Examples: 2025-08-26T20:21:56.7709429Z >>> import torch 2025-08-26T20:21:56.7709712Z >>> import numpy as np 2025-08-26T20:21:56.7710011Z >>> from torch import Tensor 2025-08-26T20:21:56.7710312Z >>> 2025-08-26T20:21:56.7710640Z >>> # Example 1: an operator without data-dependent output shape 2025-08-26T20:21:56.7711170Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-08-26T20:21:56.7711667Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-08-26T20:21:56.7712103Z >>> return (x @ weight.t()) + bias 2025-08-26T20:21:56.7712423Z >>> 2025-08-26T20:21:56.7712672Z >>> @linear.register_fake 2025-08-26T20:21:56.7712991Z >>> def _(x, weight, bias): 2025-08-26T20:21:56.7713300Z >>> assert x.dim() == 2 2025-08-26T20:21:56.7713626Z >>> assert weight.dim() == 2 2025-08-26T20:21:56.7713963Z >>> assert bias.dim() == 1 2025-08-26T20:21:56.7714313Z >>> assert x.shape[1] == weight.shape[1] 2025-08-26T20:21:56.7714676Z >>> assert weight.shape[0] == bias.shape[0] 2025-08-26T20:21:56.7715048Z >>> assert x.device == weight.device 2025-08-26T20:21:56.7715435Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-08-26T20:21:56.7715783Z >>> 2025-08-26T20:21:56.7716013Z >>> x = torch.randn(2, 2) 2025-08-26T20:21:56.7716333Z >>> weight = torch.randn(2, 2) 2025-08-26T20:21:56.7716659Z >>> bias = torch.randn(2) 2025-08-26T20:21:56.7717005Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-26T20:21:56.7717444Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-08-26T20:21:56.7717901Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-26T20:21:56.7718402Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-08-26T20:21:56.7718868Z >>> 2025-08-26T20:21:56.7719168Z >>> # Example 2: an operator with data-dependent output shape 2025-08-26T20:21:56.7719669Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-08-26T20:21:56.7720103Z >>> def nonzero(x: Tensor) -> Tensor: 2025-08-26T20:21:56.7720442Z >>> x_np = x.cpu().numpy() 2025-08-26T20:21:56.7720793Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-08-26T20:21:56.7721169Z >>> return torch.tensor(res, device=x.device) 2025-08-26T20:21:56.7721506Z >>> 2025-08-26T20:21:56.7721753Z >>> @nonzero.register_fake 2025-08-26T20:21:56.7722059Z >>> def _(x): 2025-08-26T20:21:56.7722365Z >>> # Number of nonzero-elements is data-dependent. 2025-08-26T20:21:56.7722804Z >>> # Since we cannot peek at the data in an abstract impl, 2025-08-26T20:21:56.7723248Z >>> # we use the ctx object to construct a new symint that 2025-08-26T20:21:56.7723746Z >>> # represents the data-dependent size. 2025-08-26T20:21:56.7724100Z >>> ctx = torch.library.get_ctx() 2025-08-26T20:21:56.7724456Z >>> nnz = ctx.new_dynamic_size() 2025-08-26T20:21:56.7724797Z >>> shape = [nnz, x.dim()] 2025-08-26T20:21:56.7725167Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-08-26T20:21:56.7725519Z >>> return result 2025-08-26T20:21:56.7725799Z >>> 2025-08-26T20:21:56.7726054Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-08-26T20:21:56.7726423Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-26T20:21:56.7726815Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-08-26T20:21:56.7727280Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-26T20:21:56.7727668Z >>> assert torch.allclose(out, x.nonzero()) 2025-08-26T20:21:56.7727910Z 2025-08-26T20:21:56.7728005Z 2025-08-26T20:21:56.7728653Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-08-26T20:21:56.7729289Z 2025-08-26T20:21:56.7729372Z _._ = None 2025-08-26T20:21:56.7729579Z ^ 2025-08-26T20:21:56.7825671Z msg = Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_profile.py line=94. 2025-08-26T20:21:56.7826671Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.7827051Z 2025-08-26T20:21:56.7827294Z Registers a fake kernel based on the given operator profiles. This fake 2025-08-26T20:21:56.7827874Z kernel registration will override any existing fake kernel registrations. 2025-08-26T20:21:56.7828246Z 2025-08-26T20:21:56.7828466Z The input is a dictionary mapping operator names to a set of operator 2025-08-26T20:21:56.7829038Z profiles, which we will use to generate fake kernels. The operator profiles 2025-08-26T20:21:56.7829595Z are a record of the input and output tensor metadata. Based on this 2025-08-26T20:21:56.7830155Z information we will match a given input to the recorded profile, and return 2025-08-26T20:21:56.7830726Z an output with the same metadata as in the recorded profile. If a profile 2025-08-26T20:21:56.7831211Z doesn't exist then an exception will be thrown. 2025-08-26T20:21:56.7831470Z 2025-08-26T20:21:56.7831700Z The fake kernel generation is considered unsafe because it relies on the 2025-08-26T20:21:56.7832280Z rigid, pre-defined operator profiles that do not account for potential 2025-08-26T20:21:56.7832874Z variations in output behavior. Specifically, the generated kernels assume a 2025-08-26T20:21:56.7833490Z fixed relationship between input and output ranks. However, in reality, it's 2025-08-26T20:21:56.7834113Z possible that data-dependent operations may produce outputs of different 2025-08-26T20:21:56.7834696Z ranks even when given inputs of the same rank. The generated fake kernels 2025-08-26T20:21:56.7835258Z are inflexible and unable to accommodate these nuances, making them 2025-08-26T20:21:56.7835679Z potentially unsafe. 2025-08-26T20:21:56.7835841Z 2025-08-26T20:21:56.7835921Z Args: 2025-08-26T20:21:56.7836256Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-08-26T20:21:56.7836808Z name to a set of operator profiles from which we will generate fake 2025-08-26T20:21:56.7837216Z kernels. 2025-08-26T20:21:56.7837350Z 2025-08-26T20:21:56.7837435Z Examples: 2025-08-26T20:21:56.7837565Z 2025-08-26T20:21:56.7837733Z >>> # Example: Registering an op-profile from draft-export 2025-08-26T20:21:56.7838108Z >>> import torch 2025-08-26T20:21:56.7838426Z >>> from torch.export._draft_export import draft_export 2025-08-26T20:21:56.7838778Z >>> 2025-08-26T20:21:56.7839091Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-08-26T20:21:56.7839692Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-08-26T20:21:56.7840029Z >>> return x + y 2025-08-26T20:21:56.7840268Z >>> 2025-08-26T20:21:56.7840499Z >>> class M(torch.nn.Module): 2025-08-26T20:21:56.7840808Z >>> def forward(self, a, b): 2025-08-26T20:21:56.7841169Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-08-26T20:21:56.7841513Z >>> return res 2025-08-26T20:21:56.7841768Z >>> 2025-08-26T20:21:56.7842055Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-08-26T20:21:56.7842425Z >>> 2025-08-26T20:21:56.7842824Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-08-26T20:21:56.7843365Z >>> decomp = ep.run_decompositions() 2025-08-26T20:21:56.7843601Z 2025-08-26T20:21:56.7843605Z 2025-08-26T20:21:56.7843969Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.7844354Z 2025-08-26T20:21:56.7885239Z msg = Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=96. 2025-08-26T20:21:56.7886239Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.7886875Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-08-26T20:21:56.7887242Z 2025-08-26T20:21:56.7887449Z This is a more structured way of using triton kernels with PyTorch. 2025-08-26T20:21:56.7888033Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-08-26T20:21:56.7888667Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-08-26T20:21:56.7889139Z that is simpler; 2025-08-26T20:21:56.7889554Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-08-26T20:21:56.7890159Z want to create an operator that behaves like PyTorch built-in operators. 2025-08-26T20:21:56.7890719Z For example, you may use a ``torch.library`` wrapper API to define the 2025-08-26T20:21:56.7891271Z behavior of the triton kernel when passed a tensor subclass or under 2025-08-26T20:21:56.7891891Z a TorchDispatchMode. 2025-08-26T20:21:56.7892168Z 2025-08-26T20:21:56.7892461Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-08-26T20:21:56.7893217Z when the implementation 2025-08-26T20:21:56.7893921Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-08-26T20:21:56.7894430Z custom operators as opaque (:func:`torch.compile` and 2025-08-26T20:21:56.7894979Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-08-26T20:21:56.7895559Z makes the implementation visible to these subsystems, allowing them 2025-08-26T20:21:56.7896021Z to optimize the triton kernel(s). 2025-08-26T20:21:56.7896244Z 2025-08-26T20:21:56.7896431Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-08-26T20:21:56.7896970Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-08-26T20:21:56.7897511Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-08-26T20:21:56.7897816Z 2025-08-26T20:21:56.7897912Z Args: 2025-08-26T20:21:56.7898278Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-08-26T20:21:56.7898821Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-08-26T20:21:56.7899322Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-08-26T20:21:56.7899841Z To avoid name collisions, please use your project name as the namespace; 2025-08-26T20:21:56.7900499Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-08-26T20:21:56.7901110Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-08-26T20:21:56.7901747Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-08-26T20:21:56.7902540Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-08-26T20:21:56.7903116Z schema (None | str): A schema string for the operator. If None 2025-08-26T20:21:56.7903633Z (recommended) we'll infer a schema for the operator from its type 2025-08-26T20:21:56.7904154Z annotations. We recommend letting us infer a schema unless you 2025-08-26T20:21:56.7904594Z have a specific reason not to. 2025-08-26T20:21:56.7904976Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-08-26T20:21:56.7905265Z 2025-08-26T20:21:56.7905368Z Example:: 2025-08-26T20:21:56.7905497Z 2025-08-26T20:21:56.7905644Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:21:56.7905977Z >>> import torch 2025-08-26T20:21:56.7906375Z >>> from torch.library import triton_op, wrap_triton 2025-08-26T20:21:56.7906728Z >>> 2025-08-26T20:21:56.7906954Z >>> import triton 2025-08-26T20:21:56.7907258Z >>> from triton import language as tl 2025-08-26T20:21:56.7907566Z >>> 2025-08-26T20:21:56.7907791Z >>> @triton.jit 2025-08-26T20:21:56.7908053Z >>> def add_kernel( 2025-08-26T20:21:56.7908323Z >>> in_ptr0, 2025-08-26T20:21:56.7908567Z >>> in_ptr1, 2025-08-26T20:21:56.7908824Z >>> out_ptr, 2025-08-26T20:21:56.7909084Z >>> n_elements, 2025-08-26T20:21:56.7909374Z >>> BLOCK_SIZE: "tl.constexpr", 2025-08-26T20:21:56.7909678Z >>> ): 2025-08-26T20:21:56.7909927Z >>> pid = tl.program_id(axis=0) 2025-08-26T20:21:56.7910272Z >>> block_start = pid * BLOCK_SIZE 2025-08-26T20:21:56.7910663Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-08-26T20:21:56.7911031Z >>> mask = offsets < n_elements 2025-08-26T20:21:56.7911391Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-08-26T20:21:56.7911762Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-08-26T20:21:56.7912113Z >>> output = x + y 2025-08-26T20:21:56.7912429Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-08-26T20:21:56.7912777Z >>> 2025-08-26T20:21:56.7913164Z >>> @triton_op("mylib::add", mutates_args={}) 2025-08-26T20:21:56.7913597Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-08-26T20:21:56.7914088Z >>> output = torch.empty_like(x) 2025-08-26T20:21:56.7914427Z >>> n_elements = output.numel() 2025-08-26T20:21:56.7914811Z >>> 2025-08-26T20:21:56.7915033Z >>> def grid(meta): 2025-08-26T20:21:56.7915407Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-08-26T20:21:56.7915824Z >>> 2025-08-26T20:21:56.7916131Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-08-26T20:21:56.7916696Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-08-26T20:21:56.7917174Z >>> return output 2025-08-26T20:21:56.7917432Z >>> 2025-08-26T20:21:56.7917652Z >>> @torch.compile 2025-08-26T20:21:56.7917986Z >>> def f(x, y): 2025-08-26T20:21:56.7918239Z >>> return add(x, y) 2025-08-26T20:21:56.7918514Z >>> 2025-08-26T20:21:56.7918824Z >>> x = torch.randn(3, device="cuda") 2025-08-26T20:21:56.7919173Z >>> y = torch.randn(3, device="cuda") 2025-08-26T20:21:56.7919537Z >>> 2025-08-26T20:21:56.7919754Z >>> z = f(x, y) 2025-08-26T20:21:56.7920030Z >>> assert torch.allclose(z, x + y) 2025-08-26T20:21:56.7920320Z 2025-08-26T20:21:56.7920413Z 2025-08-26T20:21:56.7920771Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.7921230Z 2025-08-26T20:21:56.7921744Z msg = Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=296. 2025-08-26T20:21:56.7922765Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.7923385Z Allows capture of a triton kernel into a graph via make_fx or 2025-08-26T20:21:56.7923803Z non-strict ``torch.export``. 2025-08-26T20:21:56.7924000Z 2025-08-26T20:21:56.7924244Z These technologies perform Dispatcher-based tracing (via 2025-08-26T20:21:56.7924757Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-08-26T20:21:56.7925270Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-08-26T20:21:56.7925704Z can actually be traced into a graph. 2025-08-26T20:21:56.7925923Z 2025-08-26T20:21:56.7926146Z Please use this API together with :func:`torch.library.triton_op`. 2025-08-26T20:21:56.7926548Z 2025-08-26T20:21:56.7926637Z Examples: 2025-08-26T20:21:56.7926780Z 2025-08-26T20:21:56.7926938Z >>> # xdoctest: +SKIP 2025-08-26T20:21:56.7927221Z >>> import torch 2025-08-26T20:21:56.7927484Z >>> import triton 2025-08-26T20:21:56.7927763Z >>> from triton import language as tl 2025-08-26T20:21:56.7928173Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-08-26T20:21:56.7928600Z >>> from torch.library import wrap_triton 2025-08-26T20:21:56.7928928Z >>> 2025-08-26T20:21:56.7929131Z >>> @triton.jit 2025-08-26T20:21:56.7929387Z >>> def add_kernel( 2025-08-26T20:21:56.7929650Z >>> in_ptr0, 2025-08-26T20:21:56.7929903Z >>> in_ptr1, 2025-08-26T20:21:56.7930173Z >>> out_ptr, 2025-08-26T20:21:56.7930463Z >>> n_elements, 2025-08-26T20:21:56.7930748Z >>> BLOCK_SIZE: "tl.constexpr", 2025-08-26T20:21:56.7931056Z >>> ): 2025-08-26T20:21:56.7931287Z >>> pid = tl.program_id(axis=0) 2025-08-26T20:21:56.7931621Z >>> block_start = pid * BLOCK_SIZE 2025-08-26T20:21:56.7932001Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-08-26T20:21:56.7932378Z >>> mask = offsets < n_elements 2025-08-26T20:21:56.7932716Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-08-26T20:21:56.7933085Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-08-26T20:21:56.7933426Z >>> output = x + y 2025-08-26T20:21:56.7933752Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-08-26T20:21:56.7934098Z >>> 2025-08-26T20:21:56.7934333Z >>> def add(x, y): 2025-08-26T20:21:56.7934618Z >>> output = torch.empty_like(x) 2025-08-26T20:21:56.7934955Z >>> n_elements = output.numel() 2025-08-26T20:21:56.7935262Z >>> 2025-08-26T20:21:56.7935473Z >>> def grid_fn(meta): 2025-08-26T20:21:56.7935844Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-08-26T20:21:56.7936222Z >>> 2025-08-26T20:21:56.7936546Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-08-26T20:21:56.7936951Z >>> return output 2025-08-26T20:21:56.7937295Z >>> 2025-08-26T20:21:56.7937535Z >>> x = torch.randn(3, device="cuda") 2025-08-26T20:21:56.7937874Z >>> y = torch.randn(3, device="cuda") 2025-08-26T20:21:56.7938188Z >>> gm = make_fx(add)(x, y) 2025-08-26T20:21:56.7938486Z >>> print(gm.code) 2025-08-26T20:21:56.7938769Z >>> # def forward(self, x_1, y_1): 2025-08-26T20:21:56.7939221Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-08-26T20:21:56.7939807Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-08-26T20:21:56.7940301Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-08-26T20:21:56.7940738Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-08-26T20:21:56.7948230Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-08-26T20:21:56.7948676Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-08-26T20:21:56.7949124Z >>> # }) 2025-08-26T20:21:56.7949385Z >>> # return empty_like 2025-08-26T20:21:56.7949602Z 2025-08-26T20:21:56.7949685Z 2025-08-26T20:21:56.7950064Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.7950433Z 2025-08-26T20:21:56.8648337Z msg = Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=286. 2025-08-26T20:21:56.8649292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.8649730Z 2025-08-26T20:21:56.8650067Z Test if two objects are equal, and print an error message if test fails. 2025-08-26T20:21:56.8650568Z 2025-08-26T20:21:56.8650803Z The test is performed with ``actual == desired``. 2025-08-26T20:21:56.8651259Z 2025-08-26T20:21:56.8651441Z Parameters 2025-08-26T20:21:56.8652169Z ---------- 2025-08-26T20:21:56.8652408Z test_string : str 2025-08-26T20:21:56.8652674Z The message supplied to AssertionError. 2025-08-26T20:21:56.8653013Z actual : object 2025-08-26T20:21:56.8653309Z The object to test for equality against `desired`. 2025-08-26T20:21:56.8653774Z desired : object 2025-08-26T20:21:56.8654151Z The expected result. 2025-08-26T20:21:56.8654383Z 2025-08-26T20:21:56.8654477Z Examples 2025-08-26T20:21:56.8654842Z -------- 2025-08-26T20:21:56.8655306Z >>> np.testing.print_assert_equal( 2025-08-26T20:21:56.8655723Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-08-26T20:21:56.8656038Z ... ) # doctest: +SKIP 2025-08-26T20:21:56.8656319Z >>> np.testing.print_assert_equal( 2025-08-26T20:21:56.8656648Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-08-26T20:21:56.8656979Z ... ) # doctest: +SKIP 2025-08-26T20:21:56.8657244Z Traceback (most recent call last): 2025-08-26T20:21:56.8657539Z ... 2025-08-26T20:21:56.8657790Z AssertionError: Test XYZ of func xyz failed 2025-08-26T20:21:56.8658119Z ACTUAL: 2025-08-26T20:21:56.8658308Z [0, 1] 2025-08-26T20:21:56.8658521Z DESIRED: 2025-08-26T20:21:56.8658818Z [0, 2] 2025-08-26T20:21:56.8659000Z 2025-08-26T20:21:56.8659006Z 2025-08-26T20:21:56.8659441Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.8659993Z 2025-08-26T20:21:56.8660748Z msg = Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=331. 2025-08-26T20:21:56.8661690Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.8662067Z 2025-08-26T20:21:56.8662287Z Raises an AssertionError if two items are not equal up to desired 2025-08-26T20:21:56.8662700Z precision. 2025-08-26T20:21:56.8662827Z 2025-08-26T20:21:56.8663012Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-26T20:21:56.8663482Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-26T20:21:56.8664065Z instead of this function for more consistent floating point 2025-08-26T20:21:56.8664620Z comparisons. 2025-08-26T20:21:56.8664782Z 2025-08-26T20:21:56.8665039Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-08-26T20:21:56.8665384Z 2025-08-26T20:21:56.8665545Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-08-26T20:21:56.8665827Z 2025-08-26T20:21:56.8666053Z That is a looser test than originally documented, but agrees with what the 2025-08-26T20:21:56.8666634Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-08-26T20:21:56.8667217Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-08-26T20:21:56.8667679Z delegates to assert_array_almost_equal 2025-08-26T20:21:56.8667909Z 2025-08-26T20:21:56.8667996Z Parameters 2025-08-26T20:21:56.8668211Z ---------- 2025-08-26T20:21:56.8668431Z actual : array_like 2025-08-26T20:21:56.8668665Z The object to check. 2025-08-26T20:21:56.8668935Z desired : array_like 2025-08-26T20:21:56.8669192Z The expected object. 2025-08-26T20:21:56.8669617Z decimal : int, optional 2025-08-26T20:21:56.8669885Z Desired precision, default is 7. 2025-08-26T20:21:56.8670205Z err_msg : str, optional 2025-08-26T20:21:56.8670527Z The error message to be printed in case of failure. 2025-08-26T20:21:56.8670899Z verbose : bool, optional 2025-08-26T20:21:56.8671267Z If True, the conflicting values are appended to the error message. 2025-08-26T20:21:56.8671595Z 2025-08-26T20:21:56.8671705Z Raises 2025-08-26T20:21:56.8671906Z ------ 2025-08-26T20:21:56.8672118Z AssertionError 2025-08-26T20:21:56.8672439Z If actual and desired are not equal up to specified precision. 2025-08-26T20:21:56.8672756Z 2025-08-26T20:21:56.8672842Z See Also 2025-08-26T20:21:56.8673046Z -------- 2025-08-26T20:21:56.8673404Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-26T20:21:56.8673940Z relative and/or absolute precision. 2025-08-26T20:21:56.8674394Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-26T20:21:56.8674730Z 2025-08-26T20:21:56.8674815Z Examples 2025-08-26T20:21:56.8675018Z -------- 2025-08-26T20:21:56.8675305Z >>> from torch._numpy.testing import assert_almost_equal 2025-08-26T20:21:56.8675702Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-08-26T20:21:56.8676124Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-08-26T20:21:56.8676525Z Traceback (most recent call last): 2025-08-26T20:21:56.8676817Z ... 2025-08-26T20:21:56.8677014Z AssertionError: 2025-08-26T20:21:56.8677280Z Arrays are not almost equal to 10 decimals 2025-08-26T20:21:56.8677602Z ACTUAL: 2.3333333333333 2025-08-26T20:21:56.8677856Z DESIRED: 2.33333334 2025-08-26T20:21:56.8678004Z 2025-08-26T20:21:56.8678099Z >>> assert_almost_equal( 2025-08-26T20:21:56.8678465Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-08-26T20:21:56.8678869Z ... ) 2025-08-26T20:21:56.8679099Z Traceback (most recent call last): 2025-08-26T20:21:56.8679380Z ... 2025-08-26T20:21:56.8679596Z AssertionError: 2025-08-26T20:21:56.8679866Z Arrays are not almost equal to 9 decimals 2025-08-26T20:21:56.8680192Z 2025-08-26T20:21:56.8680415Z Mismatched elements: 1 / 2 (50%) 2025-08-26T20:21:56.8680740Z Max absolute difference: 6.666699636781459e-09 2025-08-26T20:21:56.8681113Z Max relative difference: 2.8571569790287484e-09 2025-08-26T20:21:56.8681493Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-08-26T20:21:56.8681865Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-08-26T20:21:56.8682118Z 2025-08-26T20:21:56.8682122Z 2025-08-26T20:21:56.8682372Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.8682748Z 2025-08-26T20:21:56.8683306Z msg = Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=457. 2025-08-26T20:21:56.8684243Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.8684625Z 2025-08-26T20:21:56.8684858Z Raises an AssertionError if two items are not equal up to significant 2025-08-26T20:21:56.8685271Z digits. 2025-08-26T20:21:56.8685398Z 2025-08-26T20:21:56.8685572Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-26T20:21:56.8686038Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-26T20:21:56.8686525Z instead of this function for more consistent floating point 2025-08-26T20:21:56.8686915Z comparisons. 2025-08-26T20:21:56.8687083Z 2025-08-26T20:21:56.8687265Z Given two numbers, check that they are approximately equal. 2025-08-26T20:21:56.8687782Z Approximately equal is defined as the number of significant digits 2025-08-26T20:21:56.8688204Z that agree. 2025-08-26T20:21:56.8688327Z 2025-08-26T20:21:56.8688427Z Parameters 2025-08-26T20:21:56.8688634Z ---------- 2025-08-26T20:21:56.8688858Z actual : scalar 2025-08-26T20:21:56.8689103Z The object to check. 2025-08-26T20:21:56.8689438Z desired : scalar 2025-08-26T20:21:56.8689674Z The expected object. 2025-08-26T20:21:56.8689956Z significant : int, optional 2025-08-26T20:21:56.8690263Z Desired precision, default is 7. 2025-08-26T20:21:56.8690581Z err_msg : str, optional 2025-08-26T20:21:56.8690886Z The error message to be printed in case of failure. 2025-08-26T20:21:56.8691258Z verbose : bool, optional 2025-08-26T20:21:56.8691645Z If True, the conflicting values are appended to the error message. 2025-08-26T20:21:56.8692199Z 2025-08-26T20:21:56.8692300Z Raises 2025-08-26T20:21:56.8692493Z ------ 2025-08-26T20:21:56.8692714Z AssertionError 2025-08-26T20:21:56.8693055Z If actual and desired are not equal up to specified precision. 2025-08-26T20:21:56.8693358Z 2025-08-26T20:21:56.8693455Z See Also 2025-08-26T20:21:56.8693649Z -------- 2025-08-26T20:21:56.8694119Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-26T20:21:56.8694608Z relative and/or absolute precision. 2025-08-26T20:21:56.8695065Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-26T20:21:56.8695387Z 2025-08-26T20:21:56.8695474Z Examples 2025-08-26T20:21:56.8695678Z -------- 2025-08-26T20:21:56.8695913Z >>> np.testing.assert_approx_equal( 2025-08-26T20:21:56.8696232Z ... 0.12345677777777e-20, 0.1234567e-20 2025-08-26T20:21:56.8696529Z ... ) # doctest: +SKIP 2025-08-26T20:21:56.8696806Z >>> np.testing.assert_approx_equal( 2025-08-26T20:21:56.8697107Z ... 0.12345670e-20, 2025-08-26T20:21:56.8697376Z ... 0.12345671e-20, # doctest: +SKIP 2025-08-26T20:21:56.8697679Z ... significant=8, 2025-08-26T20:21:56.8697922Z ... ) 2025-08-26T20:21:56.8698154Z >>> np.testing.assert_approx_equal( 2025-08-26T20:21:56.8698457Z ... 0.12345670e-20, 2025-08-26T20:21:56.8698710Z ... 0.12345672e-20, # doctest: +SKIP 2025-08-26T20:21:56.8699020Z ... significant=8, 2025-08-26T20:21:56.8699263Z ... ) 2025-08-26T20:21:56.8699491Z Traceback (most recent call last): 2025-08-26T20:21:56.8699773Z ... 2025-08-26T20:21:56.8699983Z AssertionError: 2025-08-26T20:21:56.8700256Z Items are not equal to 8 significant digits: 2025-08-26T20:21:56.8700672Z ACTUAL: 1.234567e-21 2025-08-26T20:21:56.8700919Z DESIRED: 1.2345672e-21 2025-08-26T20:21:56.8701092Z 2025-08-26T20:21:56.8701292Z the evaluated condition that raises the exception is 2025-08-26T20:21:56.8701577Z 2025-08-26T20:21:56.8701769Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-08-26T20:21:56.8702167Z True 2025-08-26T20:21:56.8702277Z 2025-08-26T20:21:56.8702281Z 2025-08-26T20:21:56.8702534Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.8702916Z 2025-08-26T20:21:56.8703472Z msg = Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=744. 2025-08-26T20:21:56.8704409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.8704801Z 2025-08-26T20:21:56.8705008Z Raises an AssertionError if two array_like objects are not equal. 2025-08-26T20:21:56.8705324Z 2025-08-26T20:21:56.8705540Z Given two array_like objects, check that the shape is equal and all 2025-08-26T20:21:56.8706082Z elements of these objects are equal (but see the Notes for the special 2025-08-26T20:21:56.8706606Z handling of a scalar). An exception is raised at shape mismatch or 2025-08-26T20:21:56.8707144Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-08-26T20:21:56.8707701Z are compared like numbers, no assertion is raised if both objects have 2025-08-26T20:21:56.8708145Z NaNs in the same positions. 2025-08-26T20:21:56.8708324Z 2025-08-26T20:21:56.8708548Z The usual caution for verifying equality with floating point numbers is 2025-08-26T20:21:56.8708977Z advised. 2025-08-26T20:21:56.8709106Z 2025-08-26T20:21:56.8709197Z Parameters 2025-08-26T20:21:56.8709411Z ---------- 2025-08-26T20:21:56.8709607Z x : array_like 2025-08-26T20:21:56.8709951Z The actual object to check. 2025-08-26T20:21:56.8710235Z y : array_like 2025-08-26T20:21:56.8710483Z The desired, expected object. 2025-08-26T20:21:56.8710769Z err_msg : str, optional 2025-08-26T20:21:56.8711087Z The error message to be printed in case of failure. 2025-08-26T20:21:56.8711453Z verbose : bool, optional 2025-08-26T20:21:56.8711828Z If True, the conflicting values are appended to the error message. 2025-08-26T20:21:56.8712233Z strict : bool, optional 2025-08-26T20:21:56.8712599Z If True, raise an AssertionError when either the shape or the data 2025-08-26T20:21:56.8713089Z type of the array_like objects does not match. The special 2025-08-26T20:21:56.8713582Z handling for scalars mentioned in the Notes section is disabled. 2025-08-26T20:21:56.8713895Z 2025-08-26T20:21:56.8713988Z Raises 2025-08-26T20:21:56.8714177Z ------ 2025-08-26T20:21:56.8714451Z AssertionError 2025-08-26T20:21:56.8714743Z If actual and desired objects are not equal. 2025-08-26T20:21:56.8715110Z 2025-08-26T20:21:56.8715254Z See Also 2025-08-26T20:21:56.8715538Z -------- 2025-08-26T20:21:56.8715976Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-26T20:21:56.8716463Z relative and/or absolute precision. 2025-08-26T20:21:56.8716916Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-26T20:21:56.8717235Z 2025-08-26T20:21:56.8717316Z Notes 2025-08-26T20:21:56.8717515Z ----- 2025-08-26T20:21:56.8717819Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-08-26T20:21:56.8718341Z function checks that each element of the array_like object is equal to 2025-08-26T20:21:56.8718898Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-08-26T20:21:56.8719244Z 2025-08-26T20:21:56.8719329Z Examples 2025-08-26T20:21:56.8719535Z -------- 2025-08-26T20:21:56.8719842Z The first assert does not raise an exception: 2025-08-26T20:21:56.8720095Z 2025-08-26T20:21:56.8720209Z >>> np.testing.assert_array_equal( 2025-08-26T20:21:56.8720575Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-08-26T20:21:56.8720918Z ... ) 2025-08-26T20:21:56.8721032Z 2025-08-26T20:21:56.8721258Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-08-26T20:21:56.8721723Z functions for these cases instead: 2025-08-26T20:21:56.8721941Z 2025-08-26T20:21:56.8722050Z >>> np.testing.assert_allclose( 2025-08-26T20:21:56.8722446Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-08-26T20:21:56.8722843Z ... ) 2025-08-26T20:21:56.8722957Z 2025-08-26T20:21:56.8723161Z As mentioned in the Notes section, `assert_array_equal` has special 2025-08-26T20:21:56.8723706Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-08-26T20:21:56.8724042Z 2025-08-26T20:21:56.8724161Z >>> x = np.full((2, 5), fill_value=3) 2025-08-26T20:21:56.8724495Z >>> np.testing.assert_array_equal(x, 3) 2025-08-26T20:21:56.8724711Z 2025-08-26T20:21:56.8724926Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-08-26T20:21:56.8725344Z array: 2025-08-26T20:21:56.8725470Z 2025-08-26T20:21:56.8725620Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-08-26T20:21:56.8725996Z Traceback (most recent call last): 2025-08-26T20:21:56.8726281Z ... 2025-08-26T20:21:56.8726492Z AssertionError: 2025-08-26T20:21:56.8726727Z Arrays are not equal 2025-08-26T20:21:56.8726972Z 2025-08-26T20:21:56.8727185Z (shapes (2, 5), () mismatch) 2025-08-26T20:21:56.8727470Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-08-26T20:21:56.8727763Z [3, 3, 3, 3, 3]]) 2025-08-26T20:21:56.8728022Z y: torch.ndarray(3) 2025-08-26T20:21:56.8728172Z 2025-08-26T20:21:56.8728384Z The `strict` parameter also ensures that the array data types match: 2025-08-26T20:21:56.8728719Z 2025-08-26T20:21:56.8728814Z >>> x = np.array([2, 2, 2]) 2025-08-26T20:21:56.8729124Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-08-26T20:21:56.8729515Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-08-26T20:21:56.8730014Z Traceback (most recent call last): 2025-08-26T20:21:56.8730295Z ... 2025-08-26T20:21:56.8730509Z AssertionError: 2025-08-26T20:21:56.8730748Z Arrays are not equal 2025-08-26T20:21:56.8730980Z 2025-08-26T20:21:56.8731252Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-08-26T20:21:56.8731610Z x: torch.ndarray([2, 2, 2]) 2025-08-26T20:21:56.8731895Z y: torch.ndarray([2., 2., 2.]) 2025-08-26T20:21:56.8732079Z 2025-08-26T20:21:56.8732339Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.8732707Z 2025-08-26T20:21:56.8733291Z msg = Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=851. 2025-08-26T20:21:56.8734372Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.8734764Z 2025-08-26T20:21:56.8734977Z Raises an AssertionError if two objects are not equal up to desired 2025-08-26T20:21:56.8735400Z precision. 2025-08-26T20:21:56.8735525Z 2025-08-26T20:21:56.8735712Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-26T20:21:56.8736165Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-26T20:21:56.8736657Z instead of this function for more consistent floating point 2025-08-26T20:21:56.8737058Z comparisons. 2025-08-26T20:21:56.8737214Z 2025-08-26T20:21:56.8737469Z The test verifies identical shapes and that the elements of ``actual`` and 2025-08-26T20:21:56.8737908Z ``desired`` satisfy. 2025-08-26T20:21:56.8738075Z 2025-08-26T20:21:56.8738204Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-08-26T20:21:56.8738454Z 2025-08-26T20:21:56.8738677Z That is a looser test than originally documented, but agrees with what the 2025-08-26T20:21:56.8739270Z actual implementation did up to rounding vagaries. An exception is raised 2025-08-26T20:21:56.8739865Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-08-26T20:21:56.8740513Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-08-26T20:21:56.8740979Z objects have NaNs in the same positions. 2025-08-26T20:21:56.8741218Z 2025-08-26T20:21:56.8741307Z Parameters 2025-08-26T20:21:56.8741527Z ---------- 2025-08-26T20:21:56.8741730Z x : array_like 2025-08-26T20:21:56.8741972Z The actual object to check. 2025-08-26T20:21:56.8742254Z y : array_like 2025-08-26T20:21:56.8742502Z The desired, expected object. 2025-08-26T20:21:56.8742790Z decimal : int, optional 2025-08-26T20:21:56.8743068Z Desired precision, default is 6. 2025-08-26T20:21:56.8743378Z err_msg : str, optional 2025-08-26T20:21:56.8743691Z The error message to be printed in case of failure. 2025-08-26T20:21:56.8744044Z verbose : bool, optional 2025-08-26T20:21:56.8744428Z If True, the conflicting values are appended to the error message. 2025-08-26T20:21:56.8744758Z 2025-08-26T20:21:56.8744841Z Raises 2025-08-26T20:21:56.8745043Z ------ 2025-08-26T20:21:56.8745241Z AssertionError 2025-08-26T20:21:56.8745574Z If actual and desired are not equal up to specified precision. 2025-08-26T20:21:56.8745890Z 2025-08-26T20:21:56.8745975Z See Also 2025-08-26T20:21:56.8746180Z -------- 2025-08-26T20:21:56.8746524Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-26T20:21:56.8747005Z relative and/or absolute precision. 2025-08-26T20:21:56.8747456Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-26T20:21:56.8747775Z 2025-08-26T20:21:56.8747870Z Examples 2025-08-26T20:21:56.8748075Z -------- 2025-08-26T20:21:56.8748310Z the first assert does not raise an exception 2025-08-26T20:21:56.8748557Z 2025-08-26T20:21:56.8748804Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-08-26T20:21:56.8749190Z 2025-08-26T20:21:56.8749308Z >>> np.testing.assert_array_almost_equal( 2025-08-26T20:21:56.8749809Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-08-26T20:21:56.8750146Z ... ) 2025-08-26T20:21:56.8750377Z Traceback (most recent call last): 2025-08-26T20:21:56.8750670Z ... 2025-08-26T20:21:56.8750883Z AssertionError: 2025-08-26T20:21:56.8751132Z Arrays are not almost equal to 5 decimals 2025-08-26T20:21:56.8751450Z 2025-08-26T20:21:56.8751683Z Mismatched elements: 1 / 3 (33.3%) 2025-08-26T20:21:56.8752012Z Max absolute difference: 5.999999999994898e-05 2025-08-26T20:21:56.8752370Z Max relative difference: 2.5713661239633743e-05 2025-08-26T20:21:56.8752779Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-08-26T20:21:56.8753218Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-08-26T20:21:56.8753486Z 2025-08-26T20:21:56.8753618Z >>> np.testing.assert_array_almost_equal( 2025-08-26T20:21:56.8754026Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-08-26T20:21:56.8754366Z ... ) 2025-08-26T20:21:56.8754594Z Traceback (most recent call last): 2025-08-26T20:21:56.8754892Z ... 2025-08-26T20:21:56.8755093Z AssertionError: 2025-08-26T20:21:56.8755357Z Arrays are not almost equal to 5 decimals 2025-08-26T20:21:56.8755679Z 2025-08-26T20:21:56.8755914Z x and y nan location mismatch: 2025-08-26T20:21:56.8756252Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-08-26T20:21:56.8756683Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-08-26T20:21:56.8756960Z 2025-08-26T20:21:56.8756964Z 2025-08-26T20:21:56.8757215Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.8757584Z 2025-08-26T20:21:56.8758175Z msg = Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1848. 2025-08-26T20:21:56.8759139Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:56.8759722Z Context manager that resets warning registry for catching warnings 2025-08-26T20:21:56.8760059Z 2025-08-26T20:21:56.8760298Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-08-26T20:21:56.8760886Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-08-26T20:21:56.8761467Z it impossible to retrigger the warning in this module, whatever you put in 2025-08-26T20:21:56.8762063Z the warnings filters. This context manager accepts a sequence of `modules` 2025-08-26T20:21:56.8762553Z as a keyword argument to its constructor and: 2025-08-26T20:21:56.8762811Z 2025-08-26T20:21:56.8763035Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-08-26T20:21:56.8763469Z on entry; 2025-08-26T20:21:56.8763795Z * resets ``__warningregistry__`` to its previous state on exit. 2025-08-26T20:21:56.8764093Z 2025-08-26T20:21:56.8764328Z This makes it possible to trigger any warning afresh inside the context 2025-08-26T20:21:56.8764841Z manager without disturbing the state of warnings outside. 2025-08-26T20:21:56.8765163Z 2025-08-26T20:21:56.8765396Z For compatibility with Python 3.0, please consider all arguments to be 2025-08-26T20:21:56.8765845Z keyword-only. 2025-08-26T20:21:56.8765986Z 2025-08-26T20:21:56.8766086Z Parameters 2025-08-26T20:21:56.8766298Z ---------- 2025-08-26T20:21:56.8766567Z record : bool, optional 2025-08-26T20:21:56.8766937Z Specifies whether warnings should be captured by a custom 2025-08-26T20:21:56.8767475Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-08-26T20:21:56.8768036Z returned by the context manager. Otherwise None is returned by the 2025-08-26T20:21:56.8768575Z context manager. The objects appended to the list are arguments whose 2025-08-26T20:21:56.8769090Z attributes mirror the arguments to ``showwarning()``. 2025-08-26T20:21:56.8769484Z modules : sequence, optional 2025-08-26T20:21:56.8769903Z Sequence of modules for which to reset warnings registry on entry and 2025-08-26T20:21:56.8770485Z restore on exit. To work correctly, all 'ignore' filters should 2025-08-26T20:21:56.8770907Z filter by one of these modules. 2025-08-26T20:21:56.8771131Z 2025-08-26T20:21:56.8771216Z Examples 2025-08-26T20:21:56.8771438Z -------- 2025-08-26T20:21:56.8771651Z >>> import warnings 2025-08-26T20:21:56.8772002Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-08-26T20:21:56.8772424Z ... modules=[np.core.fromnumeric] 2025-08-26T20:21:56.8772735Z ... ): 2025-08-26T20:21:56.8772968Z ... warnings.simplefilter("always") 2025-08-26T20:21:56.8773422Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-08-26T20:21:56.8773941Z ... # do something that raises a warning but ignore those in 2025-08-26T20:21:56.8774388Z ... # np.core.fromnumeric 2025-08-26T20:21:56.8774659Z 2025-08-26T20:21:56.8775029Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:56.8775409Z 2025-08-26T20:21:57.0428789Z msg = Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2025-08-26T20:21:57.0429749Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.0430356Z Applies a 1D convolution over a quantized input signal composed of 2025-08-26T20:21:57.0430795Z several quantized input planes. 2025-08-26T20:21:57.0431019Z 2025-08-26T20:21:57.0431233Z For details on input arguments, parameters, and implementation see 2025-08-26T20:21:57.0431675Z :class:`~torch.nn.Conv1d`. 2025-08-26T20:21:57.0431864Z 2025-08-26T20:21:57.0431979Z .. note:: 2025-08-26T20:21:57.0432316Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-08-26T20:21:57.0432651Z 2025-08-26T20:21:57.0432739Z .. note:: 2025-08-26T20:21:57.0433052Z Only `torch.quint8` is supported for the input data type. 2025-08-26T20:21:57.0433363Z 2025-08-26T20:21:57.0433368Z 2025-08-26T20:21:57.0433457Z Attributes: 2025-08-26T20:21:57.0433813Z weight (Tensor): packed tensor derived from the learnable weight 2025-08-26T20:21:57.0434233Z parameter. 2025-08-26T20:21:57.0434585Z scale (Tensor): scalar for the output scale 2025-08-26T20:21:57.0435003Z zero_point (Tensor): scalar for the output zero point 2025-08-26T20:21:57.0435275Z 2025-08-26T20:21:57.0435437Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-08-26T20:21:57.0435695Z 2025-08-26T20:21:57.0435798Z Examples:: 2025-08-26T20:21:57.0435929Z 2025-08-26T20:21:57.0436074Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-08-26T20:21:57.0436471Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-08-26T20:21:57.0436844Z >>> input = torch.randn(20, 16, 100) 2025-08-26T20:21:57.0437227Z >>> # quantize input to quint8 2025-08-26T20:21:57.0437533Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.0437930Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-08-26T20:21:57.0438392Z ... dtype=torch.quint8) 2025-08-26T20:21:57.0438737Z >>> output = m(q_input) 2025-08-26T20:21:57.0438924Z 2025-08-26T20:21:57.0439024Z 2025-08-26T20:21:57.0439400Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.0439770Z 2025-08-26T20:21:57.0640078Z msg = Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=12. 2025-08-26T20:21:57.0641015Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.0641532Z A quantized long short-term memory (LSTM). 2025-08-26T20:21:57.0641767Z 2025-08-26T20:21:57.0642088Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-08-26T20:21:57.0642703Z 2025-08-26T20:21:57.0642812Z Attributes: 2025-08-26T20:21:57.0643068Z layers : instances of the `_LSTMLayer` 2025-08-26T20:21:57.0643315Z 2025-08-26T20:21:57.0643418Z .. note:: 2025-08-26T20:21:57.0643768Z To access the weights and biases, you need to access them per layer. 2025-08-26T20:21:57.0644279Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-08-26T20:21:57.0644659Z 2025-08-26T20:21:57.0644766Z Examples:: 2025-08-26T20:21:57.0644995Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.0645291Z >>> custom_module_config = { 2025-08-26T20:21:57.0645642Z ... 'float_to_observed_custom_module_class': { 2025-08-26T20:21:57.0646022Z ... nn.LSTM: nn.quantizable.LSTM, 2025-08-26T20:21:57.0646336Z ... }, 2025-08-26T20:21:57.0646723Z ... 'observed_to_quantized_custom_module_class': { 2025-08-26T20:21:57.0647134Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-08-26T20:21:57.0647483Z ... } 2025-08-26T20:21:57.0647696Z ... } 2025-08-26T20:21:57.0648048Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-08-26T20:21:57.0648602Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-08-26T20:21:57.0649015Z 2025-08-26T20:21:57.0649370Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.0649748Z 2025-08-26T20:21:57.1405212Z msg = Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-08-26T20:21:57.1406434Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.1406860Z 2025-08-26T20:21:57.1407137Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-08-26T20:21:57.1407746Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-08-26T20:21:57.1408356Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-08-26T20:21:57.1408817Z input by the user. 2025-08-26T20:21:57.1409194Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-08-26T20:21:57.1409734Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-08-26T20:21:57.1410012Z 2025-08-26T20:21:57.1410112Z Note:: 2025-08-26T20:21:57.1410560Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-08-26T20:21:57.1410995Z 2025-08-26T20:21:57.1411078Z Args: 2025-08-26T20:21:57.1411294Z model (nn.Module): 2025-08-26T20:21:57.1411693Z The model whose layers will be sparsified. The layers that needs to be 2025-08-26T20:21:57.1412278Z sparsified should be added separately using the register_layer() function 2025-08-26T20:21:57.1412760Z aggregate_fn (Optional, Callable): 2025-08-26T20:21:57.1413226Z default aggregate_fn that is used if not specified while registering the layer. 2025-08-26T20:21:57.1413779Z specifies how inputs should be aggregated over time. 2025-08-26T20:21:57.1414341Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-08-26T20:21:57.1414835Z Example 2025-08-26T20:21:57.1415139Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-08-26T20:21:57.1415549Z reduce_fn (Optional, Callable): 2025-08-26T20:21:57.1416022Z default reduce_fn that is used if not specified while registering the layer. 2025-08-26T20:21:57.1416751Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-08-26T20:21:57.1417239Z calling agg_fn() on all inputs. 2025-08-26T20:21:57.1417568Z Example 2025-08-26T20:21:57.1417934Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-08-26T20:21:57.1418373Z mask_fn (Optional, Callable): 2025-08-26T20:21:57.1419118Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-08-26T20:21:57.1419791Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-08-26T20:21:57.1420271Z register_layer(). 2025-08-26T20:21:57.1420919Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-08-26T20:21:57.1421487Z arguments. 2025-08-26T20:21:57.1421761Z features (Optional, list): 2025-08-26T20:21:57.1422118Z default selected features to sparsify. 2025-08-26T20:21:57.1422628Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-08-26T20:21:57.1423111Z For example, 2025-08-26T20:21:57.1423636Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-08-26T20:21:57.1424121Z feature_dim (Optional, int): 2025-08-26T20:21:57.1424620Z default dimension of input features. Again, features along this dim will be chosen 2025-08-26T20:21:57.1425123Z for sparsification. 2025-08-26T20:21:57.1425439Z sparse_config (Dict): 2025-08-26T20:21:57.1425845Z Default configuration for the mask_fn. This config will be passed 2025-08-26T20:21:57.1426286Z with the mask_fn() 2025-08-26T20:21:57.1426491Z 2025-08-26T20:21:57.1426575Z Example: 2025-08-26T20:21:57.1426792Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.1427051Z >>> model = SomeModel() 2025-08-26T20:21:57.1427472Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-08-26T20:21:57.1427944Z >>> # Initialize aggregate_fn 2025-08-26T20:21:57.1428244Z >>> def agg_fn(x, y): 2025-08-26T20:21:57.1428495Z >>> return x + y 2025-08-26T20:21:57.1428746Z >>> 2025-08-26T20:21:57.1428972Z >>> # Initialize reduce_fn 2025-08-26T20:21:57.1429258Z >>> def reduce_fn(x): 2025-08-26T20:21:57.1429525Z >>> return torch.mean(x, dim=0) 2025-08-26T20:21:57.1429827Z >>> 2025-08-26T20:21:57.1430047Z >>> # Initialize mask_fn 2025-08-26T20:21:57.1430329Z >>> def mask_fn(data): 2025-08-26T20:21:57.1430735Z >>> return torch.eye(data.shape).to(data.device) 2025-08-26T20:21:57.1431234Z >>> 2025-08-26T20:21:57.1431438Z >>> 2025-08-26T20:21:57.1431676Z >>> act_sparsifier.register_layer( 2025-08-26T20:21:57.1432059Z ... model.some_layer, 2025-08-26T20:21:57.1432559Z ... aggregate_fn=agg_fn, 2025-08-26T20:21:57.1433125Z ... reduce_fn=reduce_fn, 2025-08-26T20:21:57.1433420Z ... mask_fn=mask_fn, 2025-08-26T20:21:57.1433673Z ... ) 2025-08-26T20:21:57.1433881Z >>> 2025-08-26T20:21:57.1434108Z >>> # start training process 2025-08-26T20:21:57.1434398Z >>> for _ in [...]: 2025-08-26T20:21:57.1434640Z >>> # epoch starts 2025-08-26T20:21:57.1434975Z >>> # model.forward(), compute_loss() and model.backwards() 2025-08-26T20:21:57.1435355Z >>> # epoch ends 2025-08-26T20:21:57.1435669Z >>> act_sparsifier.step() 2025-08-26T20:21:57.1435962Z >>> # end training process 2025-08-26T20:21:57.1436258Z >>> sparsifier.squash_mask() 2025-08-26T20:21:57.1436453Z 2025-08-26T20:21:57.1436716Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.1437086Z 2025-08-26T20:21:57.1437958Z msg = Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-08-26T20:21:57.1439156Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.1439531Z 2025-08-26T20:21:57.1439738Z Abstract method that needs to be implemented by the child class. 2025-08-26T20:21:57.1440317Z The expected return type should is a dictionary of name to schedule_param value 2025-08-26T20:21:57.1441194Z The returned values will be updated in sparsifier when the scheduler step() function 2025-08-26T20:21:57.1441677Z is called. 2025-08-26T20:21:57.1441798Z 2025-08-26T20:21:57.1441895Z Example: 2025-08-26T20:21:57.1442117Z >>> def get_schedule_param(self): 2025-08-26T20:21:57.1442430Z ... new_param = {} 2025-08-26T20:21:57.1442768Z ... for name in self.sparsifier.data_groups.keys(): 2025-08-26T20:21:57.1443144Z ... new_param[name] = ( 2025-08-26T20:21:57.1443542Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-08-26T20:21:57.1443952Z ... ) 2025-08-26T20:21:57.1444192Z ... return new_param 2025-08-26T20:21:57.1444369Z 2025-08-26T20:21:57.1444782Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-08-26T20:21:57.1445345Z would be halved 2025-08-26T20:21:57.1445492Z 2025-08-26T20:21:57.1445745Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.1446124Z 2025-08-26T20:21:57.1754301Z msg = Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=229. 2025-08-26T20:21:57.1755390Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.1755945Z Squashes the sparse masks into the appropriate tensors. 2025-08-26T20:21:57.1756224Z 2025-08-26T20:21:57.1756443Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-08-26T20:21:57.1756945Z the module will have a `sparse_params` dict attached to it. 2025-08-26T20:21:57.1757258Z 2025-08-26T20:21:57.1757345Z Args: 2025-08-26T20:21:57.1757680Z params_to_keep: List of keys to save in the module or a dict 2025-08-26T20:21:57.1758150Z representing the modules and keys that will have 2025-08-26T20:21:57.1758554Z sparsity parameters saved 2025-08-26T20:21:57.1759015Z params_to_keep_per_layer: Dict to specify the params that should be 2025-08-26T20:21:57.1759593Z saved for specific layers. The keys in the dict 2025-08-26T20:21:57.1760033Z should be the module fqn, while the values should 2025-08-26T20:21:57.1760472Z be a list of strings with the names of the variables 2025-08-26T20:21:57.1760868Z to save in the `sparse_params` 2025-08-26T20:21:57.1761116Z 2025-08-26T20:21:57.1761205Z Examples: 2025-08-26T20:21:57.1761501Z >>> # xdoctest: +SKIP("locals are undefined") 2025-08-26T20:21:57.1761865Z >>> # Don't save any sparse params 2025-08-26T20:21:57.1762215Z >>> sparsifier.squash_mask() 2025-08-26T20:21:57.1762566Z >>> hasattr(model.submodule1, "sparse_params") 2025-08-26T20:21:57.1762912Z False 2025-08-26T20:21:57.1763061Z 2025-08-26T20:21:57.1763181Z >>> # Keep sparse params per layer 2025-08-26T20:21:57.1763524Z >>> sparsifier.squash_mask( 2025-08-26T20:21:57.1763846Z ... params_to_keep_per_layer={ 2025-08-26T20:21:57.1764208Z ... "submodule1.linear1": ("foo", "bar"), 2025-08-26T20:21:57.1764587Z ... "submodule2.linear42": ("baz",), 2025-08-26T20:21:57.1764914Z ... } 2025-08-26T20:21:57.1765138Z ... ) 2025-08-26T20:21:57.1765437Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-26T20:21:57.1765808Z {'foo': 42, 'bar': 24} 2025-08-26T20:21:57.1766163Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-26T20:21:57.1766514Z {'baz': 0.1} 2025-08-26T20:21:57.1766684Z 2025-08-26T20:21:57.1766807Z >>> # Keep sparse params for all layers 2025-08-26T20:21:57.1767223Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-08-26T20:21:57.1767861Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-26T20:21:57.1768238Z {'foo': 42, 'bar': 24} 2025-08-26T20:21:57.1768580Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-26T20:21:57.1768950Z {'foo': 42, 'bar': 24} 2025-08-26T20:21:57.1769153Z 2025-08-26T20:21:57.1769348Z >>> # Keep some sparse params for all layers, and specific ones for 2025-08-26T20:21:57.1769768Z >>> # some other layers 2025-08-26T20:21:57.1770069Z >>> sparsifier.squash_mask( 2025-08-26T20:21:57.1770403Z ... params_to_keep=("foo", "bar"), 2025-08-26T20:21:57.1770828Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-08-26T20:21:57.1771221Z ... ) 2025-08-26T20:21:57.1771617Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-26T20:21:57.1772072Z {'foo': 42, 'bar': 24} 2025-08-26T20:21:57.1772435Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-26T20:21:57.1772817Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-08-26T20:21:57.1773128Z 2025-08-26T20:21:57.1773487Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.1773866Z 2025-08-26T20:21:57.2710092Z msg = Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2025-08-26T20:21:57.2711164Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.2711542Z 2025-08-26T20:21:57.2711804Z Config object that specifies the supported data types passed as arguments to 2025-08-26T20:21:57.2712422Z quantize ops in the reference model spec, for input and output activations, 2025-08-26T20:21:57.2712876Z weights, and biases. 2025-08-26T20:21:57.2713073Z 2025-08-26T20:21:57.2713231Z For example, consider the following reference model: 2025-08-26T20:21:57.2713519Z 2025-08-26T20:21:57.2713681Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-08-26T20:21:57.2713946Z 2025-08-26T20:21:57.2714174Z The pattern in the square brackets refers to the reference pattern of 2025-08-26T20:21:57.2714734Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-08-26T20:21:57.2715323Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-08-26T20:21:57.2715897Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-08-26T20:21:57.2716487Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-08-26T20:21:57.2716943Z the second quantize op (quant2). 2025-08-26T20:21:57.2717141Z 2025-08-26T20:21:57.2717377Z Note that the dtype here does not refer to the interface dtypes of the 2025-08-26T20:21:57.2717914Z op. For example, the "input dtype" here is not the dtype of the input 2025-08-26T20:21:57.2718459Z tensor passed to the quantized linear op. Though it can still be the 2025-08-26T20:21:57.2719005Z same as the interface dtype, this is not always the case, e.g. the 2025-08-26T20:21:57.2719542Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-08-26T20:21:57.2720079Z specified in the DTypeConfig would still be quint8. The semantics of 2025-08-26T20:21:57.2720627Z dtypes here are the same as the semantics of the dtypes specified in 2025-08-26T20:21:57.2721047Z the observers. 2025-08-26T20:21:57.2721184Z 2025-08-26T20:21:57.2721402Z These dtypes are matched against the ones specified in the user's 2025-08-26T20:21:57.2721942Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-08-26T20:21:57.2722485Z specified in the DTypeConfig (if any), then we will quantize the given 2025-08-26T20:21:57.2723045Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-08-26T20:21:57.2723499Z the pattern will not be quantized. 2025-08-26T20:21:57.2723703Z 2025-08-26T20:21:57.2723839Z Example usage:: 2025-08-26T20:21:57.2724184Z 2025-08-26T20:21:57.2724288Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:21:57.2724606Z >>> dtype_config1 = DTypeConfig( 2025-08-26T20:21:57.2724928Z ... input_dtype=torch.quint8, 2025-08-26T20:21:57.2725249Z ... output_dtype=torch.quint8, 2025-08-26T20:21:57.2725557Z ... weight_dtype=torch.qint8, 2025-08-26T20:21:57.2725872Z ... bias_dtype=torch.float) 2025-08-26T20:21:57.2726083Z 2025-08-26T20:21:57.2726188Z >>> dtype_config2 = DTypeConfig( 2025-08-26T20:21:57.2726527Z ... input_dtype=DTypeWithConstraints( 2025-08-26T20:21:57.2726855Z ... dtype=torch.quint8, 2025-08-26T20:21:57.2727165Z ... quant_min_lower_bound=0, 2025-08-26T20:21:57.2727492Z ... quant_max_upper_bound=255, 2025-08-26T20:21:57.2727804Z ... ), 2025-08-26T20:21:57.2728148Z ... output_dtype=DTypeWithConstraints( 2025-08-26T20:21:57.2728492Z ... dtype=torch.quint8, 2025-08-26T20:21:57.2728805Z ... quant_min_lower_bound=0, 2025-08-26T20:21:57.2729131Z ... quant_max_upper_bound=255, 2025-08-26T20:21:57.2729437Z ... ), 2025-08-26T20:21:57.2729692Z ... weight_dtype=DTypeWithConstraints( 2025-08-26T20:21:57.2730034Z ... dtype=torch.qint8, 2025-08-26T20:21:57.2730348Z ... quant_min_lower_bound=-128, 2025-08-26T20:21:57.2730681Z ... quant_max_upper_bound=127, 2025-08-26T20:21:57.2730971Z ... ), 2025-08-26T20:21:57.2731208Z ... bias_dtype=torch.float) 2025-08-26T20:21:57.2731547Z 2025-08-26T20:21:57.2731674Z >>> dtype_config1.input_dtype 2025-08-26T20:21:57.2731979Z torch.quint8 2025-08-26T20:21:57.2732120Z 2025-08-26T20:21:57.2732228Z >>> dtype_config2.input_dtype 2025-08-26T20:21:57.2732527Z torch.quint8 2025-08-26T20:21:57.2732669Z 2025-08-26T20:21:57.2732823Z >>> dtype_config2.input_dtype_with_constraints 2025-08-26T20:21:57.2733620Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-08-26T20:21:57.2734272Z 2025-08-26T20:21:57.2734536Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.2734904Z 2025-08-26T20:21:57.3841110Z msg = Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-08-26T20:21:57.3842185Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.3842578Z 2025-08-26T20:21:57.3842881Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-08-26T20:21:57.3843640Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-08-26T20:21:57.3844377Z though the requirements on the type of model more-so depends on the specific report the user 2025-08-26T20:21:57.3845099Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-08-26T20:21:57.3845802Z a set of Detector classes, each of which generate reports on quantization configuration 2025-08-26T20:21:57.3846287Z issues a use might have. 2025-08-26T20:21:57.3846470Z 2025-08-26T20:21:57.3846597Z Currently supports generating reports on: 2025-08-26T20:21:57.3847061Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-08-26T20:21:57.3847671Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-08-26T20:21:57.3848336Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-08-26T20:21:57.3848960Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-08-26T20:21:57.3849293Z 2025-08-26T20:21:57.3849690Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-08-26T20:21:57.3850586Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-08-26T20:21:57.3851659Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-08-26T20:21:57.3852355Z has the capability to remove all the observers it inserted as well. 2025-08-26T20:21:57.3852690Z 2025-08-26T20:21:57.3852971Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-08-26T20:21:57.3853380Z 2025-08-26T20:21:57.3853753Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-08-26T20:21:57.3854562Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-08-26T20:21:57.3854988Z 2025-08-26T20:21:57.3855285Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-08-26T20:21:57.3856018Z This set is generated by calling the get_detector_name() of each detector 2025-08-26T20:21:57.3856368Z 2025-08-26T20:21:57.3856700Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-08-26T20:21:57.3857471Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-08-26T20:21:57.3858032Z can be removed at the end if desired 2025-08-26T20:21:57.3858252Z 2025-08-26T20:21:57.3858581Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-08-26T20:21:57.3859254Z This is to ensure we only insert observers once with the ModelReport instance 2025-08-26T20:21:57.3859710Z 2025-08-26T20:21:57.3859965Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-08-26T20:21:57.3860712Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-08-26T20:21:57.3861447Z instance. This also allows the functionality where we can generate the report multiple times 2025-08-26T20:21:57.3862036Z as long as we haven't removed the observers yet. 2025-08-26T20:21:57.3862288Z 2025-08-26T20:21:57.3862384Z Note: 2025-08-26T20:21:57.3862788Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-08-26T20:21:57.3863523Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-08-26T20:21:57.3864253Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-08-26T20:21:57.3864778Z the QuantizationTracer class. 2025-08-26T20:21:57.3864982Z 2025-08-26T20:21:57.3865085Z General Flow for Fx workflow: 2025-08-26T20:21:57.3865666Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-08-26T20:21:57.3866300Z 2.) Prepare your model with prepare_fx 2025-08-26T20:21:57.3866773Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-08-26T20:21:57.3867249Z 4.) Calibrate your model with data 2025-08-26T20:21:57.3867808Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-08-26T20:21:57.3868374Z Optional 2025-08-26T20:21:57.3868778Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-08-26T20:21:57.3869420Z 7.) To help in parsing report information and debugging, view report info as a: 2025-08-26T20:21:57.3869867Z - Table 2025-08-26T20:21:57.3870107Z - Histogram 2025-08-26T20:21:57.3870349Z - Line plot 2025-08-26T20:21:57.3870818Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-08-26T20:21:57.3871251Z 2025-08-26T20:21:57.3871366Z Example (with QuantizationTracer): 2025-08-26T20:21:57.3871688Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.3871972Z >>> # get the necessary qconfig 2025-08-26T20:21:57.3872305Z >>> config = PrepareCustomConfig() 2025-08-26T20:21:57.3872677Z >>> skipped_module_names, skipped_module_classes = ( 2025-08-26T20:21:57.3873182Z ... get_skipped_module_name_and_classes(config, False) 2025-08-26T20:21:57.3873544Z ... ) 2025-08-26T20:21:57.3873661Z 2025-08-26T20:21:57.3873807Z >>> # initialize our model and get GraphModule 2025-08-26T20:21:57.3874159Z >>> model = SomeModel() 2025-08-26T20:21:57.3874579Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-08-26T20:21:57.3875126Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-08-26T20:21:57.3875413Z 2025-08-26T20:21:57.3875562Z >>> # get our set of detectors and ModelReport instance 2025-08-26T20:21:57.3875938Z >>> detector_set = set( 2025-08-26T20:21:57.3876189Z ... [ 2025-08-26T20:21:57.3876458Z ... DynamicStaticDetector(tolerance=0.5), 2025-08-26T20:21:57.3876966Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-08-26T20:21:57.3877365Z ... ] 2025-08-26T20:21:57.3877571Z ... ) 2025-08-26T20:21:57.3877905Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-08-26T20:21:57.3878241Z 2025-08-26T20:21:57.3878397Z >>> # now we insert the observers and calibrate the model 2025-08-26T20:21:57.3878931Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-08-26T20:21:57.3879446Z >>> for i in range(num_callibration_batches): 2025-08-26T20:21:57.3879813Z >>> example_input = get_callibration_input() 2025-08-26T20:21:57.3880201Z >>> tracer_model_with_observers(example_input) 2025-08-26T20:21:57.3880476Z 2025-08-26T20:21:57.3880746Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-08-26T20:21:57.3881284Z >>> reports = tracer_reporter.generate_model_report( 2025-08-26T20:21:57.3881656Z ... remove_inserted_observers=True 2025-08-26T20:21:57.3881970Z ... ) 2025-08-26T20:21:57.3882098Z 2025-08-26T20:21:57.3882322Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-08-26T20:21:57.3882833Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-08-26T20:21:57.3883101Z 2025-08-26T20:21:57.3883356Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-08-26T20:21:57.3883891Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-08-26T20:21:57.3884194Z 2025-08-26T20:21:57.3884476Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-08-26T20:21:57.3885089Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-08-26T20:21:57.3885412Z 2025-08-26T20:21:57.3885416Z 2025-08-26T20:21:57.3885678Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.3886045Z 2025-08-26T20:21:57.3925686Z msg = Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2025-08-26T20:21:57.3927021Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.3927510Z 2025-08-26T20:21:57.3927895Z Takes in optional filter values and generates two tables with desired information. 2025-08-26T20:21:57.3928301Z 2025-08-26T20:21:57.3928579Z The generated tables are presented in both a list-of-lists format 2025-08-26T20:21:57.3928916Z 2025-08-26T20:21:57.3929123Z The reason for the two tables are that they handle different things: 2025-08-26T20:21:57.3929609Z 1.) the first table handles all tensor level information 2025-08-26T20:21:57.3930103Z 2.) the second table handles and displays all channel based information 2025-08-26T20:21:57.3930432Z 2025-08-26T20:21:57.3930745Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-08-26T20:21:57.3931526Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-08-26T20:21:57.3932831Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-08-26T20:21:57.3933390Z 2025-08-26T20:21:57.3933490Z Tensor table columns: 2025-08-26T20:21:57.3933844Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-08-26T20:21:57.3934299Z ---- --------- --------- --------- --------- --------- 2025-08-26T20:21:57.3934580Z 2025-08-26T20:21:57.3934687Z Per-Channel table columns: 2025-08-26T20:21:57.3935148Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-08-26T20:21:57.3935779Z ---- --------- ------- --------- --------- --------- --------- 2025-08-26T20:21:57.3936144Z 2025-08-26T20:21:57.3936240Z Args: 2025-08-26T20:21:57.3936637Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-26T20:21:57.3937303Z contain this filter substring 2025-08-26T20:21:57.3937688Z Default = "", results in all the features being printed 2025-08-26T20:21:57.3938239Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-26T20:21:57.3938856Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-26T20:21:57.3939231Z 2025-08-26T20:21:57.3939394Z Returns a dictionary with two keys: 2025-08-26T20:21:57.3939770Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-08-26T20:21:57.3940189Z "tensor_level_info", "channel_level_info" 2025-08-26T20:21:57.3940609Z Each key maps to a tuple with: 2025-08-26T20:21:57.3940946Z A list of the headers of each table 2025-08-26T20:21:57.3941347Z A list of lists containing the table information row by row 2025-08-26T20:21:57.3941837Z The 0th index row will contain the headers of the columns 2025-08-26T20:21:57.3942257Z The rest of the rows will contain data 2025-08-26T20:21:57.3942491Z 2025-08-26T20:21:57.3942598Z Example Use: 2025-08-26T20:21:57.3942848Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:57.3943240Z >>> mod_report_visualizer.generate_filtered_tables( 2025-08-26T20:21:57.3943807Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-26T20:21:57.3944398Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-08-26T20:21:57.3944785Z 2025-08-26T20:21:57.3945054Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.3945425Z 2025-08-26T20:21:57.3946335Z msg = Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=399. 2025-08-26T20:21:57.3947621Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.3948008Z 2025-08-26T20:21:57.3948277Z Takes in optional filter values and prints out formatted tables of the information. 2025-08-26T20:21:57.3948685Z 2025-08-26T20:21:57.3949022Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-08-26T20:21:57.3949652Z 1.) the first table handles all tensor level information 2025-08-26T20:21:57.3950144Z 2.) the second table handles and displays all channel based information 2025-08-26T20:21:57.3950471Z 2025-08-26T20:21:57.3950784Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-08-26T20:21:57.3951551Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-08-26T20:21:57.3952372Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-08-26T20:21:57.3952840Z 2025-08-26T20:21:57.3952950Z Tensor table columns: 2025-08-26T20:21:57.3953316Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-08-26T20:21:57.3953847Z ---- --------- --------- --------- --------- --------- 2025-08-26T20:21:57.3954126Z 2025-08-26T20:21:57.3954234Z Per-Channel table columns: 2025-08-26T20:21:57.3954428Z 2025-08-26T20:21:57.3954647Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-08-26T20:21:57.3955154Z ---- --------- ------- --------- --------- --------- --------- 2025-08-26T20:21:57.3955437Z 2025-08-26T20:21:57.3955547Z Args: 2025-08-26T20:21:57.3955939Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-26T20:21:57.3956441Z contain this filter substring 2025-08-26T20:21:57.3956826Z Default = "", results in all the features being printed 2025-08-26T20:21:57.3957373Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-26T20:21:57.3958054Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-26T20:21:57.3958433Z 2025-08-26T20:21:57.3958530Z Example Use: 2025-08-26T20:21:57.3958798Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:57.3959205Z >>> mod_report_visualizer.generate_table_visualization( 2025-08-26T20:21:57.3959672Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-26T20:21:57.3960077Z ... ) 2025-08-26T20:21:57.3960390Z >>> # prints out neatly formatted table with per_channel_min info 2025-08-26T20:21:57.3960824Z >>> # for all modules in block 1 of the model 2025-08-26T20:21:57.3961056Z 2025-08-26T20:21:57.3961319Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.3961687Z 2025-08-26T20:21:57.3962598Z msg = Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=564. 2025-08-26T20:21:57.3963877Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.3964284Z 2025-08-26T20:21:57.3964520Z Takes in a feature and optional module_filter and plots of the desired data. 2025-08-26T20:21:57.3964870Z 2025-08-26T20:21:57.3965150Z For per channel features, it averages the value across the channels and plots a point 2025-08-26T20:21:57.3965801Z per module. The reason for this is that for models with hundreds of channels, it can 2025-08-26T20:21:57.3966446Z be hard to differentiate one channel line from another, and so the point of generating 2025-08-26T20:21:57.3967114Z a single average point per module is to give a sense of general trends that encourage 2025-08-26T20:21:57.3967601Z further deep dives. 2025-08-26T20:21:57.3967750Z 2025-08-26T20:21:57.3967844Z Note: 2025-08-26T20:21:57.3968227Z Only features in the report that have tensor value data are plottable by this class 2025-08-26T20:21:57.3968770Z When the tensor information is plotted, it will plot: 2025-08-26T20:21:57.3969182Z idx as the x val, feature value as the y_val 2025-08-26T20:21:57.3969604Z When the channel information is plotted, it will plot: 2025-08-26T20:21:57.3970151Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-08-26T20:21:57.3970753Z The reason for this is that we want to be able to compare values across the 2025-08-26T20:21:57.3971339Z channels for same layer, and it will be hard if values are staggered by idx 2025-08-26T20:21:57.3971866Z This means each module is represented by only 1 x value 2025-08-26T20:21:57.3972235Z Args: 2025-08-26T20:21:57.3972583Z feature_filter (str): Filters the features presented to only those that 2025-08-26T20:21:57.3973028Z contain this filter substring 2025-08-26T20:21:57.3973501Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-26T20:21:57.3974136Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-26T20:21:57.3974498Z 2025-08-26T20:21:57.3974666Z Example Use: 2025-08-26T20:21:57.3974932Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:57.3975335Z >>> mod_report_visualizer.generate_plot_visualization( 2025-08-26T20:21:57.3975806Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-26T20:21:57.3976209Z ... ) 2025-08-26T20:21:57.3976506Z >>> # outputs line plot of per_channel_min information for all 2025-08-26T20:21:57.3976989Z >>> # modules in block1 of model each channel gets it's own line, 2025-08-26T20:21:57.3977466Z >>> # and it's plotted across the in-order modules on the x-axis 2025-08-26T20:21:57.3977751Z 2025-08-26T20:21:57.3978014Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.3978378Z 2025-08-26T20:21:57.3979380Z msg = Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=643. 2025-08-26T20:21:57.3980772Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.3981149Z 2025-08-26T20:21:57.3981426Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-08-26T20:21:57.3981837Z 2025-08-26T20:21:57.3981920Z Note: 2025-08-26T20:21:57.3982310Z Only features in the report that have tensor value data can be viewed as a histogram 2025-08-26T20:21:57.3982963Z If you want to plot a histogram from all the channel values of a specific feature for 2025-08-26T20:21:57.3983584Z a specific model, make sure to specify both the model and the feature properly 2025-08-26T20:21:57.3984194Z in the filters and you should be able to see a distribution of the channel data 2025-08-26T20:21:57.3984566Z 2025-08-26T20:21:57.3984647Z Args: 2025-08-26T20:21:57.3985034Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-26T20:21:57.3985530Z contain this filter substring 2025-08-26T20:21:57.3985897Z Default = "", results in all the features being printed 2025-08-26T20:21:57.3986435Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-26T20:21:57.3987056Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-26T20:21:57.3987646Z num_bins (int, optional): The number of bins to create the histogram with 2025-08-26T20:21:57.3988177Z Default = 10, the values will be split into 10 equal sized bins 2025-08-26T20:21:57.3988475Z 2025-08-26T20:21:57.3988566Z Example Use: 2025-08-26T20:21:57.3988798Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.3989272Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-08-26T20:21:57.3989888Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-26T20:21:57.3990270Z ... ) 2025-08-26T20:21:57.3990670Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-08-26T20:21:57.3991333Z information is gathered across all channels for all modules in block 1 for the 2025-08-26T20:21:57.3992149Z per_channel_min and is displayed in a histogram of equally sized bins 2025-08-26T20:21:57.3992482Z 2025-08-26T20:21:57.3992748Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.3993112Z 2025-08-26T20:21:57.5348052Z msg = Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py line=734. 2025-08-26T20:21:57.5348976Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.5349750Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-08-26T20:21:57.5350459Z Label will only appear if CPU activity tracing is enabled. 2025-08-26T20:21:57.5350753Z 2025-08-26T20:21:57.5351159Z It is useful when tracing the code profile. 2025-08-26T20:21:57.5351401Z 2025-08-26T20:21:57.5351505Z Args: 2025-08-26T20:21:57.5351761Z name (str): Label assigned to the block of code. 2025-08-26T20:21:57.5352204Z node_id (int): ID of node, for distributed profiling. Unset in 2025-08-26T20:21:57.5352612Z non-distributed cases. 2025-08-26T20:21:57.5352798Z 2025-08-26T20:21:57.5352895Z Example: 2025-08-26T20:21:57.5353199Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-08-26T20:21:57.5353629Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-08-26T20:21:57.5364560Z >>> with torch.autograd.profiler.profile() as prof: 2025-08-26T20:21:57.5365070Z ... y = x**2 2025-08-26T20:21:57.5365413Z ... with torch.autograd.profiler.record_function( 2025-08-26T20:21:57.5365979Z ... "label-z" 2025-08-26T20:21:57.5366273Z ... ): # label the block 2025-08-26T20:21:57.5366564Z ... z = y**3 2025-08-26T20:21:57.5366853Z ... y.backward() 2025-08-26T20:21:57.5367130Z >>> # xdoctest: +IGNORE_WANT 2025-08-26T20:21:57.5367476Z >>> # NOTE: some columns were removed for brevity 2025-08-26T20:21:57.5367932Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-08-26T20:21:57.5368504Z ----------------------------------- --------------- --------------- --------------- 2025-08-26T20:21:57.5369129Z Name Self CPU total % CPU time avg Number of Calls 2025-08-26T20:21:57.5369638Z ----------------------------------- --------------- --------------- --------------- 2025-08-26T20:21:57.5370090Z pow 60.77% 47.470us 3 2025-08-26T20:21:57.5370469Z mul 21.73% 25.465us 2 2025-08-26T20:21:57.5370901Z PowBackward0 12.03% 121.891us 1 2025-08-26T20:21:57.5371439Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-08-26T20:21:57.5371928Z label-z 2.13% 12.421us 1 2025-08-26T20:21:57.5372403Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-08-26T20:21:57.5373034Z ----------------------------------- --------------- --------------- --------------- 2025-08-26T20:21:57.5373454Z Self CPU time total: 234.344us 2025-08-26T20:21:57.5373779Z CUDA time total: 0.000us 2025-08-26T20:21:57.5373983Z 2025-08-26T20:21:57.5374063Z 2025-08-26T20:21:57.5374455Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.5374822Z 2025-08-26T20:21:57.7147626Z msg = Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=721. 2025-08-26T20:21:57.7148620Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:21:57.7149007Z 2025-08-26T20:21:57.7149294Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-08-26T20:21:57.7149941Z The submesh created consists of the dimensions and the communicators indicated by 2025-08-26T20:21:57.7150423Z ``mesh_dim_names`` 2025-08-26T20:21:57.7150580Z 2025-08-26T20:21:57.7150662Z Args: 2025-08-26T20:21:57.7151021Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-08-26T20:21:57.7151569Z mesh dimension of the DeviceMesh to create the submesh for. 2025-08-26T20:21:57.7151944Z Returns: 2025-08-26T20:21:57.7152175Z A :class:`DeviceMesh` object 2025-08-26T20:21:57.7152368Z 2025-08-26T20:21:57.7152661Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-08-26T20:21:57.7153175Z In the first example: 2025-08-26T20:21:57.7153575Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-08-26T20:21:57.7154421Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-08-26T20:21:57.7155017Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-08-26T20:21:57.7155592Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-08-26T20:21:57.7156162Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-08-26T20:21:57.7156714Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-08-26T20:21:57.7157065Z 2025-08-26T20:21:57.7157162Z In the second example: 2025-08-26T20:21:57.7157595Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-08-26T20:21:57.7158379Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-08-26T20:21:57.7159032Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-08-26T20:21:57.7159664Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-08-26T20:21:57.7160050Z 2025-08-26T20:21:57.7160157Z Example:: 2025-08-26T20:21:57.7160297Z 2025-08-26T20:21:57.7160401Z >>> # xdoctest: +SKIP("no rank") 2025-08-26T20:21:57.7160783Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-08-26T20:21:57.7161138Z >>> 2025-08-26T20:21:57.7161456Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-08-26T20:21:57.7161916Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-08-26T20:21:57.7162417Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-08-26T20:21:57.7162892Z >>> tp_mesh = mesh_2d["tp"] 2025-08-26T20:21:57.7163174Z >>> dp_mesh = mesh_2d["dp"] 2025-08-26T20:21:57.7163446Z >>> 2025-08-26T20:21:57.7163664Z >>> # Initialize a 3D mesh. 2025-08-26T20:21:57.7164149Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-08-26T20:21:57.7164839Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-08-26T20:21:57.7165374Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-08-26T20:21:57.7165705Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-08-26T20:21:57.7165919Z 2025-08-26T20:21:57.7166611Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2025-08-26T20:21:57.7167390Z 2025-08-26T20:21:57.7167645Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-08-26T20:21:57.7168129Z ^ 2025-08-26T20:21:57.7497642Z msg = Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2710. 2025-08-26T20:21:57.7498677Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.7499076Z 2025-08-26T20:21:57.7499329Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-08-26T20:21:57.7499696Z 2025-08-26T20:21:57.7499954Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-08-26T20:21:57.7500586Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-08-26T20:21:57.7500893Z 2025-08-26T20:21:57.7500975Z Args: 2025-08-26T20:21:57.7501325Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-08-26T20:21:57.7501899Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-08-26T20:21:57.7502467Z matters and it needs to match with corresponding isend/irecv on the 2025-08-26T20:21:57.7502892Z remote end. 2025-08-26T20:21:57.7503461Z 2025-08-26T20:21:57.7503575Z Returns: 2025-08-26T20:21:57.7504135Z A list of distributed request objects returned by calling the corresponding 2025-08-26T20:21:57.7504701Z op in the op_list. 2025-08-26T20:21:57.7504862Z 2025-08-26T20:21:57.7504964Z Examples: 2025-08-26T20:21:57.7505189Z >>> # xdoctest: +SKIP("no rank") 2025-08-26T20:21:57.7505596Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-08-26T20:21:57.7506060Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-08-26T20:21:57.7506536Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-08-26T20:21:57.7506960Z >>> recv_op = dist.P2POp( 2025-08-26T20:21:57.7507368Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-08-26T20:21:57.7507759Z ... ) 2025-08-26T20:21:57.7508150Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-08-26T20:21:57.7508501Z >>> for req in reqs: 2025-08-26T20:21:57.7508748Z >>> req.wait() 2025-08-26T20:21:57.7509006Z >>> recv_tensor 2025-08-26T20:21:57.7509251Z tensor([2, 3]) # Rank 0 2025-08-26T20:21:57.7509536Z tensor([0, 1]) # Rank 1 2025-08-26T20:21:57.7509713Z 2025-08-26T20:21:57.7509981Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-08-26T20:21:57.7510565Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-08-26T20:21:57.7511021Z lead to unexpected hang issues. 2025-08-26T20:21:57.7511225Z 2025-08-26T20:21:57.7511446Z In addition, if this API is the first collective call in the ``group`` 2025-08-26T20:21:57.7511998Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-08-26T20:21:57.7512551Z this API call; otherwise, the behavior is undefined. If this API call is 2025-08-26T20:21:57.7513115Z not the first collective call in the ``group``, batched P2P operations 2025-08-26T20:21:57.7513651Z involving only a subset of ranks of the ``group`` are allowed. 2025-08-26T20:21:57.7513959Z 2025-08-26T20:21:57.7514220Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.7514583Z 2025-08-26T20:21:57.7515161Z msg = Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2842. 2025-08-26T20:21:57.7516086Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.7516469Z 2025-08-26T20:21:57.7516735Z Reduces the tensor data across all machines in a way that all get the final result. 2025-08-26T20:21:57.7517132Z 2025-08-26T20:21:57.7517358Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-08-26T20:21:57.7517701Z 2025-08-26T20:21:57.7517822Z Complex tensors are supported. 2025-08-26T20:21:57.7518012Z 2025-08-26T20:21:57.7518106Z Args: 2025-08-26T20:21:57.7518421Z tensor (Tensor): Input and output of the collective. The function 2025-08-26T20:21:57.7518853Z operates in-place. 2025-08-26T20:21:57.7519151Z op (optional): One of the values from 2025-08-26T20:21:57.7519501Z ``torch.distributed.ReduceOp`` 2025-08-26T20:21:57.7519909Z enum. Specifies an operation used for element-wise reductions. 2025-08-26T20:21:57.7520464Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:21:57.7520943Z the default process group will be used. 2025-08-26T20:21:57.7521380Z async_op (bool, optional): Whether this op should be an async op 2025-08-26T20:21:57.7521689Z 2025-08-26T20:21:57.7521787Z Returns: 2025-08-26T20:21:57.7522038Z Async work handle, if async_op is set to True. 2025-08-26T20:21:57.7522569Z None, if not async_op or if not part of the group 2025-08-26T20:21:57.7522851Z 2025-08-26T20:21:57.7522990Z Examples: 2025-08-26T20:21:57.7523337Z >>> # xdoctest: +SKIP("no rank") 2025-08-26T20:21:57.7523759Z >>> # All tensors below are of torch.int64 type. 2025-08-26T20:21:57.7524310Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:21:57.7524663Z >>> device = torch.device(f"cuda:{rank}") 2025-08-26T20:21:57.7525190Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-08-26T20:21:57.7525665Z >>> tensor 2025-08-26T20:21:57.7525918Z tensor([1, 2], device='cuda:0') # Rank 0 2025-08-26T20:21:57.7526267Z tensor([3, 4], device='cuda:1') # Rank 1 2025-08-26T20:21:57.7526671Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-08-26T20:21:57.7526989Z >>> tensor 2025-08-26T20:21:57.7527292Z tensor([4, 6], device='cuda:0') # Rank 0 2025-08-26T20:21:57.7527626Z tensor([4, 6], device='cuda:1') # Rank 1 2025-08-26T20:21:57.7527892Z 2025-08-26T20:21:57.7528044Z >>> # All tensors below are of torch.cfloat type. 2025-08-26T20:21:57.7528397Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:21:57.7528854Z >>> tensor = torch.tensor( 2025-08-26T20:21:57.7529190Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-08-26T20:21:57.7529613Z ... ) + 2 * rank * (1 + 1j) 2025-08-26T20:21:57.7529884Z >>> tensor 2025-08-26T20:21:57.7530199Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-08-26T20:21:57.7530588Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-08-26T20:21:57.7531020Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-08-26T20:21:57.7531348Z >>> tensor 2025-08-26T20:21:57.7531612Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-08-26T20:21:57.7532049Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-08-26T20:21:57.7532290Z 2025-08-26T20:21:57.7532306Z 2025-08-26T20:21:57.7532614Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.7532979Z 2025-08-26T20:21:57.7533671Z msg = Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3202. 2025-08-26T20:21:57.7534695Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.7535077Z 2025-08-26T20:21:57.7535312Z Gathers picklable objects from the whole group in a single process. 2025-08-26T20:21:57.7535705Z 2025-08-26T20:21:57.7535938Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-08-26T20:21:57.7536438Z object must be picklable in order to be gathered. 2025-08-26T20:21:57.7536704Z 2025-08-26T20:21:57.7536786Z Args: 2025-08-26T20:21:57.7537035Z obj (Any): Input object. Must be picklable. 2025-08-26T20:21:57.7537484Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-08-26T20:21:57.7538051Z should be correctly sized as the size of the group for this 2025-08-26T20:21:57.7538577Z collective and will contain the output. Must be ``None`` on non-dst 2025-08-26T20:21:57.7539021Z ranks. (default is ``None``) 2025-08-26T20:21:57.7539557Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-08-26T20:21:57.7540284Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-08-26T20:21:57.7540901Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:21:57.7541487Z the default process group will be used. Default is ``None``. 2025-08-26T20:21:57.7542152Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-08-26T20:21:57.7542607Z 2025-08-26T20:21:57.7542705Z Returns: 2025-08-26T20:21:57.7542999Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-08-26T20:21:57.7543406Z output of the collective. 2025-08-26T20:21:57.7543604Z 2025-08-26T20:21:57.7543823Z .. note:: Note that this API differs slightly from the gather collective 2025-08-26T20:21:57.7544371Z since it does not provide an async_op handle and thus will be a blocking 2025-08-26T20:21:57.7544784Z call. 2025-08-26T20:21:57.7544912Z 2025-08-26T20:21:57.7545143Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-08-26T20:21:57.7545793Z of objects must be moved to the GPU device before communication takes 2025-08-26T20:21:57.7546274Z place. In this case, the device used is given by 2025-08-26T20:21:57.7546767Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-08-26T20:21:57.7547306Z ensure that this is set so that each rank has an individual GPU, via 2025-08-26T20:21:57.7547746Z ``torch.cuda.set_device()``. 2025-08-26T20:21:57.7547956Z 2025-08-26T20:21:57.7548049Z .. warning:: 2025-08-26T20:21:57.7548471Z Object collectives have a number of serious performance and scalability 2025-08-26T20:21:57.7549005Z limitations. See :ref:`object_collectives` for details. 2025-08-26T20:21:57.7549302Z 2025-08-26T20:21:57.7549390Z .. warning:: 2025-08-26T20:21:57.7549787Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-08-26T20:21:57.7550322Z known to be insecure. It is possible to construct malicious pickle data 2025-08-26T20:21:57.7550884Z which will execute arbitrary code during unpickling. Only call this 2025-08-26T20:21:57.7551315Z function with data you trust. 2025-08-26T20:21:57.7551528Z 2025-08-26T20:21:57.7551615Z .. warning:: 2025-08-26T20:21:57.7551972Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-08-26T20:21:57.7552527Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-08-26T20:21:57.7553047Z pickled. Please consider using :func:`gather` instead. 2025-08-26T20:21:57.7553339Z 2025-08-26T20:21:57.7553427Z Example:: 2025-08-26T20:21:57.7553693Z >>> # xdoctest: +SKIP("need process group init") 2025-08-26T20:21:57.7554111Z >>> # Note: Process group initialization omitted on each rank. 2025-08-26T20:21:57.7554525Z >>> import torch.distributed as dist 2025-08-26T20:21:57.7554862Z >>> # Assumes world_size of 3. 2025-08-26T20:21:57.7555240Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-08-26T20:21:57.7555646Z >>> output = [None for _ in gather_objects] 2025-08-26T20:21:57.7555982Z >>> dist.gather_object( 2025-08-26T20:21:57.7556281Z ... gather_objects[dist.get_rank()], 2025-08-26T20:21:57.7556642Z ... output if dist.get_rank() == 0 else None, 2025-08-26T20:21:57.7556960Z ... dst=0 2025-08-26T20:21:57.7557184Z ... ) 2025-08-26T20:21:57.7557392Z >>> # On rank 0 2025-08-26T20:21:57.7557619Z >>> output 2025-08-26T20:21:57.7557829Z ['foo', 12, {1: 2}] 2025-08-26T20:21:57.7557993Z 2025-08-26T20:21:57.7558242Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.7558617Z 2025-08-26T20:21:57.7564570Z msg = Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3798. 2025-08-26T20:21:57.7565537Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.7565917Z 2025-08-26T20:21:57.7566070Z Gathers tensors from the whole group in a list. 2025-08-26T20:21:57.7566324Z 2025-08-26T20:21:57.7566475Z Complex and uneven sized tensors are supported. 2025-08-26T20:21:57.7566723Z 2025-08-26T20:21:57.7566803Z Args: 2025-08-26T20:21:57.7567099Z tensor_list (list[Tensor]): Output list. It should contain 2025-08-26T20:21:57.7567625Z correctly-sized tensors to be used for output of the collective. 2025-08-26T20:21:57.7568076Z Uneven sized tensors are supported. 2025-08-26T20:21:57.7568494Z tensor (Tensor): Tensor to be broadcast from current process. 2025-08-26T20:21:57.7569020Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:21:57.7569496Z the default process group will be used. 2025-08-26T20:21:57.7569932Z async_op (bool, optional): Whether this op should be an async op 2025-08-26T20:21:57.7570241Z 2025-08-26T20:21:57.7570339Z Returns: 2025-08-26T20:21:57.7570586Z Async work handle, if async_op is set to True. 2025-08-26T20:21:57.7571082Z None, if not async_op or if not part of the group 2025-08-26T20:21:57.7571347Z 2025-08-26T20:21:57.7571435Z Examples: 2025-08-26T20:21:57.7571702Z >>> # xdoctest: +SKIP("need process group init") 2025-08-26T20:21:57.7572076Z >>> # All tensors below are of torch.int64 dtype. 2025-08-26T20:21:57.7572443Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:21:57.7572793Z >>> device = torch.device(f"cuda:{rank}") 2025-08-26T20:21:57.7573118Z >>> tensor_list = [ 2025-08-26T20:21:57.7573490Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-08-26T20:21:57.7573893Z ... ] 2025-08-26T20:21:57.7574106Z >>> tensor_list 2025-08-26T20:21:57.7574451Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-08-26T20:21:57.7575056Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-08-26T20:21:57.7575589Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-08-26T20:21:57.7576036Z >>> tensor 2025-08-26T20:21:57.7576292Z tensor([1, 2], device='cuda:0') # Rank 0 2025-08-26T20:21:57.7576635Z tensor([3, 4], device='cuda:1') # Rank 1 2025-08-26T20:21:57.7576965Z >>> dist.all_gather(tensor_list, tensor) 2025-08-26T20:21:57.7577285Z >>> tensor_list 2025-08-26T20:21:57.7577631Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-08-26T20:21:57.7578145Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-08-26T20:21:57.7578454Z 2025-08-26T20:21:57.7578595Z >>> # All tensors below are of torch.cfloat dtype. 2025-08-26T20:21:57.7578965Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:21:57.7579281Z >>> tensor_list = [ 2025-08-26T20:21:57.7579660Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-08-26T20:21:57.7580078Z ... ] 2025-08-26T20:21:57.7580279Z >>> tensor_list 2025-08-26T20:21:57.7580759Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-08-26T20:21:57.7581367Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-08-26T20:21:57.7581838Z >>> tensor = torch.tensor( 2025-08-26T20:21:57.7582167Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-08-26T20:21:57.7582529Z ... ) + 2 * rank * (1 + 1j) 2025-08-26T20:21:57.7582801Z >>> tensor 2025-08-26T20:21:57.7583078Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-08-26T20:21:57.7583451Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-08-26T20:21:57.7583814Z >>> dist.all_gather(tensor_list, tensor) 2025-08-26T20:21:57.7584130Z >>> tensor_list 2025-08-26T20:21:57.7584518Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-08-26T20:21:57.7585107Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-08-26T20:21:57.7585476Z 2025-08-26T20:21:57.7585480Z 2025-08-26T20:21:57.7585732Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.7586111Z 2025-08-26T20:21:57.7623978Z msg = Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4504. 2025-08-26T20:21:57.7624958Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.7625349Z 2025-08-26T20:21:57.7625599Z Split input tensor and then scatter the split list to all processes in a group. 2025-08-26T20:21:57.7625968Z 2025-08-26T20:21:57.7626240Z Later the received tensors are concatenated from all the processes in the group 2025-08-26T20:21:57.7626727Z and returned as a single output tensor. 2025-08-26T20:21:57.7626959Z 2025-08-26T20:21:57.7627076Z Complex tensors are supported. 2025-08-26T20:21:57.7627279Z 2025-08-26T20:21:57.7627359Z Args: 2025-08-26T20:21:57.7627805Z output (Tensor): Gathered concatenated output tensor. 2025-08-26T20:21:57.7628232Z input (Tensor): Input tensor to scatter. 2025-08-26T20:21:57.7628687Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-08-26T20:21:57.7629224Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-08-26T20:21:57.7629653Z equally by ``world_size``. 2025-08-26T20:21:57.7630072Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-08-26T20:21:57.7630609Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-08-26T20:21:57.7631020Z equally by ``world_size``. 2025-08-26T20:21:57.7631453Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:21:57.7631931Z the default process group will be used. 2025-08-26T20:21:57.7632452Z async_op (bool, optional): Whether this op should be an async op. 2025-08-26T20:21:57.7632765Z 2025-08-26T20:21:57.7632870Z Returns: 2025-08-26T20:21:57.7633123Z Async work handle, if async_op is set to True. 2025-08-26T20:21:57.7633526Z None, if not async_op or if not part of the group. 2025-08-26T20:21:57.7633904Z 2025-08-26T20:21:57.7634052Z .. warning:: 2025-08-26T20:21:57.7634517Z `all_to_all_single` is experimental and subject to change. 2025-08-26T20:21:57.7634961Z 2025-08-26T20:21:57.7635101Z Examples: 2025-08-26T20:21:57.7635377Z >>> # xdoctest: +SKIP("Undefined rank") 2025-08-26T20:21:57.7635984Z >>> input = torch.arange(4) + rank * 4 2025-08-26T20:21:57.7636497Z >>> input 2025-08-26T20:21:57.7636718Z tensor([0, 1, 2, 3]) # Rank 0 2025-08-26T20:21:57.7637020Z tensor([4, 5, 6, 7]) # Rank 1 2025-08-26T20:21:57.7637319Z tensor([8, 9, 10, 11]) # Rank 2 2025-08-26T20:21:57.7637617Z tensor([12, 13, 14, 15]) # Rank 3 2025-08-26T20:21:57.7637956Z >>> output = torch.empty([4], dtype=torch.int64) 2025-08-26T20:21:57.7638336Z >>> dist.all_to_all_single(output, input) 2025-08-26T20:21:57.7638659Z >>> output 2025-08-26T20:21:57.7638892Z tensor([0, 4, 8, 12]) # Rank 0 2025-08-26T20:21:57.7639177Z tensor([1, 5, 9, 13]) # Rank 1 2025-08-26T20:21:57.7639475Z tensor([2, 6, 10, 14]) # Rank 2 2025-08-26T20:21:57.7639799Z tensor([3, 7, 11, 15]) # Rank 3 2025-08-26T20:21:57.7640028Z 2025-08-26T20:21:57.7640203Z >>> # Essentially, it is similar to following operation: 2025-08-26T20:21:57.7640622Z >>> scatter_list = list(input.chunk(world_size)) 2025-08-26T20:21:57.7640999Z >>> gather_list = list(output.chunk(world_size)) 2025-08-26T20:21:57.7641353Z >>> for i in range(world_size): 2025-08-26T20:21:57.7641782Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-08-26T20:21:57.7642126Z 2025-08-26T20:21:57.7642255Z >>> # Another example with uneven split 2025-08-26T20:21:57.7642561Z >>> input 2025-08-26T20:21:57.7642847Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-08-26T20:21:57.7643285Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-08-26T20:21:57.7643720Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-08-26T20:21:57.7644142Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-08-26T20:21:57.7644515Z >>> input_splits 2025-08-26T20:21:57.7644797Z [2, 2, 1, 1] # Rank 0 2025-08-26T20:21:57.7645165Z [3, 2, 2, 2] # Rank 1 2025-08-26T20:21:57.7645530Z [2, 1, 1, 1] # Rank 2 2025-08-26T20:21:57.7645887Z [2, 2, 2, 1] # Rank 3 2025-08-26T20:21:57.7646228Z >>> output_splits 2025-08-26T20:21:57.7646521Z [2, 3, 2, 2] # Rank 0 2025-08-26T20:21:57.7646889Z [2, 2, 1, 2] # Rank 1 2025-08-26T20:21:57.7647350Z [1, 2, 1, 2] # Rank 2 2025-08-26T20:21:57.7647712Z [1, 2, 1, 1] # Rank 3 2025-08-26T20:21:57.7648048Z >>> output = ... 2025-08-26T20:21:57.7648410Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-08-26T20:21:57.7648821Z >>> output 2025-08-26T20:21:57.7649104Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-08-26T20:21:57.7649541Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-08-26T20:21:57.7649975Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-08-26T20:21:57.7650406Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-08-26T20:21:57.7650735Z 2025-08-26T20:21:57.7650740Z 2025-08-26T20:21:57.7650901Z >>> # Another example with tensors of torch.cfloat type. 2025-08-26T20:21:57.7651320Z >>> input = torch.tensor( 2025-08-26T20:21:57.7651654Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-08-26T20:21:57.7652013Z ... ) + 4 * rank * (1 + 1j) 2025-08-26T20:21:57.7652290Z >>> input 2025-08-26T20:21:57.7652590Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-08-26T20:21:57.7653053Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-08-26T20:21:57.7653524Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-08-26T20:21:57.7654010Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-08-26T20:21:57.7654443Z >>> output = torch.empty([4], dtype=torch.int64) 2025-08-26T20:21:57.7654819Z >>> dist.all_to_all_single(output, input) 2025-08-26T20:21:57.7655139Z >>> output 2025-08-26T20:21:57.7655513Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-08-26T20:21:57.7655970Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-08-26T20:21:57.7656438Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-08-26T20:21:57.7656906Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-08-26T20:21:57.7657196Z 2025-08-26T20:21:57.7657457Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.7657822Z 2025-08-26T20:21:57.7658429Z msg = Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4646. 2025-08-26T20:21:57.7659376Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.7659748Z 2025-08-26T20:21:57.7660129Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-08-26T20:21:57.7660695Z 2025-08-26T20:21:57.7660807Z Complex tensors are supported. 2025-08-26T20:21:57.7661016Z 2025-08-26T20:21:57.7661101Z Args: 2025-08-26T20:21:57.7661439Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-08-26T20:21:57.7661873Z per rank. 2025-08-26T20:21:57.7662250Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-08-26T20:21:57.7662836Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:21:57.7663320Z the default process group will be used. 2025-08-26T20:21:57.7663762Z async_op (bool, optional): Whether this op should be an async op. 2025-08-26T20:21:57.7664071Z 2025-08-26T20:21:57.7664158Z Returns: 2025-08-26T20:21:57.7664425Z Async work handle, if async_op is set to True. 2025-08-26T20:21:57.7664829Z None, if not async_op or if not part of the group. 2025-08-26T20:21:57.7665085Z 2025-08-26T20:21:57.7665193Z .. warning:: 2025-08-26T20:21:57.7665471Z `all_to_all` is experimental and subject to change. 2025-08-26T20:21:57.7665743Z 2025-08-26T20:21:57.7665903Z Examples: 2025-08-26T20:21:57.7666151Z >>> # xdoctest: +SKIP("Undefined rank") 2025-08-26T20:21:57.7666497Z >>> input = torch.arange(4) + rank * 4 2025-08-26T20:21:57.7666820Z >>> input = list(input.chunk(4)) 2025-08-26T20:21:57.7667124Z >>> input 2025-08-26T20:21:57.7667431Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-08-26T20:21:57.7667890Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-08-26T20:21:57.7668330Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-08-26T20:21:57.7668789Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-08-26T20:21:57.7669267Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-08-26T20:21:57.7669682Z >>> dist.all_to_all(output, input) 2025-08-26T20:21:57.7669988Z >>> output 2025-08-26T20:21:57.7670327Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-08-26T20:21:57.7670778Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-08-26T20:21:57.7671226Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-08-26T20:21:57.7671672Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-08-26T20:21:57.7671952Z 2025-08-26T20:21:57.7672113Z >>> # Essentially, it is similar to following operation: 2025-08-26T20:21:57.7672497Z >>> scatter_list = input 2025-08-26T20:21:57.7672783Z >>> gather_list = output 2025-08-26T20:21:57.7673071Z >>> for i in range(world_size): 2025-08-26T20:21:57.7673488Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-08-26T20:21:57.7673838Z 2025-08-26T20:21:57.7673925Z >>> input 2025-08-26T20:21:57.7674216Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-08-26T20:21:57.7674654Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-08-26T20:21:57.7675096Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-08-26T20:21:57.7675525Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-08-26T20:21:57.7675903Z >>> input_splits 2025-08-26T20:21:57.7676189Z [2, 2, 1, 1] # Rank 0 2025-08-26T20:21:57.7676555Z [3, 2, 2, 2] # Rank 1 2025-08-26T20:21:57.7676907Z [2, 1, 1, 1] # Rank 2 2025-08-26T20:21:57.7677273Z [2, 2, 2, 1] # Rank 3 2025-08-26T20:21:57.7677608Z >>> output_splits 2025-08-26T20:21:57.7677886Z [2, 3, 2, 2] # Rank 0 2025-08-26T20:21:57.7678250Z [2, 2, 1, 2] # Rank 1 2025-08-26T20:21:57.7678605Z [1, 2, 1, 2] # Rank 2 2025-08-26T20:21:57.7678974Z [1, 2, 1, 1] # Rank 3 2025-08-26T20:21:57.7679349Z >>> input = list(input.split(input_splits)) 2025-08-26T20:21:57.7679678Z >>> input 2025-08-26T20:21:57.7680001Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-08-26T20:21:57.7680529Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-08-26T20:21:57.7681058Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-08-26T20:21:57.7681588Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-08-26T20:21:57.7682012Z >>> output = ... 2025-08-26T20:21:57.7682269Z >>> dist.all_to_all(output, input) 2025-08-26T20:21:57.7682572Z >>> output 2025-08-26T20:21:57.7682908Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-08-26T20:21:57.7683429Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-08-26T20:21:57.7684454Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-08-26T20:21:57.7684985Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-08-26T20:21:57.7685323Z 2025-08-26T20:21:57.7685484Z >>> # Another example with tensors of torch.cfloat type. 2025-08-26T20:21:57.7685867Z >>> input = torch.tensor( 2025-08-26T20:21:57.7686195Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-08-26T20:21:57.7686531Z ... ) + 4 * rank * (1 + 1j) 2025-08-26T20:21:57.7686826Z >>> input = list(input.chunk(4)) 2025-08-26T20:21:57.7687122Z >>> input 2025-08-26T20:21:57.7687469Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-08-26T20:21:57.7688184Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-08-26T20:21:57.7688995Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-08-26T20:21:57.7689897Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-08-26T20:21:57.7690866Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-08-26T20:21:57.7691269Z >>> dist.all_to_all(output, input) 2025-08-26T20:21:57.7691575Z >>> output 2025-08-26T20:21:57.7692116Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-08-26T20:21:57.7692663Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-08-26T20:21:57.7693205Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-08-26T20:21:57.7693736Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-08-26T20:21:57.7694080Z 2025-08-26T20:21:57.7694090Z 2025-08-26T20:21:57.7694343Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.7694729Z 2025-08-26T20:21:57.7695271Z msg = Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-08-26T20:21:57.7696116Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.7696507Z 2025-08-26T20:21:57.7696625Z Module ``torch.distributed.launch``. 2025-08-26T20:21:57.7696853Z 2025-08-26T20:21:57.7697099Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-08-26T20:21:57.7697619Z training processes on each of the training nodes. 2025-08-26T20:21:57.7697878Z 2025-08-26T20:21:57.7697987Z .. warning:: 2025-08-26T20:21:57.7698116Z 2025-08-26T20:21:57.7698367Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-08-26T20:21:57.7698749Z 2025-08-26T20:21:57.7698990Z The utility can be used for single-node distributed training, in which one or 2025-08-26T20:21:57.7699585Z more processes per node will be spawned. The utility can be used for either 2025-08-26T20:21:57.7700165Z CPU training or GPU training. If the utility is used for GPU training, 2025-08-26T20:21:57.7700819Z each distributed process will be operating on a single GPU. This can achieve 2025-08-26T20:21:57.7701411Z well-improved single-node training performance. It can also be used in 2025-08-26T20:21:57.7702031Z multi-node distributed training, by spawning up multiple processes on each node 2025-08-26T20:21:57.7702656Z for well-improved multi-node distributed training performance as well. 2025-08-26T20:21:57.7703242Z This will especially be beneficial for systems with multiple Infiniband 2025-08-26T20:21:57.7703836Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-08-26T20:21:57.7704330Z aggregated communication bandwidth. 2025-08-26T20:21:57.7704556Z 2025-08-26T20:21:57.7704797Z In both cases of single-node distributed training or multi-node distributed 2025-08-26T20:21:57.7705396Z training, this utility will launch the given number of processes per node 2025-08-26T20:21:57.7706124Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-08-26T20:21:57.7706682Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-08-26T20:21:57.7707230Z and each process will be operating on a single GPU from *GPU 0 to 2025-08-26T20:21:57.7707668Z GPU (nproc_per_node - 1)*. 2025-08-26T20:21:57.7707849Z 2025-08-26T20:21:57.7707963Z **How to use this module:** 2025-08-26T20:21:57.7708141Z 2025-08-26T20:21:57.7708294Z 1. Single-Node multi-process distributed training 2025-08-26T20:21:57.7708571Z 2025-08-26T20:21:57.7708659Z :: 2025-08-26T20:21:57.7708782Z 2025-08-26T20:21:57.7709019Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-26T20:21:57.7709647Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-08-26T20:21:57.7710082Z arguments of your training script) 2025-08-26T20:21:57.7710318Z 2025-08-26T20:21:57.7710528Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-08-26T20:21:57.7710867Z 2025-08-26T20:21:57.7710870Z 2025-08-26T20:21:57.7711013Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-08-26T20:21:57.7711277Z 2025-08-26T20:21:57.7711364Z :: 2025-08-26T20:21:57.7711471Z 2025-08-26T20:21:57.7711718Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-26T20:21:57.7712226Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-08-26T20:21:57.7712695Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-08-26T20:21:57.7713172Z and all other arguments of your training script) 2025-08-26T20:21:57.7713447Z 2025-08-26T20:21:57.7713530Z Node 2: 2025-08-26T20:21:57.7713645Z 2025-08-26T20:21:57.7713736Z :: 2025-08-26T20:21:57.7713844Z 2025-08-26T20:21:57.7714091Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-26T20:21:57.7714592Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-08-26T20:21:57.7715069Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-08-26T20:21:57.7715549Z and all other arguments of your training script) 2025-08-26T20:21:57.7715814Z 2025-08-26T20:21:57.7715986Z 3. To look up what optional arguments this module offers: 2025-08-26T20:21:57.7716255Z 2025-08-26T20:21:57.7716354Z :: 2025-08-26T20:21:57.7716464Z 2025-08-26T20:21:57.7716598Z python -m torch.distributed.launch --help 2025-08-26T20:21:57.7716853Z 2025-08-26T20:21:57.7716857Z 2025-08-26T20:21:57.7716954Z **Important Notices:** 2025-08-26T20:21:57.7717129Z 2025-08-26T20:21:57.7717315Z 1. This utility and multi-process distributed (single-node or 2025-08-26T20:21:57.7717868Z multi-node) GPU training currently only achieves the best performance using 2025-08-26T20:21:57.7718480Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-08-26T20:21:57.7718955Z use for GPU training. 2025-08-26T20:21:57.7719125Z 2025-08-26T20:21:57.7719338Z 2. In your training program, you must parse the command-line argument: 2025-08-26T20:21:57.7719904Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-08-26T20:21:57.7720484Z If your training program uses GPUs, you should ensure that your code only 2025-08-26T20:21:57.7721017Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-08-26T20:21:57.7721330Z 2025-08-26T20:21:57.7721435Z Parsing the local_rank argument 2025-08-26T20:21:57.7721640Z 2025-08-26T20:21:57.7721721Z :: 2025-08-26T20:21:57.7721829Z 2025-08-26T20:21:57.7721935Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.7722191Z >>> import argparse 2025-08-26T20:21:57.7722477Z >>> parser = argparse.ArgumentParser() 2025-08-26T20:21:57.7722910Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-08-26T20:21:57.7723333Z >>> args = parser.parse_args() 2025-08-26T20:21:57.7723593Z 2025-08-26T20:21:57.7723728Z Set your device to local rank using either 2025-08-26T20:21:57.7723956Z 2025-08-26T20:21:57.7724038Z :: 2025-08-26T20:21:57.7724159Z 2025-08-26T20:21:57.7724359Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-08-26T20:21:57.7724692Z 2025-08-26T20:21:57.7724776Z or 2025-08-26T20:21:57.7724883Z 2025-08-26T20:21:57.7724979Z :: 2025-08-26T20:21:57.7725084Z 2025-08-26T20:21:57.7725215Z >>> with torch.cuda.device(args.local_rank): 2025-08-26T20:21:57.7725561Z >>> # your code to run 2025-08-26T20:21:57.7725832Z >>> ... 2025-08-26T20:21:57.7725957Z 2025-08-26T20:21:57.7726072Z .. versionchanged:: 2.0.0 2025-08-26T20:21:57.7726242Z 2025-08-26T20:21:57.7726496Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-08-26T20:21:57.7727140Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-08-26T20:21:57.7727643Z previously used underscored ``--local_rank``. 2025-08-26T20:21:57.7727905Z 2025-08-26T20:21:57.7728144Z For backward compatibility, it may be necessary for users to handle both 2025-08-26T20:21:57.7728764Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-08-26T20:21:57.7729364Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-08-26T20:21:57.7729939Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-08-26T20:21:57.7730542Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-08-26T20:21:57.7731041Z including ``"--local-rank"`` should be sufficient. 2025-08-26T20:21:57.7731300Z 2025-08-26T20:21:57.7731543Z 3. In your training program, you are supposed to call the following function 2025-08-26T20:21:57.7732120Z at the beginning to start the distributed backend. It is strongly recommended 2025-08-26T20:21:57.7732716Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-08-26T20:21:57.7733246Z but ``env://`` is the one that is officially supported by this module. 2025-08-26T20:21:57.7733546Z 2025-08-26T20:21:57.7733639Z :: 2025-08-26T20:21:57.7733744Z 2025-08-26T20:21:57.7733961Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-08-26T20:21:57.7734414Z >>> init_method='env://') 2025-08-26T20:21:57.7734663Z 2025-08-26T20:21:57.7734899Z 4. In your training program, you can either use regular distributed functions 2025-08-26T20:21:57.7735496Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-08-26T20:21:57.7736057Z training program uses GPUs for training and you would like to use 2025-08-26T20:21:57.7736573Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-08-26T20:21:57.7736968Z here is how to configure it. 2025-08-26T20:21:57.7737163Z 2025-08-26T20:21:57.7737243Z :: 2025-08-26T20:21:57.7737355Z 2025-08-26T20:21:57.7737561Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-08-26T20:21:57.7738022Z >>> device_ids=[args.local_rank], 2025-08-26T20:21:57.7738416Z >>> output_device=args.local_rank) 2025-08-26T20:21:57.7738691Z 2025-08-26T20:21:57.7738932Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-08-26T20:21:57.7739535Z that your code will be operating on. This is generally the local rank of the 2025-08-26T20:21:57.7740127Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-08-26T20:21:57.7740862Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-08-26T20:21:57.7741274Z utility 2025-08-26T20:21:57.7741404Z 2025-08-26T20:21:57.7741647Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-08-26T20:21:57.7742231Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-08-26T20:21:57.7742787Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-08-26T20:21:57.7743388Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-08-26T20:21:57.7743874Z will not pass ``--local-rank`` when you specify this flag. 2025-08-26T20:21:57.7744182Z 2025-08-26T20:21:57.7744274Z .. warning:: 2025-08-26T20:21:57.7744404Z 2025-08-26T20:21:57.7744620Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-08-26T20:21:57.7745133Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-08-26T20:21:57.7745550Z write to a networked filesystem. See 2025-08-26T20:21:57.7746003Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-08-26T20:21:57.7746508Z how things can go wrong if you don't do this correctly. 2025-08-26T20:21:57.7746784Z 2025-08-26T20:21:57.7746788Z 2025-08-26T20:21:57.7746792Z 2025-08-26T20:21:57.7746849Z 2025-08-26T20:21:57.7747117Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.7747487Z 2025-08-26T20:21:57.8406163Z msg = Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2025-08-26T20:21:57.8407265Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.8407644Z 2025-08-26T20:21:57.8407901Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-08-26T20:21:57.8408401Z Needs to be called on all ranks in an SPMD fashion. 2025-08-26T20:21:57.8408675Z 2025-08-26T20:21:57.8408760Z Args: 2025-08-26T20:21:57.8409156Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-08-26T20:21:57.8409731Z of shards that represent the local shards on this rank. 2025-08-26T20:21:57.8410248Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-08-26T20:21:57.8410715Z shape of the overall sharded tensor. 2025-08-26T20:21:57.8410963Z 2025-08-26T20:21:57.8411056Z Keyword args: 2025-08-26T20:21:57.8411459Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:21:57.8411971Z the default process group will be used. 2025-08-26T20:21:57.8412371Z init_rrefs (bool, optional): Whether or not to initialize 2025-08-26T20:21:57.8412870Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-08-26T20:21:57.8413392Z Need to initialize the RPC Framework if specified as ``True``. 2025-08-26T20:21:57.8413800Z Default: ``False``. 2025-08-26T20:21:57.8413972Z 2025-08-26T20:21:57.8414056Z Returns: 2025-08-26T20:21:57.8414328Z A :class:`ShardedTensor` object handle on this rank 2025-08-26T20:21:57.8414599Z 2025-08-26T20:21:57.8414603Z 2025-08-26T20:21:57.8414688Z Examples: 2025-08-26T20:21:57.8415068Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-08-26T20:21:57.8415624Z each shard have a (5, 5) local tensor, we can do it like below: 2025-08-26T20:21:57.8415923Z 2025-08-26T20:21:57.8416010Z on rank 0: 2025-08-26T20:21:57.8416267Z >>> # xdoctest: +SKIP("not distributed") 2025-08-26T20:21:57.8416623Z >>> local_shard_metadata = ShardMetadata( 2025-08-26T20:21:57.8416963Z >>> shard_offsets=[0, 0], 2025-08-26T20:21:57.8417251Z >>> shard_lengths=[5, 5], 2025-08-26T20:21:57.8417555Z >>> placement="rank:0/cuda:0" 2025-08-26T20:21:57.8417853Z >>> ) 2025-08-26T20:21:57.8418176Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-08-26T20:21:57.8418680Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-08-26T20:21:57.8419002Z 2025-08-26T20:21:57.8419088Z on rank 1: 2025-08-26T20:21:57.8419341Z >>> # xdoctest: +SKIP("not distributed") 2025-08-26T20:21:57.8419694Z >>> local_shard_metadata = ShardMetadata( 2025-08-26T20:21:57.8420015Z >>> shard_offsets=[5, 0], 2025-08-26T20:21:57.8420602Z >>> shard_lengths=[5, 5], 2025-08-26T20:21:57.8420908Z >>> placement="rank:1/cuda:1" 2025-08-26T20:21:57.8421205Z >>> ) 2025-08-26T20:21:57.8421509Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-08-26T20:21:57.8422021Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-08-26T20:21:57.8422345Z 2025-08-26T20:21:57.8422596Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.8422961Z 2025-08-26T20:21:57.8539974Z msg = Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=835. 2025-08-26T20:21:57.8541293Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.8541721Z 2025-08-26T20:21:57.8542177Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-08-26T20:21:57.8542740Z size and sharding spec on each rank. 2025-08-26T20:21:57.8542973Z 2025-08-26T20:21:57.8543056Z Args: 2025-08-26T20:21:57.8543465Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-08-26T20:21:57.8544137Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-08-26T20:21:57.8544690Z The specification describing how to shard the Tensor. 2025-08-26T20:21:57.8545191Z global_size (Sequence[int]): Size of the sharded tensor. 2025-08-26T20:21:57.8545791Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-08-26T20:21:57.8546349Z Default: None 2025-08-26T20:21:57.8546816Z init_rrefs (bool, optional): Whether or not to initialize 2025-08-26T20:21:57.8547414Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-08-26T20:21:57.8547951Z Need to initialize the RPC Framework if specified as ``True``. 2025-08-26T20:21:57.8548361Z Default: ``False``. 2025-08-26T20:21:57.8548533Z 2025-08-26T20:21:57.8548635Z Returns: 2025-08-26T20:21:57.8548992Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-08-26T20:21:57.8549472Z tensor stored in the current rank. 2025-08-26T20:21:57.8549704Z 2025-08-26T20:21:57.8549789Z Examples: 2025-08-26T20:21:57.8550008Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.8550314Z >>> # All tensors below are of torch.int64 type. 2025-08-26T20:21:57.8550667Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:21:57.8551069Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-08-26T20:21:57.8551560Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-08-26T20:21:57.8551973Z >>> local_tensor 2025-08-26T20:21:57.8552214Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-08-26T20:21:57.8552511Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-08-26T20:21:57.8552808Z >>> sharding_dim = 0 2025-08-26T20:21:57.8553099Z >>> sharding_spec = ChunkShardingSpec( 2025-08-26T20:21:57.8553423Z dim=sharding_dim, 2025-08-26T20:21:57.8553701Z placements=[ 2025-08-26T20:21:57.8553972Z "rank:0/cuda:0", 2025-08-26T20:21:57.8554258Z "rank:1/cuda:1", 2025-08-26T20:21:57.8554520Z ], 2025-08-26T20:21:57.8554740Z ) 2025-08-26T20:21:57.8554995Z >>> st = ShardedTensor._init_from_local_tensor( 2025-08-26T20:21:57.8555363Z ... local_tensor, sharding_spec, [2, 4] 2025-08-26T20:21:57.8555672Z ... ) 2025-08-26T20:21:57.8555877Z >>> st 2025-08-26T20:21:57.8556100Z ShardedTensor( 2025-08-26T20:21:57.8556356Z ShardedTensorMetadata( 2025-08-26T20:21:57.8556637Z shards_metadata=[ 2025-08-26T20:21:57.8557089Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-08-26T20:21:57.8557745Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-08-26T20:21:57.8558215Z ], 2025-08-26T20:21:57.8558579Z size=torch.Size([2, 4]) 2025-08-26T20:21:57.8558875Z ) 2025-08-26T20:21:57.8559096Z >>> st.local_tensor() 2025-08-26T20:21:57.8559371Z tensor([1, 2, 3, 4]) # Rank 0 2025-08-26T20:21:57.8559652Z tensor([3, 4, 5, 6]) # Rank 1 2025-08-26T20:21:57.8559853Z 2025-08-26T20:21:57.8560122Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-08-26T20:21:57.8560764Z rank validations, and we only validate the local shard on the current rank. 2025-08-26T20:21:57.8561353Z We fully rely on the user to ensure local tensor is sharded based on the 2025-08-26T20:21:57.8561784Z sharding spec. 2025-08-26T20:21:57.8561959Z 2025-08-26T20:21:57.8562256Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.8562623Z 2025-08-26T20:21:57.8563364Z msg = Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1076. 2025-08-26T20:21:57.8564421Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.8564809Z 2025-08-26T20:21:57.8565062Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-08-26T20:21:57.8565532Z single local shard. 2025-08-26T20:21:57.8565680Z 2025-08-26T20:21:57.8565919Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-08-26T20:21:57.8566484Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-08-26T20:21:57.8566962Z we swap local shards directly. 2025-08-26T20:21:57.8567416Z For more generic cases, we merge different shards across different ranks and split 2025-08-26T20:21:57.8568052Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-08-26T20:21:57.8568417Z 2025-08-26T20:21:57.8568513Z Args: 2025-08-26T20:21:57.8568912Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-08-26T20:21:57.8569517Z specification describing how the tensor is sharded. 2025-08-26T20:21:57.8569805Z 2025-08-26T20:21:57.8569902Z Returns: 2025-08-26T20:21:57.8570231Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-08-26T20:21:57.8570547Z 2025-08-26T20:21:57.8570634Z Examples: 2025-08-26T20:21:57.8570856Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.8571142Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:21:57.8571545Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-08-26T20:21:57.8571947Z >>> tensor = torch.stack([tensor, tensor]) 2025-08-26T20:21:57.8572266Z >>> tensor 2025-08-26T20:21:57.8572608Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-08-26T20:21:57.8572974Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-08-26T20:21:57.8573305Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-08-26T20:21:57.8573668Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-08-26T20:21:57.8574004Z >>> sharding_dim = 0 2025-08-26T20:21:57.8574290Z >>> spec = ChunkShardingSpec( 2025-08-26T20:21:57.8574580Z dim=sharding_dim, 2025-08-26T20:21:57.8574862Z placements=[ 2025-08-26T20:21:57.8575127Z "rank:0/cuda:0", 2025-08-26T20:21:57.8575407Z "rank:1/cuda:1", 2025-08-26T20:21:57.8575676Z "rank:2/cuda:2", 2025-08-26T20:21:57.8575957Z "rank:3/cuda:3", 2025-08-26T20:21:57.8576224Z ], 2025-08-26T20:21:57.8576440Z ) 2025-08-26T20:21:57.8576651Z >>> current_offsets = [0] * 2 2025-08-26T20:21:57.8576957Z >>> current_offsets[0] = rank * 2 2025-08-26T20:21:57.8577282Z >>> shard_metadata = ShardMetadata( 2025-08-26T20:21:57.8577654Z shard_offsets=copy.deepcopy(current_offsets), 2025-08-26T20:21:57.8578017Z shard_sizes=tensor.size(), 2025-08-26T20:21:57.8578361Z placement=spec.placements[rank], 2025-08-26T20:21:57.8578682Z ) 2025-08-26T20:21:57.8578901Z >>> local_shards = [ 2025-08-26T20:21:57.8579224Z Shard( 2025-08-26T20:21:57.8579469Z tensor=tensor, 2025-08-26T20:21:57.8579764Z metadata=shard_metadata, 2025-08-26T20:21:57.8580070Z ) 2025-08-26T20:21:57.8580272Z ] 2025-08-26T20:21:57.8580693Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-08-26T20:21:57.8581140Z >>> sharding_dim = 1 2025-08-26T20:21:57.8581439Z >>> resharding_spec = ChunkShardingSpec( 2025-08-26T20:21:57.8581761Z dim=sharding_dim, 2025-08-26T20:21:57.8582046Z placements=[ 2025-08-26T20:21:57.8582312Z "rank:0/cuda:0", 2025-08-26T20:21:57.8582597Z "rank:1/cuda:1", 2025-08-26T20:21:57.8582871Z "rank:2/cuda:2", 2025-08-26T20:21:57.8583148Z "rank:3/cuda:3", 2025-08-26T20:21:57.8583478Z ], 2025-08-26T20:21:57.8583694Z ) 2025-08-26T20:21:57.8583910Z >>> st.reshard(resharding_spec) 2025-08-26T20:21:57.8584241Z >>> tensor = st.local_shards()[0].tensor 2025-08-26T20:21:57.8584551Z >>> tensor 2025-08-26T20:21:57.8584826Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-08-26T20:21:57.8585205Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-08-26T20:21:57.8585596Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-08-26T20:21:57.8585990Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-08-26T20:21:57.8586242Z 2025-08-26T20:21:57.8586506Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.8586871Z 2025-08-26T20:21:57.8743924Z msg = Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2025-08-26T20:21:57.8744930Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.8745367Z 2025-08-26T20:21:57.8745588Z Representation of a sharding plan, describes how to shard a module 2025-08-26T20:21:57.8746222Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-08-26T20:21:57.8746903Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-08-26T20:21:57.8747557Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-08-26T20:21:57.8747919Z 2025-08-26T20:21:57.8748017Z Args: 2025-08-26T20:21:57.8748394Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-08-26T20:21:57.8748951Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-08-26T20:21:57.8749503Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-08-26T20:21:57.8750156Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-08-26T20:21:57.8750656Z a parameter to a `ShardingSpec`. 2025-08-26T20:21:57.8751143Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-08-26T20:21:57.8751626Z to a `Sharder` object. 2025-08-26T20:21:57.8752180Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-08-26T20:21:57.8752901Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-08-26T20:21:57.8753515Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-08-26T20:21:57.8753975Z Default: `None` 2025-08-26T20:21:57.8754403Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-08-26T20:21:57.8755026Z a module's sharded output to be returned as a Tensor from its local shards to 2025-08-26T20:21:57.8755643Z ensure further processing in a data parallel fashion. ("" in list means the 2025-08-26T20:21:57.8756099Z root module). 2025-08-26T20:21:57.8756355Z Default: None 2025-08-26T20:21:57.8756782Z Example: 2025-08-26T20:21:57.8757196Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-08-26T20:21:57.8757879Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-08-26T20:21:57.8758296Z 2025-08-26T20:21:57.8758468Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-08-26T20:21:57.8758869Z >>> class MyModule(nn.Module): 2025-08-26T20:21:57.8759192Z >>> def __init__(self) -> None: 2025-08-26T20:21:57.8759497Z >>> super().__init__() 2025-08-26T20:21:57.8759803Z >>> self.fc1 = nn.Linear() 2025-08-26T20:21:57.8760115Z >>> self.gelu = nn.GELU() 2025-08-26T20:21:57.8760423Z >>> self.fc2 = nn.Linear() 2025-08-26T20:21:57.8760722Z >>> self.relu = nn.Linear() 2025-08-26T20:21:57.8761019Z >>> 2025-08-26T20:21:57.8761381Z >>> def forward(self, input): 2025-08-26T20:21:57.8761768Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-08-26T20:21:57.8762058Z 2025-08-26T20:21:57.8762062Z 2025-08-26T20:21:57.8762205Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-08-26T20:21:57.8762553Z >>> sharding_plan = ShardingPlan( 2025-08-26T20:21:57.8762856Z >>> plan={ 2025-08-26T20:21:57.8763099Z >>> "fc1.weight": spec1, 2025-08-26T20:21:57.8763403Z >>> "fc2.weight": spec2 2025-08-26T20:21:57.8763674Z >>> }, 2025-08-26T20:21:57.8763900Z >>> output_plan={ 2025-08-26T20:21:57.8764172Z >>> "fc2": output_spec 2025-08-26T20:21:57.8764452Z >>> }, 2025-08-26T20:21:57.8764678Z >>> return_local_tensor=["fc2"] 2025-08-26T20:21:57.8764979Z >>> ) 2025-08-26T20:21:57.8765095Z 2025-08-26T20:21:57.8765360Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.8765730Z 2025-08-26T20:21:57.9710613Z msg = Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2025-08-26T20:21:57.9711759Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.9712137Z 2025-08-26T20:21:57.9712249Z Run post-localSGD algorithm. 2025-08-26T20:21:57.9712455Z 2025-08-26T20:21:57.9712692Z This DDP communication hook is used for running post-localSGD algorithm, 2025-08-26T20:21:57.9713209Z by combining with a model averaging component (e.g., 2025-08-26T20:21:57.9713807Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-08-26T20:21:57.9714360Z that runs after the optimizer step. 2025-08-26T20:21:57.9714580Z 2025-08-26T20:21:57.9714661Z Args: 2025-08-26T20:21:57.9715008Z state (PostLocalSGDState): State information to run post-localSGD. 2025-08-26T20:21:57.9715629Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-08-26T20:21:57.9716449Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-08-26T20:21:57.9717238Z Note that since DDP comm hook only supports single process single device mode, 2025-08-26T20:21:57.9717762Z only exactly one tensor is stored in this bucket. 2025-08-26T20:21:57.9718035Z 2025-08-26T20:21:57.9718122Z Returns: 2025-08-26T20:21:57.9718488Z Future handler of the communication, which updates the gradients in place. 2025-08-26T20:21:57.9718846Z 2025-08-26T20:21:57.9718963Z Example:: 2025-08-26T20:21:57.9719171Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.9719599Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-08-26T20:21:57.9720096Z start_localSGD_iter=10) 2025-08-26T20:21:57.9720586Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-08-26T20:21:57.9721199Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-08-26T20:21:57.9722185Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-08-26T20:21:57.9722663Z 2025-08-26T20:21:57.9722915Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.9723283Z 2025-08-26T20:21:57.9765234Z msg = Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2025-08-26T20:21:57.9766347Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.9766723Z 2025-08-26T20:21:57.9766849Z Implement PowerSGD algorithm. 2025-08-26T20:21:57.9767041Z 2025-08-26T20:21:57.9767280Z This DDP communication hook implements PowerSGD gradient compression 2025-08-26T20:21:57.9768000Z algorithm described in the `paper `_. 2025-08-26T20:21:57.9768600Z Once gradient tensors are aggregated across all workers, this hook applies 2025-08-26T20:21:57.9769068Z compression as follows: 2025-08-26T20:21:57.9769232Z 2025-08-26T20:21:57.9769682Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-08-26T20:21:57.9770234Z 2025-08-26T20:21:57.9770666Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-08-26T20:21:57.9771199Z 2025-08-26T20:21:57.9771607Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-08-26T20:21:57.9772142Z 2025-08-26T20:21:57.9772252Z 2. Handles uncompressed tensors: 2025-08-26T20:21:57.9772461Z 2025-08-26T20:21:57.9772980Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-08-26T20:21:57.9773622Z 2025-08-26T20:21:57.9773962Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-08-26T20:21:57.9774423Z 2025-08-26T20:21:57.9774668Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-08-26T20:21:57.9775013Z 2025-08-26T20:21:57.9775264Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-08-26T20:21:57.9775932Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-08-26T20:21:57.9776369Z 2025-08-26T20:21:57.9776514Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-08-26T20:21:57.9776778Z 2025-08-26T20:21:57.9776884Z 3.3. Allreduces Ps as a batch; 2025-08-26T20:21:57.9777088Z 2025-08-26T20:21:57.9777218Z 3.4. Orthogonalizes each P in Ps; 2025-08-26T20:21:57.9777430Z 2025-08-26T20:21:57.9777639Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-08-26T20:21:57.9777948Z 2025-08-26T20:21:57.9778052Z 3.6. Allreduces Qs as a batch; 2025-08-26T20:21:57.9778271Z 2025-08-26T20:21:57.9778567Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-08-26T20:21:57.9778995Z 2025-08-26T20:21:57.9779407Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-08-26T20:21:57.9780218Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-08-26T20:21:57.9781133Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-08-26T20:21:57.9781682Z 2025-08-26T20:21:57.9781762Z Args: 2025-08-26T20:21:57.9782321Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-08-26T20:21:57.9783236Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-08-26T20:21:57.9783826Z and ``min_compression_rate``. 2025-08-26T20:21:57.9784545Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-08-26T20:21:57.9785318Z Note that since DDP comm hook only supports single process single device mode, 2025-08-26T20:21:57.9785850Z only exactly one tensor is stored in this bucket. 2025-08-26T20:21:57.9786127Z 2025-08-26T20:21:57.9786214Z Returns: 2025-08-26T20:21:57.9786587Z Future handler of the communication, which updates the gradients in place. 2025-08-26T20:21:57.9786947Z 2025-08-26T20:21:57.9787061Z Example:: 2025-08-26T20:21:57.9787276Z >>> # xdoctest: +SKIP 2025-08-26T20:21:57.9787726Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-08-26T20:21:57.9799789Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-08-26T20:21:57.9800443Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-08-26T20:21:57.9800724Z 2025-08-26T20:21:57.9801004Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.9801374Z 2025-08-26T20:21:57.9814083Z msg = Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38. 2025-08-26T20:21:57.9815210Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.9815589Z 2025-08-26T20:21:57.9815782Z Averages parameters periodically after the warm-up stage. 2025-08-26T20:21:57.9816095Z 2025-08-26T20:21:57.9816354Z This can be used for running `post-local SGD `_, 2025-08-26T20:21:57.9816927Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-08-26T20:21:57.9817474Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-08-26T20:21:57.9817829Z 2025-08-26T20:21:57.9817931Z Args: 2025-08-26T20:21:57.9818207Z period (int): The number of steps per model averaging. 2025-08-26T20:21:57.9818763Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-08-26T20:21:57.9819286Z Otherwise, only DDP needs to be used. 2025-08-26T20:21:57.9819737Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-08-26T20:21:57.9820175Z model averaging is skipped. 2025-08-26T20:21:57.9820705Z process_group: The process group to be used for all-reduce. 2025-08-26T20:21:57.9821157Z If ``None``, the default process group, which 2025-08-26T20:21:57.9821612Z is created by :func:`torch.distributed.init_process_group`, 2025-08-26T20:21:57.9822047Z will be used. (default: ``None``) 2025-08-26T20:21:57.9822272Z 2025-08-26T20:21:57.9822390Z Example:: 2025-08-26T20:21:57.9822508Z 2025-08-26T20:21:57.9822630Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:57.9822958Z >>> import torch 2025-08-26T20:21:57.9823235Z >>> import torch.distributed as dist 2025-08-26T20:21:57.9823776Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-08-26T20:21:57.9824470Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-08-26T20:21:57.9824973Z >>> import torch.nn as nn 2025-08-26T20:21:57.9825253Z >>> 2025-08-26T20:21:57.9825553Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-08-26T20:21:57.9825947Z >>> torch.cuda.set_device(rank) 2025-08-26T20:21:57.9826288Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-08-26T20:21:57.9826685Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-26T20:21:57.9827084Z >>> module, device_ids=[rank], output_device=rank 2025-08-26T20:21:57.9827415Z >>> ) 2025-08-26T20:21:57.9827676Z >>> # Register a post-localSGD communication hook. 2025-08-26T20:21:57.9828249Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-08-26T20:21:57.9829013Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-26T20:21:57.9829378Z >>> 2025-08-26T20:21:57.9829755Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-08-26T20:21:57.9830305Z >>> # After 100 steps, run model averaging every 4 steps. 2025-08-26T20:21:57.9830900Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-26T20:21:57.9831587Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-08-26T20:21:57.9832063Z >>> for step in range(0, 200): 2025-08-26T20:21:57.9832362Z >>> optimizer.zero_grad() 2025-08-26T20:21:57.9832670Z >>> loss = loss_fn(output, labels) 2025-08-26T20:21:57.9832994Z >>> loss.backward() 2025-08-26T20:21:57.9833331Z >>> optimizer.step() 2025-08-26T20:21:57.9833878Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-08-26T20:21:57.9834672Z >>> # inter-node communication only occurs every 4 iterations after 2025-08-26T20:21:57.9835134Z >>> # the initial ``warmup_steps`` period. 2025-08-26T20:21:57.9835616Z >>> averager.average_parameters(model.parameters()) 2025-08-26T20:21:57.9835885Z 2025-08-26T20:21:57.9836135Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.9836514Z 2025-08-26T20:21:57.9837408Z msg = Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19. 2025-08-26T20:21:57.9838608Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:57.9838994Z 2025-08-26T20:21:57.9839336Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-08-26T20:21:57.9839799Z 2025-08-26T20:21:57.9840106Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-08-26T20:21:57.9840744Z by using different periods concurrently after the warm-up stage. 2025-08-26T20:21:57.9841465Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-08-26T20:21:57.9842312Z that supports `post-local SGD `_, which essentially only supports 2025-08-26T20:21:57.9843069Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-08-26T20:21:57.9843844Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-08-26T20:21:57.9844612Z Similarly, the process groups within this class do not have such an intra-machine process 2025-08-26T20:21:57.9845302Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-08-26T20:21:57.9845696Z 2025-08-26T20:21:57.9845778Z Args: 2025-08-26T20:21:57.9846159Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-08-26T20:21:57.9846747Z process group size, used for initializing process groups of 2025-08-26T20:21:57.9847296Z different sizes in a hierarchy to average parameters concurrently. 2025-08-26T20:21:57.9847857Z Particularly, at each iteration, there will be at most a single 2025-08-26T20:21:57.9848415Z process group that runs averaging -- the period of such group should 2025-08-26T20:21:57.9848986Z have the largest period which the current step can be divided by. 2025-08-26T20:21:57.9849494Z For example, if the dict has three keys: 2, 4, and 8, 2025-08-26T20:21:57.9849988Z then this means totally three process groups will be created to 2025-08-26T20:21:57.9850535Z average parameters every 2, 4, and 8 iterations, respectively. 2025-08-26T20:21:57.9851136Z At the 4th iteration, only the second process group will run 2025-08-26T20:21:57.9851628Z averaging, because the first process group should be a 2025-08-26T20:21:57.9852151Z subset of the second process group, and no need to execute the first 2025-08-26T20:21:57.9852621Z process group redundantly. 2025-08-26T20:21:57.9853046Z On the other hand, the third process group can only be triggered 2025-08-26T20:21:57.9853595Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-08-26T20:21:57.9854241Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-08-26T20:21:57.9855169Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-08-26T20:21:57.9855910Z If ``None``, the default process group, which is created 2025-08-26T20:21:57.9856409Z by :func:`torch.distributed.init_process_group`, will be used. 2025-08-26T20:21:57.9856872Z (default: ``None``) 2025-08-26T20:21:57.9857122Z 2025-08-26T20:21:57.9857221Z Example:: 2025-08-26T20:21:57.9857463Z >>> # xdoctest: +SKIP('undefined rank') 2025-08-26T20:21:57.9857812Z >>> from collections import OrderedDict 2025-08-26T20:21:57.9858122Z >>> import torch 2025-08-26T20:21:57.9858397Z >>> import torch.distributed as dist 2025-08-26T20:21:57.9858898Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-08-26T20:21:57.9859404Z >>> PostLocalSGDState, 2025-08-26T20:21:57.9859685Z >>> post_localSGD_hook, 2025-08-26T20:21:57.9859962Z >>> ) 2025-08-26T20:21:57.9860554Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-08-26T20:21:57.9861166Z >>> import torch.nn as nn 2025-08-26T20:21:57.9861428Z >>> 2025-08-26T20:21:57.9861730Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-08-26T20:21:57.9862136Z >>> torch.cuda.set_device(rank) 2025-08-26T20:21:57.9862484Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-08-26T20:21:57.9862875Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-26T20:21:57.9863324Z >>> module, device_ids=[rank], output_device=rank 2025-08-26T20:21:57.9863665Z >>> ) 2025-08-26T20:21:57.9863935Z >>> # Register a post-localSGD communication hook. 2025-08-26T20:21:57.9864455Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-08-26T20:21:57.9864974Z >>> subgroup, _ = dist.new_subgroups() 2025-08-26T20:21:57.9865522Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-08-26T20:21:57.9866121Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-26T20:21:57.9866482Z >>> 2025-08-26T20:21:57.9866869Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-08-26T20:21:57.9867397Z >>> # the 16 processes every 16 iterations. 2025-08-26T20:21:57.9867821Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-08-26T20:21:57.9868351Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-08-26T20:21:57.9869011Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-26T20:21:57.9869710Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-08-26T20:21:57.9870253Z >>> # After 100 steps, run model averaging at two levels. 2025-08-26T20:21:57.9870631Z >>> for step in range(0, 200): 2025-08-26T20:21:57.9870945Z >>> optimizer.zero_grad() 2025-08-26T20:21:57.9871244Z >>> loss = loss_fn(output, labels) 2025-08-26T20:21:57.9871635Z >>> loss.backward() 2025-08-26T20:21:57.9871915Z >>> optimizer.step() 2025-08-26T20:21:57.9872254Z >>> # Average parameters after ``optimizer.step()``. 2025-08-26T20:21:57.9872801Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-08-26T20:21:57.9873369Z >>> averager.average_parameters(model.parameters()) 2025-08-26T20:21:57.9873650Z 2025-08-26T20:21:57.9873742Z .. warning :: 2025-08-26T20:21:57.9874129Z The last group size in the dict must be the size of the provided ``process_group``, 2025-08-26T20:21:57.9874724Z which indicates model averaging at the highest level of the hierarchy. 2025-08-26T20:21:57.9875376Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-08-26T20:21:57.9875808Z 2025-08-26T20:21:57.9875950Z .. warning :: 2025-08-26T20:21:57.9876322Z `HierarchicalModelAverager` is experimental and subject to change. 2025-08-26T20:21:57.9876674Z 2025-08-26T20:21:57.9876934Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:57.9877299Z 2025-08-26T20:21:58.0351741Z msg = Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2025-08-26T20:21:58.0353894Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0354567Z 2025-08-26T20:21:58.0355118Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-08-26T20:21:58.0356386Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-08-26T20:21:58.0357105Z 2025-08-26T20:21:58.0357406Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-08-26T20:21:58.0357939Z 2025-08-26T20:21:58.0358141Z .. warning:: 2025-08-26T20:21:58.0358681Z Current implementation only supports loading Tensors. 2025-08-26T20:21:58.0359225Z 2025-08-26T20:21:58.0359435Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.0359929Z >>> sd = {"mode": model} 2025-08-26T20:21:58.0360203Z >>> dcp.load( 2025-08-26T20:21:58.0360427Z >>> sd, 2025-08-26T20:21:58.0360712Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-08-26T20:21:58.0361112Z >>> planner=DynamicMetaLoadPlanner(), 2025-08-26T20:21:58.0361461Z >>> checkpoint_id="path_to_model.pt" 2025-08-26T20:21:58.0361770Z >>> ) 2025-08-26T20:21:58.0361897Z 2025-08-26T20:21:58.0362165Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0362536Z 2025-08-26T20:21:58.0363236Z msg = Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2025-08-26T20:21:58.0364292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0364683Z 2025-08-26T20:21:58.0365050Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-08-26T20:21:58.0365868Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-08-26T20:21:58.0366459Z metadata file, like Torch Save files. 2025-08-26T20:21:58.0366832Z 2025-08-26T20:21:58.0367170Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-08-26T20:21:58.0367686Z 2025-08-26T20:21:58.0367844Z .. warning:: 2025-08-26T20:21:58.0368344Z Current implementation only supports loading Tensors. 2025-08-26T20:21:58.0368869Z 2025-08-26T20:21:58.0369070Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.0369638Z >>> sd = {"mode": model} 2025-08-26T20:21:58.0370086Z >>> dcp.load( 2025-08-26T20:21:58.0370474Z >>> sd, 2025-08-26T20:21:58.0370971Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-08-26T20:21:58.0371701Z >>> planner=DynamicMetaLoadPlanner(), 2025-08-26T20:21:58.0372314Z >>> checkpoint_id="path_to_model.pt" 2025-08-26T20:21:58.0373183Z >>> ) 2025-08-26T20:21:58.0373403Z 2025-08-26T20:21:58.0373868Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0374579Z 2025-08-26T20:21:58.0472145Z msg = Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=221. 2025-08-26T20:21:58.0473243Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0473640Z 2025-08-26T20:21:58.0473865Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-08-26T20:21:58.0474186Z 2025-08-26T20:21:58.0474350Z This is the current recommended way to checkpoint FSDP. 2025-08-26T20:21:58.0474728Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.0475042Z >>> import torch.distributed.checkpoint as dist_cp 2025-08-26T20:21:58.0475598Z >>> # Save 2025-08-26T20:21:58.0475818Z >>> model: torch.nn.Model 2025-08-26T20:21:58.0476114Z >>> optim_params = model.parameters() 2025-08-26T20:21:58.0476488Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-08-26T20:21:58.0476832Z >>> # Save 2025-08-26T20:21:58.0477167Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-08-26T20:21:58.0477767Z >>> state_dict = { 2025-08-26T20:21:58.0478321Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-08-26T20:21:58.0478986Z >>> "model": model.state_dict() 2025-08-26T20:21:58.0479511Z >>> } 2025-08-26T20:21:58.0479714Z >>> dist_cp.save_state_dict( 2025-08-26T20:21:58.0479897Z >>> state_dict=optim_state, 2025-08-26T20:21:58.0480230Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-08-26T20:21:58.0480467Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-08-26T20:21:58.0480603Z >>> ) 2025-08-26T20:21:58.0480758Z >>> 2025-08-26T20:21:58.0480923Z >>> # Load 2025-08-26T20:21:58.0481359Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-08-26T20:21:58.0481598Z >>> model_state_dict = model_tp.state_dict() 2025-08-26T20:21:58.0481777Z >>> checkpoint = { 2025-08-26T20:21:58.0481981Z >>> "model": model_state_dict 2025-08-26T20:21:58.0482127Z >>> } 2025-08-26T20:21:58.0482324Z >>> dist_cp.load_state_dict( 2025-08-26T20:21:58.0482506Z >>> state_dict=checkpoint, 2025-08-26T20:21:58.0482839Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-08-26T20:21:58.0483085Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-08-26T20:21:58.0483235Z >>> ) 2025-08-26T20:21:58.0483522Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-08-26T20:21:58.0483673Z >>> 2025-08-26T20:21:58.0483996Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-08-26T20:21:58.0484204Z >>> model_state_dict, 2025-08-26T20:21:58.0484436Z >>> optimizer_key="optimizer", 2025-08-26T20:21:58.0484765Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-08-26T20:21:58.0484937Z >>> ) 2025-08-26T20:21:58.0485078Z >>> 2025-08-26T20:21:58.0485338Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-08-26T20:21:58.0485578Z >>> model, optim, optim_state["optimizer"] 2025-08-26T20:21:58.0485728Z >>> ) 2025-08-26T20:21:58.0485877Z >>> 2025-08-26T20:21:58.0486111Z >>> optim.load_state_dict(flattened_osd) 2025-08-26T20:21:58.0486121Z 2025-08-26T20:21:58.0486603Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0486613Z 2025-08-26T20:21:58.0508683Z msg = Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-08-26T20:21:58.0509253Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0509264Z 2025-08-26T20:21:58.0509824Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-08-26T20:21:58.0509834Z 2025-08-26T20:21:58.0510622Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-08-26T20:21:58.0510647Z 2025-08-26T20:21:58.0511175Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-08-26T20:21:58.0511393Z will be visible to the whole process. 2025-08-26T20:21:58.0511402Z 2025-08-26T20:21:58.0511953Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-08-26T20:21:58.0511962Z 2025-08-26T20:21:58.0512193Z 1) set_up_planner - called on all ranks. 2025-08-26T20:21:58.0512437Z Signals the start of a checkpoint save. 2025-08-26T20:21:58.0512445Z 2025-08-26T20:21:58.0512672Z 2) create_local_plan - called on all ranks. 2025-08-26T20:21:58.0513228Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-08-26T20:21:58.0513235Z 2025-08-26T20:21:58.0513708Z 3) create_global_plan - called on the coordinator rank only. 2025-08-26T20:21:58.0514081Z Takes the SavePlan from all ranks and make any global decision. 2025-08-26T20:21:58.0514096Z 2025-08-26T20:21:58.0514320Z 4) finish_plan - called on all ranks. 2025-08-26T20:21:58.0514727Z This gives each rank a chance to adjust to global planning decisions. 2025-08-26T20:21:58.0514735Z 2025-08-26T20:21:58.0515029Z 5) resolve_data - called multiple times on each rank 2025-08-26T20:21:58.0515420Z Lookups a value on the `state_dict` for the storage layer to write. 2025-08-26T20:21:58.0515427Z 2025-08-26T20:21:58.0516007Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-08-26T20:21:58.0516375Z most changes can be expressed by changes in a single method. 2025-08-26T20:21:58.0516383Z 2025-08-26T20:21:58.0516605Z There are 3 usual patterns of extension: 2025-08-26T20:21:58.0516612Z 2025-08-26T20:21:58.0517089Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-08-26T20:21:58.0517460Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-08-26T20:21:58.0517475Z 2025-08-26T20:21:58.0517666Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.0517884Z >>> class RenamePlanner(DefaultSavePlanner): 2025-08-26T20:21:58.0518058Z >>> def set_up_planner( 2025-08-26T20:21:58.0518216Z >>> self, 2025-08-26T20:21:58.0518392Z >>> state_dict: STATE_DICT_TYPE, 2025-08-26T20:21:58.0518610Z >>> storage_meta: Optional[StorageMeta], 2025-08-26T20:21:58.0518788Z >>> is_coordinator: bool, 2025-08-26T20:21:58.0518942Z >>> ) -> None: 2025-08-26T20:21:58.0519144Z >>> # prefix all keys with `foo_`` 2025-08-26T20:21:58.0519666Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-08-26T20:21:58.0519676Z 2025-08-26T20:21:58.0520328Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-08-26T20:21:58.0520337Z 2025-08-26T20:21:58.0520544Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.0520781Z >>> class FP16Planner(DefaultSavePlanner): 2025-08-26T20:21:58.0520987Z >>> def create_local_plan(self): 2025-08-26T20:21:58.0521199Z >>> plan = super().create_local_plan() 2025-08-26T20:21:58.0521385Z >>> for p in plan: 2025-08-26T20:21:58.0521590Z >>> if p.tensor_data is not None: 2025-08-26T20:21:58.0521875Z >>> p.tensor_data.properties.dtype = torch.float16 2025-08-26T20:21:58.0522056Z >>> return plan 2025-08-26T20:21:58.0522200Z >>> 2025-08-26T20:21:58.0522419Z >>> def resolve_data(self, write_item): 2025-08-26T20:21:58.0522641Z >>> item = super().resolve_data(write_item) 2025-08-26T20:21:58.0523158Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-08-26T20:21:58.0523167Z 2025-08-26T20:21:58.0523851Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-08-26T20:21:58.0524765Z 2025-08-26T20:21:58.0524992Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.0525749Z >>> from itertools import zip_longest 2025-08-26T20:21:58.0526335Z >>> from dataclasses import replace 2025-08-26T20:21:58.0527013Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-08-26T20:21:58.0527701Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-08-26T20:21:58.0528226Z >>> # This sample doesn't handle ShardedTensors 2025-08-26T20:21:58.0528607Z >>> def create_global_plan(self, all_plans): 2025-08-26T20:21:58.0529004Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-08-26T20:21:58.0529378Z >>> items_per_rank = [ 2025-08-26T20:21:58.0529757Z >>> [item for item in items if item is not None] 2025-08-26T20:21:58.0530175Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-08-26T20:21:58.0530523Z >>> ] 2025-08-26T20:21:58.0530873Z >>> all_plans = [ 2025-08-26T20:21:58.0531164Z >>> replace(plan, items=items) 2025-08-26T20:21:58.0531581Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-08-26T20:21:58.0531969Z >>> ] 2025-08-26T20:21:58.0532251Z >>> return super().create_global_plan(all_plans) 2025-08-26T20:21:58.0532516Z 2025-08-26T20:21:58.0532857Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-08-26T20:21:58.0534049Z accomplished by having each rank contribute their data items in the local plan and 2025-08-26T20:21:58.0534948Z the global planner aggregate them: 2025-08-26T20:21:58.0535338Z 2025-08-26T20:21:58.0535540Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.0536220Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-08-26T20:21:58.0536955Z >>> def create_local_plan(self) -> SavePlan: 2025-08-26T20:21:58.0537630Z >>> plan = super().create_local_plan() 2025-08-26T20:21:58.0538322Z >>> return replace(plan, planner_data="per-rank-data") 2025-08-26T20:21:58.0538977Z >>> 2025-08-26T20:21:58.0539745Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-08-26T20:21:58.0540999Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-08-26T20:21:58.0541826Z >>> merged_data = [p.planner_data for p in global_plan] 2025-08-26T20:21:58.0542650Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-08-26T20:21:58.0543372Z >>> return global_plan, metadata 2025-08-26T20:21:58.0543792Z 2025-08-26T20:21:58.0544273Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0544983Z 2025-08-26T20:21:58.0546192Z msg = Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-08-26T20:21:58.0548067Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0548788Z 2025-08-26T20:21:58.0549319Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-08-26T20:21:58.0550043Z 2025-08-26T20:21:58.0550541Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-08-26T20:21:58.0551357Z 2025-08-26T20:21:58.0551878Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-08-26T20:21:58.0552857Z will be visible to the whole process. 2025-08-26T20:21:58.0553262Z 2025-08-26T20:21:58.0553790Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-08-26T20:21:58.0554550Z 2025-08-26T20:21:58.0554793Z 1) set_up_planner - called on all ranks. 2025-08-26T20:21:58.0555446Z Signals the start of loading a checkpoint. 2025-08-26T20:21:58.0555921Z 2025-08-26T20:21:58.0556150Z 2) create_local_plan - called on all ranks. 2025-08-26T20:21:58.0557154Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-08-26T20:21:58.0557934Z 2025-08-26T20:21:58.0558427Z 3) create_global_plan - called on the coordinator rank only. 2025-08-26T20:21:58.0559348Z Takes the LoadPlan from all ranks and make any global decision. 2025-08-26T20:21:58.0559964Z 2025-08-26T20:21:58.0560224Z 4) load_bytes - called multiple times on each rank 2025-08-26T20:21:58.0561026Z This is called once per non-tensor value in state_dict. 2025-08-26T20:21:58.0561563Z 2025-08-26T20:21:58.0562001Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-08-26T20:21:58.0562991Z They are called in pair for each Tensor value in state_dict. 2025-08-26T20:21:58.0563524Z 2025-08-26T20:21:58.0564094Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-08-26T20:21:58.0565207Z most changes can be expressed by changes in a single method. 2025-08-26T20:21:58.0565797Z 2025-08-26T20:21:58.0566126Z There are two usual patterns of extension: 2025-08-26T20:21:58.0566557Z 2025-08-26T20:21:58.0567054Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-08-26T20:21:58.0568201Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-08-26T20:21:58.0569379Z to keep a reference to the original state_dict as load happens in place so 2025-08-26T20:21:58.0570275Z we need to be able to perform it in place 2025-08-26T20:21:58.0570723Z 2025-08-26T20:21:58.0570927Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.0571568Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-08-26T20:21:58.0572179Z >>> def set_up_planner( 2025-08-26T20:21:58.0572627Z >>> self, 2025-08-26T20:21:58.0573058Z >>> state_dict: STATE_DICT_TYPE, 2025-08-26T20:21:58.0573621Z >>> metadata: Metadata, 2025-08-26T20:21:58.0574155Z >>> is_coordinator: bool, 2025-08-26T20:21:58.0574666Z >>> ) -> None: 2025-08-26T20:21:58.0575154Z >>> self.original_state_dict = state_dict 2025-08-26T20:21:58.0575918Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-08-26T20:21:58.0576633Z >>> 2025-08-26T20:21:58.0577040Z >>> if self.flatten_sharded_tensors: 2025-08-26T20:21:58.0577755Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-08-26T20:21:58.0578413Z >>> 2025-08-26T20:21:58.0578816Z >>> if self.flatten_state_dict: 2025-08-26T20:21:58.0579550Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-08-26T20:21:58.0580291Z >>> 2025-08-26T20:21:58.0580788Z >>> self.state_dict = state_dict 2025-08-26T20:21:58.0581385Z >>> self.metadata = metadata 2025-08-26T20:21:58.0581945Z >>> self.is_coordinator = is_coordinator 2025-08-26T20:21:58.0582524Z >>> 2025-08-26T20:21:58.0582913Z >>> def load_bytes(self, read_item, value): 2025-08-26T20:21:58.0583434Z >>> # Remove the "foo_" prefix 2025-08-26T20:21:58.0583948Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-08-26T20:21:58.0584398Z 2025-08-26T20:21:58.0584408Z 2025-08-26T20:21:58.0584668Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-08-26T20:21:58.0585060Z 2025-08-26T20:21:58.0585173Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.0585556Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-08-26T20:21:58.0585950Z >>> def resolve_tensor(self, read_item): 2025-08-26T20:21:58.0586303Z >>> tensor = super().resolve_tensor(read_item) 2025-08-26T20:21:58.0586703Z >>> return torch.empty_like(tensor, device="cpu") 2025-08-26T20:21:58.0587044Z >>> 2025-08-26T20:21:58.0587295Z >>> def commit_tensor(self, read_item, tensor): 2025-08-26T20:21:58.0587686Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-08-26T20:21:58.0588041Z 2025-08-26T20:21:58.0588462Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0589156Z 2025-08-26T20:21:58.0750542Z msg = Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-08-26T20:21:58.0752780Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0753542Z 2025-08-26T20:21:58.0753873Z Return the model state_dict and optimizers state_dict. 2025-08-26T20:21:58.0754409Z 2025-08-26T20:21:58.0754874Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-08-26T20:21:58.0756008Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-08-26T20:21:58.0757219Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-08-26T20:21:58.0758364Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-08-26T20:21:58.0759414Z with a different number of trainers and/or different parallelisms. 2025-08-26T20:21:58.0760702Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-08-26T20:21:58.0761517Z these APIs. 2025-08-26T20:21:58.0761950Z 3.) sanity checking the result state_dict. 2025-08-26T20:21:58.0762395Z 2025-08-26T20:21:58.0762799Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-08-26T20:21:58.0763884Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-08-26T20:21:58.0765013Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-08-26T20:21:58.0766084Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-08-26T20:21:58.0766983Z ``module.named_buffers()`` when the module is not distributed by any 2025-08-26T20:21:58.0767576Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-08-26T20:21:58.0768176Z a parameter, there will be a conversion from the parameter IDs to the 2025-08-26T20:21:58.0768618Z canonical FQNs when calling this API. 2025-08-26T20:21:58.0768847Z 2025-08-26T20:21:58.0769075Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-08-26T20:21:58.0769642Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-08-26T20:21:58.0770140Z optimizer parameter IDs to the canonical FQNs. 2025-08-26T20:21:58.0770390Z 2025-08-26T20:21:58.0770476Z Example: 2025-08-26T20:21:58.0770706Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.0770974Z >>> import torch 2025-08-26T20:21:58.0771373Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-26T20:21:58.0771926Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-08-26T20:21:58.0772474Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-08-26T20:21:58.0772874Z 2025-08-26T20:21:58.0773084Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-08-26T20:21:58.0773849Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-08-26T20:21:58.0774571Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-08-26T20:21:58.0775295Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-08-26T20:21:58.0775863Z 2025-08-26T20:21:58.0775879Z 2025-08-26T20:21:58.0776312Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-08-26T20:21:58.0777312Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-08-26T20:21:58.0778096Z ... fsdp_model, fsdp_optim 2025-08-26T20:21:58.0778603Z ... ) 2025-08-26T20:21:58.0778835Z 2025-08-26T20:21:58.0779223Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-08-26T20:21:58.0780036Z >>> # the asserts will fail. 2025-08-26T20:21:58.0780712Z >>> assert ddp_state_dict == fsdp_state_dict 2025-08-26T20:21:58.0781425Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-08-26T20:21:58.0781899Z 2025-08-26T20:21:58.0781907Z 2025-08-26T20:21:58.0782055Z Args: 2025-08-26T20:21:58.0782514Z model (nn.Module): the nn.Module to the model. 2025-08-26T20:21:58.0783301Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-08-26T20:21:58.0784151Z The optimizers that are used to optimize ``model``. 2025-08-26T20:21:58.0785363Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-08-26T20:21:58.0786332Z that belong to the submodules. 2025-08-26T20:21:58.0787058Z options (StateDictOptions): the options to control how 2025-08-26T20:21:58.0787998Z model state_dict and optimizer state_dict should be returned. See 2025-08-26T20:21:58.0788831Z `StateDictOptions` for the details. 2025-08-26T20:21:58.0789282Z 2025-08-26T20:21:58.0789446Z Returns: 2025-08-26T20:21:58.0790031Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-08-26T20:21:58.0790583Z 2025-08-26T20:21:58.0791026Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-08-26T20:21:58.0791666Z 2025-08-26T20:21:58.0792285Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0793165Z 2025-08-26T20:21:58.0805192Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=69. 2025-08-26T20:21:58.0807186Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0807892Z 2025-08-26T20:21:58.0808251Z Load a checkpoint into a distributed state dict in SPMD style. 2025-08-26T20:21:58.0808804Z 2025-08-26T20:21:58.0809083Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-08-26T20:21:58.0809660Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-08-26T20:21:58.0810237Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-08-26T20:21:58.0810664Z costs). 2025-08-26T20:21:58.0810780Z 2025-08-26T20:21:58.0810963Z Each rank will try to read the least amount of data necessary 2025-08-26T20:21:58.0811507Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-08-26T20:21:58.0812113Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-08-26T20:21:58.0812480Z 2025-08-26T20:21:58.0812751Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-08-26T20:21:58.0813390Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-08-26T20:21:58.0813925Z ``load_state_dict`` once the deserialization is complete. 2025-08-26T20:21:58.0814462Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-08-26T20:21:58.0814997Z it in the ``state_dict`` with the deserialized object. 2025-08-26T20:21:58.0815254Z 2025-08-26T20:21:58.0815375Z .. warning:: 2025-08-26T20:21:58.0815662Z All tensors in ``state_dict`` must be allocated on their 2025-08-26T20:21:58.0816276Z destination device *prior to* calling this function. 2025-08-26T20:21:58.0816795Z 2025-08-26T20:21:58.0817191Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-08-26T20:21:58.0817956Z on state_dict. 2025-08-26T20:21:58.0818201Z 2025-08-26T20:21:58.0818376Z .. warning:: 2025-08-26T20:21:58.0818951Z Users must call `load_state_dict` on the root module to ensure load 2025-08-26T20:21:58.0819887Z pos-processing and non-tensor data properly propagates. 2025-08-26T20:21:58.0820555Z 2025-08-26T20:21:58.0820706Z .. note: 2025-08-26T20:21:58.0821346Z If no process group is initialized, this function will assume the intent 2025-08-26T20:21:58.0822431Z is to load a checkpoint into the local process. This can be useful in the 2025-08-26T20:21:58.0823559Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-08-26T20:21:58.0824426Z or ShardedTensor) 2025-08-26T20:21:58.0824719Z 2025-08-26T20:21:58.0824886Z .. note: 2025-08-26T20:21:58.0825335Z Rank 0 is assumed to be the coordinator rank. 2025-08-26T20:21:58.0825786Z 2025-08-26T20:21:58.0825940Z Args: 2025-08-26T20:21:58.0826550Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-08-26T20:21:58.0827440Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-26T20:21:58.0828511Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-26T20:21:58.0829495Z depends on the storage. It can be a path to a folder or to a file. 2025-08-26T20:21:58.0830398Z It can also be a key if the storage is a key-value store. 2025-08-26T20:21:58.0831101Z (Default: ``None``) 2025-08-26T20:21:58.0831647Z storage_reader (Optional[StorageReader]): 2025-08-26T20:21:58.0832472Z Instance of StorageWriter used to perform reads. If this is not 2025-08-26T20:21:58.0833478Z specified, DCP will automatically infer the reader based on the 2025-08-26T20:21:58.0834492Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-26T20:21:58.0835305Z be raised. (Default: ``None``) 2025-08-26T20:21:58.0836067Z planner (Optional[LoadPlanner]): 2025-08-26T20:21:58.0836863Z Instance of LoadPlanner. If this is not specified, the default 2025-08-26T20:21:58.0837676Z planner will be used. (Default: ``None``) 2025-08-26T20:21:58.0838366Z process_group (Optional[ProcessGroup]): 2025-08-26T20:21:58.0839129Z ProcessGroup to be used for cross-rank synchronization. 2025-08-26T20:21:58.0839799Z (Default: ``None``) 2025-08-26T20:21:58.0840488Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-08-26T20:21:58.0841552Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-08-26T20:21:58.0842413Z Returns: 2025-08-26T20:21:58.0842770Z None. 2025-08-26T20:21:58.0842997Z 2025-08-26T20:21:58.0843147Z Examples 2025-08-26T20:21:58.0843539Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.0844024Z >>> my_model = MyModule() 2025-08-26T20:21:58.0844606Z >>> optimizer = Adagrad(my_model.parameters()) 2025-08-26T20:21:58.0845308Z >>> model_state_dict = my_model.state_dict() 2025-08-26T20:21:58.0846194Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-08-26T20:21:58.0847048Z ... "/checkpoint/1" 2025-08-26T20:21:58.0847490Z ... ) 2025-08-26T20:21:58.0847706Z 2025-08-26T20:21:58.0847963Z >>> torch.distributed.checkpoint.load_state_dict( 2025-08-26T20:21:58.0848638Z >>> state_dict=model_state_dict, 2025-08-26T20:21:58.0849274Z >>> storage_reader=fs_storage_reader, 2025-08-26T20:21:58.0849863Z >>> ) 2025-08-26T20:21:58.0850102Z 2025-08-26T20:21:58.0850469Z >>> # module.load_state_dict() function might have customized steps 2025-08-26T20:21:58.0851313Z >>> # to flush the state_dict, must call it to 2025-08-26T20:21:58.0851964Z >>> # ensure correct behavior. 2025-08-26T20:21:58.0852589Z >>> my_model.load_state_dict(model_state_dict) 2025-08-26T20:21:58.0853055Z 2025-08-26T20:21:58.0853229Z .. note:: 2025-08-26T20:21:58.0853860Z load_state_dict uses collectives to coordinate reads across ranks. 2025-08-26T20:21:58.0854849Z For NCCL-based process groups, internal tensor representations of 2025-08-26T20:21:58.0855870Z objects must be moved to the GPU device before communication takes place. 2025-08-26T20:21:58.0856915Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-08-26T20:21:58.0858008Z and it is the user's responsibility to ensure that this is set so that each 2025-08-26T20:21:58.0859030Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-08-26T20:21:58.0859599Z 2025-08-26T20:21:58.0860038Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0860772Z 2025-08-26T20:21:58.0862693Z msg = Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=97. 2025-08-26T20:21:58.0864645Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0865421Z 2025-08-26T20:21:58.0865647Z Save a distributed model in SPMD style. 2025-08-26T20:21:58.0866092Z 2025-08-26T20:21:58.0866580Z This function is different from ``torch.save()`` as it handles 2025-08-26T20:21:58.0867668Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-08-26T20:21:58.0868410Z 2025-08-26T20:21:58.0868918Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-08-26T20:21:58.0869872Z save will call ``state_dict`` before serialization. 2025-08-26T20:21:58.0870340Z 2025-08-26T20:21:58.0870505Z .. warning:: 2025-08-26T20:21:58.0871160Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-08-26T20:21:58.0872083Z for saved state_dicts. 2025-08-26T20:21:58.0872434Z 2025-08-26T20:21:58.0872602Z .. warning:: 2025-08-26T20:21:58.0873227Z If using the `process_group` argument, make sure that only its ranks 2025-08-26T20:21:58.0874879Z call `save_state_dict` and that all data in state_dict belong to it. 2025-08-26T20:21:58.0875522Z 2025-08-26T20:21:58.0875680Z .. note:: 2025-08-26T20:21:58.0876393Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-08-26T20:21:58.0877264Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-08-26T20:21:58.0877763Z group needs to be passed in. 2025-08-26T20:21:58.0877960Z 2025-08-26T20:21:58.0878049Z .. note:: 2025-08-26T20:21:58.0878438Z If no process group is available, this function assumes the intention is to save the 2025-08-26T20:21:58.0878938Z state_dict in the local process. 2025-08-26T20:21:58.0879149Z 2025-08-26T20:21:58.0879245Z .. note: 2025-08-26T20:21:58.0879496Z Rank 0 is assumed to be the coordinator rank. 2025-08-26T20:21:58.0879756Z 2025-08-26T20:21:58.0879760Z 2025-08-26T20:21:58.0879841Z Args: 2025-08-26T20:21:58.0880119Z state_dict (Dict[str, Any]): The state_dict to save. 2025-08-26T20:21:58.0880542Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-26T20:21:58.0881012Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-26T20:21:58.0881538Z depends on the storage. It can be a path to a folder or to a file. 2025-08-26T20:21:58.0882031Z It can also be a key if the storage is a key-value store. 2025-08-26T20:21:58.0882414Z (Default: ``None``) 2025-08-26T20:21:58.0882724Z storage_writer (Optional[StorageWriter]): 2025-08-26T20:21:58.0883373Z Instance of StorageWriter used to perform writes. If this is not 2025-08-26T20:21:58.0884346Z specified, DCP will automatically infer the writer based on the 2025-08-26T20:21:58.0885301Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-26T20:21:58.0886077Z be raised. (Default: ``None``) 2025-08-26T20:21:58.0886665Z planner (Optional[SavePlanner]): 2025-08-26T20:21:58.0887415Z Instance of SavePlanner. If this is not specified, the default 2025-08-26T20:21:58.0888256Z planner will be used. (Default: ``None``) 2025-08-26T20:21:58.0889002Z process_group (Optional[ProcessGroup]): 2025-08-26T20:21:58.0889781Z ProcessGroup to be used for cross-rank synchronization. 2025-08-26T20:21:58.0890529Z (Default: ``None``) 2025-08-26T20:21:58.0890997Z no_dist (bool): 2025-08-26T20:21:58.0891551Z If ``True``, this function will assume the intent is to load 2025-08-26T20:21:58.0892372Z a checkpoint on a single rank/process. 2025-08-26T20:21:58.0892709Z (Default: ``False``) 2025-08-26T20:21:58.0893150Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-08-26T20:21:58.0893708Z a checkpoint without using cross-rank synchronization. 2025-08-26T20:21:58.0894097Z (Default: ``True``) 2025-08-26T20:21:58.0894486Z This configuration is experimental and should be used with caution. 2025-08-26T20:21:58.0895104Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-08-26T20:21:58.0895506Z 2025-08-26T20:21:58.0895591Z Returns: 2025-08-26T20:21:58.0896019Z Metadata: Metadata object for the saved checkpoint. 2025-08-26T20:21:58.0896286Z 2025-08-26T20:21:58.0896375Z Example: 2025-08-26T20:21:58.0896600Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.0896873Z >>> my_model = MyModule() 2025-08-26T20:21:58.0897053Z 2025-08-26T20:21:58.0897174Z >>> state_dict = {"model": my_model} 2025-08-26T20:21:58.0897386Z 2025-08-26T20:21:58.0897631Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-08-26T20:21:58.0898346Z ... "/checkpoint/1" 2025-08-26T20:21:58.0898780Z ... ) 2025-08-26T20:21:58.0899205Z >>> torch.distributed.checkpoint.save( 2025-08-26T20:21:58.0899813Z >>> state_dict=state_dict, 2025-08-26T20:21:58.0900461Z >>> storage_writer=fs_storage_writer, 2025-08-26T20:21:58.0901042Z >>> ) 2025-08-26T20:21:58.0901250Z 2025-08-26T20:21:58.0901427Z .. note:: 2025-08-26T20:21:58.0902208Z save_state_dict uses collectives to coordinate writes across ranks. 2025-08-26T20:21:58.0903232Z For NCCL-based process groups, internal tensor representations of 2025-08-26T20:21:58.0904298Z objects must be moved to the GPU device before communication takes place. 2025-08-26T20:21:58.0905401Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-08-26T20:21:58.0906407Z and it is the user's responsibility to ensure that this is set so that 2025-08-26T20:21:58.0907341Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-08-26T20:21:58.0907921Z 2025-08-26T20:21:58.0908371Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0909088Z 2025-08-26T20:21:58.0910338Z msg = Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=230. 2025-08-26T20:21:58.0912295Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.0913524Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-08-26T20:21:58.0914818Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-08-26T20:21:58.0915605Z 2025-08-26T20:21:58.0915787Z .. warning:: 2025-08-26T20:21:58.0916328Z This feature is experimental and subject to change. 2025-08-26T20:21:58.0917102Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-08-26T20:21:58.0917597Z 2025-08-26T20:21:58.0917752Z Args: 2025-08-26T20:21:58.0918250Z state_dict (Dict[str, Any]): The state_dict to save. 2025-08-26T20:21:58.0919019Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-26T20:21:58.0919893Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-26T20:21:58.0920919Z depends on the storage. It can be a path to a folder or to a file. 2025-08-26T20:21:58.0921873Z It can also be a key if the storage is a key-value store. 2025-08-26T20:21:58.0922602Z (Default: ``None``) 2025-08-26T20:21:58.0923179Z storage_writer (Optional[StorageWriter]): 2025-08-26T20:21:58.0924046Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-08-26T20:21:58.0925151Z this is not specified, DCP will automatically infer the writer based on the 2025-08-26T20:21:58.0926250Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-26T20:21:58.0927080Z be raised. (Default: ``None``) 2025-08-26T20:21:58.0927725Z planner (Optional[SavePlanner]): 2025-08-26T20:21:58.0928536Z Instance of SavePlanner. If this is not specified, the default 2025-08-26T20:21:58.0929398Z planner will be used. (Default: ``None``) 2025-08-26T20:21:58.0930112Z process_group (Optional[ProcessGroup]): 2025-08-26T20:21:58.0930911Z ProcessGroup to be used for cross-rank synchronization. 2025-08-26T20:21:58.0931669Z (Default: ``None``) 2025-08-26T20:21:58.0932310Z async_checkpointer_type (AsyncCheckpointerType): 2025-08-26T20:21:58.0933295Z whether to do checkpoint in separate thread or process 2025-08-26T20:21:58.0934104Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-08-26T20:21:58.0934796Z async_stager (AsyncStager): 2025-08-26T20:21:58.0935697Z provides staging implementation. If storage_writer implements AsyncStager 2025-08-26T20:21:58.0936849Z and async_stager is provided, async_stager will be used for staging 2025-08-26T20:21:58.0937648Z no_dist (bool): 2025-08-26T20:21:58.0938264Z If ``True``, this function will assume the intent is to save 2025-08-26T20:21:58.0939054Z a checkpoint on a single rank/process. 2025-08-26T20:21:58.0939697Z (Default: ``False``) 2025-08-26T20:21:58.0940818Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-08-26T20:21:58.0942107Z This configuration is experimental and should be used with caution. 2025-08-26T20:21:58.0943307Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-08-26T20:21:58.0944065Z 2025-08-26T20:21:58.0944230Z Returns: 2025-08-26T20:21:58.0944876Z Future: A future holding the resultant Metadata object from `save`. 2025-08-26T20:21:58.0945499Z 2025-08-26T20:21:58.0945655Z Example: 2025-08-26T20:21:58.0946037Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.0946560Z >>> my_model = MyModule() 2025-08-26T20:21:58.0946895Z 2025-08-26T20:21:58.0947092Z >>> state_dict = {"model": my_model} 2025-08-26T20:21:58.0947406Z 2025-08-26T20:21:58.0947655Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-08-26T20:21:58.0948102Z ... "/checkpoint/1" 2025-08-26T20:21:58.0948380Z ... ) 2025-08-26T20:21:58.0948735Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-08-26T20:21:58.0949172Z >>> state_dict=state_dict, 2025-08-26T20:21:58.0949501Z >>> storage_writer=fs_storage_writer, 2025-08-26T20:21:58.0949821Z >>> ) 2025-08-26T20:21:58.0950036Z >>> 2025-08-26T20:21:58.0950261Z >>> # ... do some work ... 2025-08-26T20:21:58.0950536Z >>> 2025-08-26T20:21:58.0950771Z >>> checkpoint_future.result() 2025-08-26T20:21:58.0950984Z 2025-08-26T20:21:58.0951076Z 2025-08-26T20:21:58.0951444Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.0951811Z 2025-08-26T20:21:58.1243282Z msg = Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=94. 2025-08-26T20:21:58.1244402Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.1244803Z 2025-08-26T20:21:58.1245027Z Initialize rendezvous event object and record its operations. 2025-08-26T20:21:58.1245367Z 2025-08-26T20:21:58.1245450Z Args: 2025-08-26T20:21:58.1245705Z run_id (str): The run id of the rendezvous. 2025-08-26T20:21:58.1246099Z message (str): The message describing the event. 2025-08-26T20:21:58.1246601Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-08-26T20:21:58.1247564Z name (str): Event name. (E.g. Current action being performed). 2025-08-26T20:21:58.1248280Z hostname (str): Hostname of the node. 2025-08-26T20:21:58.1248907Z pid (Optional[int]): The process id of the node. 2025-08-26T20:21:58.1249815Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-08-26T20:21:58.1251049Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-08-26T20:21:58.1252106Z rank (Optional[int]): The rank of the node, if known. 2025-08-26T20:21:58.1252810Z Returns: 2025-08-26T20:21:58.1253194Z None 2025-08-26T20:21:58.1253588Z Example: 2025-08-26T20:21:58.1254355Z >>> # See DynamicRendezvousHandler class 2025-08-26T20:21:58.1254946Z >>> def _record( 2025-08-26T20:21:58.1255370Z ... self, 2025-08-26T20:21:58.1255798Z ... message: str, 2025-08-26T20:21:58.1256352Z ... node_state: NodeState = NodeState.RUNNING, 2025-08-26T20:21:58.1257035Z ... rank: Optional[int] = None, 2025-08-26T20:21:58.1257601Z ... ) -> None: 2025-08-26T20:21:58.1258080Z ... construct_and_record_rdzv_event( 2025-08-26T20:21:58.1258811Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-08-26T20:21:58.1259564Z ... run_id=self._settings.run_id, 2025-08-26T20:21:58.1260159Z ... message=message, 2025-08-26T20:21:58.1260792Z ... node_state=node_state, 2025-08-26T20:21:58.1261370Z ... hostname=self._this_node.addr, 2025-08-26T20:21:58.1262171Z ... pid=self._this_node.pid, 2025-08-26T20:21:58.1262614Z ... local_id=self._this_node.local_id, 2025-08-26T20:21:58.1262966Z ... rank=rank, 2025-08-26T20:21:58.1263219Z ... ) 2025-08-26T20:21:58.1263348Z 2025-08-26T20:21:58.1263614Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.1263984Z 2025-08-26T20:21:58.5039724Z msg = Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=114. 2025-08-26T20:21:58.5041583Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5042233Z 2025-08-26T20:21:58.5042567Z This configures FSDP-native mixed precision training. 2025-08-26T20:21:58.5043026Z 2025-08-26T20:21:58.5043180Z Attributes: 2025-08-26T20:21:58.5043850Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-08-26T20:21:58.5044934Z parameters during forward and backward and thus the dtype for 2025-08-26T20:21:58.5045973Z forward and backward computation. Outside forward and backward, the 2025-08-26T20:21:58.5047021Z *sharded* parameters are kept in full precision (e.g. for the 2025-08-26T20:21:58.5048013Z optimizer step), and for model checkpointing, the parameters are 2025-08-26T20:21:58.5048917Z always saved in full precision. (Default: ``None``) 2025-08-26T20:21:58.5049832Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-08-26T20:21:58.5050867Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-08-26T20:21:58.5051792Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-08-26T20:21:58.5052617Z the ``param_dtype`` value, still running gradient reduction in low 2025-08-26T20:21:58.5053583Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-08-26T20:21:58.5054588Z to force gradient reduction to run in full precision. (Default: 2025-08-26T20:21:58.5055371Z ``None``) 2025-08-26T20:21:58.5056034Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-08-26T20:21:58.5057066Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-08-26T20:21:58.5058059Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-08-26T20:21:58.5059076Z dtype thereafter. For model checkpointing, the buffers are saved 2025-08-26T20:21:58.5060020Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-08-26T20:21:58.5060831Z ``None``) 2025-08-26T20:21:58.5061366Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-08-26T20:21:58.5061916Z gradients to full precision after the backward pass in preparation 2025-08-26T20:21:58.5062450Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-08-26T20:21:58.5062977Z in the dtype used for gradient reduction, which can save memory if 2025-08-26T20:21:58.5063522Z using a custom optimizer that supports running in low precision. 2025-08-26T20:21:58.5063954Z (Default: ``False``) 2025-08-26T20:21:58.5064583Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-08-26T20:21:58.5065113Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-08-26T20:21:58.5065649Z that parameter and input dtypes match for forward computation, as 2025-08-26T20:21:58.5066193Z required by many ops. This may need to be set to ``True`` when only 2025-08-26T20:21:58.5066740Z applying mixed precision to some but not all FSDP modules, in which 2025-08-26T20:21:58.5067276Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-08-26T20:21:58.5067704Z (Default: ``False``) 2025-08-26T20:21:58.5068110Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-08-26T20:21:58.5068648Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-08-26T20:21:58.5069237Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-08-26T20:21:58.5069792Z this does not do anything. (Default: ``True``) 2025-08-26T20:21:58.5070651Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-08-26T20:21:58.5071606Z module classes to ignore for mixed precision when using an 2025-08-26T20:21:58.5072496Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-08-26T20:21:58.5073409Z applied to them separately with mixed precision disabled (meaning 2025-08-26T20:21:58.5074394Z that the final FSDP construction would deviate from the specified 2025-08-26T20:21:58.5075303Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-08-26T20:21:58.5076161Z not do anything. This API is experimental and subject to change. 2025-08-26T20:21:58.5076929Z (Default: ``(_BatchNorm,)``) 2025-08-26T20:21:58.5077345Z 2025-08-26T20:21:58.5077700Z .. note:: This API is experimental and subject to change. 2025-08-26T20:21:58.5078221Z 2025-08-26T20:21:58.5078642Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-08-26T20:21:58.5079288Z 2025-08-26T20:21:58.5079617Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-08-26T20:21:58.5080313Z precision, but buffers are not. 2025-08-26T20:21:58.5080699Z 2025-08-26T20:21:58.5081088Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-08-26T20:21:58.5082094Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-08-26T20:21:58.5083148Z Disabling FSDP's mixed precision for those norm modules only means that 2025-08-26T20:21:58.5084165Z the affine parameters are kept in ``float32``. However, this incurs 2025-08-26T20:21:58.5085219Z separate all-gathers and reduce-scatters for those norm modules, which 2025-08-26T20:21:58.5086270Z may be inefficient, so if the workload permits, the user should prefer 2025-08-26T20:21:58.5087200Z to still apply mixed precision to those modules. 2025-08-26T20:21:58.5087706Z 2025-08-26T20:21:58.5088097Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-08-26T20:21:58.5089011Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-08-26T20:21:58.5090038Z modules will have FSDP applied to them separately with mixed precision 2025-08-26T20:21:58.5090991Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-08-26T20:21:58.5091540Z 2025-08-26T20:21:58.5092158Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-08-26T20:21:58.5093142Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-08-26T20:21:58.5093922Z its ``cast_root_forward_inputs`` takes precedence over its 2025-08-26T20:21:58.5094759Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-08-26T20:21:58.5095720Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-08-26T20:21:58.5096777Z sufficient for the typical case where each FSDP instance has the same 2025-08-26T20:21:58.5098108Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-08-26T20:21:58.5099061Z ``param_dtype`` at the beginning of the model's forward pass. 2025-08-26T20:21:58.5099603Z 2025-08-26T20:21:58.5099974Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-08-26T20:21:58.5101093Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-08-26T20:21:58.5102141Z values to configure casting inputs or not before each instance's 2025-08-26T20:21:58.5103114Z forward. In such a case, since the casts happen before each FSDP 2025-08-26T20:21:58.5104080Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-08-26T20:21:58.5105129Z submodules run before its FSDP submodules to avoid the activation dtype 2025-08-26T20:21:58.5106347Z being changed due to a different ``MixedPrecision`` configuration. 2025-08-26T20:21:58.5106925Z 2025-08-26T20:21:58.5107105Z Example:: 2025-08-26T20:21:58.5107344Z 2025-08-26T20:21:58.5107586Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:58.5108335Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-08-26T20:21:58.5109058Z >>> model[1] = FSDP( 2025-08-26T20:21:58.5109550Z >>> model[1], 2025-08-26T20:21:58.5110441Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-08-26T20:21:58.5111308Z >>> ) 2025-08-26T20:21:58.5111611Z >>> model = FSDP( 2025-08-26T20:21:58.5111964Z >>> model, 2025-08-26T20:21:58.5112728Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-08-26T20:21:58.5113686Z >>> ) 2025-08-26T20:21:58.5113928Z 2025-08-26T20:21:58.5114316Z The above shows a working example. On the other hand, if ``model[1]`` 2025-08-26T20:21:58.5115326Z were replaced with ``model[0]``, meaning that the submodule using 2025-08-26T20:21:58.5116336Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-08-26T20:21:58.5117433Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-08-26T20:21:58.5118228Z ones. 2025-08-26T20:21:58.5118466Z 2025-08-26T20:21:58.5118474Z 2025-08-26T20:21:58.5118935Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5119617Z 2025-08-26T20:21:58.5120735Z msg = Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=295. 2025-08-26T20:21:58.5122565Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5123287Z 2025-08-26T20:21:58.5123677Z ``FullStateDictConfig`` is a config class meant to be used with 2025-08-26T20:21:58.5124641Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-08-26T20:21:58.5125600Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-08-26T20:21:58.5126599Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-08-26T20:21:58.5127596Z is meant to be used via the :func:`state_dict_type` context manager as 2025-08-26T20:21:58.5128327Z follows: 2025-08-26T20:21:58.5128559Z 2025-08-26T20:21:58.5128773Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:58.5129632Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-26T20:21:58.5130529Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-08-26T20:21:58.5131348Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-08-26T20:21:58.5132334Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-08-26T20:21:58.5133152Z >>> state = fsdp.state_dict() 2025-08-26T20:21:58.5133918Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-08-26T20:21:58.5134981Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-08-26T20:21:58.5136211Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-08-26T20:21:58.5137046Z >>> if dist.get_rank() == 0: 2025-08-26T20:21:58.5137725Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-08-26T20:21:58.5138528Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-08-26T20:21:58.5139191Z >>> model.load_state_dict(state_dict) 2025-08-26T20:21:58.5140022Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-08-26T20:21:58.5141234Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-08-26T20:21:58.5142110Z >>> fsdp = FSDP( 2025-08-26T20:21:58.5142557Z ... model, 2025-08-26T20:21:58.5143039Z ... device_id=torch.cuda.current_device(), 2025-08-26T20:21:58.5143226Z ... auto_wrap_policy=..., 2025-08-26T20:21:58.5143550Z ... sync_module_states=True, 2025-08-26T20:21:58.5143714Z ... ) 2025-08-26T20:21:58.5144109Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-08-26T20:21:58.5144121Z 2025-08-26T20:21:58.5144299Z Attributes: 2025-08-26T20:21:58.5144651Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-08-26T20:21:58.5145016Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-08-26T20:21:58.5145312Z ranks save the full state dict. (Default: ``False``) 2025-08-26T20:21:58.5145321Z 2025-08-26T20:21:58.5145780Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5145788Z 2025-08-26T20:21:58.5224639Z msg = Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=634. 2025-08-26T20:21:58.5225134Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5225621Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-08-26T20:21:58.5225639Z 2025-08-26T20:21:58.5226103Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-08-26T20:21:58.5226483Z The target module does not have to be a FSDP module. If the target 2025-08-26T20:21:58.5226845Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-08-26T20:21:58.5226854Z 2025-08-26T20:21:58.5227228Z .. note:: This API should be called for only the top-level (root) 2025-08-26T20:21:58.5227395Z module. 2025-08-26T20:21:58.5227403Z 2025-08-26T20:21:58.5227782Z .. note:: This API enables users to transparently use the conventional 2025-08-26T20:21:58.5228127Z ``state_dict`` API to take model checkpoints in cases where the 2025-08-26T20:21:58.5228474Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-08-26T20:21:58.5228825Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-08-26T20:21:58.5229262Z instances, while dispatching into `sharded_state_dict` implementation 2025-08-26T20:21:58.5229441Z for FSDP: 2025-08-26T20:21:58.5229450Z 2025-08-26T20:21:58.5229617Z Example:: 2025-08-26T20:21:58.5229625Z 2025-08-26T20:21:58.5229870Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:58.5230083Z >>> model = DDP(FSDP(...)) 2025-08-26T20:21:58.5230283Z >>> FSDP.set_state_dict_type( 2025-08-26T20:21:58.5230451Z >>> model, 2025-08-26T20:21:58.5230707Z >>> StateDictType.SHARDED_STATE_DICT, 2025-08-26T20:21:58.5231112Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-08-26T20:21:58.5231553Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-08-26T20:21:58.5231724Z >>> ) 2025-08-26T20:21:58.5231960Z >>> param_state_dict = model.state_dict() 2025-08-26T20:21:58.5232586Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-08-26T20:21:58.5232596Z 2025-08-26T20:21:58.5232791Z Args: 2025-08-26T20:21:58.5233035Z module (torch.nn.Module): Root module. 2025-08-26T20:21:58.5233477Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-08-26T20:21:58.5233923Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-08-26T20:21:58.5234138Z target ``state_dict_type``. 2025-08-26T20:21:58.5234615Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-08-26T20:21:58.5234846Z for the optimizer state dict. 2025-08-26T20:21:58.5234855Z 2025-08-26T20:21:58.5235024Z Returns: 2025-08-26T20:21:58.5235596Z A StateDictSettings that include the previous state_dict type and 2025-08-26T20:21:58.5235829Z configuration for the module. 2025-08-26T20:21:58.5235985Z 2025-08-26T20:21:58.5236484Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5236492Z 2025-08-26T20:21:58.5238131Z msg = Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=792. 2025-08-26T20:21:58.5238643Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5239103Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-08-26T20:21:58.5239113Z 2025-08-26T20:21:58.5239727Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-08-26T20:21:58.5239962Z :meth:`set_state_dict_type` for the detail. 2025-08-26T20:21:58.5239970Z 2025-08-26T20:21:58.5240138Z Example:: 2025-08-26T20:21:58.5240154Z 2025-08-26T20:21:58.5240403Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:58.5240601Z >>> model = DDP(FSDP(...)) 2025-08-26T20:21:58.5240812Z >>> with FSDP.state_dict_type( 2025-08-26T20:21:58.5240973Z >>> model, 2025-08-26T20:21:58.5241209Z >>> StateDictType.SHARDED_STATE_DICT, 2025-08-26T20:21:58.5241377Z >>> ): 2025-08-26T20:21:58.5241593Z >>> checkpoint = model.state_dict() 2025-08-26T20:21:58.5241601Z 2025-08-26T20:21:58.5241765Z Args: 2025-08-26T20:21:58.5241988Z module (torch.nn.Module): Root module. 2025-08-26T20:21:58.5242427Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-08-26T20:21:58.5242885Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-08-26T20:21:58.5243188Z configuration for the target ``state_dict_type``. 2025-08-26T20:21:58.5243661Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-08-26T20:21:58.5244038Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-08-26T20:21:58.5244190Z 2025-08-26T20:21:58.5244692Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5244700Z 2025-08-26T20:21:58.5291251Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1805. 2025-08-26T20:21:58.5292019Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5292031Z 2025-08-26T20:21:58.5292479Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-08-26T20:21:58.5292489Z 2025-08-26T20:21:58.5292844Z The given state-dict can be transformed to one of three types: 2025-08-26T20:21:58.5293429Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-08-26T20:21:58.5293438Z 2025-08-26T20:21:58.5294081Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-08-26T20:21:58.5294441Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-08-26T20:21:58.5294600Z avoid OOM. 2025-08-26T20:21:58.5294607Z 2025-08-26T20:21:58.5294995Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-08-26T20:21:58.5295352Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-08-26T20:21:58.5295502Z memory. 2025-08-26T20:21:58.5295510Z 2025-08-26T20:21:58.5295929Z For local state_dict, no transformation will be performed. But a state 2025-08-26T20:21:58.5296398Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-08-26T20:21:58.5296604Z nature (this is not supported yet). 2025-08-26T20:21:58.5296611Z 2025-08-26T20:21:58.5296813Z Example:: 2025-08-26T20:21:58.5296820Z 2025-08-26T20:21:58.5297213Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:58.5297663Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-26T20:21:58.5297973Z >>> from torch.distributed.fsdp import StateDictType 2025-08-26T20:21:58.5298292Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-08-26T20:21:58.5298676Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-08-26T20:21:58.5298852Z >>> # Save a checkpoint 2025-08-26T20:21:58.5299027Z >>> model, optim = ... 2025-08-26T20:21:58.5299241Z >>> FSDP.set_state_dict_type( 2025-08-26T20:21:58.5299413Z >>> model, 2025-08-26T20:21:58.5299641Z >>> StateDictType.FULL_STATE_DICT, 2025-08-26T20:21:58.5299882Z >>> FullStateDictConfig(rank0_only=False), 2025-08-26T20:21:58.5300139Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-26T20:21:58.5300313Z >>> ) 2025-08-26T20:21:58.5300585Z >>> state_dict = model.state_dict() 2025-08-26T20:21:58.5300910Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-08-26T20:21:58.5301175Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-08-26T20:21:58.5301358Z >>> # Load a checkpoint 2025-08-26T20:21:58.5301547Z >>> model, optim = ... 2025-08-26T20:21:58.5301809Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-08-26T20:21:58.5302011Z >>> FSDP.set_state_dict_type( 2025-08-26T20:21:58.5302162Z >>> model, 2025-08-26T20:21:58.5302367Z >>> StateDictType.FULL_STATE_DICT, 2025-08-26T20:21:58.5302605Z >>> FullStateDictConfig(rank0_only=False), 2025-08-26T20:21:58.5302862Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-26T20:21:58.5303014Z >>> ) 2025-08-26T20:21:58.5303233Z >>> model.load_state_dict(state_dict) 2025-08-26T20:21:58.5303508Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-08-26T20:21:58.5303721Z >>> model, optim, optim_state_dict 2025-08-26T20:21:58.5303868Z >>> ) 2025-08-26T20:21:58.5304101Z >>> optim.load_state_dict(optim_state_dict) 2025-08-26T20:21:58.5304111Z 2025-08-26T20:21:58.5304278Z Args: 2025-08-26T20:21:58.5304640Z model (torch.nn.Module): Root module (which may or may not be a 2025-08-26T20:21:58.5305023Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-08-26T20:21:58.5305239Z were passed into the optimizer ``optim``. 2025-08-26T20:21:58.5305570Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-08-26T20:21:58.5305756Z parameters. 2025-08-26T20:21:58.5306102Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-08-26T20:21:58.5306456Z transform. If the value is None, optim.state_dict() will be used. ( 2025-08-26T20:21:58.5306606Z Default: ``None``) 2025-08-26T20:21:58.5306932Z group (dist.ProcessGroup): Model's process group across which parameters 2025-08-26T20:21:58.5307131Z are sharded or ``None`` if using the default process group. ( 2025-08-26T20:21:58.5307234Z Default: ``None``) 2025-08-26T20:21:58.5307239Z 2025-08-26T20:21:58.5307338Z Returns: 2025-08-26T20:21:58.5307640Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-08-26T20:21:58.5307806Z ``model``. The sharding of the optimizer state is based on 2025-08-26T20:21:58.5307914Z ``state_dict_type``. 2025-08-26T20:21:58.5307918Z 2025-08-26T20:21:58.5308175Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5308180Z 2025-08-26T20:21:58.5309073Z msg = Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1903. 2025-08-26T20:21:58.5309346Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5309351Z 2025-08-26T20:21:58.5309765Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-08-26T20:21:58.5309771Z 2025-08-26T20:21:58.5309940Z Given a ``optim_state_dict`` that is transformed through 2025-08-26T20:21:58.5310173Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-08-26T20:21:58.5310380Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-08-26T20:21:58.5310577Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-08-26T20:21:58.5310582Z 2025-08-26T20:21:58.5310712Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:58.5310961Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-26T20:21:58.5311119Z >>> from torch.distributed.fsdp import StateDictType 2025-08-26T20:21:58.5311298Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-08-26T20:21:58.5311512Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-08-26T20:21:58.5311610Z >>> # Save a checkpoint 2025-08-26T20:21:58.5311719Z >>> model, optim = ... 2025-08-26T20:21:58.5311829Z >>> FSDP.set_state_dict_type( 2025-08-26T20:21:58.5311914Z >>> model, 2025-08-26T20:21:58.5312049Z >>> StateDictType.FULL_STATE_DICT, 2025-08-26T20:21:58.5312179Z >>> FullStateDictConfig(rank0_only=False), 2025-08-26T20:21:58.5312323Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-26T20:21:58.5312421Z >>> ) 2025-08-26T20:21:58.5312597Z >>> state_dict = model.state_dict() 2025-08-26T20:21:58.5312784Z >>> original_osd = optim.state_dict() 2025-08-26T20:21:58.5313004Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-08-26T20:21:58.5313158Z >>> model, 2025-08-26T20:21:58.5330042Z >>> optim, 2025-08-26T20:21:58.5330338Z >>> optim_state_dict=original_osd 2025-08-26T20:21:58.5330506Z >>> ) 2025-08-26T20:21:58.5330779Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-08-26T20:21:58.5330970Z >>> # Load a checkpoint 2025-08-26T20:21:58.5331142Z >>> model, optim = ... 2025-08-26T20:21:58.5331450Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-08-26T20:21:58.5331636Z >>> FSDP.set_state_dict_type( 2025-08-26T20:21:58.5331800Z >>> model, 2025-08-26T20:21:58.5332019Z >>> StateDictType.FULL_STATE_DICT, 2025-08-26T20:21:58.5332245Z >>> FullStateDictConfig(rank0_only=False), 2025-08-26T20:21:58.5332500Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-26T20:21:58.5332663Z >>> ) 2025-08-26T20:21:58.5332864Z >>> model.load_state_dict(state_dict) 2025-08-26T20:21:58.5333148Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-08-26T20:21:58.5333347Z >>> model, optim, optim_state_dict 2025-08-26T20:21:58.5333498Z >>> ) 2025-08-26T20:21:58.5333733Z >>> optim.load_state_dict(optim_state_dict) 2025-08-26T20:21:58.5333742Z 2025-08-26T20:21:58.5333885Z Args: 2025-08-26T20:21:58.5334233Z model (torch.nn.Module): Root module (which may or may not be a 2025-08-26T20:21:58.5334615Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-08-26T20:21:58.5334849Z were passed into the optimizer ``optim``. 2025-08-26T20:21:58.5335307Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-08-26T20:21:58.5335473Z parameters. 2025-08-26T20:21:58.5335843Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-08-26T20:21:58.5336232Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-08-26T20:21:58.5336567Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-08-26T20:21:58.5336893Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-08-26T20:21:58.5337244Z load_directly (bool): If this is set to True, this API will also 2025-08-26T20:21:58.5337601Z call optim.load_state_dict(result) before returning the result. 2025-08-26T20:21:58.5338021Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-08-26T20:21:58.5338200Z (Default: ``False``) 2025-08-26T20:21:58.5338786Z group (dist.ProcessGroup): Model's process group across which parameters 2025-08-26T20:21:58.5339101Z are sharded or ``None`` if using the default process group. ( 2025-08-26T20:21:58.5339287Z Default: ``None``) 2025-08-26T20:21:58.5339297Z 2025-08-26T20:21:58.5339754Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5339762Z 2025-08-26T20:21:58.5877061Z msg = Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=129. 2025-08-26T20:21:58.5878121Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5878508Z 2025-08-26T20:21:58.5878735Z RemoteModule instance can only be created after RPC initialization. 2025-08-26T20:21:58.5879115Z 2025-08-26T20:21:58.5879309Z It creates a user-specified module on a specified remote node. 2025-08-26T20:21:58.5879874Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-08-26T20:21:58.5880335Z executed on the remote node. 2025-08-26T20:21:58.5880777Z It takes care of autograd recording to ensure the backward pass propagates 2025-08-26T20:21:58.5881281Z gradients back to the corresponding remote module. 2025-08-26T20:21:58.5881899Z It can be shared across processors using `RPC framework `__, 2025-08-26T20:21:58.5882560Z without incurring any overheads of copying the actual module, 2025-08-26T20:21:58.5883071Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-08-26T20:21:58.5883499Z pointing to the remote module. 2025-08-26T20:21:58.5883686Z 2025-08-26T20:21:58.5883939Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-08-26T20:21:58.5884646Z the ``forward`` method of the module returned by the ``module_cls``. 2025-08-26T20:21:58.5885131Z 2025-08-26T20:21:58.5885574Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-08-26T20:21:58.5886059Z 2025-08-26T20:21:58.5886320Z Particularly, to create a hybrid model, typically the local modules should be 2025-08-26T20:21:58.5887067Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-08-26T20:21:58.5887641Z Hybrid Example: 2025-08-26T20:21:58.5887899Z >>> class HybridModel(nn.Module): 2025-08-26T20:21:58.5888237Z >>> def __init__(self) -> None: 2025-08-26T20:21:58.5888572Z >>> nn.Module.__init__(self) 2025-08-26T20:21:58.5888924Z >>> self.remote_embedding = RemoteModule(...) 2025-08-26T20:21:58.5889314Z >>> self.local_linear = nn.Linear(...) 2025-08-26T20:21:58.5889557Z 2025-08-26T20:21:58.5889757Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-08-26T20:21:58.5890328Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-08-26T20:21:58.5890941Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-08-26T20:21:58.5891394Z ``def forward(input: Tensor) -> Tensor:`` and 2025-08-26T20:21:58.5892253Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-08-26T20:21:58.5892539Z 2025-08-26T20:21:58.5892648Z .. note:: 2025-08-26T20:21:58.5892921Z If the remote module is placed on a cuda device, 2025-08-26T20:21:58.5893606Z any input CPU tensors will be automatically moved to the same cuda device, 2025-08-26T20:21:58.5894611Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-08-26T20:21:58.5895143Z 2025-08-26T20:21:58.5895225Z Args: 2025-08-26T20:21:58.5895640Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-26T20:21:58.5896348Z The device can be a local device or a remote device specified by one of the following remote 2025-08-26T20:21:58.5896836Z formats: 2025-08-26T20:21:58.5897114Z 2025-08-26T20:21:58.5897259Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-08-26T20:21:58.5897676Z 2. "/" (ex: "trainer0/cuda:0"). 2025-08-26T20:21:58.5897938Z 2025-08-26T20:21:58.5898203Z In addition, the device field can be optional and the default value is "cpu". 2025-08-26T20:21:58.5898693Z module_cls (nn.Module): For example, 2025-08-26T20:21:58.5899016Z >>> class MyModule(nn.Module): 2025-08-26T20:21:58.5899336Z >>> def forward(input): 2025-08-26T20:21:58.5899641Z >>> return input + 1 2025-08-26T20:21:58.5899930Z >>> 2025-08-26T20:21:58.5900143Z >>> module_cls = MyModule 2025-08-26T20:21:58.5900612Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-08-26T20:21:58.5901125Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-08-26T20:21:58.5901717Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-08-26T20:21:58.5902464Z to be created. The type object should be decorated by @torch.jit.interface. 2025-08-26T20:21:58.5903053Z If not provided, the generated RemoteModule is not torchscript-able. 2025-08-26T20:21:58.5903630Z Warning, this is an experimental API and susceptible to frequent changes. 2025-08-26T20:21:58.5903982Z 2025-08-26T20:21:58.5904083Z Returns: 2025-08-26T20:21:58.5904453Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-26T20:21:58.5905032Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-08-26T20:21:58.5905652Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-26T20:21:58.5906188Z on the user-provided module on the remote side. 2025-08-26T20:21:58.5906440Z 2025-08-26T20:21:58.5906548Z Example:: 2025-08-26T20:21:58.5906815Z Run the following code in two different processes: 2025-08-26T20:21:58.5907086Z 2025-08-26T20:21:58.5907204Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:21:58.5907517Z >>> # On worker 0: 2025-08-26T20:21:58.5907770Z >>> import torch 2025-08-26T20:21:58.5908037Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.5908381Z >>> from torch import nn, Tensor 2025-08-26T20:21:58.5908808Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-26T20:21:58.5909231Z >>> 2025-08-26T20:21:58.5909479Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-26T20:21:58.5909849Z >>> remote_linear_module = RemoteModule( 2025-08-26T20:21:58.5910207Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-26T20:21:58.5910534Z >>> ) 2025-08-26T20:21:58.5910758Z >>> input = torch.randn(128, 20) 2025-08-26T20:21:58.5911130Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-08-26T20:21:58.5911498Z >>> ret = ret_fut.wait() 2025-08-26T20:21:58.5911778Z >>> rpc.shutdown() 2025-08-26T20:21:58.5911935Z 2025-08-26T20:21:58.5912044Z >>> # On worker 1: 2025-08-26T20:21:58.5912280Z >>> import torch 2025-08-26T20:21:58.5912564Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.5913007Z >>> 2025-08-26T20:21:58.5913266Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-26T20:21:58.5913602Z >>> rpc.shutdown() 2025-08-26T20:21:58.5913770Z 2025-08-26T20:21:58.5914018Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5914384Z 2025-08-26T20:21:58.5915137Z msg = Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=506. 2025-08-26T20:21:58.5916206Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5916596Z 2025-08-26T20:21:58.5916967Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-08-26T20:21:58.5917422Z 2025-08-26T20:21:58.5917816Z This alternate initialization method can be particularly useful if we want to create multiple 2025-08-26T20:21:58.5918582Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-08-26T20:21:58.5919019Z 2025-08-26T20:21:58.5919307Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-08-26T20:21:58.5919964Z which is not supported. The recommended way is as follows: 2025-08-26T20:21:58.5920271Z 2025-08-26T20:21:58.5920393Z 1. the sender creates a RemoteModule; 2025-08-26T20:21:58.5920769Z 2. the sender sends its ``module_rref`` over RPC; 2025-08-26T20:21:58.5921367Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-08-26T20:21:58.5921819Z 2025-08-26T20:21:58.5921933Z Example:: 2025-08-26T20:21:58.5922203Z Run the following code in two different processes: 2025-08-26T20:21:58.5922475Z 2025-08-26T20:21:58.5922589Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:21:58.5922908Z >>> # On worker 0: 2025-08-26T20:21:58.5923160Z >>> import torch 2025-08-26T20:21:58.5923424Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.5923769Z >>> from torch import nn, Tensor 2025-08-26T20:21:58.5924193Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-26T20:21:58.5924612Z >>> 2025-08-26T20:21:58.5924858Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-26T20:21:58.5925223Z >>> remote_module = RemoteModule( 2025-08-26T20:21:58.5925564Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-26T20:21:58.5925888Z >>> ) 2025-08-26T20:21:58.5926081Z >>> 2025-08-26T20:21:58.5926311Z >>> remote_module1 = rpc.rpc_sync( 2025-08-26T20:21:58.5926623Z >>> "worker1/cpu", 2025-08-26T20:21:58.5926919Z >>> RemoteModule.init_from_module_rref, 2025-08-26T20:21:58.5927363Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-08-26T20:21:58.5927719Z >>> ) 2025-08-26T20:21:58.5927943Z >>> rpc.shutdown() 2025-08-26T20:21:58.5928098Z 2025-08-26T20:21:58.5928204Z >>> # On worker 1: 2025-08-26T20:21:58.5928440Z >>> import torch 2025-08-26T20:21:58.5928719Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.5929037Z >>> 2025-08-26T20:21:58.5929291Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-26T20:21:58.5929625Z >>> rpc.shutdown() 2025-08-26T20:21:58.5929797Z 2025-08-26T20:21:58.5929878Z Args: 2025-08-26T20:21:58.5930296Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-26T20:21:58.5931004Z The device can be a local device or a remote device specified by one of the following remote 2025-08-26T20:21:58.5931494Z formats: 2025-08-26T20:21:58.5931644Z 2025-08-26T20:21:58.5931785Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-08-26T20:21:58.5932193Z 2. "/" (ex: "trainer0/cuda:0"). 2025-08-26T20:21:58.5932453Z 2025-08-26T20:21:58.5932724Z In addition, the device field can be optional and the default value is "cpu". 2025-08-26T20:21:58.5933425Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-08-26T20:21:58.5933892Z the created remote module. 2025-08-26T20:21:58.5934379Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-08-26T20:21:58.5935013Z to be created. The type object should be decorated by @torch.jit.interface. 2025-08-26T20:21:58.5935588Z If not provided, the generated RemoteModule is not torchscript-able. 2025-08-26T20:21:58.5936164Z Warning, this is an experimental API and susceptible to frequent changes. 2025-08-26T20:21:58.5936513Z 2025-08-26T20:21:58.5936598Z Returns: 2025-08-26T20:21:58.5937035Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-26T20:21:58.5937641Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-08-26T20:21:58.5938356Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-26T20:21:58.5938897Z on the user-provided module on the remote side. 2025-08-26T20:21:58.5939164Z 2025-08-26T20:21:58.5939416Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5939795Z 2025-08-26T20:21:58.5940476Z msg = Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=598. 2025-08-26T20:21:58.5941467Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.5941845Z 2025-08-26T20:21:58.5942090Z A RemoteModule instance can only be created after RPC initialization. 2025-08-26T20:21:58.5942436Z 2025-08-26T20:21:58.5942650Z It creates a user-specified module on a specified remote node. 2025-08-26T20:21:58.5943184Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-08-26T20:21:58.5943653Z executed on the remote node. 2025-08-26T20:21:58.5944086Z It takes care of autograd recording to ensure the backward pass propagates 2025-08-26T20:21:58.5944600Z gradients back to the corresponding remote module. 2025-08-26T20:21:58.5944864Z 2025-08-26T20:21:58.5945079Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-08-26T20:21:58.5945636Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-08-26T20:21:58.5946217Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-08-26T20:21:58.5946785Z and ``forward`` are the same as the ``forward`` method of the module 2025-08-26T20:21:58.5947211Z returned by the ``module_cls``. 2025-08-26T20:21:58.5947415Z 2025-08-26T20:21:58.5947612Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-08-26T20:21:58.5948180Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-08-26T20:21:58.5948818Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-08-26T20:21:58.5949163Z 2025-08-26T20:21:58.5949296Z | ``def forward(input: Tensor) -> Tensor:`` 2025-08-26T20:21:58.5949684Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-08-26T20:21:58.5949971Z 2025-08-26T20:21:58.5950051Z Args: 2025-08-26T20:21:58.5950467Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-26T20:21:58.5951220Z The format should be "/", where the device field can be parsed as torch.device type. 2025-08-26T20:21:58.5951818Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-08-26T20:21:58.5952318Z In addition, the device field can be optional and the default value is "cpu". 2025-08-26T20:21:58.5952923Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-08-26T20:21:58.5953371Z 2025-08-26T20:21:58.5953488Z >>> class MyModule(nn.Module): 2025-08-26T20:21:58.5953816Z >>> def forward(input): 2025-08-26T20:21:58.5954125Z >>> return input + 1 2025-08-26T20:21:58.5954491Z >>> 2025-08-26T20:21:58.5954710Z >>> module_cls = MyModule 2025-08-26T20:21:58.5954917Z 2025-08-26T20:21:58.5955115Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-08-26T20:21:58.5955626Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-08-26T20:21:58.5955934Z 2025-08-26T20:21:58.5956035Z Returns: 2025-08-26T20:21:58.5956396Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-26T20:21:58.5956990Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-08-26T20:21:58.5957607Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-26T20:21:58.5958143Z on the user-provided module on the remote side. 2025-08-26T20:21:58.5958396Z 2025-08-26T20:21:58.5958500Z Example:: 2025-08-26T20:21:58.5958820Z Run the following code in two different processes: 2025-08-26T20:21:58.5959098Z 2025-08-26T20:21:58.5959212Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:21:58.5959531Z >>> # On worker 0: 2025-08-26T20:21:58.5959780Z >>> import torch 2025-08-26T20:21:58.5960046Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.5960385Z >>> from torch import nn, Tensor 2025-08-26T20:21:58.5960817Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-26T20:21:58.5961239Z >>> 2025-08-26T20:21:58.5961632Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-26T20:21:58.5962006Z >>> remote_linear_module = RemoteModule( 2025-08-26T20:21:58.5962364Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-26T20:21:58.5962689Z >>> ) 2025-08-26T20:21:58.5962903Z >>> input = torch.randn(128, 20) 2025-08-26T20:21:58.5963264Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-08-26T20:21:58.5963638Z >>> ret = ret_fut.wait() 2025-08-26T20:21:58.5963915Z >>> rpc.shutdown() 2025-08-26T20:21:58.5964137Z 2025-08-26T20:21:58.5964269Z >>> # On worker 1: 2025-08-26T20:21:58.5964530Z >>> import torch 2025-08-26T20:21:58.5964808Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.5965130Z >>> 2025-08-26T20:21:58.5965375Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-26T20:21:58.5965722Z >>> rpc.shutdown() 2025-08-26T20:21:58.5965887Z 2025-08-26T20:21:58.5966157Z Furthermore, a more practical example that is combined with 2025-08-26T20:21:58.5966960Z `DistributedDataParallel `__ (DDP) 2025-08-26T20:21:58.5967877Z can be found in this `tutorial `__. 2025-08-26T20:21:58.5968321Z 2025-08-26T20:21:58.5968622Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.5969005Z 2025-08-26T20:21:58.6127356Z msg = Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-08-26T20:21:58.6128459Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.6128852Z 2025-08-26T20:21:58.6129096Z DistributedOptimizer takes remote references to parameters scattered 2025-08-26T20:21:58.6129698Z across workers and applies the given optimizer locally for each parameter. 2025-08-26T20:21:58.6130055Z 2025-08-26T20:21:58.6130304Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-08-26T20:21:58.6130803Z to retrieve the gradients for specific parameters. 2025-08-26T20:21:58.6131081Z 2025-08-26T20:21:58.6131176Z Concurrent calls to 2025-08-26T20:21:58.6131537Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-08-26T20:21:58.6132006Z either from the same or different clients, will 2025-08-26T20:21:58.6132499Z be serialized on each worker -- as each worker's optimizer can only work 2025-08-26T20:21:58.6133039Z on one set of gradients at a time. However, there is no guarantee that 2025-08-26T20:21:58.6133842Z the full forward-backward-optimizer sequence will execute for one client 2025-08-26T20:21:58.6134430Z at a time. This means that the gradients being applied may not correspond 2025-08-26T20:21:58.6134996Z to the latest forward pass executed on a given worker. Also, there is no 2025-08-26T20:21:58.6135446Z guaranteed ordering across workers. 2025-08-26T20:21:58.6135673Z 2025-08-26T20:21:58.6135934Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-08-26T20:21:58.6136549Z by default, so that optimizer updates are not blocked by the Python Global 2025-08-26T20:21:58.6137142Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-08-26T20:21:58.6137744Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-08-26T20:21:58.6138432Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-08-26T20:21:58.6138922Z for your own custom optimizers. 2025-08-26T20:21:58.6139127Z 2025-08-26T20:21:58.6139208Z Args: 2025-08-26T20:21:58.6139530Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-08-26T20:21:58.6139936Z instantiate on each worker. 2025-08-26T20:21:58.6140447Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-08-26T20:21:58.6140884Z to optimize. 2025-08-26T20:21:58.6141258Z args: arguments to pass to the optimizer constructor on each worker. 2025-08-26T20:21:58.6141803Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-08-26T20:21:58.6142157Z 2025-08-26T20:21:58.6142259Z Example:: 2025-08-26T20:21:58.6142501Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:21:58.6142885Z >>> import torch.distributed.autograd as dist_autograd 2025-08-26T20:21:58.6143334Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.6143737Z >>> from torch import optim 2025-08-26T20:21:58.6144117Z >>> from torch.distributed.optim import DistributedOptimizer 2025-08-26T20:21:58.6144508Z >>> 2025-08-26T20:21:58.6144762Z >>> with dist_autograd.context() as context_id: 2025-08-26T20:21:58.6145301Z >>> # Forward pass. 2025-08-26T20:21:58.6145904Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-08-26T20:21:58.6146420Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-08-26T20:21:58.6146864Z >>> loss = rref1.to_here() + rref2.to_here() 2025-08-26T20:21:58.6147178Z >>> 2025-08-26T20:21:58.6147396Z >>> # Backward pass. 2025-08-26T20:21:58.6147720Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-08-26T20:21:58.6148074Z >>> 2025-08-26T20:21:58.6148274Z >>> # Optimizer. 2025-08-26T20:21:58.6148560Z >>> dist_optim = DistributedOptimizer( 2025-08-26T20:21:58.6148883Z >>> optim.SGD, 2025-08-26T20:21:58.6149146Z >>> [rref1, rref2], 2025-08-26T20:21:58.6149401Z >>> lr=0.05, 2025-08-26T20:21:58.6149650Z >>> ) 2025-08-26T20:21:58.6149890Z >>> dist_optim.step(context_id) 2025-08-26T20:21:58.6150100Z 2025-08-26T20:21:58.6150273Z __ https://github.com/pytorch/tutorials/pull/1465 2025-08-26T20:21:58.6150533Z 2025-08-26T20:21:58.6150785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.6151166Z 2025-08-26T20:21:58.6151886Z msg = Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2025-08-26T20:21:58.6152961Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.6153348Z 2025-08-26T20:21:58.6153727Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-08-26T20:21:58.6154395Z This optimizer runs local optimizer at every step. 2025-08-26T20:21:58.6154999Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-08-26T20:21:58.6155547Z 2025-08-26T20:21:58.6155630Z Args: 2025-08-26T20:21:58.6155861Z optim: The local optimizer. 2025-08-26T20:21:58.6156281Z averager: A model averager instance to run post-localSGD algorithm. 2025-08-26T20:21:58.6156612Z 2025-08-26T20:21:58.6156721Z Example:: 2025-08-26T20:21:58.6156843Z 2025-08-26T20:21:58.6156981Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:21:58.6157300Z >>> import torch 2025-08-26T20:21:58.6157573Z >>> import torch.distributed as dist 2025-08-26T20:21:58.6158071Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-08-26T20:21:58.6158563Z >>> import torch.nn as nn 2025-08-26T20:21:58.6158958Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-08-26T20:21:58.6159630Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-08-26T20:21:58.6160133Z >>> PostLocalSGDState, 2025-08-26T20:21:58.6160430Z >>> post_localSGD_hook, 2025-08-26T20:21:58.6160689Z >>> ) 2025-08-26T20:21:58.6160896Z >>> 2025-08-26T20:21:58.6161173Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-26T20:21:58.6161583Z >>> module, device_ids=[rank], output_device=rank 2025-08-26T20:21:58.6161907Z >>> ) 2025-08-26T20:21:58.6162114Z >>> 2025-08-26T20:21:58.6162374Z >>> # Register a post-localSGD communication hook. 2025-08-26T20:21:58.6162924Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-08-26T20:21:58.6163494Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-26T20:21:58.6163853Z >>> 2025-08-26T20:21:58.6164176Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-08-26T20:21:58.6164744Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-08-26T20:21:58.6165296Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-26T20:21:58.6165784Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-08-26T20:21:58.6166237Z >>> opt = PostLocalSGDOptimizer( 2025-08-26T20:21:58.6166564Z >>> optim=local_optim, 2025-08-26T20:21:58.6167002Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-08-26T20:21:58.6167489Z >>> ) 2025-08-26T20:21:58.6167701Z >>> 2025-08-26T20:21:58.6168042Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-08-26T20:21:58.6168692Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-08-26T20:21:58.6169480Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-08-26T20:21:58.6170079Z >>> for step in range(0, 200): 2025-08-26T20:21:58.6170380Z >>> opt.zero_grad() 2025-08-26T20:21:58.6170668Z >>> loss = loss_fn(output, labels) 2025-08-26T20:21:58.6170983Z >>> loss.backward() 2025-08-26T20:21:58.6171253Z >>> opt.step() 2025-08-26T20:21:58.6171415Z 2025-08-26T20:21:58.6171665Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.6172034Z 2025-08-26T20:21:58.6266847Z msg = Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=284. 2025-08-26T20:21:58.6267968Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.6268343Z 2025-08-26T20:21:58.6268757Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-08-26T20:21:58.6269281Z 2025-08-26T20:21:58.6269551Z The sharing is done as described by `ZeRO `_. 2025-08-26T20:21:58.6269912Z 2025-08-26T20:21:58.6270066Z The local optimizer instance in each rank is only 2025-08-26T20:21:58.6270568Z responsible for updating approximately ``1 / world_size`` parameters and 2025-08-26T20:21:58.6271316Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-08-26T20:21:58.6271883Z parameters are updated locally, each rank will broadcast its parameters to 2025-08-26T20:21:58.6272417Z all other peers to keep all model replicas in the same state. 2025-08-26T20:21:58.6272920Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-08-26T20:21:58.6273492Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-08-26T20:21:58.6273971Z memory consumption. 2025-08-26T20:21:58.6274119Z 2025-08-26T20:21:58.6274387Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-08-26T20:21:58.6274982Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-08-26T20:21:58.6275648Z not divided among ranks. The partition is arbitrary and might not match the 2025-08-26T20:21:58.6276139Z the parameter registration or usage order. 2025-08-26T20:21:58.6276374Z 2025-08-26T20:21:58.6276476Z Arguments: 2025-08-26T20:21:58.6276783Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-08-26T20:21:58.6277283Z or :class:`dict` s giving all parameters, which will be sharded 2025-08-26T20:21:58.6277683Z across ranks. 2025-08-26T20:21:58.6277839Z 2025-08-26T20:21:58.6277939Z Keyword Args: 2025-08-26T20:21:58.6278285Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-08-26T20:21:58.6278735Z optimizer. 2025-08-26T20:21:58.6279099Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-08-26T20:21:58.6279630Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-08-26T20:21:58.6280085Z :meth:`torch.distributed.init_process_group`). 2025-08-26T20:21:58.6280583Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-08-26T20:21:58.6281152Z packed into buckets to speed up communication, and ``param.data`` 2025-08-26T20:21:58.6281718Z fields point to bucket views at different offsets; if ``False``, 2025-08-26T20:21:58.6282251Z each individual parameter is communicated separately, and each 2025-08-26T20:21:58.6282714Z ``params.data`` stays intact (default: ``False``). 2025-08-26T20:21:58.6283181Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-08-26T20:21:58.6283697Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-08-26T20:21:58.6284235Z synchronization; this requires (1) either a functional optimizer 2025-08-26T20:21:58.6284741Z for the ``optimizer_class`` argument or one with a functional 2025-08-26T20:21:58.6285218Z equivalent and (2) registering a DDP communication hook 2025-08-26T20:21:58.6285717Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-08-26T20:21:58.6286211Z parameters are packed into buckets matching those in 2025-08-26T20:21:58.6286650Z :class:`DistributedDataParallel`, meaning that the 2025-08-26T20:21:58.6287063Z ``parameters_as_bucket_view`` argument is ignored. 2025-08-26T20:21:58.6287515Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-08-26T20:21:58.6287920Z (per normal). 2025-08-26T20:21:58.6288180Z (default: ``False``) 2025-08-26T20:21:58.6288562Z **defaults: any trailing arguments, which are forwarded to the local 2025-08-26T20:21:58.6289009Z optimizer. 2025-08-26T20:21:58.6289162Z 2025-08-26T20:21:58.6289262Z Example:: 2025-08-26T20:21:58.6289383Z 2025-08-26T20:21:58.6289491Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.6289754Z >>> import torch.nn as nn 2025-08-26T20:21:58.6290143Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-08-26T20:21:58.6290663Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-08-26T20:21:58.6291213Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-08-26T20:21:58.6291868Z >>> ddp = DDP(model, device_ids=[rank]) 2025-08-26T20:21:58.6292339Z >>> opt = ZeroRedundancyOptimizer( 2025-08-26T20:21:58.6292669Z >>> ddp.parameters(), 2025-08-26T20:21:58.6292983Z >>> optimizer_class=torch.optim.Adam, 2025-08-26T20:21:58.6293311Z >>> lr=0.01 2025-08-26T20:21:58.6293530Z >>> ) 2025-08-26T20:21:58.6293763Z >>> ddp(inputs).sum().backward() 2025-08-26T20:21:58.6294067Z >>> opt.step() 2025-08-26T20:21:58.6294210Z 2025-08-26T20:21:58.6294310Z .. warning:: 2025-08-26T20:21:58.6294638Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-08-26T20:21:58.6295102Z passed-in parameters are the same dense type. 2025-08-26T20:21:58.6295355Z 2025-08-26T20:21:58.6295455Z .. warning:: 2025-08-26T20:21:58.6295800Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-08-26T20:21:58.6296395Z the way that overlapping :class:`DistributedDataParallel` with 2025-08-26T20:21:58.6296948Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-08-26T20:21:58.6297520Z two or three training iterations do not perform parameter updates in 2025-08-26T20:21:58.6298054Z the optimizer step, depending on if ``static_graph=False`` or 2025-08-26T20:21:58.6298538Z ``static_graph=True``, respectively. This is because it needs 2025-08-26T20:21:58.6299027Z information about the gradient bucketing strategy used by 2025-08-26T20:21:58.6299555Z :class:`DistributedDataParallel`, which is not finalized until the 2025-08-26T20:21:58.6300098Z second forward pass if ``static_graph=False`` or until the third 2025-08-26T20:21:58.6300711Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-08-26T20:21:58.6301137Z is to prepend dummy inputs. 2025-08-26T20:21:58.6301345Z 2025-08-26T20:21:58.6301607Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-08-26T20:21:58.6301989Z 2025-08-26T20:21:58.6302239Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.6302613Z 2025-08-26T20:21:58.6512963Z msg = Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-08-26T20:21:58.6513976Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.6514354Z 2025-08-26T20:21:58.6514602Z Custom reducer class that can be used to specify a custom operation that 2025-08-26T20:21:58.6515112Z reduces losses of multiple microbatches into one value. 2025-08-26T20:21:58.6515403Z 2025-08-26T20:21:58.6515488Z Example: 2025-08-26T20:21:58.6515706Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.6515974Z >>> sum_reducer = _CustomReducer( 2025-08-26T20:21:58.6516264Z >>> torch.tensor(0.0), 2025-08-26T20:21:58.6516536Z >>> lambda a, b: a + b 2025-08-26T20:21:58.6516790Z >>> ) 2025-08-26T20:21:58.6516914Z 2025-08-26T20:21:58.6517178Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.6517548Z 2025-08-26T20:21:58.7061300Z msg = Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6. 2025-08-26T20:21:58.7063153Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.7063534Z 2025-08-26T20:21:58.7063802Z A decorator for a function indicating that the return value of the function 2025-08-26T20:21:58.7064361Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-08-26T20:21:58.7064927Z function can run asynchronously on the RPC callee. More specifically, the 2025-08-26T20:21:58.7065525Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-08-26T20:21:58.7066116Z function and installs subsequent processing steps as a callback to that 2025-08-26T20:21:58.7066726Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-08-26T20:21:58.7067271Z from the :class:`~torch.futures.Future` when completed and send the 2025-08-26T20:21:58.7068037Z value back as the RPC response. That also means the returned 2025-08-26T20:21:58.7068566Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-08-26T20:21:58.7069142Z sent through RPC. This decorator is useful when the wrapped function's 2025-08-26T20:21:58.7069671Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-08-26T20:21:58.7070213Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-08-26T20:21:58.7070566Z 2025-08-26T20:21:58.7070806Z .. note:: To enable asynchronous execution, applications must pass the 2025-08-26T20:21:58.7071367Z function object returned by this decorator to RPC APIs. If RPC detected 2025-08-26T20:21:58.7071943Z attributes installed by this decorator, it knows that this function 2025-08-26T20:21:58.7072563Z returns a ``Future`` object and will handle that accordingly. 2025-08-26T20:21:58.7073083Z However, this does not mean this decorator has to be outmost one when 2025-08-26T20:21:58.7073646Z defining a function. For example, when combined with ``@staticmethod`` 2025-08-26T20:21:58.7074203Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-08-26T20:21:58.7074743Z inner decorator to allow the target function be recognized as a static 2025-08-26T20:21:58.7075317Z or class function. This target function can still execute asynchronously 2025-08-26T20:21:58.7075896Z because, when accessed, the static or class method preserves attributes 2025-08-26T20:21:58.7076399Z installed by ``@rpc.functions.async_execution``. 2025-08-26T20:21:58.7076662Z 2025-08-26T20:21:58.7076666Z 2025-08-26T20:21:58.7076770Z Example:: 2025-08-26T20:21:58.7077087Z The returned :class:`~torch.futures.Future` object can come from 2025-08-26T20:21:58.7077546Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-08-26T20:21:58.7078019Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-08-26T20:21:58.7078550Z constructor. The example below shows directly using the 2025-08-26T20:21:58.7078965Z :class:`~torch.futures.Future` returned by 2025-08-26T20:21:58.7079328Z :meth:`~torch.futures.Future.then`. 2025-08-26T20:21:58.7079563Z 2025-08-26T20:21:58.7079683Z >>> from torch.distributed import rpc 2025-08-26T20:21:58.7079999Z >>> 2025-08-26T20:21:58.7080237Z >>> # omitting setup and shutdown RPC 2025-08-26T20:21:58.7080535Z >>> 2025-08-26T20:21:58.7080753Z >>> # On all workers 2025-08-26T20:21:58.7081040Z >>> @rpc.functions.async_execution 2025-08-26T20:21:58.7081366Z >>> def async_add_chained(to, x, y, z): 2025-08-26T20:21:58.7081794Z >>> # This function runs on "worker1" and returns immediately when 2025-08-26T20:21:58.7082292Z >>> # the callback is installed through the `then(cb)` API. In the 2025-08-26T20:21:58.7082791Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-08-26T20:21:58.7083260Z >>> # When the return value of that `rpc_async` arrives at 2025-08-26T20:21:58.7083712Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-08-26T20:21:58.7084207Z >>> # and set the value for the previously returned `Future`, which 2025-08-26T20:21:58.7084696Z >>> # will then trigger RPC to send the result back to "worker0". 2025-08-26T20:21:58.7085167Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-26T20:21:58.7085548Z >>> lambda fut: fut.wait() + z 2025-08-26T20:21:58.7085852Z >>> ) 2025-08-26T20:21:58.7086067Z >>> 2025-08-26T20:21:58.7086273Z >>> # On worker0 2025-08-26T20:21:58.7086514Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.7086781Z >>> ret = rpc.rpc_sync( 2025-08-26T20:21:58.7087047Z >>> "worker1", 2025-08-26T20:21:58.7087298Z >>> async_add_chained, 2025-08-26T20:21:58.7087592Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-08-26T20:21:58.7087920Z >>> ) 2025-08-26T20:21:58.7088270Z >>> print(ret) # prints tensor([3., 3.]) 2025-08-26T20:21:58.7088496Z 2025-08-26T20:21:58.7088736Z When combined with TorchScript decorators, this decorator must be the 2025-08-26T20:21:58.7089159Z outmost one. 2025-08-26T20:21:58.7089311Z 2025-08-26T20:21:58.7089418Z >>> from torch import Tensor 2025-08-26T20:21:58.7089738Z >>> from torch.futures import Future 2025-08-26T20:21:58.7090088Z >>> from torch.distributed import rpc 2025-08-26T20:21:58.7090401Z >>> 2025-08-26T20:21:58.7090621Z >>> # omitting setup and shutdown RPC 2025-08-26T20:21:58.7090926Z >>> 2025-08-26T20:21:58.7091133Z >>> # On all workers 2025-08-26T20:21:58.7091387Z >>> @torch.jit.script 2025-08-26T20:21:58.7091914Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-08-26T20:21:58.7092271Z >>> return x + y 2025-08-26T20:21:58.7092640Z >>> 2025-08-26T20:21:58.7092869Z >>> @rpc.functions.async_execution 2025-08-26T20:21:58.7093196Z >>> @torch.jit.script 2025-08-26T20:21:58.7093570Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-08-26T20:21:58.7094024Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-08-26T20:21:58.7094370Z >>> 2025-08-26T20:21:58.7094565Z >>> # On worker0 2025-08-26T20:21:58.7094814Z >>> ret = rpc.rpc_sync( 2025-08-26T20:21:58.7095082Z >>> "worker1", 2025-08-26T20:21:58.7095316Z >>> async_add, 2025-08-26T20:21:58.7095586Z >>> args=("worker2", torch.ones(2), 1) 2025-08-26T20:21:58.7095899Z >>> ) 2025-08-26T20:21:58.7096136Z >>> print(ret) # prints tensor([2., 2.]) 2025-08-26T20:21:58.7096360Z 2025-08-26T20:21:58.7096590Z When combined with static or class method, this decorator must be the 2025-08-26T20:21:58.7097003Z inner one. 2025-08-26T20:21:58.7097147Z 2025-08-26T20:21:58.7097266Z >>> from torch.distributed import rpc 2025-08-26T20:21:58.7097577Z >>> 2025-08-26T20:21:58.7097808Z >>> # omitting setup and shutdown RPC 2025-08-26T20:21:58.7098103Z >>> 2025-08-26T20:21:58.7098313Z >>> # On all workers 2025-08-26T20:21:58.7098588Z >>> class AsyncExecutionClass: 2025-08-26T20:21:58.7098879Z >>> 2025-08-26T20:21:58.7099076Z >>> @staticmethod 2025-08-26T20:21:58.7099362Z >>> @rpc.functions.async_execution 2025-08-26T20:21:58.7099702Z >>> def static_async_add(to, x, y, z): 2025-08-26T20:21:58.7100099Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-26T20:21:58.7100567Z >>> lambda fut: fut.wait() + z 2025-08-26T20:21:58.7100878Z >>> ) 2025-08-26T20:21:58.7101103Z >>> 2025-08-26T20:21:58.7101313Z >>> @classmethod 2025-08-26T20:21:58.7101582Z >>> @rpc.functions.async_execution 2025-08-26T20:21:58.7101932Z >>> def class_async_add(cls, to, x, y, z): 2025-08-26T20:21:58.7102302Z >>> ret_fut = torch.futures.Future() 2025-08-26T20:21:58.7102688Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-26T20:21:58.7103101Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-08-26T20:21:58.7103454Z >>> ) 2025-08-26T20:21:58.7103699Z >>> return ret_fut 2025-08-26T20:21:58.7103971Z >>> 2025-08-26T20:21:58.7104196Z >>> @rpc.functions.async_execution 2025-08-26T20:21:58.7104546Z >>> def bound_async_add(self, to, x, y, z): 2025-08-26T20:21:58.7104958Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-26T20:21:58.7105359Z >>> lambda fut: fut.wait() + z 2025-08-26T20:21:58.7105661Z >>> ) 2025-08-26T20:21:58.7105883Z >>> 2025-08-26T20:21:58.7106087Z >>> # On worker0 2025-08-26T20:21:58.7106339Z >>> ret = rpc.rpc_sync( 2025-08-26T20:21:58.7106594Z >>> "worker1", 2025-08-26T20:21:58.7106881Z >>> AsyncExecutionClass.static_async_add, 2025-08-26T20:21:58.7107016Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-08-26T20:21:58.7107113Z >>> ) 2025-08-26T20:21:58.7107348Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:21:58.7107445Z >>> 2025-08-26T20:21:58.7107543Z >>> ret = rpc.rpc_sync( 2025-08-26T20:21:58.7107631Z >>> "worker1", 2025-08-26T20:21:58.7107775Z >>> AsyncExecutionClass.class_async_add, 2025-08-26T20:21:58.7107891Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-08-26T20:21:58.7108018Z >>> ) 2025-08-26T20:21:58.7108179Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:21:58.7108186Z 2025-08-26T20:21:58.7108371Z This decorator also works with RRef helpers, i.e., . 2025-08-26T20:21:58.7108525Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-08-26T20:21:58.7108681Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-08-26T20:21:58.7108833Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-08-26T20:21:58.7108838Z 2025-08-26T20:21:58.7109030Z >>> from torch.distributed import rpc 2025-08-26T20:21:58.7109112Z >>> 2025-08-26T20:21:58.7109260Z >>> # reuse the AsyncExecutionClass class above 2025-08-26T20:21:58.7109467Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-26T20:21:58.7109819Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-08-26T20:21:58.7110033Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:21:58.7110200Z >>> 2025-08-26T20:21:58.7110512Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-26T20:21:58.7110806Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-08-26T20:21:58.7110937Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:21:58.7111020Z >>> 2025-08-26T20:21:58.7111169Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-26T20:21:58.7111415Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-08-26T20:21:58.7111535Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:21:58.7111540Z 2025-08-26T20:21:58.7111801Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.7111809Z 2025-08-26T20:21:58.7112547Z msg = Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=113. 2025-08-26T20:21:58.7112818Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.7112823Z 2025-08-26T20:21:58.7113027Z Set device mapping between each RPC caller and callee pair. This 2025-08-26T20:21:58.7113219Z function can be called multiple times to incrementally add 2025-08-26T20:21:58.7113335Z device placement configurations. 2025-08-26T20:21:58.7113340Z 2025-08-26T20:21:58.7113420Z Args: 2025-08-26T20:21:58.7113529Z to (str): Callee name. 2025-08-26T20:21:58.7113727Z device_map (Dict of int, str, or torch.device): Device placement 2025-08-26T20:21:58.7113923Z mappings from this worker to the callee. This map must be 2025-08-26T20:21:58.7114010Z invertible. 2025-08-26T20:21:58.7114018Z 2025-08-26T20:21:58.7114103Z Example: 2025-08-26T20:21:58.7114225Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:21:58.7114314Z >>> # both workers 2025-08-26T20:21:58.7114416Z >>> def add(x, y): 2025-08-26T20:21:58.7114549Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-08-26T20:21:58.7114650Z >>> return x + y, (x + y).to(2) 2025-08-26T20:21:58.7114747Z >>> 2025-08-26T20:21:58.7114834Z >>> # on worker 0 2025-08-26T20:21:58.7114977Z >>> options = TensorPipeRpcBackendOptions( 2025-08-26T20:21:58.7115075Z >>> num_worker_threads=8, 2025-08-26T20:21:58.7115184Z >>> device_maps={"worker1": {0: 1}} 2025-08-26T20:21:58.7115321Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-08-26T20:21:58.7115404Z >>> ) 2025-08-26T20:21:58.7115530Z >>> options.set_device_map("worker1", {1: 2}) 2025-08-26T20:21:58.7115669Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-08-26T20:21:58.7115750Z >>> 2025-08-26T20:21:58.7115922Z >>> rpc.init_rpc( 2025-08-26T20:21:58.7116010Z >>> "worker0", 2025-08-26T20:21:58.7116094Z >>> rank=0, 2025-08-26T20:21:58.7116198Z >>> world_size=2, 2025-08-26T20:21:58.7116326Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-08-26T20:21:58.7116449Z >>> rpc_backend_options=options 2025-08-26T20:21:58.7116532Z >>> ) 2025-08-26T20:21:58.7116612Z >>> 2025-08-26T20:21:58.7116719Z >>> x = torch.ones(2) 2025-08-26T20:21:58.7116876Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-08-26T20:21:58.7117074Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-08-26T20:21:58.7117259Z >>> # sending the return value back, it will follow the invert of 2025-08-26T20:21:58.7117436Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-08-26T20:21:58.7117603Z >>> # cuda:1 on worker0 2025-08-26T20:21:58.7117749Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-08-26T20:21:58.7117912Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-08-26T20:21:58.7117917Z 2025-08-26T20:21:58.7118168Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.7118173Z 2025-08-26T20:21:58.7147230Z msg = Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19. 2025-08-26T20:21:58.7147501Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.7147529Z 2025-08-26T20:21:58.7147741Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-08-26T20:21:58.7148015Z except that it enables profiling on all threads running RPC server request callbacks. 2025-08-26T20:21:58.7148020Z 2025-08-26T20:21:58.7148322Z Context manager that manages autograd profiler state and holds a summary of results. 2025-08-26T20:21:58.7148550Z Under the hood it just records events of functions being executed in C++ and 2025-08-26T20:21:58.7148787Z exposes those events to Python. You can wrap any code into it and it will 2025-08-26T20:21:58.7148924Z only report runtime of PyTorch functions. 2025-08-26T20:21:58.7149189Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-08-26T20:21:58.7149194Z 2025-08-26T20:21:58.7149287Z Args: 2025-08-26T20:21:58.7149553Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-08-26T20:21:58.7149648Z Default: ``True``. 2025-08-26T20:21:58.7149653Z 2025-08-26T20:21:58.7149948Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-08-26T20:21:58.7150144Z Adds approximately 4us of overhead to each tensor operation. 2025-08-26T20:21:58.7150249Z Default: ``False`` 2025-08-26T20:21:58.7150253Z 2025-08-26T20:21:58.7150481Z record_shapes (bool, optional): If shapes recording is set, information 2025-08-26T20:21:58.7150724Z about input dimensions will be collected. This allows one to see which 2025-08-26T20:21:58.7150934Z dimensions have been used under the hood and further group by them 2025-08-26T20:21:58.7151150Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-08-26T20:21:58.7151383Z shape recording might skew your profiling data. It is recommended to 2025-08-26T20:21:58.7151619Z use separate runs with and without shape recording to validate the timing. 2025-08-26T20:21:58.7151859Z Most likely the skew will be negligible for bottom most events (in a case 2025-08-26T20:21:58.7152070Z of nested function calls). But for higher level functions the total 2025-08-26T20:21:58.7152272Z self cpu time might be artificially increased because of the shape 2025-08-26T20:21:58.7152377Z collection. 2025-08-26T20:21:58.7152386Z 2025-08-26T20:21:58.7152655Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-08-26T20:21:58.7152776Z 2025-08-26T20:21:58.7152899Z .. warning:: 2025-08-26T20:21:58.7153104Z Enabling memory profiling incurs additional profiler overhead 2025-08-26T20:21:58.7153108Z 2025-08-26T20:21:58.7153196Z .. warning:: 2025-08-26T20:21:58.7153496Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-08-26T20:21:58.7153695Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-08-26T20:21:58.7153944Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-08-26T20:21:58.7154107Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-08-26T20:21:58.7154111Z 2025-08-26T20:21:58.7154211Z Example: 2025-08-26T20:21:58.7154308Z >>> # xdoctest: +SKIP 2025-08-26T20:21:58.7154395Z >>> # On worker 0: 2025-08-26T20:21:58.7154495Z >>> import torch 2025-08-26T20:21:58.7154678Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.7154819Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-26T20:21:58.7154953Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-08-26T20:21:58.7155065Z >>> outer_profile_rref = rpc.remote( 2025-08-26T20:21:58.7155235Z ... dst_worker_name, rpc._server_process_global_profile 2025-08-26T20:21:58.7155318Z ... ) 2025-08-26T20:21:58.7155445Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-08-26T20:21:58.7155606Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-08-26T20:21:58.7155718Z >>> inner_profile_rref = rpc.remote( 2025-08-26T20:21:58.7155892Z ... dst_worker_name, rpc._server_process_global_profile 2025-08-26T20:21:58.7155972Z ... ) 2025-08-26T20:21:58.7156098Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-08-26T20:21:58.7156255Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-08-26T20:21:58.7156429Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-08-26T20:21:58.7156609Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-08-26T20:21:58.7156773Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-08-26T20:21:58.7157009Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:21:58.7157327Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-08-26T20:21:58.7157555Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:21:58.7157756Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-08-26T20:21:58.7157944Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-08-26T20:21:58.7158182Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:21:58.7158287Z Self CPU time total: 89.667us 2025-08-26T20:21:58.7158450Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-08-26T20:21:58.7158684Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:21:58.7158984Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-08-26T20:21:58.7159215Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:21:58.7159392Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-08-26T20:21:58.7159589Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-08-26T20:21:58.7159766Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-08-26T20:21:58.7159989Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:21:58.7160186Z Self CPU time total: 213.926us 2025-08-26T20:21:58.7160281Z >>> rpc.shutdown() 2025-08-26T20:21:58.7160285Z 2025-08-26T20:21:58.7160385Z >>> # On worker 1: 2025-08-26T20:21:58.7160510Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:21:58.7160648Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-26T20:21:58.7160816Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-08-26T20:21:58.7160909Z >>> rpc.shutdown() 2025-08-26T20:21:58.7160913Z 2025-08-26T20:21:58.7161178Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.7161182Z 2025-08-26T20:21:58.8462011Z msg = Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=35. 2025-08-26T20:21:58.8462524Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.8462564Z 2025-08-26T20:21:58.8462842Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-08-26T20:21:58.8463132Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-08-26T20:21:58.8463401Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-08-26T20:21:58.8463574Z :class:`DTensor` according to the ``out_placements``. 2025-08-26T20:21:58.8463579Z 2025-08-26T20:21:58.8463661Z Args: 2025-08-26T20:21:58.8463872Z func (Callable): the function to be applied on each local shard of 2025-08-26T20:21:58.8464010Z :class:`DTensor` s. 2025-08-26T20:21:58.8464237Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-08-26T20:21:58.8464503Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-08-26T20:21:58.8464745Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-08-26T20:21:58.8464987Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-08-26T20:21:58.8465248Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-08-26T20:21:58.8465368Z mapping to the flattened ``output``. 2025-08-26T20:21:58.8465580Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-08-26T20:21:58.8465851Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-08-26T20:21:58.8465948Z should be `None`. 2025-08-26T20:21:58.8466194Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-08-26T20:21:58.8466414Z in. In this case, even if `out_placements` is not `None`, the result function 2025-08-26T20:21:58.8466677Z should ignore the desired placements because the function is not running with 2025-08-26T20:21:58.8466777Z :class:`DTensor` s. 2025-08-26T20:21:58.8466941Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-08-26T20:21:58.8467237Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-08-26T20:21:58.8467467Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-08-26T20:21:58.8467704Z placements of each :class:`DTensor` argument is the same as the required 2025-08-26T20:21:58.8467884Z placements or not. If the placements are not the same and 2025-08-26T20:21:58.8468138Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-08-26T20:21:58.8468377Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-08-26T20:21:58.8468632Z the required sharding placements before passing its local tensor to ``func``. 2025-08-26T20:21:58.8468870Z The only exception is when required placements are not ``None`` and the 2025-08-26T20:21:58.8469110Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-08-26T20:21:58.8469423Z will be skipped and the argument will be directly passed to ``func``. 2025-08-26T20:21:58.8469644Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-08-26T20:21:58.8469739Z Default: None 2025-08-26T20:21:58.8469938Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-08-26T20:21:58.8470147Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-08-26T20:21:58.8470375Z to the flattened input DTensor. This argument is the hint that user 2025-08-26T20:21:58.8470560Z can give to :meth:`to_local` in case the gradient layout of the 2025-08-26T20:21:58.8470772Z local tensor input does not match its :class:`DTensor` input layout. 2025-08-26T20:21:58.8470984Z If not specified, we will assume the gradient layout of the local 2025-08-26T20:21:58.8471255Z tensor input remains the same as the original :class:`DTensor` input 2025-08-26T20:21:58.8471433Z and use that for gradient computation. Default: None. 2025-08-26T20:21:58.8471575Z device_mesh (:class:`DeviceMesh`, optional): 2025-08-26T20:21:58.8471797Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-08-26T20:21:58.8472063Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-08-26T20:21:58.8472164Z mesh. Default: None. 2025-08-26T20:21:58.8472169Z 2025-08-26T20:21:58.8472274Z Keyword Args: 2025-08-26T20:21:58.8472396Z redistribute_inputs (bool, optional): 2025-08-26T20:21:58.8472662Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-08-26T20:21:58.8472911Z their placements are different from the required input placements. If this 2025-08-26T20:21:58.8473137Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-08-26T20:21:58.8473294Z an exception will be raised. Default: False. 2025-08-26T20:21:58.8473299Z 2025-08-26T20:21:58.8473390Z Returns: 2025-08-26T20:21:58.8473660Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-08-26T20:21:58.8473901Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-08-26T20:21:58.8473906Z 2025-08-26T20:21:58.8473991Z Raises: 2025-08-26T20:21:58.8474246Z AssertionError: For any non-DTensor output, we require its corresponding 2025-08-26T20:21:58.8474504Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-08-26T20:21:58.8474618Z if this is not the case. 2025-08-26T20:21:58.8474623Z 2025-08-26T20:21:58.8474884Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-08-26T20:21:58.8475051Z a redistribution according to ``in_placements``. 2025-08-26T20:21:58.8475055Z 2025-08-26T20:21:58.8475139Z Example: 2025-08-26T20:21:58.8475257Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:21:58.8475407Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-08-26T20:21:58.8475531Z >>> partial_sum_tensor = torch.mm(W, X) 2025-08-26T20:21:58.8475782Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-08-26T20:21:58.8475884Z >>> return reduced_tensor 2025-08-26T20:21:58.8475967Z >>> 2025-08-26T20:21:58.8476104Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-08-26T20:21:58.8476225Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-08-26T20:21:58.8476319Z >>> Y = torch.mm(W, X) 2025-08-26T20:21:58.8476523Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-08-26T20:21:58.8476706Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-08-26T20:21:58.8476800Z >>> 2025-08-26T20:21:58.8477069Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-08-26T20:21:58.8477195Z >>> local_mm_allreduce_forward = local_map( 2025-08-26T20:21:58.8477307Z >>> mm_allreduce_forward, 2025-08-26T20:21:58.8477477Z >>> out_placements=[Replicate()], 2025-08-26T20:21:58.8477604Z >>> in_placements=[col_wise, row_wise], 2025-08-26T20:21:58.8477706Z >>> device_mesh=device_mesh, 2025-08-26T20:21:58.8477787Z >>> ) 2025-08-26T20:21:58.8477879Z >>> 2025-08-26T20:21:58.8477979Z >>> W_dt = distribute_tensor( 2025-08-26T20:21:58.8478094Z ... W, device_mesh, (col_wise) 2025-08-26T20:21:58.8478201Z ... ) # col-wisely sharded W tensor 2025-08-26T20:21:58.8478300Z >>> X_dt = distribute_tensor( 2025-08-26T20:21:58.8478416Z ... X, device_mesh, (row_wise) 2025-08-26T20:21:58.8478522Z ... ) # row-wisely sharded X tensor 2025-08-26T20:21:58.8478643Z >>> Y_dt = local_mm_allreduce_forward( 2025-08-26T20:21:58.8478742Z ... device_mesh, W_dt, X_dt 2025-08-26T20:21:58.8478936Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-08-26T20:21:58.8478941Z 2025-08-26T20:21:58.8479170Z .. note:: This API is currently experimental and subject to change 2025-08-26T20:21:58.8479178Z 2025-08-26T20:21:58.8479430Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.8479435Z 2025-08-26T20:21:58.8482094Z msg = Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2025-08-26T20:21:58.8482375Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.8482383Z 2025-08-26T20:21:58.8482675Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-08-26T20:21:58.8482910Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-08-26T20:21:58.8483172Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-08-26T20:21:58.8483417Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-08-26T20:21:58.8483696Z when users would like to overwrite default sharding strategies of existing operators. 2025-08-26T20:21:58.8483712Z 2025-08-26T20:21:58.8483796Z Args: 2025-08-26T20:21:58.8483925Z op (Union[OpOverload, List[OpOverload]]): 2025-08-26T20:21:58.8484178Z An op or a list of ops to register the customized sharding function. 2025-08-26T20:21:58.8484185Z 2025-08-26T20:21:58.8484293Z Returns: 2025-08-26T20:21:58.8484572Z A function decorator which can be used to wrap a function that defines the sharding 2025-08-26T20:21:58.8484842Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-08-26T20:21:58.8485113Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-08-26T20:21:58.8485423Z already implemented the operator. The customized sharding function takes the same inputs 2025-08-26T20:21:58.8485667Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-08-26T20:21:58.8485951Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-08-26T20:21:58.8486222Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-08-26T20:21:58.8486337Z corresponding input placements. 2025-08-26T20:21:58.8486341Z 2025-08-26T20:21:58.8486439Z Example: 2025-08-26T20:21:58.8486552Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:21:58.8486693Z >>> @register_sharding(aten._softmax.default) 2025-08-26T20:21:58.8486848Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-08-26T20:21:58.8486989Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-08-26T20:21:58.8487113Z >>> acceptable_shardings = [] 2025-08-26T20:21:58.8487194Z >>> 2025-08-26T20:21:58.8487383Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-08-26T20:21:58.8487527Z >>> acceptable_shardings.append(all_replicate) 2025-08-26T20:21:58.8487611Z >>> 2025-08-26T20:21:58.8487832Z >>> for sharding_dim in range(x.ndim): 2025-08-26T20:21:58.8487947Z >>> if sharding_dim != softmax_dim: 2025-08-26T20:21:58.8488061Z >>> all_sharded = ( 2025-08-26T20:21:58.8488171Z >>> [Shard(sharding_dim)], 2025-08-26T20:21:58.8488296Z >>> [Shard(sharding_dim), None, None], 2025-08-26T20:21:58.8488398Z >>> ) 2025-08-26T20:21:58.8488539Z >>> acceptable_shardings.append(all_sharded) 2025-08-26T20:21:58.8488636Z >>> 2025-08-26T20:21:58.8488747Z >>> return acceptable_shardings 2025-08-26T20:21:58.8488752Z 2025-08-26T20:21:58.8488949Z .. note:: This API is currently experimental and subject to change 2025-08-26T20:21:58.8488953Z 2025-08-26T20:21:58.8489222Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.8489226Z 2025-08-26T20:21:58.8740431Z msg = Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=428. 2025-08-26T20:21:58.8740732Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.8740739Z 2025-08-26T20:21:58.8741122Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-08-26T20:21:58.8741460Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-08-26T20:21:58.8741465Z 2025-08-26T20:21:58.8741556Z Keyword Args: 2025-08-26T20:21:58.8741785Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-26T20:21:58.8742115Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-08-26T20:21:58.8742485Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-08-26T20:21:58.8742601Z as a placeholder. default: None. 2025-08-26T20:21:58.8742833Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-26T20:21:58.8743222Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-26T20:21:58.8743614Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-08-26T20:21:58.8743755Z input_kwarg_layouts (Dict[str, Placement]): 2025-08-26T20:21:58.8744127Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-08-26T20:21:58.8744218Z default: None 2025-08-26T20:21:58.8744477Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-08-26T20:21:58.8745054Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-26T20:21:58.8745227Z have the desired DTensor layouts. default: None. 2025-08-26T20:21:58.8745341Z use_local_output (bool, optional): 2025-08-26T20:21:58.8745801Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-08-26T20:21:58.8745960Z Returns: 2025-08-26T20:21:58.8746570Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-08-26T20:21:58.8746581Z 2025-08-26T20:21:58.8746772Z Example:: 2025-08-26T20:21:58.8746891Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:21:58.8747200Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-08-26T20:21:58.8747411Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-26T20:21:58.8747492Z >>> ... 2025-08-26T20:21:58.8747809Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-26T20:21:58.8747935Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-26T20:21:58.8748017Z >>> 2025-08-26T20:21:58.8748366Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-08-26T20:21:58.8748628Z >>> # and then redistributed to Replicated DTensor. 2025-08-26T20:21:58.8748744Z >>> parallelize_module( 2025-08-26T20:21:58.8748874Z >>> block, # this can be a submodule or module 2025-08-26T20:21:58.8748959Z >>> tp_mesh, 2025-08-26T20:21:58.8749074Z >>> parallelize_plan={ 2025-08-26T20:21:58.8749191Z >>> "attn": PrepareModuleInput( 2025-08-26T20:21:58.8749334Z >>> input_layouts=(Shard(0), None, None, ...), 2025-08-26T20:21:58.8749503Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-08-26T20:21:58.8749587Z >>> ), 2025-08-26T20:21:58.8749681Z >>> } 2025-08-26T20:21:58.8749762Z >>> ) 2025-08-26T20:21:58.8749767Z 2025-08-26T20:21:58.8750097Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.8750102Z 2025-08-26T20:21:58.8750781Z msg = Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=597. 2025-08-26T20:21:58.8751054Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.8751058Z 2025-08-26T20:21:58.8751448Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-08-26T20:21:58.8751794Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-08-26T20:21:58.8751798Z 2025-08-26T20:21:58.8751885Z Keyword Args: 2025-08-26T20:21:58.8752049Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-26T20:21:58.8752402Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-08-26T20:21:58.8752788Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-08-26T20:21:58.8752948Z ``None`` need to be specified as a placeholder. 2025-08-26T20:21:58.8753143Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-26T20:21:58.8753543Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-08-26T20:21:58.8753662Z have the desired DTensor layouts. 2025-08-26T20:21:58.8753848Z use_local_output (bool, optional): 2025-08-26T20:21:58.8754290Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-08-26T20:21:58.8754378Z Returns: 2025-08-26T20:21:58.8754867Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-08-26T20:21:58.8754877Z 2025-08-26T20:21:58.8755022Z Example:: 2025-08-26T20:21:58.8755235Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:21:58.8755802Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-08-26T20:21:58.8755997Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-26T20:21:58.8756091Z >>> ... 2025-08-26T20:21:58.8756394Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-26T20:21:58.8756517Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-26T20:21:58.8756612Z >>> 2025-08-26T20:21:58.8757012Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-08-26T20:21:58.8757160Z >>> # and then redistributed to Sharded DTensor. 2025-08-26T20:21:58.8757262Z >>> parallelize_module( 2025-08-26T20:21:58.8757392Z >>> block, # this can be a submodule or module 2025-08-26T20:21:58.8757508Z >>> tp_mesh, 2025-08-26T20:21:58.8757643Z >>> parallelize_plan = PrepareModuleOutput( 2025-08-26T20:21:58.8757774Z >>> output_layouts=Replicate(), 2025-08-26T20:21:58.8757891Z >>> desired_output_layouts=Shard(0) 2025-08-26T20:21:58.8758047Z >>> ) 2025-08-26T20:21:58.8758142Z >>> ) 2025-08-26T20:21:58.8758146Z 2025-08-26T20:21:58.8758401Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.8758405Z 2025-08-26T20:21:58.8759129Z msg = Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-08-26T20:21:58.8759392Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.8759396Z 2025-08-26T20:21:58.8759839Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-08-26T20:21:58.8760322Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-08-26T20:21:58.8760709Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-08-26T20:21:58.8760924Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-08-26T20:21:58.8760928Z 2025-08-26T20:21:58.8761018Z Keyword Args: 2025-08-26T20:21:58.8761230Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-26T20:21:58.8761560Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-08-26T20:21:58.8761933Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-08-26T20:21:58.8762049Z as a placeholder. default: None. 2025-08-26T20:21:58.8762277Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-26T20:21:58.8762674Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-26T20:21:58.8763070Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-08-26T20:21:58.8763220Z input_kwarg_layouts (Dict[str, Placement]): 2025-08-26T20:21:58.8763597Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-08-26T20:21:58.8763703Z default: None 2025-08-26T20:21:58.8763864Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-08-26T20:21:58.8764231Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-26T20:21:58.8764393Z have the desired DTensor layouts. default: None. 2025-08-26T20:21:58.8764507Z use_local_input (bool, optional): 2025-08-26T20:21:58.8764877Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-08-26T20:21:58.8765046Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-26T20:21:58.8765377Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-08-26T20:21:58.8765772Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-08-26T20:21:58.8765913Z ``None`` need to be specified as a placeholder. 2025-08-26T20:21:58.8766121Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-26T20:21:58.8766504Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-08-26T20:21:58.8766620Z have the desired DTensor layouts. 2025-08-26T20:21:58.8766747Z use_local_output (bool, optional): 2025-08-26T20:21:58.8767106Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-08-26T20:21:58.8767205Z Returns: 2025-08-26T20:21:58.8767574Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-08-26T20:21:58.8767637Z 2025-08-26T20:21:58.8767743Z Example:: 2025-08-26T20:21:58.8767846Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:21:58.8768184Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-08-26T20:21:58.8768387Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-26T20:21:58.8768469Z >>> ... 2025-08-26T20:21:58.8768783Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-26T20:21:58.8768907Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-26T20:21:58.8768988Z >>> 2025-08-26T20:21:58.8769336Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-08-26T20:21:58.8769740Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-08-26T20:21:58.8769962Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-08-26T20:21:58.8770067Z >>> parallelize_module( 2025-08-26T20:21:58.8770200Z >>> block, # this can be a submodule or module 2025-08-26T20:21:58.8770299Z >>> tp_mesh, 2025-08-26T20:21:58.8770401Z >>> parallelize_plan={ 2025-08-26T20:21:58.8770544Z >>> "attn": PrepareModuleInputOutput( 2025-08-26T20:21:58.8770679Z >>> input_layouts=(Shard(0), None, None, ...), 2025-08-26T20:21:58.8770849Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-08-26T20:21:58.8770978Z >>> output_layouts=Replicate(), 2025-08-26T20:21:58.8771101Z >>> desired_output_layouts=Shard(0), 2025-08-26T20:21:58.8771197Z >>> ), 2025-08-26T20:21:58.8771280Z >>> } 2025-08-26T20:21:58.8771362Z >>> ) 2025-08-26T20:21:58.8771366Z 2025-08-26T20:21:58.8771632Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.8771637Z 2025-08-26T20:21:58.9490630Z msg = Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-08-26T20:21:58.9490941Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.9490946Z 2025-08-26T20:21:58.9491253Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-08-26T20:21:58.9491465Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-08-26T20:21:58.9491469Z 2025-08-26T20:21:58.9491645Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-08-26T20:21:58.9491650Z 2025-08-26T20:21:58.9491949Z Example: 2025-08-26T20:21:58.9492103Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-08-26T20:21:58.9492245Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:21:58.9492383Z >>> m = LowRankMultivariateNormal( 2025-08-26T20:21:58.9492561Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-08-26T20:21:58.9492661Z ... ) 2025-08-26T20:21:58.9492948Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-08-26T20:21:58.9493051Z tensor([-0.2102, -0.5429]) 2025-08-26T20:21:58.9493055Z 2025-08-26T20:21:58.9493146Z Args: 2025-08-26T20:21:58.9493383Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-08-26T20:21:58.9493651Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-08-26T20:21:58.9493768Z `batch_shape + event_shape + (rank,)` 2025-08-26T20:21:58.9494021Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-08-26T20:21:58.9494139Z `batch_shape + event_shape` 2025-08-26T20:21:58.9494143Z 2025-08-26T20:21:58.9494223Z Note: 2025-08-26T20:21:58.9494505Z The computation for determinant and inverse of covariance matrix is avoided when 2025-08-26T20:21:58.9494744Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-08-26T20:21:58.9495143Z `_ and 2025-08-26T20:21:58.9495447Z `matrix determinant lemma `_. 2025-08-26T20:21:58.9495698Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-08-26T20:21:58.9495835Z the small size "capacitance" matrix:: 2025-08-26T20:21:58.9495840Z 2025-08-26T20:21:58.9496022Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-08-26T20:21:58.9496026Z 2025-08-26T20:21:58.9496289Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.9496294Z 2025-08-26T20:21:58.9514588Z msg = Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=15. 2025-08-26T20:21:58.9514892Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.9514902Z 2025-08-26T20:21:58.9515130Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-08-26T20:21:58.9515390Z distribution where all component are from different parameterizations of 2025-08-26T20:21:58.9515607Z the same distribution type. It is parameterized by a `Categorical` 2025-08-26T20:21:58.9515803Z "selecting distribution" (over `k` component) and a component 2025-08-26T20:21:58.9516024Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-08-26T20:21:58.9516180Z (equal to `[k]`) which indexes each (batch of) component. 2025-08-26T20:21:58.9516185Z 2025-08-26T20:21:58.9516291Z Examples:: 2025-08-26T20:21:58.9516295Z 2025-08-26T20:21:58.9516411Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:21:58.9516612Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-08-26T20:21:58.9516738Z >>> # weighted normal distributions 2025-08-26T20:21:58.9516852Z >>> mix = D.Categorical(torch.ones(5,)) 2025-08-26T20:21:58.9517012Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-08-26T20:21:58.9517128Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-26T20:21:58.9517132Z 2025-08-26T20:21:58.9517343Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-08-26T20:21:58.9517472Z >>> # weighted bivariate normal distributions 2025-08-26T20:21:58.9517585Z >>> mix = D.Categorical(torch.ones(5,)) 2025-08-26T20:21:58.9517707Z >>> comp = D.Independent(D.Normal( 2025-08-26T20:21:58.9517832Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-08-26T20:21:58.9517947Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-26T20:21:58.9517967Z 2025-08-26T20:21:58.9518146Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-08-26T20:21:58.9518351Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-08-26T20:21:58.9518480Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-08-26T20:21:58.9518592Z >>> comp = D.Independent(D.Normal( 2025-08-26T20:21:58.9518741Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-08-26T20:21:58.9518857Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-26T20:21:58.9518861Z 2025-08-26T20:21:58.9518942Z Args: 2025-08-26T20:21:58.9519156Z mixture_distribution: `torch.distributions.Categorical`-like 2025-08-26T20:21:58.9519342Z instance. Manages the probability of selecting component. 2025-08-26T20:21:58.9519523Z The number of categories must match the rightmost batch 2025-08-26T20:21:58.9519708Z dimension of the `component_distribution`. Must have either 2025-08-26T20:21:58.9519846Z scalar `batch_shape` or `batch_shape` matching 2025-08-26T20:21:58.9519998Z `component_distribution.batch_shape[:-1]` 2025-08-26T20:21:58.9520311Z component_distribution: `torch.distributions.Distribution`-like 2025-08-26T20:21:58.9520507Z instance. Right-most batch dimension indexes component. 2025-08-26T20:21:58.9520578Z 2025-08-26T20:21:58.9520831Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.9520836Z 2025-08-26T20:21:58.9671445Z msg = Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-08-26T20:21:58.9671723Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.9671728Z 2025-08-26T20:21:58.9671927Z Creates a RelaxedBernoulli distribution, parametrized by 2025-08-26T20:21:58.9672117Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-08-26T20:21:58.9672332Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-08-26T20:21:58.9672524Z so the values are in (0, 1), and has reparametrizable samples. 2025-08-26T20:21:58.9672528Z 2025-08-26T20:21:58.9672624Z Example:: 2025-08-26T20:21:58.9672770Z 2025-08-26T20:21:58.9672925Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:21:58.9673059Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-08-26T20:21:58.9673182Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-08-26T20:21:58.9673284Z >>> m.sample() 2025-08-26T20:21:58.9673398Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-08-26T20:21:58.9673402Z 2025-08-26T20:21:58.9673494Z Args: 2025-08-26T20:21:58.9673630Z temperature (Tensor): relaxation temperature 2025-08-26T20:21:58.9673802Z probs (Number, Tensor): the probability of sampling `1` 2025-08-26T20:21:58.9673975Z logits (Number, Tensor): the log-odds of sampling `1` 2025-08-26T20:21:58.9673979Z 2025-08-26T20:21:58.9674229Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.9674233Z 2025-08-26T20:21:58.9692254Z msg = Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-08-26T20:21:58.9692521Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:58.9692532Z 2025-08-26T20:21:58.9692757Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-08-26T20:21:58.9692948Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-08-26T20:21:58.9693196Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-08-26T20:21:58.9693358Z its samples are on simplex, and are reparametrizable. 2025-08-26T20:21:58.9693363Z 2025-08-26T20:21:58.9693456Z Example:: 2025-08-26T20:21:58.9693460Z 2025-08-26T20:21:58.9693612Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:21:58.9693766Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-08-26T20:21:58.9693908Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-08-26T20:21:58.9693997Z >>> m.sample() 2025-08-26T20:21:58.9694118Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-08-26T20:21:58.9694122Z 2025-08-26T20:21:58.9694223Z Args: 2025-08-26T20:21:58.9694360Z temperature (Tensor): relaxation temperature 2025-08-26T20:21:58.9694488Z probs (Tensor): event probabilities 2025-08-26T20:21:58.9694673Z logits (Tensor): unnormalized log probability for each event 2025-08-26T20:21:58.9694677Z 2025-08-26T20:21:58.9694925Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:58.9694929Z 2025-08-26T20:21:59.3853266Z msg = Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2025-08-26T20:21:59.3854489Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.3855442Z Return a new dict with new, potentially nested, key value pair 2025-08-26T20:21:59.3855760Z 2025-08-26T20:21:59.3855852Z >>> purchase = { 2025-08-26T20:21:59.3856127Z ... "name": "Alice", 2025-08-26T20:21:59.3856525Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-26T20:21:59.3867286Z ... "credit card": "5555-1234-1234-1234", 2025-08-26T20:21:59.3867956Z ... } 2025-08-26T20:21:59.3868336Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-08-26T20:21:59.3868781Z {'credit card': '5555-1234-1234-1234', 2025-08-26T20:21:59.3869079Z 'name': 'Alice', 2025-08-26T20:21:59.3869414Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-08-26T20:21:59.3869784Z 2025-08-26T20:21:59.3870156Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.3870526Z 2025-08-26T20:21:59.3871237Z msg = Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2025-08-26T20:21:59.3872456Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.3872984Z Update value in a (potentially) nested dictionary 2025-08-26T20:21:59.3873266Z 2025-08-26T20:21:59.3873354Z inputs: 2025-08-26T20:21:59.3873611Z d - dictionary on which to operate 2025-08-26T20:21:59.3874056Z keys - list or tuple giving the location of the value to be changed in d 2025-08-26T20:21:59.3874512Z func - function to operate on that value 2025-08-26T20:21:59.3874763Z 2025-08-26T20:21:59.3874953Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-08-26T20:21:59.3875502Z original dictionary with v replaced by func(v), but does not mutate the 2025-08-26T20:21:59.3875958Z original dictionary. 2025-08-26T20:21:59.3876129Z 2025-08-26T20:21:59.3876347Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-08-26T20:21:59.3876878Z specified by the keys, with the innermost value set to func(default). 2025-08-26T20:21:59.3877209Z 2025-08-26T20:21:59.3877304Z >>> inc = lambda x: x + 1 2025-08-26T20:21:59.3877585Z >>> update_in({"a": 0}, ["a"], inc) 2025-08-26T20:21:59.3877861Z {'a': 1} 2025-08-26T20:21:59.3877989Z 2025-08-26T20:21:59.3878084Z >>> transaction = { 2025-08-26T20:21:59.3878349Z ... "name": "Alice", 2025-08-26T20:21:59.3878716Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-26T20:21:59.3879134Z ... "credit card": "5555-1234-1234-1234", 2025-08-26T20:21:59.3879449Z ... } 2025-08-26T20:21:59.3879788Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-08-26T20:21:59.3880229Z {'credit card': '5555-1234-1234-1234', 2025-08-26T20:21:59.3880525Z 'name': 'Alice', 2025-08-26T20:21:59.3880852Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-08-26T20:21:59.3881143Z 2025-08-26T20:21:59.3881265Z >>> # updating a value when k0 is not in d 2025-08-26T20:21:59.3881623Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-08-26T20:21:59.3881952Z {1: {2: {3: 'bar'}}} 2025-08-26T20:21:59.3882218Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-08-26T20:21:59.3882554Z {1: 'foo', 2: {3: {4: 1}}} 2025-08-26T20:21:59.3882818Z 2025-08-26T20:21:59.3883192Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.3883559Z 2025-08-26T20:21:59.3884198Z msg = Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2025-08-26T20:21:59.3885214Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.3885758Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-08-26T20:21:59.3886030Z 2025-08-26T20:21:59.3886224Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-08-26T20:21:59.3886721Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-08-26T20:21:59.3887030Z 2025-08-26T20:21:59.3887244Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-08-26T20:21:59.3887790Z structures such as dictionaries and lists. 2025-08-26T20:21:59.3888040Z 2025-08-26T20:21:59.3888135Z >>> transaction = { 2025-08-26T20:21:59.3888398Z ... "name": "Alice", 2025-08-26T20:21:59.3888752Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-26T20:21:59.3889175Z ... "credit card": "5555-1234-1234-1234", 2025-08-26T20:21:59.3889490Z ... } 2025-08-26T20:21:59.3889754Z >>> get_in(["purchase", "items", 0], transaction) 2025-08-26T20:21:59.3890088Z 'Apple' 2025-08-26T20:21:59.3890308Z >>> get_in(["name"], transaction) 2025-08-26T20:21:59.3890601Z 'Alice' 2025-08-26T20:21:59.3890857Z >>> get_in(["purchase", "total"], transaction) 2025-08-26T20:21:59.3891247Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-08-26T20:21:59.3891923Z >>> get_in(["purchase", "items", 10], transaction) 2025-08-26T20:21:59.3892367Z >>> get_in(["purchase", "total"], transaction, 0) 2025-08-26T20:21:59.3892706Z 0 2025-08-26T20:21:59.3892933Z >>> get_in(["y"], {}, no_default=True) 2025-08-26T20:21:59.3893254Z Traceback (most recent call last): 2025-08-26T20:21:59.3893560Z ... 2025-08-26T20:21:59.3893776Z KeyError: 'y' 2025-08-26T20:21:59.3893918Z 2025-08-26T20:21:59.3894014Z See Also: 2025-08-26T20:21:59.3894228Z itertoolz.get 2025-08-26T20:21:59.3894475Z operator.getitem 2025-08-26T20:21:59.3894725Z 2025-08-26T20:21:59.3895096Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.3895467Z 2025-08-26T20:21:59.3896120Z msg = Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2025-08-26T20:21:59.3897144Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.3897644Z Group a collection by a key function 2025-08-26T20:21:59.3897855Z 2025-08-26T20:21:59.3898029Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-08-26T20:21:59.3898438Z >>> groupby(len, names) # doctest: +SKIP 2025-08-26T20:21:59.3898820Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-08-26T20:21:59.3899101Z 2025-08-26T20:21:59.3899206Z >>> iseven = lambda x: x % 2 == 0 2025-08-26T20:21:59.3899584Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-08-26T20:21:59.3899979Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-08-26T20:21:59.3900196Z 2025-08-26T20:21:59.3900337Z Non-callable keys imply grouping on a member. 2025-08-26T20:21:59.3900670Z 2025-08-26T20:21:59.3900758Z >>> groupby( 2025-08-26T20:21:59.3900993Z ... "gender", 2025-08-26T20:21:59.3901234Z ... [ 2025-08-26T20:21:59.3901471Z ... {"name": "Alice", "gender": "F"}, 2025-08-26T20:21:59.3901827Z ... {"name": "Bob", "gender": "M"}, 2025-08-26T20:21:59.3902180Z ... {"name": "Charlie", "gender": "M"}, 2025-08-26T20:21:59.3902511Z ... ], 2025-08-26T20:21:59.3902726Z ... ) # doctest:+SKIP 2025-08-26T20:21:59.3903015Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-08-26T20:21:59.3903345Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-08-26T20:21:59.3903675Z {'gender': 'M', 'name': 'Charlie'}]} 2025-08-26T20:21:59.3903894Z 2025-08-26T20:21:59.3904046Z Not to be confused with ``itertools.groupby`` 2025-08-26T20:21:59.3904292Z 2025-08-26T20:21:59.3904376Z See Also: 2025-08-26T20:21:59.3904591Z countby 2025-08-26T20:21:59.3904806Z 2025-08-26T20:21:59.3905174Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.3905541Z 2025-08-26T20:21:59.7016383Z msg = Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py line=142. 2025-08-26T20:21:59.7017301Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.7017961Z Return the recommended gain value for the given nonlinearity function. 2025-08-26T20:21:59.7018600Z 2025-08-26T20:21:59.7018712Z The values are as follows: 2025-08-26T20:21:59.7018901Z 2025-08-26T20:21:59.7019033Z ================= ==================================================== 2025-08-26T20:21:59.7019387Z nonlinearity gain 2025-08-26T20:21:59.7019672Z ================= ==================================================== 2025-08-26T20:21:59.7020024Z Linear / Identity :math:`1` 2025-08-26T20:21:59.7020318Z Conv{1,2,3}D :math:`1` 2025-08-26T20:21:59.7020680Z Sigmoid :math:`1` 2025-08-26T20:21:59.7020962Z Tanh :math:`\frac{5}{3}` 2025-08-26T20:21:59.7021290Z ReLU :math:`\sqrt{2}` 2025-08-26T20:21:59.7021692Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-08-26T20:21:59.7022230Z SELU :math:`\frac{3}{4}` 2025-08-26T20:21:59.7022556Z ================= ==================================================== 2025-08-26T20:21:59.7022811Z 2025-08-26T20:21:59.7022926Z .. warning:: 2025-08-26T20:21:59.7023263Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-08-26T20:21:59.7023812Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-08-26T20:21:59.7024335Z This gives the initial weights a variance of ``1 / N``, 2025-08-26T20:21:59.7024843Z which is necessary to induce a stable fixed point in the forward pass. 2025-08-26T20:21:59.7025414Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-08-26T20:21:59.7025943Z effect for more stable gradient flow in rectangular layers. 2025-08-26T20:21:59.7026241Z 2025-08-26T20:21:59.7026337Z Args: 2025-08-26T20:21:59.7026647Z nonlinearity: the non-linear function (`nn.functional` name) 2025-08-26T20:21:59.7027138Z param: optional parameter for the non-linear function 2025-08-26T20:21:59.7027428Z 2025-08-26T20:21:59.7027519Z Examples: 2025-08-26T20:21:59.7027768Z >>> gain = nn.init.calculate_gain( 2025-08-26T20:21:59.7028080Z ... "leaky_relu", 0.2 2025-08-26T20:21:59.7028401Z ... ) # leaky_relu with negative_slope=0.2 2025-08-26T20:21:59.7028652Z 2025-08-26T20:21:59.7029144Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-08-26T20:21:59.7029814Z 2025-08-26T20:21:59.7030179Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.7030543Z 2025-08-26T20:21:59.7653690Z msg = Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=603. 2025-08-26T20:21:59.7654653Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.7655236Z Applies Batch Normalization over a N-Dimensional input. 2025-08-26T20:21:59.7655523Z 2025-08-26T20:21:59.7655880Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-08-26T20:21:59.7656571Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-08-26T20:21:59.7657192Z Internal Covariate Shift `__ . 2025-08-26T20:21:59.7657615Z 2025-08-26T20:21:59.7657739Z .. math:: 2025-08-26T20:21:59.7657867Z 2025-08-26T20:21:59.7658179Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-08-26T20:21:59.7658521Z 2025-08-26T20:21:59.7658761Z The mean and standard-deviation are calculated per-dimension over all 2025-08-26T20:21:59.7659329Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-08-26T20:21:59.7659928Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-08-26T20:21:59.7660553Z By default, the elements of :math:`\gamma` are sampled from 2025-08-26T20:21:59.7661052Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-08-26T20:21:59.7661877Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-08-26T20:21:59.7662379Z `torch.var(input, unbiased=False)`. 2025-08-26T20:21:59.7662612Z 2025-08-26T20:21:59.7662850Z Also by default, during training this layer keeps running estimates of its 2025-08-26T20:21:59.7663437Z computed mean and variance, which are then used for normalization during 2025-08-26T20:21:59.7664034Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-08-26T20:21:59.7664478Z of 0.1. 2025-08-26T20:21:59.7664615Z 2025-08-26T20:21:59.7664840Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-08-26T20:21:59.7665406Z keep running estimates, and batch statistics are instead used during 2025-08-26T20:21:59.7665959Z evaluation time as well. 2025-08-26T20:21:59.7666147Z 2025-08-26T20:21:59.7666238Z .. note:: 2025-08-26T20:21:59.7666603Z This :attr:`momentum` argument is different from one used in optimizer 2025-08-26T20:21:59.7667175Z classes and the conventional notion of momentum. Mathematically, the 2025-08-26T20:21:59.7667659Z update rule for running statistics here is 2025-08-26T20:21:59.7668174Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-08-26T20:21:59.7668750Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-08-26T20:21:59.7669220Z new observed value. 2025-08-26T20:21:59.7669408Z 2025-08-26T20:21:59.7669707Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-08-26T20:21:59.7670376Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-08-26T20:21:59.7670929Z Normalization or Spatio-temporal Batch Normalization. 2025-08-26T20:21:59.7671213Z 2025-08-26T20:21:59.7671373Z Currently :class:`SyncBatchNorm` only supports 2025-08-26T20:21:59.7671912Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-08-26T20:21:59.7672528Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-08-26T20:21:59.7673065Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-08-26T20:21:59.7673486Z Network with DDP. 2025-08-26T20:21:59.7673642Z 2025-08-26T20:21:59.7673724Z Args: 2025-08-26T20:21:59.7674011Z num_features: :math:`C` from an expected input of size 2025-08-26T20:21:59.7674386Z :math:`(N, C, +)` 2025-08-26T20:21:59.7674757Z eps: a value added to the denominator for numerical stability. 2025-08-26T20:21:59.7675150Z Default: ``1e-5`` 2025-08-26T20:21:59.7675524Z momentum: the value used for the running_mean and running_var 2025-08-26T20:21:59.7676047Z computation. Can be set to ``None`` for cumulative moving average 2025-08-26T20:21:59.7676502Z (i.e. simple average). Default: 0.1 2025-08-26T20:21:59.7676943Z affine: a boolean value that when set to ``True``, this module has 2025-08-26T20:21:59.7677403Z learnable affine parameters. Default: ``True`` 2025-08-26T20:21:59.7677882Z track_running_stats: a boolean value that when set to ``True``, this 2025-08-26T20:21:59.7678443Z module tracks the running mean and variance, and when set to ``False``, 2025-08-26T20:21:59.7679017Z this module does not track such statistics, and initializes statistics 2025-08-26T20:21:59.7679556Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-08-26T20:21:59.7680107Z When these buffers are ``None``, this module always uses batch statistics. 2025-08-26T20:21:59.7680614Z in both training and eval modes. Default: ``True`` 2025-08-26T20:21:59.7681143Z process_group: synchronization of stats happen within each process group 2025-08-26T20:21:59.7681744Z individually. Default behavior is synchronization across the whole 2025-08-26T20:21:59.7682251Z world 2025-08-26T20:21:59.7682404Z 2025-08-26T20:21:59.7682492Z Shape: 2025-08-26T20:21:59.7682742Z - Input: :math:`(N, C, +)` 2025-08-26T20:21:59.7683096Z - Output: :math:`(N, C, +)` (same shape as input) 2025-08-26T20:21:59.7683351Z 2025-08-26T20:21:59.7683441Z .. note:: 2025-08-26T20:21:59.7683821Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-08-26T20:21:59.7684388Z synchronization is disabled when ``model.eval()`` is set or if 2025-08-26T20:21:59.7684835Z ``self.training`` is otherwise ``False``. 2025-08-26T20:21:59.7685068Z 2025-08-26T20:21:59.7685158Z Examples:: 2025-08-26T20:21:59.7685303Z 2025-08-26T20:21:59.7685401Z >>> # xdoctest: +SKIP 2025-08-26T20:21:59.7685774Z >>> # With Learnable Parameters 2025-08-26T20:21:59.7686102Z >>> m = nn.SyncBatchNorm(100) 2025-08-26T20:21:59.7686438Z >>> # creating process group (optional) 2025-08-26T20:21:59.7686806Z >>> # ranks is a list of int identifying rank ids. 2025-08-26T20:21:59.7687162Z >>> ranks = list(range(8)) 2025-08-26T20:21:59.7687468Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-08-26T20:21:59.7687838Z >>> # Note: every rank calls into new_group for every 2025-08-26T20:21:59.7688242Z >>> # process group created, even if that rank is not 2025-08-26T20:21:59.7688612Z >>> # part of the group. 2025-08-26T20:21:59.7689051Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-08-26T20:21:59.7689626Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-08-26T20:21:59.7690055Z >>> # Without Learnable Parameters 2025-08-26T20:21:59.7690482Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-08-26T20:21:59.7690935Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-08-26T20:21:59.7691272Z >>> output = m(input) 2025-08-26T20:21:59.7691450Z 2025-08-26T20:21:59.7691564Z >>> # network is nn.BatchNorm layer 2025-08-26T20:21:59.7692497Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-08-26T20:21:59.7693253Z >>> # only single gpu per process is currently supported 2025-08-26T20:21:59.7693754Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-08-26T20:21:59.7694209Z >>> sync_bn_network, 2025-08-26T20:21:59.7694554Z >>> device_ids=[args.local_rank], 2025-08-26T20:21:59.7694933Z >>> output_device=args.local_rank) 2025-08-26T20:21:59.7695273Z 2025-08-26T20:21:59.7695644Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.7696012Z 2025-08-26T20:21:59.7696676Z msg = Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=830. 2025-08-26T20:21:59.7697708Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.7698397Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-08-26T20:21:59.7698833Z 2025-08-26T20:21:59.7698918Z Args: 2025-08-26T20:21:59.7699294Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-08-26T20:21:59.7699862Z process_group (optional): process group to scope synchronization, 2025-08-26T20:21:59.7700310Z default is the whole world 2025-08-26T20:21:59.7700606Z 2025-08-26T20:21:59.7700692Z Returns: 2025-08-26T20:21:59.7701086Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-08-26T20:21:59.7701687Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-08-26T20:21:59.7702229Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-08-26T20:21:59.7702810Z instead. 2025-08-26T20:21:59.7702981Z 2025-08-26T20:21:59.7703079Z Example:: 2025-08-26T20:21:59.7703220Z 2025-08-26T20:21:59.7703357Z >>> # Network with nn.BatchNorm layer 2025-08-26T20:21:59.7703727Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:21:59.7704108Z >>> module = torch.nn.Sequential( 2025-08-26T20:21:59.7704459Z >>> torch.nn.Linear(20, 100), 2025-08-26T20:21:59.7704822Z >>> torch.nn.BatchNorm1d(100), 2025-08-26T20:21:59.7705152Z >>> ).cuda() 2025-08-26T20:21:59.7705454Z >>> # creating process group (optional) 2025-08-26T20:21:59.7705837Z >>> # ranks is a list of int identifying rank ids. 2025-08-26T20:21:59.7706277Z >>> ranks = list(range(8)) 2025-08-26T20:21:59.7706598Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-08-26T20:21:59.7706960Z >>> # Note: every rank calls into new_group for every 2025-08-26T20:21:59.7707385Z >>> # process group created, even if that rank is not 2025-08-26T20:21:59.7707765Z >>> # part of the group. 2025-08-26T20:21:59.7708093Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:21:59.7708562Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-08-26T20:21:59.7709136Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-08-26T20:21:59.7709776Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-08-26T20:21:59.7710187Z 2025-08-26T20:21:59.7710281Z 2025-08-26T20:21:59.7710651Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.7711016Z 2025-08-26T20:21:59.7933111Z msg = Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=66. 2025-08-26T20:21:59.7934022Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.7934411Z 2025-08-26T20:21:59.7934718Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-08-26T20:21:59.7935153Z 2025-08-26T20:21:59.7935420Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-08-26T20:21:59.7936036Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-08-26T20:21:59.7936375Z 2025-08-26T20:21:59.7936698Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-08-26T20:21:59.7937388Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-08-26T20:21:59.7937934Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-08-26T20:21:59.7938224Z 2025-08-26T20:21:59.7938306Z Shape: 2025-08-26T20:21:59.7938646Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-08-26T20:21:59.7939225Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-08-26T20:21:59.7939816Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-08-26T20:21:59.7940313Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-08-26T20:21:59.7940618Z 2025-08-26T20:21:59.7940726Z Args: 2025-08-26T20:21:59.7940995Z dim (Union[int, str]): Dimension to be unflattened 2025-08-26T20:21:59.7941590Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-08-26T20:21:59.7942063Z 2025-08-26T20:21:59.7942151Z Examples: 2025-08-26T20:21:59.7942390Z >>> input = torch.randn(2, 50) 2025-08-26T20:21:59.7942701Z >>> # With tuple of ints 2025-08-26T20:21:59.7942990Z >>> m = nn.Sequential( 2025-08-26T20:21:59.7943252Z >>> nn.Linear(50, 50), 2025-08-26T20:21:59.7943712Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-08-26T20:21:59.7944008Z >>> ) 2025-08-26T20:21:59.7944228Z >>> output = m(input) 2025-08-26T20:21:59.7944485Z >>> output.size() 2025-08-26T20:21:59.7944749Z torch.Size([2, 2, 5, 5]) 2025-08-26T20:21:59.7945029Z >>> # With torch.Size 2025-08-26T20:21:59.7945282Z >>> m = nn.Sequential( 2025-08-26T20:21:59.7945553Z >>> nn.Linear(50, 50), 2025-08-26T20:21:59.7945857Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-08-26T20:21:59.7946182Z >>> ) 2025-08-26T20:21:59.7946387Z >>> output = m(input) 2025-08-26T20:21:59.7946654Z >>> output.size() 2025-08-26T20:21:59.7946910Z torch.Size([2, 2, 5, 5]) 2025-08-26T20:21:59.7947209Z >>> # With namedshape (tuple of tuples) 2025-08-26T20:21:59.7947574Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-08-26T20:21:59.7948168Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-08-26T20:21:59.7948606Z >>> output = unflatten(input) 2025-08-26T20:21:59.7948908Z >>> output.size() 2025-08-26T20:21:59.7949168Z torch.Size([2, 2, 5, 5]) 2025-08-26T20:21:59.7949344Z 2025-08-26T20:21:59.7949593Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.7949977Z 2025-08-26T20:21:59.8302541Z msg = Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1798. 2025-08-26T20:21:59.8303614Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.8304201Z Creates a criterion that measures the triplet loss given input 2025-08-26T20:21:59.8304718Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-08-26T20:21:59.8305248Z positive, and negative examples, respectively), and a nonnegative, 2025-08-26T20:21:59.8305827Z real-valued function ("distance function") used to compute the relationship 2025-08-26T20:21:59.8306417Z between the anchor and positive example ("positive distance") and the 2025-08-26T20:21:59.8306916Z anchor and negative example ("negative distance"). 2025-08-26T20:21:59.8307175Z 2025-08-26T20:21:59.8307392Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-08-26T20:21:59.8307813Z can be described as: 2025-08-26T20:21:59.8307978Z 2025-08-26T20:21:59.8308078Z .. math:: 2025-08-26T20:21:59.8308353Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-08-26T20:21:59.8308759Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-08-26T20:21:59.8309021Z 2025-08-26T20:21:59.8309275Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-08-26T20:21:59.8309924Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-08-26T20:21:59.8310586Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-08-26T20:21:59.8311201Z between the positive and negative distances that is required for the loss to 2025-08-26T20:21:59.8311803Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-08-26T20:21:59.8312276Z that the distance function can handle. 2025-08-26T20:21:59.8312501Z 2025-08-26T20:21:59.8312614Z If :attr:`reduction` is not ``'none'`` 2025-08-26T20:21:59.8312942Z (default ``'mean'``), then: 2025-08-26T20:21:59.8313138Z 2025-08-26T20:21:59.8313222Z .. math:: 2025-08-26T20:21:59.8313443Z \ell(x, y) = 2025-08-26T20:21:59.8313675Z \begin{cases} 2025-08-26T20:21:59.8314032Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-08-26T20:21:59.8314800Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-08-26T20:21:59.8315408Z \end{cases} 2025-08-26T20:21:59.8315585Z 2025-08-26T20:21:59.8315935Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-08-26T20:21:59.8316887Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-08-26T20:21:59.8317527Z 2025-08-26T20:21:59.8317615Z Args: 2025-08-26T20:21:59.8318021Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-08-26T20:21:59.8318615Z quantifies the closeness of two tensors. If not specified, 2025-08-26T20:21:59.8319085Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-08-26T20:21:59.8319645Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-08-26T20:21:59.8320307Z between the positive and negative distances required for the loss to be 0. Larger 2025-08-26T20:21:59.8320984Z margins penalize cases where the negative examples are not distant enough from the 2025-08-26T20:21:59.8321562Z anchors, relative to the positives. Default: :math:`1`. 2025-08-26T20:21:59.8322165Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-08-26T20:21:59.8322799Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-08-26T20:21:59.8323417Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-08-26T20:21:59.8324043Z negative example than the anchor is, swaps the positive example and the anchor in 2025-08-26T20:21:59.8324573Z the loss computation. Default: ``False``. 2025-08-26T20:21:59.8325086Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-08-26T20:21:59.8325668Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-08-26T20:21:59.8326155Z ``'mean'``: the sum of the output will be divided by the number of 2025-08-26T20:21:59.8326694Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-08-26T20:21:59.8327055Z 2025-08-26T20:21:59.8327059Z 2025-08-26T20:21:59.8327155Z Shape: 2025-08-26T20:21:59.8327516Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-08-26T20:21:59.8328005Z as supported by the distance function. 2025-08-26T20:21:59.8328498Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-08-26T20:21:59.8328960Z otherwise. 2025-08-26T20:21:59.8329109Z 2025-08-26T20:21:59.8329211Z Examples: 2025-08-26T20:21:59.8329354Z 2025-08-26T20:21:59.8329454Z >>> # Initialize embeddings 2025-08-26T20:21:59.8329765Z >>> embedding = nn.Embedding(1000, 128) 2025-08-26T20:21:59.8330111Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-08-26T20:21:59.8330483Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-08-26T20:21:59.8330859Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-08-26T20:21:59.8331219Z >>> anchor = embedding(anchor_ids) 2025-08-26T20:21:59.8331548Z >>> positive = embedding(positive_ids) 2025-08-26T20:21:59.8331916Z >>> negative = embedding(negative_ids) 2025-08-26T20:21:59.8332233Z >>> 2025-08-26T20:21:59.8332464Z >>> # Built-in Distance Function 2025-08-26T20:21:59.8332762Z >>> triplet_loss = \ 2025-08-26T20:21:59.8333214Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-08-26T20:21:59.8333776Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-26T20:21:59.8334146Z >>> output.backward() 2025-08-26T20:21:59.8334407Z >>> 2025-08-26T20:21:59.8334626Z >>> # Custom Distance Function 2025-08-26T20:21:59.8334931Z >>> def l_infinity(x1, x2): 2025-08-26T20:21:59.8335304Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-08-26T20:21:59.8335659Z >>> 2025-08-26T20:21:59.8335955Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-08-26T20:21:59.8336357Z >>> triplet_loss = ( 2025-08-26T20:21:59.8336807Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-08-26T20:21:59.8337441Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-26T20:21:59.8337794Z >>> output.backward() 2025-08-26T20:21:59.8338052Z >>> 2025-08-26T20:21:59.8338287Z >>> # Custom Distance Function (Lambda) 2025-08-26T20:21:59.8338612Z >>> triplet_loss = ( 2025-08-26T20:21:59.8338899Z >>> nn.TripletMarginWithDistanceLoss( 2025-08-26T20:21:59.8339360Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-08-26T20:21:59.8339851Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-26T20:21:59.8340222Z >>> output.backward() 2025-08-26T20:21:59.8340477Z 2025-08-26T20:21:59.8340566Z Reference: 2025-08-26T20:21:59.8341012Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-08-26T20:21:59.8341739Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-08-26T20:21:59.8342162Z 2025-08-26T20:21:59.8342524Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-08-26T20:21:59.8342912Z 2025-08-26T20:21:59.8343454Z msg = Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1933. 2025-08-26T20:21:59.8344325Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.8344841Z The Connectionist Temporal Classification loss. 2025-08-26T20:21:59.8345105Z 2025-08-26T20:21:59.8345483Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-08-26T20:21:59.8346338Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-08-26T20:21:59.8347151Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-08-26T20:21:59.8347907Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-08-26T20:21:59.8348320Z 2025-08-26T20:21:59.8348410Z Args: 2025-08-26T20:21:59.8348735Z blank (int, optional): blank label. Default :math:`0`. 2025-08-26T20:21:59.8349256Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-08-26T20:21:59.8349807Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-08-26T20:21:59.8350325Z ``'mean'``: the output losses will be divided by the target lengths and 2025-08-26T20:21:59.8350889Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-08-26T20:21:59.8351359Z Default: ``'mean'`` 2025-08-26T20:21:59.8351662Z zero_infinity (bool, optional): 2025-08-26T20:21:59.8352080Z Whether to zero infinite losses and the associated gradients. 2025-08-26T20:21:59.8352480Z Default: ``False`` 2025-08-26T20:21:59.8352855Z Infinite losses mainly occur when the inputs are too short 2025-08-26T20:21:59.8353269Z to be aligned to the targets. 2025-08-26T20:21:59.8353486Z 2025-08-26T20:21:59.8353582Z Shape: 2025-08-26T20:21:59.8353882Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-08-26T20:21:59.8354307Z where :math:`T = \text{input length}`, 2025-08-26T20:21:59.8354655Z :math:`N = \text{batch size}`, and 2025-08-26T20:21:59.8355035Z :math:`C = \text{number of classes (including blank)}`. 2025-08-26T20:21:59.8355532Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-08-26T20:21:59.8355994Z :func:`torch.nn.functional.log_softmax`). 2025-08-26T20:21:59.8356371Z - Targets: Tensor of size :math:`(N, S)` or 2025-08-26T20:21:59.8356774Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-08-26T20:21:59.8357171Z where :math:`N = \text{batch size}` and 2025-08-26T20:21:59.8357561Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-08-26T20:21:59.8358037Z It represents the target sequences. Each element in the target 2025-08-26T20:21:59.8358659Z sequence is a class index. And the target index cannot be blank (default=0). 2025-08-26T20:21:59.8359175Z In the :math:`(N, S)` form, targets are padded to the 2025-08-26T20:21:59.8359572Z length of the longest sequence, and stacked. 2025-08-26T20:21:59.8360016Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-08-26T20:21:59.8360465Z the targets are assumed to be un-padded and 2025-08-26T20:21:59.8360836Z concatenated within 1 dimension. 2025-08-26T20:21:59.8361266Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-08-26T20:21:59.8361783Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-08-26T20:21:59.8362360Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-08-26T20:21:59.8362912Z for each sequence to achieve masking under the assumption that sequences 2025-08-26T20:21:59.8363381Z are padded to equal lengths. 2025-08-26T20:21:59.8363795Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-08-26T20:21:59.8364343Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-08-26T20:21:59.8364910Z Lengths are specified for each sequence to achieve masking under the 2025-08-26T20:21:59.8365502Z assumption that sequences are padded to equal lengths. If target shape is 2025-08-26T20:21:59.8366047Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-08-26T20:21:59.8366581Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-08-26T20:21:59.8367136Z each target in a batch. Lengths must each be :math:`\leq S` 2025-08-26T20:21:59.8367677Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-08-26T20:21:59.8368277Z targets, the target_lengths must add up to the total length of the tensor. 2025-08-26T20:21:59.8368833Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-08-26T20:21:59.8369363Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-08-26T20:21:59.8369914Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-08-26T20:21:59.8370237Z 2025-08-26T20:21:59.8370323Z Examples: 2025-08-26T20:21:59.8370456Z 2025-08-26T20:21:59.8370576Z >>> # Target are to be padded 2025-08-26T20:21:59.8370890Z >>> T = 50 # Input sequence length 2025-08-26T20:21:59.8371252Z >>> C = 20 # Number of classes (including blank) 2025-08-26T20:21:59.8371602Z >>> N = 16 # Batch size 2025-08-26T20:21:59.8372021Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-08-26T20:21:59.8372559Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-08-26T20:21:59.8372938Z >>> 2025-08-26T20:21:59.8373274Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-08-26T20:21:59.8373812Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-08-26T20:21:59.8374233Z >>> 2025-08-26T20:21:59.8374545Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-26T20:21:59.8375081Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-08-26T20:21:59.8375507Z >>> 2025-08-26T20:21:59.8375855Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-08-26T20:21:59.8376305Z >>> target_lengths = torch.randint( 2025-08-26T20:21:59.8376619Z ... low=S_min, 2025-08-26T20:21:59.8376879Z ... high=S, 2025-08-26T20:21:59.8377128Z ... size=(N,), 2025-08-26T20:21:59.8377389Z ... dtype=torch.long, 2025-08-26T20:21:59.8377670Z ... ) 2025-08-26T20:21:59.8378009Z >>> ctc_loss = nn.CTCLoss() 2025-08-26T20:21:59.8378398Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-26T20:21:59.8378790Z >>> loss.backward() 2025-08-26T20:21:59.8379056Z >>> 2025-08-26T20:21:59.8379263Z >>> 2025-08-26T20:21:59.8379499Z >>> # Target are to be un-padded 2025-08-26T20:21:59.8379816Z >>> T = 50 # Input sequence length 2025-08-26T20:21:59.8380169Z >>> C = 20 # Number of classes (including blank) 2025-08-26T20:21:59.8380608Z >>> N = 16 # Batch size 2025-08-26T20:21:59.8380882Z >>> 2025-08-26T20:21:59.8381191Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-08-26T20:21:59.8381727Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-08-26T20:21:59.8382338Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-08-26T20:21:59.8382766Z >>> 2025-08-26T20:21:59.8383073Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-26T20:21:59.8383633Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-08-26T20:21:59.8384104Z >>> target = torch.randint( 2025-08-26T20:21:59.8384400Z ... low=1, 2025-08-26T20:21:59.8384645Z ... high=C, 2025-08-26T20:21:59.8384905Z ... size=(sum(target_lengths),), 2025-08-26T20:21:59.8385280Z ... dtype=torch.long, 2025-08-26T20:21:59.8385560Z ... ) 2025-08-26T20:21:59.8385789Z >>> ctc_loss = nn.CTCLoss() 2025-08-26T20:21:59.8386161Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-26T20:21:59.8386560Z >>> loss.backward() 2025-08-26T20:21:59.8386822Z >>> 2025-08-26T20:21:59.8387031Z >>> 2025-08-26T20:21:59.8387337Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-08-26T20:21:59.8387753Z >>> T = 50 # Input sequence length 2025-08-26T20:21:59.8388115Z >>> C = 20 # Number of classes (including blank) 2025-08-26T20:21:59.8388447Z >>> 2025-08-26T20:21:59.8388754Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-08-26T20:21:59.8389202Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-08-26T20:21:59.8389649Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-08-26T20:21:59.8390124Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-08-26T20:21:59.8390464Z >>> 2025-08-26T20:21:59.8390780Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-26T20:21:59.8391327Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-08-26T20:21:59.8392328Z >>> target = torch.randint( 2025-08-26T20:21:59.8392652Z ... low=1, 2025-08-26T20:21:59.8392908Z ... high=C, 2025-08-26T20:21:59.8393173Z ... size=(target_lengths,), 2025-08-26T20:21:59.8393496Z ... dtype=torch.long, 2025-08-26T20:21:59.8393765Z ... ) 2025-08-26T20:21:59.8394000Z >>> ctc_loss = nn.CTCLoss() 2025-08-26T20:21:59.8394388Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-26T20:21:59.8394793Z >>> loss.backward() 2025-08-26T20:21:59.8394966Z 2025-08-26T20:21:59.8395052Z Reference: 2025-08-26T20:21:59.8395362Z A. Graves et al.: Connectionist Temporal Classification: 2025-08-26T20:21:59.8395881Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-08-26T20:21:59.8396400Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-08-26T20:21:59.8396662Z 2025-08-26T20:21:59.8396757Z Note: 2025-08-26T20:21:59.8397107Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-08-26T20:21:59.8397728Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-08-26T20:21:59.8398324Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-08-26T20:21:59.8399332Z dtype :attr:`torch.int32`. 2025-08-26T20:21:59.8399532Z 2025-08-26T20:21:59.8399801Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-08-26T20:21:59.8400204Z 2025-08-26T20:21:59.8400208Z 2025-08-26T20:21:59.8400291Z Note: 2025-08-26T20:21:59.8400660Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-08-26T20:21:59.8401287Z may select a nondeterministic algorithm to increase performance. If this is 2025-08-26T20:21:59.8401914Z undesirable, you can try to make the operation deterministic (potentially at 2025-08-26T20:21:59.8402510Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-08-26T20:21:59.8402952Z True``. 2025-08-26T20:21:59.8403377Z Please see the notes on :doc:`/notes/randomness` for background. 2025-08-26T20:21:59.8403780Z 2025-08-26T20:21:59.8404142Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.8404520Z 2025-08-26T20:21:59.8874503Z msg = Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=410. 2025-08-26T20:21:59.8875438Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.8876004Z Computes a partial inverse of :class:`MaxPool2d`. 2025-08-26T20:21:59.8876280Z 2025-08-26T20:21:59.8876540Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-08-26T20:21:59.8876920Z 2025-08-26T20:21:59.8877161Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-08-26T20:21:59.8877767Z including the indices of the maximal values and computes a partial inverse 2025-08-26T20:21:59.8878276Z in which all non-maximal values are set to zero. 2025-08-26T20:21:59.8878545Z 2025-08-26T20:21:59.8878632Z Note: 2025-08-26T20:21:59.8879090Z This operation may behave nondeterministically when the input indices has repeat values. 2025-08-26T20:21:59.8879924Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-08-26T20:21:59.8880414Z 2025-08-26T20:21:59.8880670Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-08-26T20:21:59.8881178Z sizes. Hence, the inversion process can get ambiguous. 2025-08-26T20:21:59.8881665Z To accommodate this, you can provide the needed output size 2025-08-26T20:21:59.8882191Z as an additional argument :attr:`output_size` in the forward call. 2025-08-26T20:21:59.8882649Z See the Inputs and Example below. 2025-08-26T20:21:59.8882880Z 2025-08-26T20:21:59.8882981Z Args: 2025-08-26T20:21:59.8883285Z kernel_size (int or tuple): Size of the max pooling window. 2025-08-26T20:21:59.8883763Z stride (int or tuple): Stride of the max pooling window. 2025-08-26T20:21:59.8884196Z It is set to :attr:`kernel_size` by default. 2025-08-26T20:21:59.8884636Z padding (int or tuple): Padding that was added to the input 2025-08-26T20:21:59.8884932Z 2025-08-26T20:21:59.8885016Z Inputs: 2025-08-26T20:21:59.8885274Z - `input`: the input Tensor to invert 2025-08-26T20:21:59.8885715Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-08-26T20:21:59.8886198Z - `output_size` (optional): the targeted output size 2025-08-26T20:21:59.8886465Z 2025-08-26T20:21:59.8886552Z Shape: 2025-08-26T20:21:59.8886870Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-08-26T20:21:59.8887387Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-08-26T20:21:59.8887716Z 2025-08-26T20:21:59.8887824Z .. math:: 2025-08-26T20:21:59.8888233Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-08-26T20:21:59.8888854Z 2025-08-26T20:21:59.8888943Z .. math:: 2025-08-26T20:21:59.8889346Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-08-26T20:21:59.8889733Z 2025-08-26T20:21:59.8889898Z or as given by :attr:`output_size` in the call operator 2025-08-26T20:21:59.8890170Z 2025-08-26T20:21:59.8890272Z Example:: 2025-08-26T20:21:59.8890403Z 2025-08-26T20:21:59.8890575Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-08-26T20:21:59.8890966Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-08-26T20:21:59.8891338Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-08-26T20:21:59.8891926Z [ 5., 6., 7., 8.], 2025-08-26T20:21:59.8892371Z [ 9., 10., 11., 12.], 2025-08-26T20:21:59.8892698Z [13., 14., 15., 16.]]]]) 2025-08-26T20:21:59.8893058Z >>> output, indices = pool(input) 2025-08-26T20:21:59.8893398Z >>> unpool(output, indices) 2025-08-26T20:21:59.8893711Z tensor([[[[ 0., 0., 0., 0.], 2025-08-26T20:21:59.8894028Z [ 0., 6., 0., 8.], 2025-08-26T20:21:59.8894322Z [ 0., 0., 0., 0.], 2025-08-26T20:21:59.8894631Z [ 0., 14., 0., 16.]]]]) 2025-08-26T20:21:59.8895053Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-08-26T20:21:59.8895530Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-08-26T20:21:59.8895888Z [ 6., 7., 8., 9., 10.], 2025-08-26T20:21:59.8896228Z [11., 12., 13., 14., 15.], 2025-08-26T20:21:59.8896570Z [16., 17., 18., 19., 20.]]]]) 2025-08-26T20:21:59.8896918Z >>> output, indices = pool(input) 2025-08-26T20:21:59.8897303Z >>> # This call will not work without specifying output_size 2025-08-26T20:21:59.8897756Z >>> unpool(output, indices, output_size=input.size()) 2025-08-26T20:21:59.8898134Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-08-26T20:21:59.8898449Z [ 0., 7., 0., 9., 0.], 2025-08-26T20:21:59.8898745Z [ 0., 0., 0., 0., 0.], 2025-08-26T20:21:59.8899059Z [ 0., 17., 0., 19., 0.]]]]) 2025-08-26T20:21:59.8899281Z 2025-08-26T20:21:59.8899285Z 2025-08-26T20:21:59.8899367Z 2025-08-26T20:21:59.8899737Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.8900103Z 2025-08-26T20:21:59.9161301Z msg = Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=272. 2025-08-26T20:21:59.9162442Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.9163132Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-08-26T20:21:59.9163584Z 2025-08-26T20:21:59.9163902Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-08-26T20:21:59.9164447Z and with 2D inputs, this class 2025-08-26T20:21:59.9164646Z 2025-08-26T20:21:59.9164965Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-08-26T20:21:59.9165705Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-08-26T20:21:59.9166420Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-08-26T20:21:59.9166846Z 2025-08-26T20:21:59.9167195Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-08-26T20:21:59.9167768Z operations. 2025-08-26T20:21:59.9167903Z 2025-08-26T20:21:59.9168229Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-08-26T20:21:59.9169095Z pass. This scales the output of the Embedding before performing a weighted 2025-08-26T20:21:59.9169690Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-08-26T20:21:59.9170301Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-08-26T20:21:59.9170765Z :attr:`per_sample_weights`. 2025-08-26T20:21:59.9170956Z 2025-08-26T20:21:59.9171053Z Args: 2025-08-26T20:21:59.9171345Z num_embeddings (int): size of the dictionary of embeddings 2025-08-26T20:21:59.9171811Z embedding_dim (int): the size of each embedding vector 2025-08-26T20:21:59.9172407Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-08-26T20:21:59.9173087Z is renormalized to have norm :attr:`max_norm`. 2025-08-26T20:21:59.9173770Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-08-26T20:21:59.9174558Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-08-26T20:21:59.9175159Z the words in the mini-batch. Default ``False``. 2025-08-26T20:21:59.9175629Z Note: this option is not supported when ``mode="max"``. 2025-08-26T20:21:59.9176184Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-08-26T20:21:59.9176776Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-08-26T20:21:59.9177342Z into consideration. ``"mean"`` computes the average of the values 2025-08-26T20:21:59.9177852Z in the bag, ``"max"`` computes the max value over each bag. 2025-08-26T20:21:59.9178273Z Default: ``"mean"`` 2025-08-26T20:21:59.9178844Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-08-26T20:21:59.9179540Z Notes for more details regarding sparse gradients. Note: this option is not 2025-08-26T20:21:59.9180048Z supported when ``mode="max"``. 2025-08-26T20:21:59.9180734Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-08-26T20:21:59.9181477Z is equivalent to the size of `indices`. This matches the CSR format. 2025-08-26T20:21:59.9182157Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-08-26T20:21:59.9182888Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-08-26T20:21:59.9183549Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-08-26T20:21:59.9184177Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-08-26T20:21:59.9184815Z zeros, but can be updated to another value to be used as the padding vector. 2025-08-26T20:21:59.9185438Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-08-26T20:21:59.9185917Z reduction. 2025-08-26T20:21:59.9186135Z 2025-08-26T20:21:59.9186239Z Attributes: 2025-08-26T20:21:59.9186685Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-08-26T20:21:59.9187275Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-08-26T20:21:59.9187545Z 2025-08-26T20:21:59.9187656Z Examples:: 2025-08-26T20:21:59.9187863Z 2025-08-26T20:21:59.9188054Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-08-26T20:21:59.9188512Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-08-26T20:21:59.9188907Z >>> # a batch of 2 samples of 4 indices each 2025-08-26T20:21:59.9189424Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-08-26T20:21:59.9189950Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-08-26T20:21:59.9190364Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:21:59.9190764Z >>> embedding_sum(input, offsets) 2025-08-26T20:21:59.9191135Z tensor([[-0.8861, -5.4350, -0.0523], 2025-08-26T20:21:59.9191455Z [ 1.1306, -2.5798, -1.0044]]) 2025-08-26T20:21:59.9191666Z 2025-08-26T20:21:59.9192074Z >>> # Example with padding_idx 2025-08-26T20:21:59.9192607Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-08-26T20:21:59.9193131Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-08-26T20:21:59.9193600Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-08-26T20:21:59.9193974Z >>> embedding_sum(input, offsets) 2025-08-26T20:21:59.9194308Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-08-26T20:21:59.9194622Z [-0.7082, 3.2145, -2.6251]]) 2025-08-26T20:21:59.9194845Z 2025-08-26T20:21:59.9195018Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-08-26T20:21:59.9195459Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-08-26T20:21:59.9195880Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-08-26T20:21:59.9196247Z embedding.weight, 2025-08-26T20:21:59.9196574Z padding_idx=embedding.padding_idx, 2025-08-26T20:21:59.9196913Z mode='sum') 2025-08-26T20:21:59.9197176Z 2025-08-26T20:21:59.9197532Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.9197915Z 2025-08-26T20:21:59.9235722Z msg = Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py line=186. 2025-08-26T20:21:59.9236714Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.9237251Z Take in and process masked source/target sequences. 2025-08-26T20:21:59.9237522Z 2025-08-26T20:21:59.9237625Z .. note:: 2025-08-26T20:21:59.9237763Z 2025-08-26T20:21:59.9238168Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-08-26T20:21:59.9238874Z not allowed to participate in the attention, 2025-08-26T20:21:59.9239319Z which is the opposite of the definition for :attr:`attn_mask` 2025-08-26T20:21:59.9239830Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-08-26T20:21:59.9240145Z 2025-08-26T20:21:59.9240229Z Args: 2025-08-26T20:21:59.9240506Z src: the sequence to the encoder (required). 2025-08-26T20:21:59.9240880Z tgt: the sequence to the decoder (required). 2025-08-26T20:21:59.9241314Z src_mask: the additive mask for the src sequence (optional). 2025-08-26T20:21:59.9241801Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-08-26T20:21:59.9242316Z memory_mask: the additive mask for the encoder output (optional). 2025-08-26T20:21:59.9242876Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-08-26T20:21:59.9243445Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-08-26T20:21:59.9244051Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-08-26T20:21:59.9244642Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-08-26T20:21:59.9245117Z Default: ``None``; try to detect a causal mask. 2025-08-26T20:21:59.9245618Z Warning: 2025-08-26T20:21:59.9245948Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-08-26T20:21:59.9246408Z the causal mask. Providing incorrect hints can result in 2025-08-26T20:21:59.9246871Z incorrect execution, including forward and backward 2025-08-26T20:21:59.9247297Z compatibility. 2025-08-26T20:21:59.9247680Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-08-26T20:21:59.9248158Z Default: ``None``; try to detect a causal mask. 2025-08-26T20:21:59.9248511Z Warning: 2025-08-26T20:21:59.9248833Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-08-26T20:21:59.9249275Z the causal mask. Providing incorrect hints can result in 2025-08-26T20:21:59.9249826Z incorrect execution, including forward and backward 2025-08-26T20:21:59.9250211Z compatibility. 2025-08-26T20:21:59.9250580Z memory_is_causal: If specified, applies a causal mask as 2025-08-26T20:21:59.9250962Z ``memory_mask``. 2025-08-26T20:21:59.9251257Z Default: ``False``. 2025-08-26T20:21:59.9251550Z Warning: 2025-08-26T20:21:59.9251848Z ``memory_is_causal`` provides a hint that 2025-08-26T20:21:59.9252252Z ``memory_mask`` is the causal mask. Providing incorrect 2025-08-26T20:21:59.9252698Z hints can result in incorrect execution, including 2025-08-26T20:21:59.9253145Z forward and backward compatibility. 2025-08-26T20:21:59.9253380Z 2025-08-26T20:21:59.9253476Z Shape: 2025-08-26T20:21:59.9253857Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-08-26T20:21:59.9254330Z `(N, S, E)` if `batch_first=True`. 2025-08-26T20:21:59.9254797Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-08-26T20:21:59.9255276Z `(N, T, E)` if `batch_first=True`. 2025-08-26T20:21:59.9255694Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-08-26T20:21:59.9256180Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-08-26T20:21:59.9256607Z - memory_mask: :math:`(T, S)`. 2025-08-26T20:21:59.9257079Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-08-26T20:21:59.9257690Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-08-26T20:21:59.9258311Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-08-26T20:21:59.9258682Z 2025-08-26T20:21:59.9258999Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-08-26T20:21:59.9259645Z positions. If a BoolTensor is provided, positions with ``True`` 2025-08-26T20:21:59.9260241Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-08-26T20:21:59.9260899Z is provided, it will be added to the attention weight. 2025-08-26T20:21:59.9261487Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-08-26T20:21:59.9262117Z the attention. If a BoolTensor is provided, the positions with the 2025-08-26T20:21:59.9262762Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-08-26T20:21:59.9263201Z 2025-08-26T20:21:59.9263458Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-08-26T20:21:59.9263957Z `(N, T, E)` if `batch_first=True`. 2025-08-26T20:21:59.9264186Z 2025-08-26T20:21:59.9264454Z Note: Due to the multi-head attention architecture in the transformer model, 2025-08-26T20:21:59.9265129Z the output sequence length of a transformer is same as the input sequence 2025-08-26T20:21:59.9265622Z (i.e. target) length of the decoder. 2025-08-26T20:21:59.9265865Z 2025-08-26T20:21:59.9266189Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-08-26T20:21:59.9266774Z batch size, :math:`E` is the feature number 2025-08-26T20:21:59.9267015Z 2025-08-26T20:21:59.9267120Z Examples: 2025-08-26T20:21:59.9267355Z >>> # xdoctest: +SKIP 2025-08-26T20:21:59.9267676Z >>> output = transformer_model( 2025-08-26T20:21:59.9268058Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-08-26T20:21:59.9268413Z ... ) 2025-08-26T20:21:59.9268683Z 2025-08-26T20:21:59.9269125Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.9269514Z 2025-08-26T20:21:59.9508790Z msg = Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1766. 2025-08-26T20:21:59.9509834Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.9510212Z 2025-08-26T20:21:59.9510462Z Context manager for training with uneven inputs across processes in DDP. 2025-08-26T20:21:59.9510809Z 2025-08-26T20:21:59.9511032Z This context manager will keep track of already-joined DDP processes, 2025-08-26T20:21:59.9511579Z and "shadow" the forward and backward passes by inserting collective 2025-08-26T20:21:59.9512145Z communication operations to match with the ones created by non-joined 2025-08-26T20:21:59.9512728Z DDP processes. This will ensure each collective call has a corresponding 2025-08-26T20:21:59.9513310Z call by already-joined DDP processes, preventing hangs or errors that 2025-08-26T20:21:59.9513835Z would otherwise happen when training with uneven inputs across 2025-08-26T20:21:59.9514418Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-08-26T20:21:59.9514979Z specified to be ``True``, all trainers will throw an error once one rank 2025-08-26T20:21:59.9515502Z runs out of inputs, allowing these errors to be caught and handled 2025-08-26T20:21:59.9515926Z according to application logic. 2025-08-26T20:21:59.9516121Z 2025-08-26T20:21:59.9516348Z Once all DDP processes have joined, the context manager will broadcast 2025-08-26T20:21:59.9516897Z the model corresponding to the last joined process to all processes to 2025-08-26T20:21:59.9517389Z ensure the model is the same across all processes 2025-08-26T20:21:59.9517758Z (which is guaranteed by DDP). 2025-08-26T20:21:59.9517946Z 2025-08-26T20:21:59.9518162Z To use this to enable training with uneven inputs across processes, 2025-08-26T20:21:59.9518694Z simply wrap this context manager around your training loop. No further 2025-08-26T20:21:59.9519207Z modifications to the model or data loading is required. 2025-08-26T20:21:59.9519566Z 2025-08-26T20:21:59.9519673Z .. warning:: 2025-08-26T20:21:59.9520013Z If the model or training loop this context manager is wrapped around 2025-08-26T20:21:59.9520526Z has additional distributed collective operations, such as 2025-08-26T20:21:59.9521005Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-08-26T20:21:59.9521516Z ``throw_on_early_termination`` must be enabled. This is because this 2025-08-26T20:21:59.9522057Z context manager is not aware of non-DDP collective communication. 2025-08-26T20:21:59.9522554Z This flag will cause all ranks to throw when any one rank 2025-08-26T20:21:59.9523037Z exhausts inputs, allowing these errors to be caught and recovered 2025-08-26T20:21:59.9523468Z from across all ranks. 2025-08-26T20:21:59.9523651Z 2025-08-26T20:21:59.9523732Z Args: 2025-08-26T20:21:59.9524039Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-08-26T20:21:59.9524530Z gradients by the initial ``world_size`` DDP training was launched 2025-08-26T20:21:59.9525231Z with. If ``False``, will compute the effective world size 2025-08-26T20:21:59.9525709Z (number of ranks that have not depleted their inputs yet) and 2025-08-26T20:21:59.9526166Z divide gradients by that during allreduce. Set 2025-08-26T20:21:59.9526613Z ``divide_by_initial_world_size=True`` to ensure every input 2025-08-26T20:21:59.9527107Z sample including the uneven inputs have equal weight in terms of 2025-08-26T20:21:59.9527606Z how much they contribute to the global gradient. This is 2025-08-26T20:21:59.9528066Z achieved by always dividing the gradient by the initial 2025-08-26T20:21:59.9528542Z ``world_size`` even when we encounter uneven inputs. If you set 2025-08-26T20:21:59.9529089Z this to ``False``, we divide the gradient by the remaining 2025-08-26T20:21:59.9529580Z number of nodes. This ensures parity with training on a smaller 2025-08-26T20:21:59.9530087Z ``world_size`` although it also means the uneven inputs would 2025-08-26T20:21:59.9530590Z contribute more towards the global gradient. Typically, you 2025-08-26T20:21:59.9531089Z would want to set this to ``True`` for cases where the last few 2025-08-26T20:21:59.9531584Z inputs of your training job are uneven. In extreme cases, where 2025-08-26T20:21:59.9532090Z there is a large discrepancy in the number of inputs, setting 2025-08-26T20:21:59.9532536Z this to ``False`` might provide better results. 2025-08-26T20:21:59.9533002Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-08-26T20:21:59.9533488Z in ``enable=False`` to disable in cases where you know that 2025-08-26T20:21:59.9533962Z inputs are even across participating processes. Default is 2025-08-26T20:21:59.9534354Z ``True``. 2025-08-26T20:21:59.9534680Z throw_on_early_termination (bool): Whether to throw an error 2025-08-26T20:21:59.9535168Z or continue training when at least one rank has exhausted 2025-08-26T20:21:59.9535693Z inputs. If ``True``, will throw upon the first rank reaching end 2025-08-26T20:21:59.9536258Z of data. If ``False``, will continue training with a smaller 2025-08-26T20:21:59.9537058Z effective world size until all ranks are joined. Note that if 2025-08-26T20:21:59.9537492Z this flag is specified, then the flag 2025-08-26T20:21:59.9537886Z ``divide_by_initial_world_size`` would be ignored. Default 2025-08-26T20:21:59.9538265Z is ``False``. 2025-08-26T20:21:59.9538415Z 2025-08-26T20:21:59.9538431Z 2025-08-26T20:21:59.9538526Z Example:: 2025-08-26T20:21:59.9538646Z 2025-08-26T20:21:59.9538769Z >>> # xdoctest: +SKIP("Distributed") 2025-08-26T20:21:59.9539072Z >>> import torch 2025-08-26T20:21:59.9539349Z >>> import torch.distributed as dist 2025-08-26T20:21:59.9539665Z >>> import os 2025-08-26T20:21:59.9539935Z >>> import torch.multiprocessing as mp 2025-08-26T20:21:59.9540264Z >>> import torch.nn as nn 2025-08-26T20:21:59.9540668Z >>> # On each spawned worker 2025-08-26T20:21:59.9540964Z >>> def worker(rank): 2025-08-26T20:21:59.9541315Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-08-26T20:21:59.9541709Z >>> torch.cuda.set_device(rank) 2025-08-26T20:21:59.9542067Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-08-26T20:21:59.9542491Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-08-26T20:21:59.9542921Z >>> model, device_ids=[rank], output_device=rank 2025-08-26T20:21:59.9543268Z >>> ) 2025-08-26T20:21:59.9543516Z >>> # Rank 1 gets one more input than rank 0. 2025-08-26T20:21:59.9543947Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-08-26T20:21:59.9544367Z >>> with model.join(): 2025-08-26T20:21:59.9544658Z >>> for _ in range(5): 2025-08-26T20:21:59.9544950Z >>> for inp in inputs: 2025-08-26T20:21:59.9545389Z >>> loss = model(inp).sum() 2025-08-26T20:21:59.9545724Z >>> loss.backward() 2025-08-26T20:21:59.9546134Z >>> # Without the join() API, the below synchronization will hang 2025-08-26T20:21:59.9557841Z >>> # blocking for rank 1's allreduce to complete. 2025-08-26T20:21:59.9558360Z >>> torch.cuda.synchronize(device=rank) 2025-08-26T20:21:59.9558617Z 2025-08-26T20:21:59.9558875Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.9559262Z 2025-08-26T20:21:59.9560012Z msg = Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2057. 2025-08-26T20:21:59.9561258Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.9561644Z 2025-08-26T20:21:59.9561967Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-08-26T20:21:59.9562401Z 2025-08-26T20:21:59.9562623Z Registers an optimizer with DDP such that the optimization for a 2025-08-26T20:21:59.9563147Z parameter will run immediately when that parameter's gradient is 2025-08-26T20:21:59.9563683Z finished with reduction, instead of waiting for all parameters' 2025-08-26T20:21:59.9564230Z gradients to finish reduction. This can result in a training speedup 2025-08-26T20:21:59.9564790Z depending on your workload since the optimizer can run while gradient 2025-08-26T20:21:59.9565347Z reduction for other parameters are still ongoing. In addition, this has 2025-08-26T20:21:59.9565929Z the potential to reduce peak memory consumption during training, as it 2025-08-26T20:21:59.9566474Z only needs to load the per-parameter optimizer states of a single 2025-08-26T20:21:59.9567015Z parameter at a time, instead of loading all per-parameter optimizer 2025-08-26T20:21:59.9567438Z states at once. 2025-08-26T20:21:59.9567584Z 2025-08-26T20:21:59.9567665Z Args: 2025-08-26T20:21:59.9567989Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-08-26T20:21:59.9568406Z as a fused optimizer. 2025-08-26T20:21:59.9568754Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-08-26T20:21:59.9569233Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-08-26T20:21:59.9569794Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-08-26T20:21:59.9570395Z Optimizers. If this is omitted, all DDP model parameters will be 2025-08-26T20:21:59.9570796Z optimized. 2025-08-26T20:21:59.9571133Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-08-26T20:21:59.9571456Z 2025-08-26T20:21:59.9571554Z .. warning :: 2025-08-26T20:21:59.9571902Z _register_fused_optim should only be called once on a DDP instance, 2025-08-26T20:21:59.9572650Z and registering multiple fused optimizers for the same DDP model 2025-08-26T20:21:59.9573115Z is not currently supported. Please ping 2025-08-26T20:21:59.9573599Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-26T20:21:59.9574041Z for your use case. 2025-08-26T20:21:59.9574198Z 2025-08-26T20:21:59.9574304Z .. warning :: 2025-08-26T20:21:59.9574625Z _register_fused_optim and register_comm_hook currently do not 2025-08-26T20:21:59.9575149Z compose together, meaning that custom DDP communication hooks are 2025-08-26T20:21:59.9575652Z not supported with overlapped optimizers. Please ping 2025-08-26T20:21:59.9576165Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-26T20:21:59.9576594Z for your use case. 2025-08-26T20:21:59.9576763Z 2025-08-26T20:21:59.9576851Z .. warning :: 2025-08-26T20:21:59.9577213Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-08-26T20:21:59.9577683Z with overlapped optimizer. Please ping 2025-08-26T20:21:59.9578128Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-26T20:21:59.9578661Z for your use case. 2025-08-26T20:21:59.9578831Z 2025-08-26T20:21:59.9578919Z Example:: 2025-08-26T20:21:59.9579039Z 2025-08-26T20:21:59.9579183Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-08-26T20:21:59.9579725Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-08-26T20:21:59.9580334Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-08-26T20:21:59.9580849Z >>> lr = 1e-2 2025-08-26T20:21:59.9581091Z >>> betas = (0.9, 0.99) 2025-08-26T20:21:59.9581356Z >>> eps = 1e-6 2025-08-26T20:21:59.9581712Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-08-26T20:21:59.9582182Z >>> # Example with subset of parameters 2025-08-26T20:21:59.9582543Z >>> params_to_opt = [list(net.parameters())[0]] 2025-08-26T20:21:59.9582959Z >>> net._register_fused_optim( 2025-08-26T20:21:59.9583380Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-08-26T20:21:59.9583820Z ... ) 2025-08-26T20:21:59.9583951Z 2025-08-26T20:21:59.9584200Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.9584564Z 2025-08-26T20:21:59.9794819Z msg = Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=14. 2025-08-26T20:21:59.9795983Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.9796584Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-08-26T20:21:59.9796949Z 2025-08-26T20:21:59.9797223Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-08-26T20:21:59.9797903Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-08-26T20:21:59.9798556Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-08-26T20:21:59.9799253Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-08-26T20:21:59.9799676Z 2025-08-26T20:21:59.9799788Z .. note:: 2025-08-26T20:21:59.9800164Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-08-26T20:21:59.9800886Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-08-26T20:21:59.9801447Z layer with 4d weight will be affected by ``model.to``, which does not 2025-08-26T20:21:59.9802003Z necessarily benefit from conversion to specified ``memory_format``. 2025-08-26T20:21:59.9802573Z One place we are confident in is that NHWC(channels_last) conversion for 2025-08-26T20:21:59.9803122Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-08-26T20:21:59.9803666Z even in cases where we have to apply permutation to input tensors. 2025-08-26T20:21:59.9803997Z 2025-08-26T20:21:59.9804226Z Hence our strategy here is to convert only the weight of convolution to 2025-08-26T20:21:59.9804685Z channels_last. This ensures that; 2025-08-26T20:21:59.9805118Z 1. Fast convolution kernels will be used, the benefit of which could 2025-08-26T20:21:59.9805733Z outweigh overhead of permutation (if input is not in the same format). 2025-08-26T20:21:59.9806319Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-08-26T20:21:59.9806787Z from memory_format conversion. 2025-08-26T20:21:59.9806998Z 2025-08-26T20:21:59.9807235Z The optimal case is that, layers between convolution layers are channels 2025-08-26T20:21:59.9807805Z last compatible. Input tensor would be permuted to channels last when it 2025-08-26T20:21:59.9808398Z encounters the first convolution layer and stay in that memory format. 2025-08-26T20:21:59.9808994Z Hence following convolutions will not need to permute its input tensor. 2025-08-26T20:21:59.9809540Z 2025-08-26T20:21:59.9809779Z In case where a channels last incompatible layer is between convolution 2025-08-26T20:21:59.9810340Z layers, we need to permute the input tensor back to contiguous format 2025-08-26T20:21:59.9810889Z for that layer. The input tensor will go through the remaining layers in 2025-08-26T20:21:59.9811466Z contiguous format and be permuted to channels last when it encounters 2025-08-26T20:21:59.9812028Z another convolution layer. There's no point in propagating that 2025-08-26T20:21:59.9812606Z permutation to an earlier layer, as most layers are quite agnostic to 2025-08-26T20:21:59.9813047Z ``memory_format``. 2025-08-26T20:21:59.9813217Z 2025-08-26T20:21:59.9813458Z This claim might change when PyTorch supports fusion of permutation, as 2025-08-26T20:21:59.9814108Z there might have been a better spot to fuse the permutation other than 2025-08-26T20:21:59.9814567Z immediately before a convolution. 2025-08-26T20:21:59.9814802Z 2025-08-26T20:21:59.9814885Z Args: 2025-08-26T20:21:59.9815223Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-08-26T20:21:59.9815646Z ``nn.Module`` 2025-08-26T20:21:59.9816013Z memory_format: user specified ``memory_format``, 2025-08-26T20:21:59.9816459Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-08-26T20:21:59.9816749Z 2025-08-26T20:21:59.9816845Z Returns: 2025-08-26T20:21:59.9817108Z The original module with updated ``nn.Conv2d`` 2025-08-26T20:21:59.9817419Z 2025-08-26T20:21:59.9817504Z Example: 2025-08-26T20:21:59.9817772Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:21:59.9818176Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-08-26T20:21:59.9818552Z >>> input = torch.randint( 2025-08-26T20:21:59.9818892Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-08-26T20:21:59.9819247Z ... ) 2025-08-26T20:21:59.9819480Z >>> model = nn.Sequential( 2025-08-26T20:21:59.9819795Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-08-26T20:21:59.9820114Z >>> # This is identical to: 2025-08-26T20:21:59.9820643Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-08-26T20:21:59.9821181Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-08-26T20:21:59.9821573Z ... model, torch.channels_last 2025-08-26T20:21:59.9821874Z ... ) 2025-08-26T20:21:59.9822106Z >>> out = model(input) 2025-08-26T20:21:59.9822375Z 2025-08-26T20:21:59.9822745Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.9823113Z 2025-08-26T20:21:59.9823751Z msg = Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=93. 2025-08-26T20:21:59.9824758Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.9825352Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-08-26T20:21:59.9825957Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-08-26T20:21:59.9826630Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-08-26T20:21:59.9827267Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-08-26T20:21:59.9827956Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-08-26T20:21:59.9828390Z 2025-08-26T20:21:59.9828484Z .. note:: 2025-08-26T20:21:59.9828868Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-08-26T20:21:59.9829464Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-08-26T20:21:59.9830008Z layer with 4d weight will be affected by ``model.to``, which does not 2025-08-26T20:21:59.9830669Z necessarily benefit from conversion to specified ``memory_format``. 2025-08-26T20:21:59.9831284Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-08-26T20:21:59.9831880Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-08-26T20:21:59.9832427Z even in cases where we have to apply permutation to input tensors. 2025-08-26T20:21:59.9832750Z 2025-08-26T20:21:59.9832975Z Hence our strategy here is to convert only the weight of convolution to 2025-08-26T20:21:59.9833442Z channels_last_3d. This ensures that; 2025-08-26T20:21:59.9833886Z 1. Fast convolution kernels will be used, the benefit of which could 2025-08-26T20:21:59.9834455Z outweigh overhead of permutation (if input is not in the same format). 2025-08-26T20:21:59.9835080Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-08-26T20:21:59.9835553Z from memory_format conversion. 2025-08-26T20:21:59.9835773Z 2025-08-26T20:21:59.9835992Z The optimal case is that, layers between convolution layers are channels 2025-08-26T20:21:59.9836571Z last compatible. Input tensor would be permuted to channels last when it 2025-08-26T20:21:59.9837165Z encounters the first convolution layer and stay in that memory format. 2025-08-26T20:21:59.9837744Z Hence following convolutions will not need to permute its input tensor. 2025-08-26T20:21:59.9838109Z 2025-08-26T20:21:59.9838328Z In case where a channels last incompatible layer is between convolution 2025-08-26T20:21:59.9838885Z layers, we need to permute the input tensor back to contiguous format 2025-08-26T20:21:59.9839442Z for that layer. The input tensor will go through the remaining layers in 2025-08-26T20:21:59.9840015Z contiguous format and be permuted to channels last when it encounters 2025-08-26T20:21:59.9840558Z another convolution layer. There's no point in propagating that 2025-08-26T20:21:59.9841111Z permutation to an earlier layer, as most layers are quite agnostic to 2025-08-26T20:21:59.9841546Z ``memory_format``. 2025-08-26T20:21:59.9841715Z 2025-08-26T20:21:59.9841955Z This claim might change when PyTorch supports fusion of permutation, as 2025-08-26T20:21:59.9842513Z there might have been a better spot to fuse the permutation other than 2025-08-26T20:21:59.9842972Z immediately before a convolution. 2025-08-26T20:21:59.9843205Z 2025-08-26T20:21:59.9843288Z Args: 2025-08-26T20:21:59.9843624Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-08-26T20:21:59.9844056Z ``nn.Module`` 2025-08-26T20:21:59.9844409Z memory_format: user specified ``memory_format``, 2025-08-26T20:21:59.9844856Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-08-26T20:21:59.9845161Z 2025-08-26T20:21:59.9845251Z Returns: 2025-08-26T20:21:59.9845528Z The original module with updated ``nn.Conv3d`` 2025-08-26T20:21:59.9845778Z 2025-08-26T20:21:59.9845864Z Example: 2025-08-26T20:21:59.9846138Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:21:59.9846544Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-08-26T20:21:59.9846920Z >>> input = torch.randint( 2025-08-26T20:21:59.9847268Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-08-26T20:21:59.9847623Z ... ) 2025-08-26T20:21:59.9847854Z >>> model = nn.Sequential( 2025-08-26T20:21:59.9848168Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-08-26T20:21:59.9848527Z >>> # This is identical to: 2025-08-26T20:21:59.9848983Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-08-26T20:21:59.9849527Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-08-26T20:21:59.9849999Z ... model, torch.channels_last_3d 2025-08-26T20:21:59.9850315Z ... ) 2025-08-26T20:21:59.9850580Z >>> out = model(input) 2025-08-26T20:21:59.9850854Z 2025-08-26T20:21:59.9851230Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.9851597Z 2025-08-26T20:21:59.9908209Z msg = Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py line=424. 2025-08-26T20:21:59.9909281Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:21:59.9909817Z Register a parametrization to a tensor in a module. 2025-08-26T20:21:59.9910101Z 2025-08-26T20:21:59.9910376Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-08-26T20:21:59.9911249Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-08-26T20:21:59.9911926Z If the original tensor requires a gradient, the backward pass will differentiate 2025-08-26T20:21:59.9912616Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-08-26T20:21:59.9913026Z 2025-08-26T20:21:59.9913339Z The first time that a module registers a parametrization, this function will add an attribute 2025-08-26T20:21:59.9914018Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-08-26T20:21:59.9914392Z 2025-08-26T20:21:59.9914645Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-08-26T20:21:59.9915145Z ``module.parametrizations.weight``. 2025-08-26T20:21:59.9915372Z 2025-08-26T20:21:59.9915518Z The original tensor will be accessible under 2025-08-26T20:21:59.9915901Z ``module.parametrizations.weight.original``. 2025-08-26T20:21:59.9916167Z 2025-08-26T20:21:59.9916434Z Parametrizations may be concatenated by registering several parametrizations 2025-08-26T20:21:59.9916925Z on the same attribute. 2025-08-26T20:21:59.9917101Z 2025-08-26T20:21:59.9917357Z The training mode of a registered parametrization is updated on registration 2025-08-26T20:21:59.9917859Z to match the training mode of the host module 2025-08-26T20:21:59.9918102Z 2025-08-26T20:21:59.9918419Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-08-26T20:21:59.9918961Z using the context manager :func:`cached`. 2025-08-26T20:21:59.9919206Z 2025-08-26T20:21:59.9919446Z A :attr:`parametrization` may optionally implement a method with signature 2025-08-26T20:21:59.9919815Z 2025-08-26T20:21:59.9919931Z .. code-block:: python 2025-08-26T20:21:59.9920109Z 2025-08-26T20:21:59.9920337Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-08-26T20:21:59.9920670Z 2025-08-26T20:21:59.9920952Z This method is called on the unparametrized tensor when the first parametrization 2025-08-26T20:21:59.9921534Z is registered to compute the initial value of the original tensor. 2025-08-26T20:21:59.9922173Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-08-26T20:21:59.9922178Z 2025-08-26T20:21:59.9922498Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-08-26T20:21:59.9922785Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-08-26T20:21:59.9922790Z 2025-08-26T20:21:59.9923027Z It is possible for the first parametrization to depend on several inputs. 2025-08-26T20:21:59.9923290Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-08-26T20:21:59.9923533Z (see the example implementation of a ``RankOne`` parametrization below). 2025-08-26T20:21:59.9923537Z 2025-08-26T20:21:59.9923873Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-08-26T20:21:59.9924001Z with names ``original0``, ``original1``,... 2025-08-26T20:21:59.9924117Z 2025-08-26T20:21:59.9924208Z .. note:: 2025-08-26T20:21:59.9924212Z 2025-08-26T20:21:59.9924479Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-08-26T20:21:59.9924637Z once to perform a number of consistency checks. 2025-08-26T20:21:59.9924903Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-08-26T20:21:59.9925038Z and nothing will be called otherwise. 2025-08-26T20:21:59.9925042Z 2025-08-26T20:21:59.9925128Z .. note:: 2025-08-26T20:21:59.9925133Z 2025-08-26T20:21:59.9925333Z In most situations, ``right_inverse`` will be a function such that 2025-08-26T20:21:59.9925470Z ``forward(right_inverse(X)) == X`` (see 2025-08-26T20:21:59.9925772Z `right inverse `_). 2025-08-26T20:21:59.9926085Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-08-26T20:21:59.9926182Z to relax this. 2025-08-26T20:21:59.9926186Z 2025-08-26T20:21:59.9926289Z .. warning:: 2025-08-26T20:21:59.9926293Z 2025-08-26T20:21:59.9926564Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-08-26T20:21:59.9926823Z will register a number of new parameters. If such parametrization is registered 2025-08-26T20:21:59.9927101Z after the optimizer is created, these new parameters will need to be added manually 2025-08-26T20:21:59.9927295Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-08-26T20:21:59.9927299Z 2025-08-26T20:21:59.9927398Z Args: 2025-08-26T20:21:59.9927608Z module (nn.Module): module on which to register the parametrization 2025-08-26T20:21:59.9927819Z tensor_name (str): name of the parameter or buffer on which to register 2025-08-26T20:21:59.9927938Z the parametrization 2025-08-26T20:21:59.9928140Z parametrization (nn.Module): the parametrization to register 2025-08-26T20:21:59.9928246Z Keyword args: 2025-08-26T20:21:59.9928459Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-08-26T20:21:59.9928649Z may change the dtype and shape of the tensor. Default: `False` 2025-08-26T20:21:59.9928924Z Warning: the parametrization is not checked for consistency upon registration. 2025-08-26T20:21:59.9929040Z Enable this flag at your own risk. 2025-08-26T20:21:59.9929045Z 2025-08-26T20:21:59.9929138Z Raises: 2025-08-26T20:21:59.9929426Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-08-26T20:21:59.9929430Z 2025-08-26T20:21:59.9929527Z Examples: 2025-08-26T20:21:59.9929672Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-08-26T20:21:59.9929764Z >>> import torch 2025-08-26T20:21:59.9929883Z >>> import torch.nn as nn 2025-08-26T20:21:59.9930016Z >>> import torch.nn.utils.parametrize as P 2025-08-26T20:21:59.9930103Z >>> 2025-08-26T20:21:59.9930222Z >>> class Symmetric(nn.Module): 2025-08-26T20:21:59.9930325Z >>> def forward(self, X): 2025-08-26T20:21:59.9930513Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-08-26T20:21:59.9930596Z >>> 2025-08-26T20:21:59.9930709Z >>> def right_inverse(self, A): 2025-08-26T20:21:59.9930818Z >>> return A.triu() 2025-08-26T20:21:59.9930901Z >>> 2025-08-26T20:21:59.9931011Z >>> m = nn.Linear(5, 5) 2025-08-26T20:21:59.9931181Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-08-26T20:21:59.9931419Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-08-26T20:21:59.9931517Z True 2025-08-26T20:21:59.9931619Z >>> A = torch.rand(5, 5) 2025-08-26T20:21:59.9931750Z >>> A = A + A.T # A is now symmetric 2025-08-26T20:21:59.9931947Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-08-26T20:21:59.9932148Z >>> print(torch.allclose(m.weight, A)) 2025-08-26T20:21:59.9932246Z True 2025-08-26T20:21:59.9932250Z 2025-08-26T20:21:59.9932358Z >>> class RankOne(nn.Module): 2025-08-26T20:21:59.9932476Z >>> def forward(self, x, y): 2025-08-26T20:21:59.9932616Z >>> # Form a rank 1 matrix multiplying two vectors 2025-08-26T20:21:59.9932751Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-08-26T20:21:59.9932848Z >>> 2025-08-26T20:21:59.9932961Z >>> def right_inverse(self, Z): 2025-08-26T20:21:59.9933093Z >>> # Project Z onto the rank 1 matrices 2025-08-26T20:21:59.9933246Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-08-26T20:21:59.9933366Z >>> # Return rescaled singular vectors 2025-08-26T20:21:59.9933550Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-08-26T20:21:59.9933700Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-08-26T20:21:59.9933797Z >>> 2025-08-26T20:21:59.9933943Z >>> linear_rank_one = P.register_parametrization( 2025-08-26T20:21:59.9934071Z ... nn.Linear(4, 4), "weight", RankOne() 2025-08-26T20:21:59.9934165Z ... ) 2025-08-26T20:21:59.9934365Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-08-26T20:21:59.9934458Z 1 2025-08-26T20:21:59.9934463Z 2025-08-26T20:21:59.9934544Z 2025-08-26T20:21:59.9934798Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:21:59.9934802Z 2025-08-26T20:22:00.0020907Z msg = Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=979. 2025-08-26T20:22:00.0021198Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0021522Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-08-26T20:22:00.0021537Z 2025-08-26T20:22:00.0021789Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-08-26T20:22:00.0022016Z by removing the specified ``amount`` of (currently unpruned) channels 2025-08-26T20:22:00.0022190Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-08-26T20:22:00.0022400Z Modifies module in place (and also return the modified module) 2025-08-26T20:22:00.0022485Z by: 2025-08-26T20:22:00.0022490Z 2025-08-26T20:22:00.0022699Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-26T20:22:00.0022927Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-26T20:22:00.0023137Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-26T20:22:00.0023361Z original (unpruned) parameter is stored in a new parameter named 2025-08-26T20:22:00.0023458Z ``name+'_orig'``. 2025-08-26T20:22:00.0023463Z 2025-08-26T20:22:00.0023550Z Args: 2025-08-26T20:22:00.0023740Z module (nn.Module): module containing the tensor to prune 2025-08-26T20:22:00.0023921Z name (str): parameter name within ``module`` on which pruning 2025-08-26T20:22:00.0024024Z will act. 2025-08-26T20:22:00.0024193Z amount (int or float): quantity of parameters to prune. 2025-08-26T20:22:00.0024363Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-08-26T20:22:00.0024620Z fraction of parameters to prune. If ``int``, it represents the 2025-08-26T20:22:00.0024821Z absolute number of parameters to prune. 2025-08-26T20:22:00.0025073Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-08-26T20:22:00.0025278Z entries for argument ``p`` in :func:`torch.norm`. 2025-08-26T20:22:00.0025546Z dim (int): index of the dim along which we define channels to prune. 2025-08-26T20:22:00.0025935Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-08-26T20:22:00.0026572Z shape as module parameter) used to compute mask for pruning. 2025-08-26T20:22:00.0026913Z The values in this tensor indicate the importance of the corresponding 2025-08-26T20:22:00.0027043Z elements in the parameter being pruned. 2025-08-26T20:22:00.0027269Z If unspecified or None, the module parameter will be used in its place. 2025-08-26T20:22:00.0027288Z 2025-08-26T20:22:00.0027374Z Returns: 2025-08-26T20:22:00.0027589Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-08-26T20:22:00.0027594Z 2025-08-26T20:22:00.0027699Z Examples: 2025-08-26T20:22:00.0027819Z >>> from torch.nn.utils import prune 2025-08-26T20:22:00.0027944Z >>> m = prune.ln_structured( 2025-08-26T20:22:00.0028205Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-08-26T20:22:00.0028288Z ... ) 2025-08-26T20:22:00.0028386Z 2025-08-26T20:22:00.0028639Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0028643Z 2025-08-26T20:22:00.0029223Z msg = Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1026. 2025-08-26T20:22:00.0029484Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0029491Z 2025-08-26T20:22:00.0030013Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-08-26T20:22:00.0030018Z 2025-08-26T20:22:00.0030160Z Modifies modules in place by: 2025-08-26T20:22:00.0030167Z 2025-08-26T20:22:00.0030468Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-26T20:22:00.0030753Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-26T20:22:00.0031167Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-26T20:22:00.0031506Z original (unpruned) parameter is stored in a new parameter named 2025-08-26T20:22:00.0031598Z ``name+'_orig'``. 2025-08-26T20:22:00.0031602Z 2025-08-26T20:22:00.0031697Z Args: 2025-08-26T20:22:00.0031895Z parameters (Iterable of (module, name) tuples): parameters of 2025-08-26T20:22:00.0032096Z the model to prune in a global fashion, i.e. by aggregating all 2025-08-26T20:22:00.0032301Z weights prior to deciding which ones to prune. module must be of 2025-08-26T20:22:00.0032453Z type :class:`nn.Module`, and name must be a string. 2025-08-26T20:22:00.0032682Z pruning_method (function): a valid pruning function from this module, 2025-08-26T20:22:00.0032855Z or a custom one implemented by the user that satisfies the 2025-08-26T20:22:00.0033094Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-08-26T20:22:00.0033322Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-08-26T20:22:00.0033546Z the corresponding parameter's importance scores tensor. The tensor 2025-08-26T20:22:00.0033766Z should be the same shape as the parameter, and is used for computing 2025-08-26T20:22:00.0033865Z mask for pruning. 2025-08-26T20:22:00.0034082Z If unspecified or None, the parameter will be used in place of its 2025-08-26T20:22:00.0034181Z importance scores. 2025-08-26T20:22:00.0034304Z kwargs: other keyword arguments such as: 2025-08-26T20:22:00.0034510Z amount (int or float): quantity of parameters to prune across the 2025-08-26T20:22:00.0034615Z specified parameters. 2025-08-26T20:22:00.0034796Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-08-26T20:22:00.0034993Z fraction of parameters to prune. If ``int``, it represents the 2025-08-26T20:22:00.0035125Z absolute number of parameters to prune. 2025-08-26T20:22:00.0035130Z 2025-08-26T20:22:00.0035225Z Raises: 2025-08-26T20:22:00.0035445Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-08-26T20:22:00.0035450Z 2025-08-26T20:22:00.0035543Z Note: 2025-08-26T20:22:00.0035757Z Since global structured pruning doesn't make much sense unless the 2025-08-26T20:22:00.0036000Z norm is normalized by the size of the parameter, we now limit the 2025-08-26T20:22:00.0036161Z scope of global pruning to unstructured methods. 2025-08-26T20:22:00.0036165Z 2025-08-26T20:22:00.0036252Z Examples: 2025-08-26T20:22:00.0036379Z >>> from torch.nn.utils import prune 2025-08-26T20:22:00.0036497Z >>> from collections import OrderedDict 2025-08-26T20:22:00.0036597Z >>> net = nn.Sequential( 2025-08-26T20:22:00.0036701Z ... OrderedDict( 2025-08-26T20:22:00.0036784Z ... [ 2025-08-26T20:22:00.0036913Z ... ("first", nn.Linear(10, 4)), 2025-08-26T20:22:00.0037079Z ... ("second", nn.Linear(4, 1)), 2025-08-26T20:22:00.0037164Z ... ] 2025-08-26T20:22:00.0037258Z ... ) 2025-08-26T20:22:00.0037345Z ... ) 2025-08-26T20:22:00.0037451Z >>> parameters_to_prune = ( 2025-08-26T20:22:00.0037564Z ... (net.first, "weight"), 2025-08-26T20:22:00.0037666Z ... (net.second, "weight"), 2025-08-26T20:22:00.0037760Z ... ) 2025-08-26T20:22:00.0037867Z >>> prune.global_unstructured( 2025-08-26T20:22:00.0037968Z ... parameters_to_prune, 2025-08-26T20:22:00.0038109Z ... pruning_method=prune.L1Unstructured, 2025-08-26T20:22:00.0038198Z ... amount=10, 2025-08-26T20:22:00.0038292Z ... ) 2025-08-26T20:22:00.0038511Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-08-26T20:22:00.0038598Z tensor(10) 2025-08-26T20:22:00.0038602Z 2025-08-26T20:22:00.0038606Z 2025-08-26T20:22:00.0038870Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0038874Z 2025-08-26T20:22:00.0039409Z msg = Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1149. 2025-08-26T20:22:00.0039684Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0040074Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-08-26T20:22:00.0040079Z 2025-08-26T20:22:00.0040301Z Modifies module in place (and also return the modified module) by: 2025-08-26T20:22:00.0040306Z 2025-08-26T20:22:00.0040513Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-26T20:22:00.0040742Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-26T20:22:00.0040951Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-26T20:22:00.0041158Z original (unpruned) parameter is stored in a new parameter named 2025-08-26T20:22:00.0041266Z ``name+'_orig'``. 2025-08-26T20:22:00.0041270Z 2025-08-26T20:22:00.0041355Z Args: 2025-08-26T20:22:00.0041546Z module (nn.Module): module containing the tensor to prune 2025-08-26T20:22:00.0041725Z name (str): parameter name within ``module`` on which pruning 2025-08-26T20:22:00.0041812Z will act. 2025-08-26T20:22:00.0041996Z mask (Tensor): binary mask to be applied to the parameter. 2025-08-26T20:22:00.0042001Z 2025-08-26T20:22:00.0042086Z Returns: 2025-08-26T20:22:00.0042313Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-08-26T20:22:00.0042317Z 2025-08-26T20:22:00.0042402Z Examples: 2025-08-26T20:22:00.0042521Z >>> from torch.nn.utils import prune 2025-08-26T20:22:00.0042640Z >>> m = prune.custom_from_mask( 2025-08-26T20:22:00.0042809Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-08-26T20:22:00.0042903Z ... ) 2025-08-26T20:22:00.0043006Z >>> print(m.bias_mask) 2025-08-26T20:22:00.0043100Z tensor([0., 1., 0.]) 2025-08-26T20:22:00.0043104Z 2025-08-26T20:22:00.0043251Z 2025-08-26T20:22:00.0043503Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0043508Z 2025-08-26T20:22:00.0087400Z msg = Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py line=350. 2025-08-26T20:22:00.0087663Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0087820Z Pad a packed batch of variable length sequences. 2025-08-26T20:22:00.0087826Z 2025-08-26T20:22:00.0088000Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-08-26T20:22:00.0088004Z 2025-08-26T20:22:00.0088303Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-08-26T20:22:00.0088632Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-08-26T20:22:00.0088749Z sequence and ``B`` is the batch size. 2025-08-26T20:22:00.0088760Z 2025-08-26T20:22:00.0088859Z Example: 2025-08-26T20:22:00.0089098Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-08-26T20:22:00.0089258Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-08-26T20:22:00.0089351Z >>> lens = [2, 1, 3] 2025-08-26T20:22:00.0089465Z >>> packed = pack_padded_sequence( 2025-08-26T20:22:00.0089627Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-08-26T20:22:00.0089742Z ... ) 2025-08-26T20:22:00.0089839Z >>> packed 2025-08-26T20:22:00.0090077Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-08-26T20:22:00.0090288Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-08-26T20:22:00.0090546Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-08-26T20:22:00.0090638Z >>> seq_unpacked 2025-08-26T20:22:00.0090755Z tensor([[1, 2, 0], 2025-08-26T20:22:00.0090845Z [3, 0, 0], 2025-08-26T20:22:00.0090932Z [4, 5, 6]]) 2025-08-26T20:22:00.0091035Z >>> lens_unpacked 2025-08-26T20:22:00.0091125Z tensor([2, 1, 3]) 2025-08-26T20:22:00.0091129Z 2025-08-26T20:22:00.0091237Z .. note:: 2025-08-26T20:22:00.0091378Z :attr:`total_length` is useful to implement the 2025-08-26T20:22:00.0091603Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-08-26T20:22:00.0092005Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-08-26T20:22:00.0092237Z See :ref:`this FAQ section ` for 2025-08-26T20:22:00.0092337Z details. 2025-08-26T20:22:00.0092342Z 2025-08-26T20:22:00.0092424Z Args: 2025-08-26T20:22:00.0092557Z sequence (PackedSequence): batch to pad 2025-08-26T20:22:00.0092807Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-08-26T20:22:00.0092922Z format, ``T x B x *`` otherwise. 2025-08-26T20:22:00.0093125Z padding_value (float, optional): values for padded elements. 2025-08-26T20:22:00.0093354Z total_length (int, optional): if not ``None``, the output will be padded to 2025-08-26T20:22:00.0093600Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-08-26T20:22:00.0093786Z if :attr:`total_length` is less than the max sequence length in 2025-08-26T20:22:00.0093884Z :attr:`sequence`. 2025-08-26T20:22:00.0093889Z 2025-08-26T20:22:00.0093986Z Returns: 2025-08-26T20:22:00.0094173Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-08-26T20:22:00.0094379Z containing the list of lengths of each sequence in the batch. 2025-08-26T20:22:00.0094607Z Batch elements will be re-ordered as they were ordered originally when 2025-08-26T20:22:00.0094820Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-08-26T20:22:00.0095049Z 2025-08-26T20:22:00.0095302Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0095307Z 2025-08-26T20:22:00.0767807Z msg = Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=808. 2025-08-26T20:22:00.0768753Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0769467Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-08-26T20:22:00.0769914Z 2025-08-26T20:22:00.0770296Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-08-26T20:22:00.0770991Z intervals by which each scheduler should be called at a given epoch. 2025-08-26T20:22:00.0771336Z 2025-08-26T20:22:00.0771629Z Args: 2025-08-26T20:22:00.0771893Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:00.0772303Z schedulers (list): List of chained schedulers. 2025-08-26T20:22:00.0772763Z milestones (list): List of integers that reflects milestone points. 2025-08-26T20:22:00.0773271Z last_epoch (int): The index of last epoch. Default: -1. 2025-08-26T20:22:00.0773563Z 2025-08-26T20:22:00.0773648Z Example: 2025-08-26T20:22:00.0773883Z >>> # xdoctest: +SKIP 2025-08-26T20:22:00.0774219Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-08-26T20:22:00.0774577Z >>> # lr = 0.005 if epoch == 0 2025-08-26T20:22:00.0774896Z >>> # lr = 0.005 if epoch == 1 2025-08-26T20:22:00.0775210Z >>> # lr = 0.005 if epoch == 2 2025-08-26T20:22:00.0775506Z >>> # ... 2025-08-26T20:22:00.0775764Z >>> # lr = 0.05 if epoch == 20 2025-08-26T20:22:00.0776072Z >>> # lr = 0.045 if epoch == 21 2025-08-26T20:22:00.0776397Z >>> # lr = 0.0405 if epoch == 22 2025-08-26T20:22:00.0776813Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-08-26T20:22:00.0777304Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-08-26T20:22:00.0777677Z >>> scheduler = SequentialLR( 2025-08-26T20:22:00.0777994Z ... optimizer, 2025-08-26T20:22:00.0778306Z ... schedulers=[scheduler1, scheduler2], 2025-08-26T20:22:00.0778656Z ... milestones=[20], 2025-08-26T20:22:00.0778924Z ... ) 2025-08-26T20:22:00.0779166Z >>> for epoch in range(100): 2025-08-26T20:22:00.0779473Z >>> train(...) 2025-08-26T20:22:00.0779743Z >>> validate(...) 2025-08-26T20:22:00.0780016Z >>> scheduler.step() 2025-08-26T20:22:00.0780220Z 2025-08-26T20:22:00.0780493Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-08-26T20:22:00.0780872Z 2025-08-26T20:22:00.0781258Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0781626Z 2025-08-26T20:22:00.0803294Z msg = Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1233. 2025-08-26T20:22:00.0804236Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:00.0804774Z Reduce learning rate when a metric has stopped improving. 2025-08-26T20:22:00.0805076Z 2025-08-26T20:22:00.0805280Z Models often benefit from reducing the learning rate by a factor 2025-08-26T20:22:00.0805786Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-08-26T20:22:00.0806281Z quantity and if no improvement is seen for a 'patience' number 2025-08-26T20:22:00.0806699Z of epochs, the learning rate is reduced. 2025-08-26T20:22:00.0806937Z 2025-08-26T20:22:00.0807019Z Args: 2025-08-26T20:22:00.0807274Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:00.0807689Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-08-26T20:22:00.0808118Z be reduced when the quantity monitored has stopped 2025-08-26T20:22:00.0808752Z decreasing; in `max` mode it will be reduced when the 2025-08-26T20:22:00.0809227Z quantity monitored has stopped increasing. Default: 'min'. 2025-08-26T20:22:00.0809714Z factor (float): Factor by which the learning rate will be 2025-08-26T20:22:00.0810143Z reduced. new_lr = lr * factor. Default: 0.1. 2025-08-26T20:22:00.0810603Z patience (int): The number of allowed epochs with no improvement after 2025-08-26T20:22:00.0811091Z which the learning rate will be reduced. 2025-08-26T20:22:00.0811568Z For example, consider the case of having no patience (`patience = 0`). 2025-08-26T20:22:00.0812337Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-08-26T20:22:00.0813524Z In the second epoch, if the performance is worse than the baseline, 2025-08-26T20:22:00.0814018Z we have what is considered an intolerable epoch. 2025-08-26T20:22:00.0814613Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-08-26T20:22:00.0815171Z the learning rate is reduced at the end of this epoch. 2025-08-26T20:22:00.0815776Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-08-26T20:22:00.0816537Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-08-26T20:22:00.0817079Z the learning rate is not adjusted. 2025-08-26T20:22:00.0817408Z Default: 10. 2025-08-26T20:22:00.0817764Z threshold (float): Threshold for measuring the new optimum, 2025-08-26T20:22:00.0818231Z to only focus on significant changes. Default: 1e-4. 2025-08-26T20:22:00.0818689Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-08-26T20:22:00.0819131Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-08-26T20:22:00.0819561Z mode or best * ( 1 - threshold ) in `min` mode. 2025-08-26T20:22:00.0819977Z In `abs` mode, dynamic_threshold = best + threshold in 2025-08-26T20:22:00.0820560Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-08-26T20:22:00.0821060Z cooldown (int): Number of epochs to wait before resuming 2025-08-26T20:22:00.0821590Z normal operation after lr has been reduced. Default: 0. 2025-08-26T20:22:00.0822170Z min_lr (float or list): A scalar or a list of scalars. A 2025-08-26T20:22:00.0822611Z lower bound on the learning rate of all param groups 2025-08-26T20:22:00.0823009Z or each group respectively. Default: 0. 2025-08-26T20:22:00.0823502Z eps (float): Minimal decay applied to lr. If the difference 2025-08-26T20:22:00.0823984Z between new and old lr is smaller than eps, the update is 2025-08-26T20:22:00.0824386Z ignored. Default: 1e-8. 2025-08-26T20:22:00.0824590Z 2025-08-26T20:22:00.0824688Z Example: 2025-08-26T20:22:00.0824907Z >>> # xdoctest: +SKIP 2025-08-26T20:22:00.0825318Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-08-26T20:22:00.0825825Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-08-26T20:22:00.0826198Z >>> for epoch in range(10): 2025-08-26T20:22:00.0826482Z >>> train(...) 2025-08-26T20:22:00.0826761Z >>> val_loss = validate(...) 2025-08-26T20:22:00.0827126Z >>> # Note that step should be called after validate() 2025-08-26T20:22:00.0827503Z >>> scheduler.step(val_loss) 2025-08-26T20:22:00.0827718Z 2025-08-26T20:22:00.0827922Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-08-26T20:22:00.0828308Z 2025-08-26T20:22:00.0828837Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-08-26T20:22:00.0829469Z 2025-08-26T20:22:00.0829585Z scheduler.step(val_loss) 2025-08-26T20:22:00.0829856Z ^ 2025-08-26T20:22:00.0830524Z msg = Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1430. 2025-08-26T20:22:00.0831559Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0832331Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-08-26T20:22:00.0832782Z 2025-08-26T20:22:00.0833065Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-08-26T20:22:00.0833731Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-08-26T20:22:00.0834343Z The distance between the two boundaries can be scaled on a per-iteration 2025-08-26T20:22:00.0834877Z or per-cycle basis. 2025-08-26T20:22:00.0835043Z 2025-08-26T20:22:00.0835286Z Cyclical learning rate policy changes the learning rate after every batch. 2025-08-26T20:22:00.0835859Z `step` should be called after a batch has been used for training. 2025-08-26T20:22:00.0836180Z 2025-08-26T20:22:00.0836387Z This class has three built-in policies, as put forth in the paper: 2025-08-26T20:22:00.0836707Z 2025-08-26T20:22:00.0836924Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-08-26T20:22:00.0837542Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-08-26T20:22:00.0838270Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-08-26T20:22:00.0838821Z at each cycle iteration. 2025-08-26T20:22:00.0839017Z 2025-08-26T20:22:00.0839254Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-08-26T20:22:00.0839606Z 2025-08-26T20:22:00.0839707Z Args: 2025-08-26T20:22:00.0839965Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:00.0840387Z base_lr (float or list): Initial learning rate which is the 2025-08-26T20:22:00.0840853Z lower boundary in the cycle for each parameter group. 2025-08-26T20:22:00.0841340Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-08-26T20:22:00.0841795Z for each parameter group. Functionally, 2025-08-26T20:22:00.0842190Z it defines the cycle amplitude (max_lr - base_lr). 2025-08-26T20:22:00.0842643Z The lr at any cycle is the sum of base_lr 2025-08-26T20:22:00.0843247Z and some scaling of the amplitude; therefore 2025-08-26T20:22:00.0843840Z max_lr may not actually be reached depending on 2025-08-26T20:22:00.0844458Z scaling function. 2025-08-26T20:22:00.0845025Z step_size_up (int): Number of training iterations in the 2025-08-26T20:22:00.0845461Z increasing half of a cycle. Default: 2000 2025-08-26T20:22:00.0845891Z step_size_down (int): Number of training iterations in the 2025-08-26T20:22:00.0846368Z decreasing half of a cycle. If step_size_down is None, 2025-08-26T20:22:00.0846779Z it is set to step_size_up. Default: None 2025-08-26T20:22:00.0847190Z mode (str): One of {triangular, triangular2, exp_range}. 2025-08-26T20:22:00.0847624Z Values correspond to policies detailed above. 2025-08-26T20:22:00.0848036Z If scale_fn is not None, this argument is ignored. 2025-08-26T20:22:00.0848398Z Default: 'triangular' 2025-08-26T20:22:00.0848769Z gamma (float): Constant in 'exp_range' scaling function: 2025-08-26T20:22:00.0849161Z gamma**(cycle iterations) 2025-08-26T20:22:00.0849465Z Default: 1.0 2025-08-26T20:22:00.0849810Z scale_fn (function): Custom scaling policy defined by a single 2025-08-26T20:22:00.0850248Z argument lambda function, where 2025-08-26T20:22:00.0850590Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-08-26T20:22:00.0851104Z If specified, then 'mode' is ignored. 2025-08-26T20:22:00.0851420Z Default: None 2025-08-26T20:22:00.0851720Z scale_mode (str): {'cycle', 'iterations'}. 2025-08-26T20:22:00.0852095Z Defines whether scale_fn is evaluated on 2025-08-26T20:22:00.0852475Z cycle number or cycle iterations (training 2025-08-26T20:22:00.0852844Z iterations since start of cycle). 2025-08-26T20:22:00.0853162Z Default: 'cycle' 2025-08-26T20:22:00.0853539Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-08-26T20:22:00.0854044Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-08-26T20:22:00.0854444Z Default: True 2025-08-26T20:22:00.0854821Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-08-26T20:22:00.0855424Z for each parameter group. Note that momentum is cycled inversely 2025-08-26T20:22:00.0855919Z to learning rate; at the peak of a cycle, momentum is 2025-08-26T20:22:00.0856342Z 'base_momentum' and learning rate is 'max_lr'. 2025-08-26T20:22:00.0856680Z Default: 0.8 2025-08-26T20:22:00.0857060Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-08-26T20:22:00.0857525Z for each parameter group. Functionally, 2025-08-26T20:22:00.0857968Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-08-26T20:22:00.0858451Z The momentum at any cycle is the difference of max_momentum 2025-08-26T20:22:00.0858895Z and some scaling of the amplitude; therefore 2025-08-26T20:22:00.0859321Z base_momentum may not actually be reached depending on 2025-08-26T20:22:00.0859790Z scaling function. Note that momentum is cycled inversely 2025-08-26T20:22:00.0860302Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-08-26T20:22:00.0860824Z and learning rate is 'base_lr' 2025-08-26T20:22:00.0861154Z Default: 0.9 2025-08-26T20:22:00.0861551Z last_epoch (int): The index of the last batch. This parameter is used when 2025-08-26T20:22:00.0862120Z resuming a training job. Since `step()` should be invoked after each 2025-08-26T20:22:00.0862662Z batch instead of after each epoch, this number represents the total 2025-08-26T20:22:00.0863222Z number of *batches* computed, not the total number of epochs computed. 2025-08-26T20:22:00.0863761Z When last_epoch=-1, the schedule is started from the beginning. 2025-08-26T20:22:00.0864164Z Default: -1 2025-08-26T20:22:00.0864321Z 2025-08-26T20:22:00.0864421Z Example: 2025-08-26T20:22:00.0864642Z >>> # xdoctest: +SKIP 2025-08-26T20:22:00.0865056Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-08-26T20:22:00.0865566Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-08-26T20:22:00.0865931Z ... optimizer, 2025-08-26T20:22:00.0866184Z ... base_lr=0.01, 2025-08-26T20:22:00.0866454Z ... max_lr=0.1, 2025-08-26T20:22:00.0866728Z ... step_size_up=10, 2025-08-26T20:22:00.0867004Z ... ) 2025-08-26T20:22:00.0867271Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-08-26T20:22:00.0867638Z >>> for epoch in range(10): 2025-08-26T20:22:00.0867949Z >>> for batch in data_loader: 2025-08-26T20:22:00.0868267Z >>> train_batch(...) 2025-08-26T20:22:00.0868558Z >>> scheduler.step() 2025-08-26T20:22:00.0868771Z 2025-08-26T20:22:00.0868933Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-08-26T20:22:00.0869208Z 2025-08-26T20:22:00.0869518Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-08-26T20:22:00.0870105Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-08-26T20:22:00.0870462Z 2025-08-26T20:22:00.0870894Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0871272Z 2025-08-26T20:22:00.0871901Z msg = Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1722. 2025-08-26T20:22:00.0872902Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0873537Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-08-26T20:22:00.0873902Z 2025-08-26T20:22:00.0874094Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-08-26T20:22:00.0874616Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-08-26T20:22:00.0875106Z of epochs between two warm restarts in SGDR: 2025-08-26T20:22:00.0875363Z 2025-08-26T20:22:00.0875452Z .. math:: 2025-08-26T20:22:00.0875825Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-08-26T20:22:00.0876261Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-08-26T20:22:00.0876522Z 2025-08-26T20:22:00.0876688Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-08-26T20:22:00.0877156Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-08-26T20:22:00.0877457Z 2025-08-26T20:22:00.0877573Z It has been proposed in 2025-08-26T20:22:00.0877928Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-08-26T20:22:00.0878211Z 2025-08-26T20:22:00.0878295Z Args: 2025-08-26T20:22:00.0878554Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:00.0878963Z T_0 (int): Number of iterations until the first restart. 2025-08-26T20:22:00.0879521Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-08-26T20:22:00.0880117Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-08-26T20:22:00.0880640Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-08-26T20:22:00.0880987Z 2025-08-26T20:22:00.0881160Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-08-26T20:22:00.0881571Z https://arxiv.org/abs/1608.03983 2025-08-26T20:22:00.0881793Z 2025-08-26T20:22:00.0881894Z Example: 2025-08-26T20:22:00.0882115Z >>> # xdoctest: +SKIP 2025-08-26T20:22:00.0882487Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-08-26T20:22:00.0883025Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-08-26T20:22:00.0883486Z ... optimizer, T_0=20 2025-08-26T20:22:00.0883764Z ... ) 2025-08-26T20:22:00.0884005Z >>> for epoch in range(100): 2025-08-26T20:22:00.0884310Z >>> train(...) 2025-08-26T20:22:00.0884581Z >>> validate(...) 2025-08-26T20:22:00.0884860Z >>> scheduler.step() 2025-08-26T20:22:00.0885067Z 2025-08-26T20:22:00.0885305Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-08-26T20:22:00.0885746Z 2025-08-26T20:22:00.0886116Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0886481Z 2025-08-26T20:22:00.0887047Z msg = Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1872. 2025-08-26T20:22:00.0887951Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0888623Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-08-26T20:22:00.0889053Z 2025-08-26T20:22:00.0889347Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-08-26T20:22:00.0890056Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-08-26T20:22:00.0890591Z lower than the initial learning rate. 2025-08-26T20:22:00.0891040Z This policy was initially described in the paper `Super-Convergence: 2025-08-26T20:22:00.0891669Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-08-26T20:22:00.0892217Z 2025-08-26T20:22:00.0892460Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-08-26T20:22:00.0893023Z `step` should be called after a batch has been used for training. 2025-08-26T20:22:00.0893331Z 2025-08-26T20:22:00.0893453Z This scheduler is not chainable. 2025-08-26T20:22:00.0893659Z 2025-08-26T20:22:00.0893913Z Note also that the total number of steps in the cycle can be determined in one 2025-08-26T20:22:00.0894398Z of two ways (listed in order of precedence): 2025-08-26T20:22:00.0894651Z 2025-08-26T20:22:00.0894800Z #. A value for total_steps is explicitly provided. 2025-08-26T20:22:00.0895250Z #. A number of epochs (epochs) and a number of steps per epoch 2025-08-26T20:22:00.0895775Z (steps_per_epoch) are provided. 2025-08-26T20:22:00.0896148Z In this case, the number of total steps is inferred by 2025-08-26T20:22:00.0896556Z total_steps = epochs * steps_per_epoch 2025-08-26T20:22:00.0896795Z 2025-08-26T20:22:00.0897032Z You must either provide a value for total_steps or provide a value for both 2025-08-26T20:22:00.0897501Z epochs and steps_per_epoch. 2025-08-26T20:22:00.0897690Z 2025-08-26T20:22:00.0898008Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-08-26T20:22:00.0898714Z claims that "unpublished work has shown even better results by using only two phases". To 2025-08-26T20:22:00.0899370Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-08-26T20:22:00.0899737Z 2025-08-26T20:22:00.0899818Z Args: 2025-08-26T20:22:00.0900076Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:00.0900611Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-08-26T20:22:00.0901057Z for each parameter group. 2025-08-26T20:22:00.0901488Z total_steps (int): The total number of steps in the cycle. Note that 2025-08-26T20:22:00.0902035Z if a value is not provided here, then it must be inferred by providing 2025-08-26T20:22:00.0902504Z a value for epochs and steps_per_epoch. 2025-08-26T20:22:00.0902834Z Default: None 2025-08-26T20:22:00.0903211Z epochs (int): The number of epochs to train for. This is used along 2025-08-26T20:22:00.0903774Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-08-26T20:22:00.0904274Z if a value for total_steps is not provided. 2025-08-26T20:22:00.0904607Z Default: None 2025-08-26T20:22:00.0905004Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-08-26T20:22:00.0905590Z used along with epochs in order to infer the total number of steps in the 2025-08-26T20:22:00.0906092Z cycle if a value for total_steps is not provided. 2025-08-26T20:22:00.0906457Z Default: None 2025-08-26T20:22:00.0906842Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-08-26T20:22:00.0907309Z increasing the learning rate. 2025-08-26T20:22:00.0907626Z Default: 0.3 2025-08-26T20:22:00.0907914Z anneal_strategy (str): {'cos', 'linear'} 2025-08-26T20:22:00.0908385Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-08-26T20:22:00.0908855Z linear annealing. 2025-08-26T20:22:00.0909139Z Default: 'cos' 2025-08-26T20:22:00.0909509Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-08-26T20:22:00.0909998Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-08-26T20:22:00.0910397Z Default: True 2025-08-26T20:22:00.0910790Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-08-26T20:22:00.0911336Z for each parameter group. Note that momentum is cycled inversely 2025-08-26T20:22:00.0911922Z to learning rate; at the peak of a cycle, momentum is 2025-08-26T20:22:00.0912332Z 'base_momentum' and learning rate is 'max_lr'. 2025-08-26T20:22:00.0912684Z Default: 0.85 2025-08-26T20:22:00.0913069Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-08-26T20:22:00.0913537Z for each parameter group. Functionally, 2025-08-26T20:22:00.0913968Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-08-26T20:22:00.0914408Z Note that momentum is cycled inversely 2025-08-26T20:22:00.0914859Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-08-26T20:22:00.0915306Z and learning rate is 'base_lr' 2025-08-26T20:22:00.0915610Z Default: 0.95 2025-08-26T20:22:00.0916027Z div_factor (float): Determines the initial learning rate via 2025-08-26T20:22:00.0916448Z initial_lr = max_lr/div_factor 2025-08-26T20:22:00.0916771Z Default: 25 2025-08-26T20:22:00.0917130Z final_div_factor (float): Determines the minimum learning rate via 2025-08-26T20:22:00.0917582Z min_lr = initial_lr/final_div_factor 2025-08-26T20:22:00.0917911Z Default: 1e4 2025-08-26T20:22:00.0918324Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-08-26T20:22:00.0918945Z learning rate according to 'final_div_factor' instead of modifying the second 2025-08-26T20:22:00.0919550Z phase (the first two phases will be symmetrical about the step indicated by 2025-08-26T20:22:00.0920012Z 'pct_start'). 2025-08-26T20:22:00.0920403Z last_epoch (int): The index of the last batch. This parameter is used when 2025-08-26T20:22:00.0920971Z resuming a training job. Since `step()` should be invoked after each 2025-08-26T20:22:00.0921513Z batch instead of after each epoch, this number represents the total 2025-08-26T20:22:00.0922077Z number of *batches* computed, not the total number of epochs computed. 2025-08-26T20:22:00.0922614Z When last_epoch=-1, the schedule is started from the beginning. 2025-08-26T20:22:00.0923021Z Default: -1 2025-08-26T20:22:00.0923179Z 2025-08-26T20:22:00.0923280Z Example: 2025-08-26T20:22:00.0923504Z >>> # xdoctest: +SKIP 2025-08-26T20:22:00.0923842Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-08-26T20:22:00.0924494Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-08-26T20:22:00.0925015Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-08-26T20:22:00.0925496Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-08-26T20:22:00.0925926Z ... ) 2025-08-26T20:22:00.0926174Z >>> for epoch in range(10): 2025-08-26T20:22:00.0926495Z >>> for batch in data_loader: 2025-08-26T20:22:00.0926809Z >>> train_batch(...) 2025-08-26T20:22:00.0927121Z >>> optimizer.step() 2025-08-26T20:22:00.0927429Z >>> scheduler.step() 2025-08-26T20:22:00.0927627Z 2025-08-26T20:22:00.0927812Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-08-26T20:22:00.0928086Z 2025-08-26T20:22:00.0928393Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-08-26T20:22:00.0928915Z https://arxiv.org/abs/1708.07120 2025-08-26T20:22:00.0929226Z 2025-08-26T20:22:00.0929601Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0929967Z 2025-08-26T20:22:00.0976624Z msg = Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/optimizer.py line=867. 2025-08-26T20:22:00.0977597Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.0978190Z Load the optimizer state. 2025-08-26T20:22:00.0978381Z 2025-08-26T20:22:00.0978466Z Args: 2025-08-26T20:22:00.0978796Z state_dict (dict): optimizer state. Should be an object returned 2025-08-26T20:22:00.0979238Z from a call to :meth:`state_dict`. 2025-08-26T20:22:00.0979467Z 2025-08-26T20:22:00.0979562Z .. warning:: 2025-08-26T20:22:00.0980051Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-08-26T20:22:00.0980787Z as calling it beforehand will overwrite the loaded learning rates. 2025-08-26T20:22:00.0981111Z 2025-08-26T20:22:00.0981209Z .. note:: 2025-08-26T20:22:00.0981638Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-08-26T20:22:00.0982279Z in :meth:`state_dict`) will not affect the loading process. 2025-08-26T20:22:00.0982905Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-08-26T20:22:00.0983515Z differ from those initialized in the optimizer), 2025-08-26T20:22:00.0984100Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-08-26T20:22:00.0984630Z accordingly. 2025-08-26T20:22:00.0985084Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-08-26T20:22:00.0985833Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-08-26T20:22:00.0986435Z the optimizer ``param_names`` will remain unchanged. 2025-08-26T20:22:00.0986711Z 2025-08-26T20:22:00.0986810Z Example: 2025-08-26T20:22:00.0987052Z >>> # xdoctest: +SKIP 2025-08-26T20:22:00.0987354Z >>> model = torch.nn.Linear(10, 10) 2025-08-26T20:22:00.0987760Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-08-26T20:22:00.0988215Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-08-26T20:22:00.0988583Z ... optim, 2025-08-26T20:22:00.0988843Z ... start_factor=0.1, 2025-08-26T20:22:00.0989149Z ... end_factor=1, 2025-08-26T20:22:00.0989441Z ... total_iters=20, 2025-08-26T20:22:00.0989728Z ... ) 2025-08-26T20:22:00.0990047Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-08-26T20:22:00.0990449Z ... optim, 2025-08-26T20:22:00.0990711Z ... T_max=80, 2025-08-26T20:22:00.0990985Z ... eta_min=3e-5, 2025-08-26T20:22:00.0991248Z ... ) 2025-08-26T20:22:00.0991531Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-08-26T20:22:00.0992061Z ... optim, 2025-08-26T20:22:00.0992374Z ... schedulers=[scheduler1, scheduler2], 2025-08-26T20:22:00.0992711Z ... milestones=[20], 2025-08-26T20:22:00.0993009Z ... ) 2025-08-26T20:22:00.0993301Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-08-26T20:22:00.0993768Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-08-26T20:22:00.0994249Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-08-26T20:22:00.0994540Z 2025-08-26T20:22:00.0994622Z 2025-08-26T20:22:00.0994993Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.0995357Z 2025-08-26T20:22:00.1143217Z msg = Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=120. 2025-08-26T20:22:00.1144382Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.1145130Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-08-26T20:22:00.1145610Z 2025-08-26T20:22:00.1145865Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-08-26T20:22:00.1146623Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-08-26T20:22:00.1147178Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-08-26T20:22:00.1147605Z (UAI 2018). 2025-08-26T20:22:00.1147738Z 2025-08-26T20:22:00.1147968Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-08-26T20:22:00.1148530Z but using exponential weights instead of equal weights across iterations. 2025-08-26T20:22:00.1148901Z 2025-08-26T20:22:00.1149140Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-08-26T20:22:00.1149722Z on the device :attr:`device` and allows to compute running averages of the 2025-08-26T20:22:00.1150178Z parameters of the :attr:`model`. 2025-08-26T20:22:00.1150388Z 2025-08-26T20:22:00.1150482Z Args: 2025-08-26T20:22:00.1151265Z model (torch.nn.Module): model to use with SWA/EMA 2025-08-26T20:22:00.1151780Z device (torch.device, optional): if provided, the averaged model will be 2025-08-26T20:22:00.1152345Z stored on the :attr:`device` 2025-08-26T20:22:00.1152907Z avg_fn (function, optional): the averaging function used to update 2025-08-26T20:22:00.1153642Z parameters; the function must take in the current value of the 2025-08-26T20:22:00.1154550Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-08-26T20:22:00.1155137Z parameter, and the number of models already averaged; if None, 2025-08-26T20:22:00.1155613Z an equally weighted average is used (default: None) 2025-08-26T20:22:00.1156117Z multi_avg_fn (function, optional): the averaging function used to update 2025-08-26T20:22:00.1156690Z parameters inplace; the function must take in the current values of the 2025-08-26T20:22:00.1157323Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-08-26T20:22:00.1157950Z parameters as a list, and the number of models already averaged; if None, 2025-08-26T20:22:00.1158461Z an equally weighted average is used (default: None) 2025-08-26T20:22:00.1158930Z use_buffers (bool): if ``True``, it will compute running averages for 2025-08-26T20:22:00.1159490Z both the parameters and the buffers of the model. (default: ``False``) 2025-08-26T20:22:00.1159845Z 2025-08-26T20:22:00.1159932Z Example: 2025-08-26T20:22:00.1160204Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:00.1160581Z >>> loader, optimizer, model, loss_fn = ... 2025-08-26T20:22:00.1160993Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-08-26T20:22:00.1161512Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-08-26T20:22:00.1161977Z >>> T_max=300) 2025-08-26T20:22:00.1162308Z >>> swa_start = 160 2025-08-26T20:22:00.1162622Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-08-26T20:22:00.1162982Z >>> for i in range(300): 2025-08-26T20:22:00.1163295Z >>> for input, target in loader: 2025-08-26T20:22:00.1163639Z >>> optimizer.zero_grad() 2025-08-26T20:22:00.1164050Z >>> loss_fn(model(input), target).backward() 2025-08-26T20:22:00.1164411Z >>> optimizer.step() 2025-08-26T20:22:00.1164720Z >>> if i > swa_start: 2025-08-26T20:22:00.1165051Z >>> swa_model.update_parameters(model) 2025-08-26T20:22:00.1165394Z >>> swa_scheduler.step() 2025-08-26T20:22:00.1165704Z >>> else: 2025-08-26T20:22:00.1166031Z >>> scheduler.step() 2025-08-26T20:22:00.1166328Z >>> 2025-08-26T20:22:00.1166600Z >>> # Update bn statistics for the swa_model at the end 2025-08-26T20:22:00.1167040Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-08-26T20:22:00.1167434Z 2025-08-26T20:22:00.1167731Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-08-26T20:22:00.1168353Z If no averaging function is provided, the default is to compute 2025-08-26T20:22:00.1168816Z equally-weighted average of the weights (SWA). 2025-08-26T20:22:00.1169071Z 2025-08-26T20:22:00.1169156Z Example: 2025-08-26T20:22:00.1169418Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:00.1169870Z >>> # Compute exponential moving averages of the weights and buffers 2025-08-26T20:22:00.1170368Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-08-26T20:22:00.1170870Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-08-26T20:22:00.1171205Z 2025-08-26T20:22:00.1171303Z .. note:: 2025-08-26T20:22:00.1171724Z When using SWA/EMA with models containing Batch Normalization you may 2025-08-26T20:22:00.1172276Z need to update the activation statistics for Batch Normalization. 2025-08-26T20:22:00.1172840Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-08-26T20:22:00.1173410Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-08-26T20:22:00.1174000Z statistics in a post-training step by passing data through the model. The 2025-08-26T20:22:00.1174604Z second does it during the parameter update phase by averaging all buffers. 2025-08-26T20:22:00.1175213Z Empirical evidence has shown that updating the statistics in normalization 2025-08-26T20:22:00.1175810Z layers increases accuracy, but you may wish to empirically test which 2025-08-26T20:22:00.1176302Z approach yields the best results in your problem. 2025-08-26T20:22:00.1176578Z 2025-08-26T20:22:00.1176665Z .. note:: 2025-08-26T20:22:00.1177056Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-08-26T20:22:00.1177427Z 2025-08-26T20:22:00.1177525Z .. note:: 2025-08-26T20:22:00.1177846Z When :meth:`update_parameters` is called for the first time (i.e. 2025-08-26T20:22:00.1178353Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-08-26T20:22:00.1178859Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-08-26T20:22:00.1179398Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-08-26T20:22:00.1179812Z to update the parameters. 2025-08-26T20:22:00.1180011Z 2025-08-26T20:22:00.1180234Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-08-26T20:22:00.1180808Z https://arxiv.org/abs/1803.05407 2025-08-26T20:22:00.1181280Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-08-26T20:22:00.1181733Z Average: 2025-08-26T20:22:00.1181986Z https://arxiv.org/abs/1806.05594 2025-08-26T20:22:00.1182413Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-08-26T20:22:00.1182851Z https://arxiv.org/abs/1904.11943 2025-08-26T20:22:00.1183299Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-08-26T20:22:00.1183730Z Generalizes Well: 2025-08-26T20:22:00.1184018Z https://arxiv.org/abs/2001.02312 2025-08-26T20:22:00.1184335Z .. _Polyak averaging: 2025-08-26T20:22:00.1184674Z https://paperswithcode.com/method/polyak-averaging 2025-08-26T20:22:00.1185026Z 2025-08-26T20:22:00.1185390Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.1185766Z 2025-08-26T20:22:00.1186259Z msg = Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=375. 2025-08-26T20:22:00.1187102Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.1187700Z Anneals the learning rate in each parameter group to a fixed value. 2025-08-26T20:22:00.1188098Z 2025-08-26T20:22:00.1188342Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-08-26T20:22:00.1188896Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-08-26T20:22:00.1189234Z 2025-08-26T20:22:00.1189318Z Args: 2025-08-26T20:22:00.1189618Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-08-26T20:22:00.1190113Z swa_lrs (float or list): the learning rate value for all param groups 2025-08-26T20:22:00.1190569Z together or separately for each group. 2025-08-26T20:22:00.1191003Z annealing_epochs (int): number of epochs in the annealing phase 2025-08-26T20:22:00.1191428Z (default: 10) 2025-08-26T20:22:00.1191994Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-08-26T20:22:00.1192655Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-08-26T20:22:00.1193074Z (default: "cos") 2025-08-26T20:22:00.1193443Z last_epoch (int): the index of the last epoch (default: -1) 2025-08-26T20:22:00.1193752Z 2025-08-26T20:22:00.1193930Z The :class:`SWALR` scheduler can be used together with other 2025-08-26T20:22:00.1194445Z schedulers to switch to a constant learning rate late in the training 2025-08-26T20:22:00.1194873Z as in the example below. 2025-08-26T20:22:00.1195066Z 2025-08-26T20:22:00.1195152Z Example: 2025-08-26T20:22:00.1195419Z >>> # xdoctest: +SKIP("Undefined variables") 2025-08-26T20:22:00.1195782Z >>> loader, optimizer, model = ... 2025-08-26T20:22:00.1196120Z >>> lr_lambda = lambda epoch: 0.9 2025-08-26T20:22:00.1196552Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-08-26T20:22:00.1196999Z >>> lr_lambda=lr_lambda) 2025-08-26T20:22:00.1197385Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-08-26T20:22:00.1197853Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-08-26T20:22:00.1198240Z >>> swa_start = 160 2025-08-26T20:22:00.1198517Z >>> for i in range(300): 2025-08-26T20:22:00.1198823Z >>> for input, target in loader: 2025-08-26T20:22:00.1199158Z >>> optimizer.zero_grad() 2025-08-26T20:22:00.1199501Z >>> loss_fn(model(input), target).backward() 2025-08-26T20:22:00.1199909Z >>> optimizer.step() 2025-08-26T20:22:00.1200221Z >>> if i > swa_start: 2025-08-26T20:22:00.1200530Z >>> swa_scheduler.step() 2025-08-26T20:22:00.1200826Z >>> else: 2025-08-26T20:22:00.1201089Z >>> scheduler.step() 2025-08-26T20:22:00.1201303Z 2025-08-26T20:22:00.1201529Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-08-26T20:22:00.1202084Z https://arxiv.org/abs/1803.05407 2025-08-26T20:22:00.1213644Z 2025-08-26T20:22:00.1214115Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.1214510Z 2025-08-26T20:22:00.5927386Z msg = Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1331. 2025-08-26T20:22:00.5928762Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:00.5929287Z Asserts that ``actual`` and ``expected`` are close. 2025-08-26T20:22:00.5929562Z 2025-08-26T20:22:00.5929932Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-08-26T20:22:00.5930426Z 2025-08-26T20:22:00.5930535Z .. math:: 2025-08-26T20:22:00.5930677Z 2025-08-26T20:22:00.5931049Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-08-26T20:22:00.5931537Z 2025-08-26T20:22:00.5931898Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-08-26T20:22:00.5932799Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-08-26T20:22:00.5933117Z 2025-08-26T20:22:00.5933328Z In addition, they are only considered close if they have the same 2025-08-26T20:22:00.5933639Z 2025-08-26T20:22:00.5933873Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-08-26T20:22:00.5934300Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-08-26T20:22:00.5934686Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-08-26T20:22:00.5935068Z - stride (if ``check_stride`` is ``True``). 2025-08-26T20:22:00.5935302Z 2025-08-26T20:22:00.5935606Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-08-26T20:22:00.5936020Z 2025-08-26T20:22:00.5936386Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-08-26T20:22:00.5937326Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-08-26T20:22:00.5938055Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-08-26T20:22:00.5938787Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-08-26T20:22:00.5939307Z 2025-08-26T20:22:00.5939599Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-08-26T20:22:00.5940421Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-08-26T20:22:00.5940989Z definition above. 2025-08-26T20:22:00.5941159Z 2025-08-26T20:22:00.5941465Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-08-26T20:22:00.5942299Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-08-26T20:22:00.5943158Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-08-26T20:22:00.5944023Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-08-26T20:22:00.5944743Z their elements are considered close according to the above definition. 2025-08-26T20:22:00.5945101Z 2025-08-26T20:22:00.5945191Z .. note:: 2025-08-26T20:22:00.5945333Z 2025-08-26T20:22:00.5945664Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-08-26T20:22:00.5946443Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-08-26T20:22:00.5947159Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-08-26T20:22:00.5947553Z 2025-08-26T20:22:00.5947636Z Args: 2025-08-26T20:22:00.5947912Z actual (Any): Actual input. 2025-08-26T20:22:00.5948234Z expected (Any): Expected input. 2025-08-26T20:22:00.5948821Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-08-26T20:22:00.5949445Z are allowed. Otherwise type equality is required. 2025-08-26T20:22:00.5950087Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-08-26T20:22:00.5950837Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-08-26T20:22:00.5951583Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-08-26T20:22:00.5952325Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-08-26T20:22:00.5952950Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-08-26T20:22:00.5953608Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-08-26T20:22:00.5954336Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-08-26T20:22:00.5954940Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-08-26T20:22:00.5955651Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-08-26T20:22:00.5956453Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-08-26T20:22:00.5957084Z :func:`torch.promote_types`) before being compared. 2025-08-26T20:22:00.5957721Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-08-26T20:22:00.5958642Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-08-26T20:22:00.5959198Z compared. 2025-08-26T20:22:00.5959711Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-08-26T20:22:00.5960548Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-08-26T20:22:00.5961400Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-08-26T20:22:00.5961991Z should return the new message. 2025-08-26T20:22:00.5962212Z 2025-08-26T20:22:00.5962309Z Raises: 2025-08-26T20:22:00.5962661Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-08-26T20:22:00.5963178Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-08-26T20:22:00.5963787Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-08-26T20:22:00.5964603Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-08-26T20:22:00.5965196Z different types. 2025-08-26T20:22:00.5965735Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-08-26T20:22:00.5966588Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-08-26T20:22:00.5967396Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-08-26T20:22:00.5968133Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-08-26T20:22:00.5968669Z :attr:`~torch.Tensor.layout`. 2025-08-26T20:22:00.5969107Z AssertionError: If only one of corresponding tensors is quantized. 2025-08-26T20:22:00.5969835Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-08-26T20:22:00.5970636Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-08-26T20:22:00.5971172Z :attr:`~torch.Tensor.device`. 2025-08-26T20:22:00.5971721Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-08-26T20:22:00.5972541Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-08-26T20:22:00.5973389Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-08-26T20:22:00.5973867Z 2025-08-26T20:22:00.5974243Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-08-26T20:22:00.5974873Z ``dtype``'s, the maximum of both tolerances is used. 2025-08-26T20:22:00.5975133Z 2025-08-26T20:22:00.5975267Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5975642Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-08-26T20:22:00.5976050Z +===========================+============+==========+ 2025-08-26T20:22:00.5976417Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-08-26T20:22:00.5976793Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5977154Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-08-26T20:22:00.5977527Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5977906Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:00.5978281Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5978642Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-08-26T20:22:00.5979017Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5979397Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-08-26T20:22:00.5979825Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5980195Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:00.5980663Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5981045Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-08-26T20:22:00.5981426Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5981802Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:00.5982160Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5982544Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:00.5982919Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5983294Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:00.5983652Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5984068Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:00.5984447Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5984817Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:00.5985180Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5985539Z | other | ``0.0`` | ``0.0`` | 2025-08-26T20:22:00.5985898Z +---------------------------+------------+----------+ 2025-08-26T20:22:00.5986131Z 2025-08-26T20:22:00.5986235Z .. note:: 2025-08-26T20:22:00.5986365Z 2025-08-26T20:22:00.5986760Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-08-26T20:22:00.5987604Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-08-26T20:22:00.5988333Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-08-26T20:22:00.5988717Z 2025-08-26T20:22:00.5988816Z >>> import functools 2025-08-26T20:22:00.5989260Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-08-26T20:22:00.5989750Z >>> assert_equal(1e-9, 1e-10) 2025-08-26T20:22:00.5990082Z Traceback (most recent call last): 2025-08-26T20:22:00.5990393Z ... 2025-08-26T20:22:00.5990644Z AssertionError: Scalars are not equal! 2025-08-26T20:22:00.5990975Z 2025-08-26T20:22:00.5991224Z Expected 1e-10 but got 1e-09. 2025-08-26T20:22:00.5991570Z Absolute difference: 9.000000000000001e-10 2025-08-26T20:22:00.5992147Z Relative difference: 9.0 2025-08-26T20:22:00.5992343Z 2025-08-26T20:22:00.5992444Z Examples: 2025-08-26T20:22:00.5992679Z >>> # tensor to tensor comparison 2025-08-26T20:22:00.5993040Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-08-26T20:22:00.5993420Z >>> actual = torch.acos(torch.cos(expected)) 2025-08-26T20:22:00.5993815Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:00.5994075Z 2025-08-26T20:22:00.5994186Z >>> # scalar to scalar comparison 2025-08-26T20:22:00.5994672Z >>> import math 2025-08-26T20:22:00.5994943Z >>> expected = math.sqrt(2.0) 2025-08-26T20:22:00.5995260Z >>> actual = 2.0 / math.sqrt(2.0) 2025-08-26T20:22:00.5995611Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:00.5995879Z 2025-08-26T20:22:00.5996007Z >>> # numpy array to numpy array comparison 2025-08-26T20:22:00.5996350Z >>> import numpy as np 2025-08-26T20:22:00.5996658Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-08-26T20:22:00.5997024Z >>> actual = np.arccos(np.cos(expected)) 2025-08-26T20:22:00.5997394Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:00.5997661Z 2025-08-26T20:22:00.5997778Z >>> # sequence to sequence comparison 2025-08-26T20:22:00.5998111Z >>> import numpy as np 2025-08-26T20:22:00.5998628Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-08-26T20:22:00.5999123Z >>> # length and their elements have to match. 2025-08-26T20:22:00.5999526Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-08-26T20:22:00.5999909Z >>> actual = tuple(expected) 2025-08-26T20:22:00.6000269Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:00.6000521Z 2025-08-26T20:22:00.6000652Z >>> # mapping to mapping comparison 2025-08-26T20:22:00.6000996Z >>> from collections import OrderedDict 2025-08-26T20:22:00.6001340Z >>> import numpy as np 2025-08-26T20:22:00.6001633Z >>> foo = torch.tensor(1.0) 2025-08-26T20:22:00.6001924Z >>> bar = 2.0 2025-08-26T20:22:00.6002163Z >>> baz = np.array(3.0) 2025-08-26T20:22:00.6002600Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-08-26T20:22:00.6003180Z >>> # have to have the same set of keys and their elements have to match. 2025-08-26T20:22:00.6003706Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-08-26T20:22:00.6004157Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-08-26T20:22:00.6004551Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:00.6004817Z 2025-08-26T20:22:00.6004941Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-08-26T20:22:00.6005290Z >>> actual = expected.clone() 2025-08-26T20:22:00.6005660Z >>> # By default, directly related instances can be compared 2025-08-26T20:22:00.6006161Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-08-26T20:22:00.6006688Z >>> # This check can be made more strict with allow_subclasses=False 2025-08-26T20:22:00.6007113Z >>> torch.testing.assert_close( 2025-08-26T20:22:00.6007533Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-08-26T20:22:00.6008079Z ... ) 2025-08-26T20:22:00.6008337Z Traceback (most recent call last): 2025-08-26T20:22:00.6008648Z ... 2025-08-26T20:22:00.6008984Z TypeError: No comparison pair was able to handle inputs of type 2025-08-26T20:22:00.6009519Z and . 2025-08-26T20:22:00.6010085Z >>> # If the inputs are not directly related, they are never considered close 2025-08-26T20:22:00.6010611Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-08-26T20:22:00.6011018Z Traceback (most recent call last): 2025-08-26T20:22:00.6011319Z ... 2025-08-26T20:22:00.6011737Z TypeError: No comparison pair was able to handle inputs of type 2025-08-26T20:22:00.6012290Z and . 2025-08-26T20:22:00.6012762Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-08-26T20:22:00.6013268Z >>> # their type if check_dtype=False. 2025-08-26T20:22:00.6013663Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-08-26T20:22:00.6013956Z 2025-08-26T20:22:00.6014137Z >>> # NaN != NaN by default. 2025-08-26T20:22:00.6014470Z >>> expected = torch.tensor(float("Nan")) 2025-08-26T20:22:00.6014825Z >>> actual = expected.clone() 2025-08-26T20:22:00.6015174Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:00.6015555Z Traceback (most recent call last): 2025-08-26T20:22:00.6015868Z ... 2025-08-26T20:22:00.6016119Z AssertionError: Scalars are not close! 2025-08-26T20:22:00.6016431Z 2025-08-26T20:22:00.6016684Z Expected nan but got nan. 2025-08-26T20:22:00.6017028Z Absolute difference: nan (up to 1e-05 allowed) 2025-08-26T20:22:00.6017431Z Relative difference: nan (up to 1.3e-06 allowed) 2025-08-26T20:22:00.6017877Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-08-26T20:22:00.6018202Z 2025-08-26T20:22:00.6018401Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-08-26T20:22:00.6018929Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-08-26T20:22:00.6019318Z >>> # The default error message can be overwritten. 2025-08-26T20:22:00.6019696Z >>> torch.testing.assert_close( 2025-08-26T20:22:00.6020087Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-08-26T20:22:00.6020546Z ... ) 2025-08-26T20:22:00.6020794Z Traceback (most recent call last): 2025-08-26T20:22:00.6021108Z ... 2025-08-26T20:22:00.6021375Z AssertionError: Argh, the tensors are not close! 2025-08-26T20:22:00.6021861Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-08-26T20:22:00.6022311Z >>> # extra information 2025-08-26T20:22:00.6022617Z >>> torch.testing.assert_close( 2025-08-26T20:22:00.6023024Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-08-26T20:22:00.6023430Z ... ) 2025-08-26T20:22:00.6023679Z Traceback (most recent call last): 2025-08-26T20:22:00.6023994Z ... 2025-08-26T20:22:00.6024213Z AssertionError: Header 2025-08-26T20:22:00.6024488Z 2025-08-26T20:22:00.6024740Z Tensor-likes are not close! 2025-08-26T20:22:00.6025035Z 2025-08-26T20:22:00.6025277Z Mismatched elements: 2 / 3 (66.7%) 2025-08-26T20:22:00.6025721Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-08-26T20:22:00.6026299Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-08-26T20:22:00.6026736Z 2025-08-26T20:22:00.6026953Z Footer 2025-08-26T20:22:00.6027175Z 2025-08-26T20:22:00.6027545Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:00.6027912Z 2025-08-26T20:22:01.9408828Z msg = Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=134. 2025-08-26T20:22:01.9409830Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:01.9410376Z Register a container-like type as pytree node. 2025-08-26T20:22:01.9410632Z 2025-08-26T20:22:01.9410722Z Args: 2025-08-26T20:22:01.9411048Z cls (type): A Python type to treat as an internal pytree node. 2025-08-26T20:22:01.9411628Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-08-26T20:22:01.9412274Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-08-26T20:22:01.9412937Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-08-26T20:22:01.9413456Z passed to the ``unflatten_fn``. 2025-08-26T20:22:01.9413965Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-08-26T20:22:01.9414631Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-08-26T20:22:01.9415431Z The function should return an instance of ``cls``. 2025-08-26T20:22:01.9415973Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-08-26T20:22:01.9416525Z qualified name used when serializing the tree spec. 2025-08-26T20:22:01.9417121Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-08-26T20:22:01.9417831Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-08-26T20:22:01.9418504Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-08-26T20:22:01.9419186Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-08-26T20:22:01.9419878Z how to convert the custom json dumpable representation of the context back to the 2025-08-26T20:22:01.9420720Z original context. This is used for json deserialization, which is being used in 2025-08-26T20:22:01.9421229Z :mod:`torch.export` right now. 2025-08-26T20:22:01.9421454Z 2025-08-26T20:22:01.9421580Z Example:: 2025-08-26T20:22:01.9421712Z 2025-08-26T20:22:01.9421816Z >>> # xdoctest: +SKIP 2025-08-26T20:22:01.9422146Z >>> # Registry a Python type with lambda functions 2025-08-26T20:22:01.9422520Z >>> register_pytree_node( 2025-08-26T20:22:01.9422806Z ... set, 2025-08-26T20:22:01.9423068Z ... lambda s: (sorted(s), None, None), 2025-08-26T20:22:01.9423432Z ... lambda children, _: set(children), 2025-08-26T20:22:01.9423752Z ... ) 2025-08-26T20:22:01.9423967Z 2025-08-26T20:22:01.9424323Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:01.9424701Z 2025-08-26T20:22:01.9964594Z msg = Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1218. 2025-08-26T20:22:01.9965615Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:01.9966006Z 2025-08-26T20:22:01.9966222Z Context passed to policy function during selective checkpointing. 2025-08-26T20:22:01.9966547Z 2025-08-26T20:22:01.9966800Z This class is used to pass relevant metadata to the policy function during 2025-08-26T20:22:01.9967411Z selective checkpointing. The metadata includes whether the current invocation 2025-08-26T20:22:01.9967950Z of the policy function is during recomputation or not. 2025-08-26T20:22:01.9968234Z 2025-08-26T20:22:01.9968324Z Example: 2025-08-26T20:22:01.9968555Z >>> # xdoctest: +SKIP(stub) 2025-08-26T20:22:01.9968832Z >>> 2025-08-26T20:22:01.9969065Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-08-26T20:22:01.9969471Z >>> print(ctx.is_recompute) 2025-08-26T20:22:01.9969761Z >>> 2025-08-26T20:22:01.9970218Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-08-26T20:22:01.9970712Z >>> 2025-08-26T20:22:01.9971130Z >>> out = torch.utils.checkpoint.checkpoint( 2025-08-26T20:22:01.9971814Z >>> fn, x, y, 2025-08-26T20:22:01.9972135Z >>> use_reentrant=False, 2025-08-26T20:22:01.9972420Z >>> context_fn=context_fn, 2025-08-26T20:22:01.9972709Z >>> ) 2025-08-26T20:22:01.9972839Z 2025-08-26T20:22:01.9973093Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:01.9973459Z 2025-08-26T20:22:01.9974122Z msg = Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1358. 2025-08-26T20:22:01.9975123Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:01.9975497Z 2025-08-26T20:22:01.9975745Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-08-26T20:22:01.9976105Z 2025-08-26T20:22:01.9976317Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-08-26T20:22:01.9977028Z operations are recomputed during the backward pass. 2025-08-26T20:22:01.9977312Z 2025-08-26T20:22:01.9977396Z Args: 2025-08-26T20:22:01.9977640Z policy_fn_or_list (Callable or List): 2025-08-26T20:22:01.9978046Z - If a policy function is provided, it should accept a 2025-08-26T20:22:01.9978553Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-08-26T20:22:01.9979124Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-08-26T20:22:01.9979685Z indicating whether the execution of the op should be recomputed or not. 2025-08-26T20:22:01.9980245Z - If a list of operations is provided, it is equivalent to a policy 2025-08-26T20:22:01.9980804Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-08-26T20:22:01.9981429Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-08-26T20:22:01.9981860Z operations. 2025-08-26T20:22:01.9982226Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-08-26T20:22:01.9982779Z raised if any tensors cached by selective activation checkpoint are 2025-08-26T20:22:01.9983310Z mutated in order to ensure correctness. If set to `True`, this check 2025-08-26T20:22:01.9983727Z is disabled. 2025-08-26T20:22:01.9983968Z Returns: 2025-08-26T20:22:01.9984204Z A tuple of two context managers. 2025-08-26T20:22:01.9984410Z 2025-08-26T20:22:01.9984493Z Example: 2025-08-26T20:22:01.9984719Z >>> # xdoctest: +REQUIRES(LINUX) 2025-08-26T20:22:01.9985024Z >>> import functools 2025-08-26T20:22:01.9985274Z >>> 2025-08-26T20:22:01.9985503Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-08-26T20:22:01.9985880Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-08-26T20:22:01.9986200Z >>> 2025-08-26T20:22:01.9986414Z >>> ops_to_save = [ 2025-08-26T20:22:01.9986685Z >>> torch.ops.aten.mm.default, 2025-08-26T20:22:01.9986987Z >>> ] 2025-08-26T20:22:01.9987197Z >>> 2025-08-26T20:22:01.9987439Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-08-26T20:22:01.9987768Z >>> if op in ops_to_save: 2025-08-26T20:22:01.9988089Z >>> return CheckpointPolicy.MUST_SAVE 2025-08-26T20:22:01.9988417Z >>> else: 2025-08-26T20:22:01.9988699Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-08-26T20:22:01.9989025Z >>> 2025-08-26T20:22:01.9989410Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-08-26T20:22:01.9989888Z >>> 2025-08-26T20:22:01.9990105Z >>> # or equivalently 2025-08-26T20:22:01.9990539Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-08-26T20:22:01.9991021Z >>> 2025-08-26T20:22:01.9991231Z >>> def fn(x, y): 2025-08-26T20:22:01.9991594Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-08-26T20:22:01.9992175Z >>> 2025-08-26T20:22:01.9992441Z >>> out = torch.utils.checkpoint.checkpoint( 2025-08-26T20:22:01.9992799Z >>> fn, x, y, 2025-08-26T20:22:01.9993063Z >>> use_reentrant=False, 2025-08-26T20:22:01.9993354Z >>> context_fn=context_fn, 2025-08-26T20:22:01.9993644Z >>> ) 2025-08-26T20:22:01.9993759Z 2025-08-26T20:22:01.9994022Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:01.9994387Z 2025-08-26T20:22:02.0217949Z msg = Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1159. 2025-08-26T20:22:02.0218901Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.0219279Z 2025-08-26T20:22:02.0219427Z Create a :class:`setuptools.Extension` for C++. 2025-08-26T20:22:02.0219691Z 2025-08-26T20:22:02.0219939Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-26T20:22:02.0220584Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-08-26T20:22:02.0221196Z 2025-08-26T20:22:02.0221403Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-26T20:22:02.0221875Z constructor. Full list arguments can be found at 2025-08-26T20:22:02.0222478Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-08-26T20:22:02.0222915Z 2025-08-26T20:22:02.0223023Z .. warning:: 2025-08-26T20:22:02.0223416Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-26T20:22:02.0224146Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-26T20:22:02.0224863Z the user's responsibility in their library to not use APIs from 2025-08-26T20:22:02.0225899Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-26T20:22:02.0226488Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-26T20:22:02.0227205Z example, to give access to custom ops from python, the library should 2025-08-26T20:22:02.0227679Z register the ops through the dispatcher. 2025-08-26T20:22:02.0227912Z 2025-08-26T20:22:02.0228153Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-26T20:22:02.0228689Z as a compile flag when py_limited_api is specified as an option for 2025-08-26T20:22:02.0229223Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-26T20:22:02.0229770Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-26T20:22:02.0230389Z safety, and sanity in order to encourage best practices. To target a 2025-08-26T20:22:02.0231186Z different version, set min_supported_cpython to the hexcode of the 2025-08-26T20:22:02.0231884Z CPython version of choice. 2025-08-26T20:22:02.0232083Z 2025-08-26T20:22:02.0232167Z Example: 2025-08-26T20:22:02.0232393Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.0232721Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:02.0233080Z >>> from setuptools import setup 2025-08-26T20:22:02.0233518Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-08-26T20:22:02.0233939Z >>> setup( 2025-08-26T20:22:02.0234172Z ... name='extension', 2025-08-26T20:22:02.0234437Z ... ext_modules=[ 2025-08-26T20:22:02.0234708Z ... CppExtension( 2025-08-26T20:22:02.0234995Z ... name='extension', 2025-08-26T20:22:02.0235321Z ... sources=['extension.cpp'], 2025-08-26T20:22:02.0235660Z ... extra_compile_args=['-g'], 2025-08-26T20:22:02.0236044Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-08-26T20:22:02.0236403Z ... ], 2025-08-26T20:22:02.0236629Z ... cmdclass={ 2025-08-26T20:22:02.0236887Z ... 'build_ext': BuildExtension 2025-08-26T20:22:02.0237195Z ... }) 2025-08-26T20:22:02.0237337Z 2025-08-26T20:22:02.0237592Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.0237966Z 2025-08-26T20:22:02.0238572Z msg = Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1229. 2025-08-26T20:22:02.0239495Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.0240021Z 2025-08-26T20:22:02.0240271Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-08-26T20:22:02.0240539Z 2025-08-26T20:22:02.0240796Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-26T20:22:02.0241801Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-08-26T20:22:02.0242491Z extension. This includes the CUDA include path, library path and runtime 2025-08-26T20:22:02.0242929Z library. 2025-08-26T20:22:02.0243047Z 2025-08-26T20:22:02.0243267Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-26T20:22:02.0243750Z constructor. Full list arguments can be found at 2025-08-26T20:22:02.0244321Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-08-26T20:22:02.0244902Z 2025-08-26T20:22:02.0245000Z .. warning:: 2025-08-26T20:22:02.0245366Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-26T20:22:02.0245927Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-26T20:22:02.0246449Z the user's responsibility in their library to not use APIs from 2025-08-26T20:22:02.0247001Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-26T20:22:02.0247574Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-26T20:22:02.0248127Z example, to give access to custom ops from python, the library should 2025-08-26T20:22:02.0248586Z register the ops through the dispatcher. 2025-08-26T20:22:02.0248815Z 2025-08-26T20:22:02.0249098Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-26T20:22:02.0249643Z as a compile flag when py_limited_api is specified as an option for 2025-08-26T20:22:02.0250265Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-26T20:22:02.0251035Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-26T20:22:02.0251788Z safety, and sanity in order to encourage best practices. To target a 2025-08-26T20:22:02.0252662Z different version, set min_supported_cpython to the hexcode of the 2025-08-26T20:22:02.0253103Z CPython version of choice. 2025-08-26T20:22:02.0253288Z 2025-08-26T20:22:02.0253387Z Example: 2025-08-26T20:22:02.0253609Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.0253915Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:02.0254285Z >>> from setuptools import setup 2025-08-26T20:22:02.0254722Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-08-26T20:22:02.0255152Z >>> setup( 2025-08-26T20:22:02.0255380Z ... name='cuda_extension', 2025-08-26T20:22:02.0255674Z ... ext_modules=[ 2025-08-26T20:22:02.0255946Z ... CUDAExtension( 2025-08-26T20:22:02.0256244Z ... name='cuda_extension', 2025-08-26T20:22:02.0256621Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-26T20:22:02.0257033Z ... extra_compile_args={'cxx': ['-g'], 2025-08-26T20:22:02.0257399Z ... 'nvcc': ['-O2']}, 2025-08-26T20:22:02.0257790Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-08-26T20:22:02.0258143Z ... ], 2025-08-26T20:22:02.0258368Z ... cmdclass={ 2025-08-26T20:22:02.0258644Z ... 'build_ext': BuildExtension 2025-08-26T20:22:02.0258958Z ... }) 2025-08-26T20:22:02.0259085Z 2025-08-26T20:22:02.0259191Z Compute capabilities: 2025-08-26T20:22:02.0259363Z 2025-08-26T20:22:02.0259665Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-08-26T20:22:02.0260460Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-08-26T20:22:02.0261176Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-08-26T20:22:02.0261890Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-08-26T20:22:02.0262587Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-08-26T20:22:02.0263116Z support (see below for details on PTX). 2025-08-26T20:22:02.0263351Z 2025-08-26T20:22:02.0263662Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-08-26T20:22:02.0264212Z CCs you want the extension to support: 2025-08-26T20:22:02.0264429Z 2025-08-26T20:22:02.0264633Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-08-26T20:22:02.0265171Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-08-26T20:22:02.0265540Z 2025-08-26T20:22:02.0265981Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-08-26T20:22:02.0266748Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-08-26T20:22:02.0267493Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-08-26T20:22:02.0268222Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-08-26T20:22:02.0268948Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-08-26T20:22:02.0269670Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-08-26T20:22:02.0270395Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-08-26T20:22:02.0271219Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-08-26T20:22:02.0271758Z "8.0 8.6" would be better. 2025-08-26T20:22:02.0271933Z 2025-08-26T20:22:02.0272233Z Note that while it's possible to include all supported archs, the more archs get included the 2025-08-26T20:22:02.0272947Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-08-26T20:22:02.0273368Z 2025-08-26T20:22:02.0273699Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-08-26T20:22:02.0274370Z To workaround the issue, move python binding logic to pure C++ file. 2025-08-26T20:22:02.0274692Z 2025-08-26T20:22:02.0274793Z Example use: 2025-08-26T20:22:02.0275014Z #include 2025-08-26T20:22:02.0275349Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-08-26T20:22:02.0275618Z 2025-08-26T20:22:02.0275702Z Instead of: 2025-08-26T20:22:02.0275943Z #include 2025-08-26T20:22:02.0276288Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-08-26T20:22:02.0276562Z 2025-08-26T20:22:02.0276835Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-08-26T20:22:02.0277738Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-08-26T20:22:02.0278355Z 2025-08-26T20:22:02.0278474Z Relocatable device code linking: 2025-08-26T20:22:02.0278669Z 2025-08-26T20:22:02.0278958Z If you want to reference device symbols across compilation units (across object files), 2025-08-26T20:22:02.0279609Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-08-26T20:22:02.0280349Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-08-26T20:22:02.0281189Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-08-26T20:22:02.0281956Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-08-26T20:22:02.0282577Z helps reduce the protentional perf degradation of `-rdc`. 2025-08-26T20:22:02.0283028Z Note that it needs to be used at both steps to be useful. 2025-08-26T20:22:02.0283315Z 2025-08-26T20:22:02.0283681Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-08-26T20:22:02.0284342Z There is also a case where `-dlink` is used without `-rdc`: 2025-08-26T20:22:02.0284893Z when an extension is linked against a static lib containing rdc-compiled objects 2025-08-26T20:22:02.0285479Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-08-26T20:22:02.0285801Z 2025-08-26T20:22:02.0286002Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-08-26T20:22:02.0286329Z 2025-08-26T20:22:02.0286419Z Example: 2025-08-26T20:22:02.0286643Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.0286973Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:02.0287319Z >>> CUDAExtension( 2025-08-26T20:22:02.0287653Z ... name='cuda_extension', 2025-08-26T20:22:02.0288025Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-26T20:22:02.0288395Z ... dlink=True, 2025-08-26T20:22:02.0288672Z ... dlink_libraries=["dlink_lib"], 2025-08-26T20:22:02.0289030Z ... extra_compile_args={'cxx': ['-g'], 2025-08-26T20:22:02.0289393Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-08-26T20:22:02.0289629Z 2025-08-26T20:22:02.0289890Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.0290256Z 2025-08-26T20:22:02.0290818Z msg = Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1420. 2025-08-26T20:22:02.0292042Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.0292562Z 2025-08-26T20:22:02.0292734Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-08-26T20:22:02.0293023Z 2025-08-26T20:22:02.0293264Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-26T20:22:02.0293825Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-08-26T20:22:02.0294266Z extension. 2025-08-26T20:22:02.0294486Z 2025-08-26T20:22:02.0294901Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-26T20:22:02.0295387Z constructor. 2025-08-26T20:22:02.0295528Z 2025-08-26T20:22:02.0295620Z .. warning:: 2025-08-26T20:22:02.0295982Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-26T20:22:02.0296529Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-26T20:22:02.0297057Z the user's responsibility in their library to not use APIs from 2025-08-26T20:22:02.0297601Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-26T20:22:02.0298173Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-26T20:22:02.0298724Z example, to give access to custom ops from python, the library should 2025-08-26T20:22:02.0299172Z register the ops through the dispatcher. 2025-08-26T20:22:02.0299416Z 2025-08-26T20:22:02.0299636Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-26T20:22:02.0300181Z as a compile flag when py_limited_api is specified as an option for 2025-08-26T20:22:02.0300783Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-26T20:22:02.0301327Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-26T20:22:02.0301863Z safety, and sanity in order to encourage best practices. To target a 2025-08-26T20:22:02.0302410Z different version, set min_supported_cpython to the hexcode of the 2025-08-26T20:22:02.0302848Z CPython version of choice. 2025-08-26T20:22:02.0303034Z 2025-08-26T20:22:02.0303133Z Example: 2025-08-26T20:22:02.0303345Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.0303666Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:02.0304153Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-08-26T20:22:02.0304582Z >>> setup( 2025-08-26T20:22:02.0304812Z ... name='xpu_extension', 2025-08-26T20:22:02.0305104Z ... ext_modules=[ 2025-08-26T20:22:02.0305369Z ... SyclExtension( 2025-08-26T20:22:02.0305656Z ... name='xpu_extension', 2025-08-26T20:22:02.0306035Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-08-26T20:22:02.0306505Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-08-26T20:22:02.0306898Z ... ], 2025-08-26T20:22:02.0307121Z ... cmdclass={ 2025-08-26T20:22:02.0307382Z ... 'build_ext': BuildExtension 2025-08-26T20:22:02.0307694Z ... }) 2025-08-26T20:22:02.0307833Z 2025-08-26T20:22:02.0308136Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-08-26T20:22:02.0308960Z building process of the extension. If down the road a new card is installed the 2025-08-26T20:22:02.0309591Z extension may need to be recompiled. You can override the default behavior using 2025-08-26T20:22:02.0310258Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-08-26T20:22:02.0310779Z to support: 2025-08-26T20:22:02.0310920Z 2025-08-26T20:22:02.0311125Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-08-26T20:22:02.0311434Z 2025-08-26T20:22:02.0311744Z Note that while it's possible to include all supported archs, the more archs get included the 2025-08-26T20:22:02.0312460Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-08-26T20:22:02.0312869Z 2025-08-26T20:22:02.0313006Z Note: Ninja is required to build SyclExtension. 2025-08-26T20:22:02.0313267Z 2025-08-26T20:22:02.0313579Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.0313963Z 2025-08-26T20:22:02.0314530Z msg = Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1597. 2025-08-26T20:22:02.0315396Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.0315787Z 2025-08-26T20:22:02.0315934Z Load a PyTorch C++ extension just-in-time (JIT). 2025-08-26T20:22:02.0316197Z 2025-08-26T20:22:02.0316405Z To load an extension, a Ninja build file is emitted, which is used to 2025-08-26T20:22:02.0316941Z compile the given sources into a dynamic library. This library is 2025-08-26T20:22:02.0317489Z subsequently loaded into the current Python process as a module and 2025-08-26T20:22:02.0317951Z returned from this function, ready for use. 2025-08-26T20:22:02.0318198Z 2025-08-26T20:22:02.0318416Z By default, the directory to which the build file is emitted and the 2025-08-26T20:22:02.0318979Z resulting library compiled to is ``/torch_extensions/``, where 2025-08-26T20:22:02.0319547Z ```` is the temporary folder on the current platform and ```` 2025-08-26T20:22:02.0320076Z the name of the extension. This location can be overridden in two ways. 2025-08-26T20:22:02.0320623Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-08-26T20:22:02.0321168Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-08-26T20:22:02.0321725Z into subfolders of this directory. Second, if the ``build_directory`` 2025-08-26T20:22:02.0322288Z argument to this function is supplied, it overrides the entire path, i.e. 2025-08-26T20:22:02.0322793Z the library will be compiled into that folder directly. 2025-08-26T20:22:02.0323074Z 2025-08-26T20:22:02.0323283Z To compile the sources, the default system compiler (``c++``) is used, 2025-08-26T20:22:02.0323852Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-08-26T20:22:02.0324438Z additional arguments to the compilation process, ``extra_cflags`` or 2025-08-26T20:22:02.0324991Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-08-26T20:22:02.0325544Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-08-26T20:22:02.0326025Z ``extra_cflags`` to pass further include directories. 2025-08-26T20:22:02.0326287Z 2025-08-26T20:22:02.0326532Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-08-26T20:22:02.0327068Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-08-26T20:22:02.0327599Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-08-26T20:22:02.0328172Z passing the CUDA lib64 directory as a library directory, and linking 2025-08-26T20:22:02.0328654Z ``cudart``. You can pass additional flags to nvcc via 2025-08-26T20:22:02.0329126Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-08-26T20:22:02.0329675Z heuristics for finding the CUDA install directory are used, which usually 2025-08-26T20:22:02.0330306Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-08-26T20:22:02.0330727Z safest option. 2025-08-26T20:22:02.0330864Z 2025-08-26T20:22:02.0331113Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-08-26T20:22:02.0331668Z files (``.sycl``) along with other sources. Such files will be detected 2025-08-26T20:22:02.0332193Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-08-26T20:22:02.0332735Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-08-26T20:22:02.0333254Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-08-26T20:22:02.0333772Z SYCL compiler is expected to be found via system PATH environment 2025-08-26T20:22:02.0334170Z variable. 2025-08-26T20:22:02.0334301Z 2025-08-26T20:22:02.0334382Z Args: 2025-08-26T20:22:02.0334766Z name: The name of the extension to build. This MUST be the same as the 2025-08-26T20:22:02.0335202Z name of the pybind11 module! 2025-08-26T20:22:02.0335604Z sources: A list of relative or absolute paths to C++ source files. 2025-08-26T20:22:02.0336149Z extra_cflags: optional list of compiler flags to forward to the build. 2025-08-26T20:22:02.0336712Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-08-26T20:22:02.0337161Z when building CUDA sources. 2025-08-26T20:22:02.0337594Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-08-26T20:22:02.0338051Z compiler when building SYCL sources. 2025-08-26T20:22:02.0338499Z extra_ldflags: optional list of linker flags to forward to the build. 2025-08-26T20:22:02.0339061Z extra_include_paths: optional list of include directories to forward 2025-08-26T20:22:02.0339494Z to the build. 2025-08-26T20:22:02.0339818Z build_directory: optional path to use as build workspace. 2025-08-26T20:22:02.0340299Z verbose: If ``True``, turns on verbose logging of load steps. 2025-08-26T20:22:02.0340910Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-08-26T20:22:02.0341417Z the build. If set to ``None`` (default), this value is 2025-08-26T20:22:02.0341903Z automatically determined based on the existence of ``.cu`` or 2025-08-26T20:22:02.0342384Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-08-26T20:22:02.0342787Z and libraries to be included. 2025-08-26T20:22:02.0343226Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-08-26T20:22:02.0343729Z the build. If set to ``None`` (default), this value is 2025-08-26T20:22:02.0344202Z automatically determined based on the existence of ``.sycl`` in 2025-08-26T20:22:02.0344691Z ``sources``. Set it to `True`` to force SYCL headers and 2025-08-26T20:22:02.0345070Z libraries to be included. 2025-08-26T20:22:02.0345478Z is_python_module: If ``True`` (default), imports the produced shared 2025-08-26T20:22:02.0345977Z library as a Python module. If ``False``, behavior depends on 2025-08-26T20:22:02.0346383Z ``is_standalone``. 2025-08-26T20:22:02.0346762Z is_standalone: If ``False`` (default) loads the constructed extension 2025-08-26T20:22:02.0347280Z into the process as a plain dynamic library. If ``True``, build a 2025-08-26T20:22:02.0347702Z standalone executable. 2025-08-26T20:22:02.0347885Z 2025-08-26T20:22:02.0347971Z Returns: 2025-08-26T20:22:02.0348204Z If ``is_python_module`` is ``True``: 2025-08-26T20:22:02.0348601Z Returns the loaded PyTorch extension as a Python module. 2025-08-26T20:22:02.0348888Z 2025-08-26T20:22:02.0349099Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-08-26T20:22:02.0349621Z Returns nothing. (The shared library is loaded into the process as 2025-08-26T20:22:02.0350048Z a side effect.) 2025-08-26T20:22:02.0350221Z 2025-08-26T20:22:02.0350330Z If ``is_standalone`` is ``True``. 2025-08-26T20:22:02.0350825Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-08-26T20:22:02.0351312Z added to the PATH environment variable as a side effect.) 2025-08-26T20:22:02.0351616Z 2025-08-26T20:22:02.0351701Z Example: 2025-08-26T20:22:02.0351927Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.0352235Z >>> from torch.utils.cpp_extension import load 2025-08-26T20:22:02.0352579Z >>> module = load( 2025-08-26T20:22:02.0352825Z ... name='extension', 2025-08-26T20:22:02.0353168Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-26T20:22:02.0353545Z ... extra_cflags=['-O2'], 2025-08-26T20:22:02.0353835Z ... verbose=True) 2025-08-26T20:22:02.0353996Z 2025-08-26T20:22:02.0354247Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.0354626Z 2025-08-26T20:22:02.0355207Z msg = Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1882. 2025-08-26T20:22:02.0356114Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.0356490Z 2025-08-26T20:22:02.0356712Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-08-26T20:22:02.0357031Z 2025-08-26T20:22:02.0357276Z This function behaves exactly like :func:`load`, but takes its sources as 2025-08-26T20:22:02.0357843Z strings rather than filenames. These strings are stored to files in the 2025-08-26T20:22:02.0358409Z build directory, after which the behavior of :func:`load_inline` is 2025-08-26T20:22:02.0358845Z identical to :func:`load`. 2025-08-26T20:22:02.0359022Z 2025-08-26T20:22:02.0359120Z See `the 2025-08-26T20:22:02.0359560Z tests `_ 2025-08-26T20:22:02.0360129Z for good examples of using this function. 2025-08-26T20:22:02.0360377Z 2025-08-26T20:22:02.0360610Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-08-26T20:22:02.0361217Z the necessary header includes, as well as the (pybind11) binding code. More 2025-08-26T20:22:02.0361828Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-08-26T20:22:02.0362367Z single ``.cpp`` file. This file is then prepended with ``#include 2025-08-26T20:22:02.0362774Z `` 2025-08-26T20:22:02.0362945Z 2025-08-26T20:22:02.0363171Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-08-26T20:22:02.0363760Z automatically generated for each function specified. ``functions`` can 2025-08-26T20:22:02.0364337Z either be a list of function names, or a dictionary mapping from function 2025-08-26T20:22:02.0364892Z names to docstrings. If a list is given, the name of each function is used 2025-08-26T20:22:02.0365333Z as its docstring. 2025-08-26T20:22:02.0365477Z 2025-08-26T20:22:02.0365708Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-08-26T20:22:02.0366221Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-08-26T20:22:02.0366712Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-08-26T20:22:02.0367323Z separately, but ultimately linked into a single library. Note that no 2025-08-26T20:22:02.0367897Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-08-26T20:22:02.0368470Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-08-26T20:22:02.0369017Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-08-26T20:22:02.0369443Z include its name in ``functions``). 2025-08-26T20:22:02.0369659Z 2025-08-26T20:22:02.0369879Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-08-26T20:22:02.0370435Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-08-26T20:22:02.0370970Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-08-26T20:22:02.0371487Z linked into a single library. Note that no bindings are generated for 2025-08-26T20:22:02.0372099Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-08-26T20:22:02.0372642Z create a C++ function that calls it, and either declare or define this 2025-08-26T20:22:02.0373163Z C++ function in one of the ``cpp_sources`` (and include its name 2025-08-26T20:22:02.0373561Z in ``functions``). 2025-08-26T20:22:02.0373704Z 2025-08-26T20:22:02.0373708Z 2025-08-26T20:22:02.0373712Z 2025-08-26T20:22:02.0373901Z See :func:`load` for a description of arguments omitted below. 2025-08-26T20:22:02.0374207Z 2025-08-26T20:22:02.0374288Z Args: 2025-08-26T20:22:02.0374627Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-08-26T20:22:02.0375186Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-08-26T20:22:02.0375804Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-08-26T20:22:02.0376344Z functions: A list of function names for which to generate function 2025-08-26T20:22:02.0376883Z bindings. If a dictionary is given, it should map function names to 2025-08-26T20:22:02.0377400Z docstrings (which are otherwise just the function names). 2025-08-26T20:22:02.0377921Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-08-26T20:22:02.0378410Z the build. If set to ``None`` (default), this value is 2025-08-26T20:22:02.0378891Z automatically determined based on whether ``cuda_sources`` is 2025-08-26T20:22:02.0379366Z provided. Set it to ``True`` to force CUDA headers 2025-08-26T20:22:02.0379744Z and libraries to be included. 2025-08-26T20:22:02.0380169Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-08-26T20:22:02.0380798Z the build. If set to ``None`` (default), this value is 2025-08-26T20:22:02.0381288Z automatically determined based on whether ``sycl_sources`` is 2025-08-26T20:22:02.0381767Z provided. Set it to ``True`` to force SYCL headers 2025-08-26T20:22:02.0382153Z and libraries to be included. 2025-08-26T20:22:02.0382561Z with_pytorch_error_handling: Determines whether pytorch error and 2025-08-26T20:22:02.0383088Z warning macros are handled by pytorch instead of pybind. To do 2025-08-26T20:22:02.0383622Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-08-26T20:22:02.0384159Z function. This redirection might cause issues in obscure cases 2025-08-26T20:22:02.0384648Z of cpp. This flag should be set to ``False`` when this redirect 2025-08-26T20:22:02.0385046Z causes issues. 2025-08-26T20:22:02.0385455Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-08-26T20:22:02.0386055Z ``#include `` and ``#include `` lines. 2025-08-26T20:22:02.0386559Z Use this option to improve cold start times when you 2025-08-26T20:22:02.0387079Z already include the necessary headers in your source code. Default: ``False``. 2025-08-26T20:22:02.0387472Z 2025-08-26T20:22:02.0387558Z Example: 2025-08-26T20:22:02.0387933Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:02.0388524Z >>> from torch.utils.cpp_extension import load_inline 2025-08-26T20:22:02.0388993Z >>> source = """ 2025-08-26T20:22:02.0389300Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-08-26T20:22:02.0389652Z return x.sin() + y.sin(); 2025-08-26T20:22:02.0389923Z } 2025-08-26T20:22:02.0390111Z """ 2025-08-26T20:22:02.0390367Z >>> module = load_inline(name='inline_extension', 2025-08-26T20:22:02.0390736Z ... cpp_sources=[source], 2025-08-26T20:22:02.0391085Z ... functions=['sin_add']) 2025-08-26T20:22:02.0391315Z 2025-08-26T20:22:02.0391402Z .. note:: 2025-08-26T20:22:02.0391953Z Since load_inline will just-in-time compile the source code, please ensure 2025-08-26T20:22:02.0392548Z that you have the right toolchains installed in the runtime. For example, 2025-08-26T20:22:02.0393341Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-08-26T20:22:02.0393925Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-08-26T20:22:02.0394530Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-08-26T20:22:02.0395139Z are not included when you install torch and must be additionally installed. 2025-08-26T20:22:02.0395503Z 2025-08-26T20:22:02.0395756Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-08-26T20:22:02.0396353Z the extension. This may use up too many resources on some systems. One 2025-08-26T20:22:02.0396906Z can control the number of workers by setting the `MAX_JOBS` environment 2025-08-26T20:22:02.0397454Z variable to a non-negative number. 2025-08-26T20:22:02.0397686Z 2025-08-26T20:22:02.0397936Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.0398319Z 2025-08-26T20:22:02.0444765Z msg = Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61. 2025-08-26T20:22:02.0445769Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.0446145Z 2025-08-26T20:22:02.0446455Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-08-26T20:22:02.0446857Z 2025-08-26T20:22:02.0447161Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-08-26T20:22:02.0447812Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-08-26T20:22:02.0448412Z server like load. It can emulate multiple calling threads to a single module 2025-08-26T20:22:02.0449009Z provided. In the future we plan to enhance this component to support inter and 2025-08-26T20:22:02.0449634Z intra-op parallelism as well as multiple models running in a single process. 2025-08-26T20:22:02.0449999Z 2025-08-26T20:22:02.0450251Z Please note that even though nn.Module is supported, it might incur an overhead 2025-08-26T20:22:02.0450850Z from the need to hold GIL every time we execute Python code or pass around 2025-08-26T20:22:02.0451435Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-08-26T20:22:02.0452033Z model for inference deployment it is better to switch to using it in this 2025-08-26T20:22:02.0452482Z benchmark. 2025-08-26T20:22:02.0452607Z 2025-08-26T20:22:02.0452698Z Example:: 2025-08-26T20:22:02.0452836Z 2025-08-26T20:22:02.0452956Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:02.0453331Z >>> from torch.utils import ThroughputBenchmark 2025-08-26T20:22:02.0453725Z >>> bench = ThroughputBenchmark(my_module) 2025-08-26T20:22:02.0454120Z >>> # Pre-populate benchmark's data set with the inputs 2025-08-26T20:22:02.0454505Z >>> for input in inputs: 2025-08-26T20:22:02.0454924Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-08-26T20:22:02.0455395Z ... bench.add_input(input[0], x2=input[1]) 2025-08-26T20:22:02.0455840Z >>> # Inputs supplied above are randomly used during the execution 2025-08-26T20:22:02.0456252Z >>> stats = bench.benchmark( 2025-08-26T20:22:02.0456552Z ... num_calling_threads=4, 2025-08-26T20:22:02.0456857Z ... num_warmup_iters = 100, 2025-08-26T20:22:02.0457162Z ... num_iters = 1000, 2025-08-26T20:22:02.0457417Z ... ) 2025-08-26T20:22:02.0457717Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-08-26T20:22:02.0458184Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-08-26T20:22:02.0458469Z 2025-08-26T20:22:02.0458733Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.0459098Z 2025-08-26T20:22:02.1368007Z msg = Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=18. 2025-08-26T20:22:02.1369279Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1369872Z Sampler that restricts data loading to a subset of the dataset. 2025-08-26T20:22:02.1370191Z 2025-08-26T20:22:02.1370343Z It is especially useful in conjunction with 2025-08-26T20:22:02.1370843Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-08-26T20:22:02.1371456Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-08-26T20:22:02.1372067Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-08-26T20:22:02.1372543Z original dataset that is exclusive to it. 2025-08-26T20:22:02.1372778Z 2025-08-26T20:22:02.1372900Z .. note:: 2025-08-26T20:22:02.1373363Z Dataset is assumed to be of constant size and that any instance of it always 2025-08-26T20:22:02.1373867Z returns the same elements in the same order. 2025-08-26T20:22:02.1374125Z 2025-08-26T20:22:02.1374210Z Args: 2025-08-26T20:22:02.1374452Z dataset: Dataset used for sampling. 2025-08-26T20:22:02.1374885Z num_replicas (int, optional): Number of processes participating in 2025-08-26T20:22:02.1375470Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-08-26T20:22:02.1375952Z current distributed group. 2025-08-26T20:22:02.1376405Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-08-26T20:22:02.1376969Z By default, :attr:`rank` is retrieved from the current distributed 2025-08-26T20:22:02.1377366Z group. 2025-08-26T20:22:02.1377730Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-08-26T20:22:02.1378168Z indices. 2025-08-26T20:22:02.1378511Z seed (int, optional): random seed used to shuffle the sampler if 2025-08-26T20:22:02.1379013Z :attr:`shuffle=True`. This number should be identical across all 2025-08-26T20:22:02.1379495Z processes in the distributed group. Default: ``0``. 2025-08-26T20:22:02.1379995Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-08-26T20:22:02.1380602Z tail of the data to make it evenly divisible across the number of 2025-08-26T20:22:02.1381121Z replicas. If ``False``, the sampler will add extra indices to make 2025-08-26T20:22:02.1381637Z the data evenly divisible across the replicas. Default: ``False``. 2025-08-26T20:22:02.1381975Z 2025-08-26T20:22:02.1382072Z .. warning:: 2025-08-26T20:22:02.1382406Z In distributed mode, calling the :meth:`set_epoch` method at 2025-08-26T20:22:02.1382967Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-08-26T20:22:02.1383586Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-08-26T20:22:02.1384098Z the same ordering will be always used. 2025-08-26T20:22:02.1384386Z 2025-08-26T20:22:02.1384474Z Example:: 2025-08-26T20:22:02.1384604Z 2025-08-26T20:22:02.1384717Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.1385123Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-08-26T20:22:02.1385626Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-08-26T20:22:02.1386039Z ... sampler=sampler) 2025-08-26T20:22:02.1386406Z >>> for epoch in range(start_epoch, n_epochs): 2025-08-26T20:22:02.1386758Z ... if is_distributed: 2025-08-26T20:22:02.1387062Z ... sampler.set_epoch(epoch) 2025-08-26T20:22:02.1387385Z ... train(loader) 2025-08-26T20:22:02.1387651Z 2025-08-26T20:22:02.1388025Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1388393Z 2025-08-26T20:22:02.1427499Z msg = Cannot scrape callname=WeightedRandomSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=227. 2025-08-26T20:22:02.1428712Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1429581Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-08-26T20:22:02.1430298Z 2025-08-26T20:22:02.1430471Z Args: 2025-08-26T20:22:02.1430934Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-08-26T20:22:02.1431439Z num_samples (int): number of samples to draw 2025-08-26T20:22:02.1431902Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-08-26T20:22:02.1432436Z If not, they are drawn without replacement, which means that when a 2025-08-26T20:22:02.1433095Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-08-26T20:22:02.1433602Z generator (Generator): Generator used in sampling. 2025-08-26T20:22:02.1433876Z 2025-08-26T20:22:02.1433963Z Example: 2025-08-26T20:22:02.1434244Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:22:02.1434588Z >>> list( 2025-08-26T20:22:02.1434845Z ... WeightedRandomSampler( 2025-08-26T20:22:02.1435197Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-08-26T20:22:02.1435542Z ... ) 2025-08-26T20:22:02.1435773Z ... ) 2025-08-26T20:22:02.1435997Z [4, 4, 1, 4, 5] 2025-08-26T20:22:02.1436230Z >>> list( 2025-08-26T20:22:02.1436482Z ... WeightedRandomSampler( 2025-08-26T20:22:02.1436844Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-08-26T20:22:02.1437188Z ... ) 2025-08-26T20:22:02.1445198Z ... ) 2025-08-26T20:22:02.1445457Z [0, 1, 4, 3, 2] 2025-08-26T20:22:02.1445726Z 2025-08-26T20:22:02.1446094Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1446491Z 2025-08-26T20:22:02.1447051Z msg = Cannot scrape callname=BatchSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=300. 2025-08-26T20:22:02.1447960Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1448518Z Wraps another sampler to yield a mini-batch of indices. 2025-08-26T20:22:02.1448799Z 2025-08-26T20:22:02.1448893Z Args: 2025-08-26T20:22:02.1449240Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-08-26T20:22:02.1449711Z batch_size (int): Size of mini-batch. 2025-08-26T20:22:02.1450149Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-08-26T20:22:02.1450607Z its size would be less than ``batch_size`` 2025-08-26T20:22:02.1450848Z 2025-08-26T20:22:02.1450953Z Example: 2025-08-26T20:22:02.1451164Z >>> list( 2025-08-26T20:22:02.1451409Z ... BatchSampler( 2025-08-26T20:22:02.1451805Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-08-26T20:22:02.1452215Z ... ) 2025-08-26T20:22:02.1452430Z ... ) 2025-08-26T20:22:02.1452669Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-08-26T20:22:02.1452972Z >>> list( 2025-08-26T20:22:02.1453365Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-08-26T20:22:02.1453836Z ... ) 2025-08-26T20:22:02.1454063Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-08-26T20:22:02.1454355Z 2025-08-26T20:22:02.1454703Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1455080Z 2025-08-26T20:22:02.1618020Z msg = Cannot scrape callname=IterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py line=56. 2025-08-26T20:22:02.1619027Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1619622Z 2025-08-26T20:22:02.1619729Z Iterable-style DataPipe. 2025-08-26T20:22:02.1619903Z 2025-08-26T20:22:02.1620174Z All DataPipes that represent an iterable of data samples should subclass this. 2025-08-26T20:22:02.1620883Z This style of DataPipes is particularly useful when data come from a stream, or 2025-08-26T20:22:02.1621604Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-08-26T20:22:02.1622382Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-08-26T20:22:02.1622806Z 2025-08-26T20:22:02.1623035Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-08-26T20:22:02.1623732Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-08-26T20:22:02.1624683Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-08-26T20:22:02.1625454Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-08-26T20:22:02.1626068Z and various state variables within the custom ``IterDataPipe``. 2025-08-26T20:22:02.1626393Z 2025-08-26T20:22:02.1626477Z Note: 2025-08-26T20:22:02.1626814Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-08-26T20:22:02.1627495Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-08-26T20:22:02.1628330Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-08-26T20:22:02.1629095Z The code example below presents details on how this constraint looks in practice. 2025-08-26T20:22:02.1634770Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-08-26T20:22:02.1635274Z 2025-08-26T20:22:02.1635573Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-08-26T20:22:02.1636338Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-08-26T20:22:02.1637132Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-08-26T20:22:02.1637673Z operations in succession. 2025-08-26T20:22:02.1637848Z 2025-08-26T20:22:02.1637997Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-08-26T20:22:02.1638394Z https://github.com/pytorch/data/issues/45 2025-08-26T20:22:02.1638635Z 2025-08-26T20:22:02.1638716Z Note: 2025-08-26T20:22:02.1639068Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-08-26T20:22:02.1639681Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-08-26T20:22:02.1640322Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-08-26T20:22:02.1640887Z different copy of the DataPipe object, so it is often desired to configure 2025-08-26T20:22:02.1641480Z each copy independently to avoid having duplicate data returned from the 2025-08-26T20:22:02.1642073Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-08-26T20:22:02.1642684Z process, returns information about the worker. It can be used in either the 2025-08-26T20:22:02.1643292Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-08-26T20:22:02.1643843Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-08-26T20:22:02.1644148Z 2025-08-26T20:22:02.1644237Z Examples: 2025-08-26T20:22:02.1644455Z General Usage: 2025-08-26T20:22:02.1644710Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.1645096Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-08-26T20:22:02.1645550Z >>> dp = IterableWrapper(range(10)) 2025-08-26T20:22:02.1645976Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-08-26T20:22:02.1646388Z >>> map_dp_2 = dp.map( 2025-08-26T20:22:02.1646735Z ... lambda x: x + 1 2025-08-26T20:22:02.1647058Z ... ) # Using functional form (recommended) 2025-08-26T20:22:02.1647402Z >>> list(map_dp_1) 2025-08-26T20:22:02.1647672Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-08-26T20:22:02.1647960Z >>> list(map_dp_2) 2025-08-26T20:22:02.1648226Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-08-26T20:22:02.1648575Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-08-26T20:22:02.1648935Z >>> list(filter_dp) 2025-08-26T20:22:02.1649187Z [2, 4, 6, 8, 10] 2025-08-26T20:22:02.1649467Z Single Iterator Constraint Example: 2025-08-26T20:22:02.1649897Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-08-26T20:22:02.1650354Z >>> source_dp = IterableWrapper(range(10)) 2025-08-26T20:22:02.1650757Z >>> it1 = iter(source_dp) 2025-08-26T20:22:02.1651076Z >>> list(it1) 2025-08-26T20:22:02.1651325Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-08-26T20:22:02.1651635Z >>> it1 = iter(source_dp) 2025-08-26T20:22:02.1651908Z >>> it2 = iter( 2025-08-26T20:22:02.1652161Z ... source_dp 2025-08-26T20:22:02.1652482Z ... ) # The creation of a new iterator invalidates `it1` 2025-08-26T20:22:02.1652842Z >>> next(it2) 2025-08-26T20:22:02.1653064Z 0 2025-08-26T20:22:02.1653374Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-08-26T20:22:02.1653687Z 2025-08-26T20:22:02.1653936Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1654300Z 2025-08-26T20:22:02.1830454Z msg = Cannot scrape callname=DemultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=375. 2025-08-26T20:22:02.1831760Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1832136Z 2025-08-26T20:22:02.1832677Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-08-26T20:22:02.1833499Z 2025-08-26T20:22:02.1833703Z A list of the child DataPipes is returned from this operation. 2025-08-26T20:22:02.1834229Z 2025-08-26T20:22:02.1834395Z Args: 2025-08-26T20:22:02.1834810Z datapipe: Iterable DataPipe being filtered 2025-08-26T20:22:02.1835246Z num_instances: number of instances of the DataPipe to create 2025-08-26T20:22:02.1835931Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-08-26T20:22:02.1836726Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-08-26T20:22:02.1837716Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-08-26T20:22:02.1838790Z DataPipes while waiting for their values to be yielded. 2025-08-26T20:22:02.1839256Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-08-26T20:22:02.1839537Z 2025-08-26T20:22:02.1839636Z Examples: 2025-08-26T20:22:02.1839877Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-26T20:22:02.1840291Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-26T20:22:02.1840681Z >>> def odd_or_even(n): 2025-08-26T20:22:02.1840950Z ... return n % 2 2025-08-26T20:22:02.1841229Z >>> source_dp = IterableWrapper(range(5)) 2025-08-26T20:22:02.1841676Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-08-26T20:22:02.1842095Z >>> list(dp1) 2025-08-26T20:22:02.1842327Z [0, 2, 4] 2025-08-26T20:22:02.1842532Z >>> list(dp2) 2025-08-26T20:22:02.1842760Z [1, 3] 2025-08-26T20:22:02.1843123Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-08-26T20:22:02.1843591Z >>> def odd_or_even_no_zero(n): 2025-08-26T20:22:02.1843897Z ... return n % 2 if n != 0 else None 2025-08-26T20:22:02.1844336Z >>> dp1, dp2 = source_dp.demux( 2025-08-26T20:22:02.1844759Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-08-26T20:22:02.1845182Z ... ) 2025-08-26T20:22:02.1845381Z >>> list(dp1) 2025-08-26T20:22:02.1845610Z [2, 4] 2025-08-26T20:22:02.1845825Z >>> list(dp2) 2025-08-26T20:22:02.1846052Z [1, 3] 2025-08-26T20:22:02.1846170Z 2025-08-26T20:22:02.1846419Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1846798Z 2025-08-26T20:22:02.1847507Z msg = Cannot scrape callname=MultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=594. 2025-08-26T20:22:02.1848569Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1848963Z 2025-08-26T20:22:02.1849371Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-08-26T20:22:02.1849812Z 2025-08-26T20:22:02.1850158Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-08-26T20:22:02.1850814Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-08-26T20:22:02.1851109Z 2025-08-26T20:22:02.1851207Z Args: 2025-08-26T20:22:02.1851724Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-08-26T20:22:02.1852257Z 2025-08-26T20:22:02.1852347Z Example: 2025-08-26T20:22:02.1852601Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-26T20:22:02.1853015Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-26T20:22:02.1853389Z >>> dp1, dp2, dp3 = ( 2025-08-26T20:22:02.1853733Z ... IterableWrapper(range(3)), 2025-08-26T20:22:02.1854072Z ... IterableWrapper(range(10, 15)), 2025-08-26T20:22:02.1854424Z ... IterableWrapper(range(20, 25)), 2025-08-26T20:22:02.1854790Z ... ) 2025-08-26T20:22:02.1855048Z >>> list(dp1.mux(dp2, dp3)) 2025-08-26T20:22:02.1855334Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-08-26T20:22:02.1855526Z 2025-08-26T20:22:02.1855851Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1856219Z 2025-08-26T20:22:02.1857160Z msg = Cannot scrape callname=ZipperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=665. 2025-08-26T20:22:02.1858657Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1859046Z 2025-08-26T20:22:02.1859351Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-08-26T20:22:02.1859783Z 2025-08-26T20:22:02.1860015Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-08-26T20:22:02.1860433Z 2025-08-26T20:22:02.1860532Z Args: 2025-08-26T20:22:02.1860802Z *datapipes: Iterable DataPipes being aggregated 2025-08-26T20:22:02.1861060Z 2025-08-26T20:22:02.1861144Z Example: 2025-08-26T20:22:02.1861394Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-26T20:22:02.1861836Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-26T20:22:02.1862225Z >>> dp1, dp2, dp3 = ( 2025-08-26T20:22:02.1862498Z ... IterableWrapper(range(5)), 2025-08-26T20:22:02.1862838Z ... IterableWrapper(range(10, 15)), 2025-08-26T20:22:02.1863186Z ... IterableWrapper(range(20, 25)), 2025-08-26T20:22:02.1863500Z ... ) 2025-08-26T20:22:02.1863716Z >>> list(dp1.zip(dp2, dp3)) 2025-08-26T20:22:02.1864039Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-08-26T20:22:02.1864294Z 2025-08-26T20:22:02.1864548Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1864919Z 2025-08-26T20:22:02.1865635Z msg = Cannot scrape callname=FileOpenerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py line=18. 2025-08-26T20:22:02.1866757Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1867133Z 2025-08-26T20:22:02.1867515Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-08-26T20:22:02.1868006Z 2025-08-26T20:22:02.1868087Z Args: 2025-08-26T20:22:02.1868371Z datapipe: Iterable datapipe that provides pathnames 2025-08-26T20:22:02.1868870Z mode: An optional string that specifies the mode in which 2025-08-26T20:22:02.1869374Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-08-26T20:22:02.1869859Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-08-26T20:22:02.1870341Z encoding: An optional string that specifies the encoding of the 2025-08-26T20:22:02.1870979Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-08-26T20:22:02.1871489Z length: Nominal length of the datapipe 2025-08-26T20:22:02.1871713Z 2025-08-26T20:22:02.1871809Z Note: 2025-08-26T20:22:02.1872185Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-08-26T20:22:02.1872692Z to close them explicitly. 2025-08-26T20:22:02.1872893Z 2025-08-26T20:22:02.1872979Z Example: 2025-08-26T20:22:02.1873198Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.1873490Z >>> from torchdata.datapipes.iter import ( 2025-08-26T20:22:02.1873829Z ... FileLister, 2025-08-26T20:22:02.1874080Z ... FileOpener, 2025-08-26T20:22:02.1874334Z ... StreamReader, 2025-08-26T20:22:02.1874574Z ... ) 2025-08-26T20:22:02.1874913Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-08-26T20:22:02.1875386Z >>> dp = FileOpener(dp) 2025-08-26T20:22:02.1875667Z >>> dp = StreamReader(dp) 2025-08-26T20:22:02.1875932Z >>> list(dp) 2025-08-26T20:22:02.1876166Z [('./abc.txt', 'abc')] 2025-08-26T20:22:02.1876350Z 2025-08-26T20:22:02.1876601Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1876965Z 2025-08-26T20:22:02.1893223Z msg = Cannot scrape callname=GrouperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py line=155. 2025-08-26T20:22:02.1894292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:02.1894671Z 2025-08-26T20:22:02.1895089Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-08-26T20:22:02.1895608Z 2025-08-26T20:22:02.1895716Z (functional name: ``groupby``). 2025-08-26T20:22:02.1895920Z 2025-08-26T20:22:02.1896301Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-08-26T20:22:02.1897099Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-08-26T20:22:02.1897816Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-08-26T20:22:02.1898561Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-08-26T20:22:02.1898997Z 2025-08-26T20:22:02.1899383Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-08-26T20:22:02.1900204Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-08-26T20:22:02.1900709Z 2025-08-26T20:22:02.1900794Z Args: 2025-08-26T20:22:02.1901056Z datapipe: Iterable datapipe to be grouped 2025-08-26T20:22:02.1901567Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-08-26T20:22:02.1902208Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-08-26T20:22:02.1902742Z resulting in `(key, [items])` otherwise returning [items] 2025-08-26T20:22:02.1903298Z buffer_size: The size of buffer for ungrouped data 2025-08-26T20:22:02.1903843Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-08-26T20:22:02.1904570Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-08-26T20:22:02.1905388Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-08-26T20:22:02.1905970Z when the buffer is full 2025-08-26T20:22:02.1906157Z 2025-08-26T20:22:02.1906254Z Example: 2025-08-26T20:22:02.1906452Z >>> import os 2025-08-26T20:22:02.1906693Z >>> # xdoctest: +SKIP 2025-08-26T20:22:02.1907039Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-26T20:22:02.1907428Z >>> def group_fn(file): 2025-08-26T20:22:02.1907852Z ... return os.path.basename(file).split(".")[0] 2025-08-26T20:22:02.1908221Z >>> source_dp = IterableWrapper( 2025-08-26T20:22:02.1908596Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-08-26T20:22:02.1908959Z ... ) 2025-08-26T20:22:02.1909227Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-08-26T20:22:02.1909556Z >>> list(dp0) 2025-08-26T20:22:02.1909868Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-08-26T20:22:02.1910327Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-08-26T20:22:02.1910812Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-08-26T20:22:02.1911184Z >>> list(dp1) 2025-08-26T20:22:02.1911486Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-08-26T20:22:02.1912112Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-08-26T20:22:02.1912739Z >>> dp2 = source_dp.groupby( 2025-08-26T20:22:02.1913031Z ... group_key_fn=group_fn, 2025-08-26T20:22:02.1913330Z ... buffer_size=3, 2025-08-26T20:22:02.1913597Z ... group_size=3, 2025-08-26T20:22:02.1913879Z ... guaranteed_group_size=2, 2025-08-26T20:22:02.1914160Z ... ) 2025-08-26T20:22:02.1914371Z >>> list(dp2) 2025-08-26T20:22:02.1914672Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-08-26T20:22:02.1914947Z 2025-08-26T20:22:02.1915213Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:02.1915582Z 2025-08-26T20:22:02.3329865Z gathering tests 2025-08-26T20:22:02.3342492Z running 731 test(s) 2025-08-26T20:22:02.3382647Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0, line 1082 <- wrt source file 2025-08-26T20:22:02.3384622Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::typename:0 2025-08-26T20:22:02.3385751Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0, line 1118 <- wrt source file 2025-08-26T20:22:02.3387002Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::is_tensor:0 2025-08-26T20:22:02.3388653Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0, line 1203 <- wrt source file 2025-08-26T20:22:02.3390704Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_device:0 2025-08-26T20:22:02.3392648Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0, line 1252 <- wrt source file 2025-08-26T20:22:02.3393901Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_tensor_type:0 2025-08-26T20:22:02.3395109Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0, line 1289 <- wrt source file 2025-08-26T20:22:02.3396476Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::set_default_dtype:0 2025-08-26T20:22:02.3397697Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0, line 1444 <- wrt source file 2025-08-26T20:22:02.3399011Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::use_deterministic_algorithms:0 2025-08-26T20:22:02.3400177Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0, line 2567 <- wrt source file 2025-08-26T20:22:02.3401284Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::compile:0 2025-08-26T20:22:02.3402610Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0, line 2840 <- wrt source file 2025-08-26T20:22:02.3403968Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/__init__.py::_is_device_backend_autoload_enabled:0 2025-08-26T20:22:02.3405280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0, line 15 <- wrt source file 2025-08-26T20:22:02.3406624Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::Generator:0 2025-08-26T20:22:02.3407946Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0, line 5 <- wrt source file 2025-08-26T20:22:02.3409305Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_C.cpython-313-x86_64-linux-gnu.so::_LinAlgError:0 2025-08-26T20:22:02.3410580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0, line 55 <- wrt source file 2025-08-26T20:22:02.3411718Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::custom_op:0 2025-08-26T20:22:02.3412798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0, line 138 <- wrt source file 2025-08-26T20:22:02.3413891Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl:0 2025-08-26T20:22:02.3415007Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0, line 208 <- wrt source file 2025-08-26T20:22:02.3806056Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_custom_ops.py::impl_abstract:0 2025-08-26T20:22:02.3807361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0, line 118 <- wrt source file 2025-08-26T20:22:02.3808681Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_namedtensor_internals.py::update_names:0 2025-08-26T20:22:02.3809909Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0, line 649 <- wrt source file 2025-08-26T20:22:02.3817601Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_hook:0 2025-08-26T20:22:02.3818916Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0, line 706 <- wrt source file 2025-08-26T20:22:02.3837701Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.register_post_accumulate_grad_hook:0 2025-08-26T20:22:02.3839027Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0, line 1333 <- wrt source file 2025-08-26T20:22:02.3948256Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.refine_names:0 2025-08-26T20:22:02.3952039Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0, line 1378 <- wrt source file 2025-08-26T20:22:02.3957121Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.align_to:0 2025-08-26T20:22:02.3958251Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0, line 1451 <- wrt source file 2025-08-26T20:22:02.3964675Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.rename:0 2025-08-26T20:22:02.3965840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0, line 1481 <- wrt source file 2025-08-26T20:22:02.3971192Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py::Tensor.to_sparse_coo:0 2025-08-26T20:22:02.3972392Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0, line 53 <- wrt source file 2025-08-26T20:22:02.3992383Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor_str.py::set_printoptions:0 2025-08-26T20:22:02.3993578Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0, line 64 <- wrt source file 2025-08-26T20:22:02.3999358Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_tensors:0 2025-08-26T20:22:02.4000566Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0, line 92 <- wrt source file 2025-08-26T20:22:02.4003342Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::broadcast_shapes:0 2025-08-26T20:22:02.4004497Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0, line 144 <- wrt source file 2025-08-26T20:22:02.4016161Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::split:0 2025-08-26T20:22:02.4017263Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0, line 258 <- wrt source file 2025-08-26T20:22:02.4040626Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::einsum:0 2025-08-26T20:22:02.4041826Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0, line 992 <- wrt source file 2025-08-26T20:22:02.4053111Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_unique_consecutive_impl:0 2025-08-26T20:22:02.4054325Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0, line 1267 <- wrt source file 2025-08-26T20:22:02.4064757Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::tensordot:0 2025-08-26T20:22:02.4065919Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0, line 1351 <- wrt source file 2025-08-26T20:22:02.4072463Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cartesian_prod:0 2025-08-26T20:22:02.4073626Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0, line 1385 <- wrt source file 2025-08-26T20:22:02.4082573Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::block_diag:0 2025-08-26T20:22:02.4083706Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0, line 1441 <- wrt source file 2025-08-26T20:22:02.4097320Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::cdist:0 2025-08-26T20:22:02.4098442Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0, line 1482 <- wrt source file 2025-08-26T20:22:02.4114741Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_1d:0 2025-08-26T20:22:02.4115881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0, line 1520 <- wrt source file 2025-08-26T20:22:02.4132707Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_2d:0 2025-08-26T20:22:02.4134050Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0, line 1560 <- wrt source file 2025-08-26T20:22:02.4155011Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::atleast_3d:0 2025-08-26T20:22:02.4156130Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0, line 1735 <- wrt source file 2025-08-26T20:22:02.4188012Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::norm:0 2025-08-26T20:22:02.4189143Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0, line 1903 <- wrt source file 2025-08-26T20:22:02.4215312Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::unravel_index:0 2025-08-26T20:22:02.4216646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0, line 2003 <- wrt source file 2025-08-26T20:22:02.4218381Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::chain_matmul:0 2025-08-26T20:22:02.4219825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0, line 2104 <- wrt source file 2025-08-26T20:22:02.4221038Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py::_lu_impl:0 2025-08-26T20:22:02.4222094Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0, line 473 <- wrt source file 2025-08-26T20:22:02.4223154Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::list:0 2025-08-26T20:22:02.4224359Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0, line 533 <- wrt source file 2025-08-26T20:22:02.4225395Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py::help:0 2025-08-26T20:22:02.4226480Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0, line 153 <- wrt source file 2025-08-26T20:22:02.4227634Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.define:0 2025-08-26T20:22:02.4228854Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0, line 247 <- wrt source file 2025-08-26T20:22:02.4236952Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library._impl_with_aoti_compile:0 2025-08-26T20:22:02.4238174Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0, line 307 <- wrt source file 2025-08-26T20:22:02.4241531Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::Library.impl:0 2025-08-26T20:22:02.4242647Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0, line 512 <- wrt source file 2025-08-26T20:22:02.4270776Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::define:0 2025-08-26T20:22:02.4271849Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0, line 618 <- wrt source file 2025-08-26T20:22:02.4286644Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::impl:0 2025-08-26T20:22:02.4288072Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0, line 799 <- wrt source file 2025-08-26T20:22:02.4289341Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_kernel:0 2025-08-26T20:22:02.4290663Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0, line 867 <- wrt source file 2025-08-26T20:22:02.4292013Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_autocast:0 2025-08-26T20:22:02.4293228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0, line 1232 <- wrt source file 2025-08-26T20:22:02.4379457Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_torch_dispatch:0 2025-08-26T20:22:02.4380710Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0, line 1321 <- wrt source file 2025-08-26T20:22:02.4530027Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py::register_vmap:0 2025-08-26T20:22:02.4531225Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0, line 116 <- wrt source file 2025-08-26T20:22:02.4536854Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_ignored_functions:0 2025-08-26T20:22:02.4538163Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0, line 422 <- wrt source file 2025-08-26T20:22:02.4574745Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::get_testing_overrides:0 2025-08-26T20:22:02.4575991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0, line 1577 <- wrt source file 2025-08-26T20:22:02.4578157Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::wrap_torch_function:0 2025-08-26T20:22:02.4579398Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0, line 1712 <- wrt source file 2025-08-26T20:22:02.4581273Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::handle_torch_function:0 2025-08-26T20:22:02.4582539Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0, line 1960 <- wrt source file 2025-08-26T20:22:02.4608402Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_method_or_property:0 2025-08-26T20:22:02.4609646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0, line 1979 <- wrt source file 2025-08-26T20:22:02.4615733Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/overrides.py::is_tensor_like:0 2025-08-26T20:22:02.4617160Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0, line 39 <- wrt source file 2025-08-26T20:22:02.4618687Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/quasirandom.py::SobolEngine:0 2025-08-26T20:22:02.4620189Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0, line 299 <- wrt source file 2025-08-26T20:22:02.4621518Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::add_safe_globals:0 2025-08-26T20:22:02.4622728Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0, line 324 <- wrt source file 2025-08-26T20:22:02.4624181Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::safe_globals:0 2025-08-26T20:22:02.4625999Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0, line 400 <- wrt source file 2025-08-26T20:22:02.4627387Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::skip_data:0 2025-08-26T20:22:02.4628717Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0, line 472 <- wrt source file 2025-08-26T20:22:02.4630023Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::register_package:0 2025-08-26T20:22:02.4631190Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0, line 950 <- wrt source file 2025-08-26T20:22:02.4632343Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py::save:0 2025-08-26T20:22:02.4633487Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0, line 19 <- wrt source file 2025-08-26T20:22:02.4634761Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/torch_version.py::TorchVersion:0 2025-08-26T20:22:02.4635990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0, line 320 <- wrt source file 2025-08-26T20:22:02.4637282Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_mode_options:0 2025-08-26T20:22:02.4638516Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0, line 357 <- wrt source file 2025-08-26T20:22:02.4642943Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/__init__.py::list_options:0 2025-08-26T20:22:02.4644240Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0, line 113 <- wrt source file 2025-08-26T20:22:02.4648938Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::current_accelerator:0 2025-08-26T20:22:02.4650637Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0, line 249 <- wrt source file 2025-08-26T20:22:02.4651954Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py::device_index:0 2025-08-26T20:22:02.4653188Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0, line 125 <- wrt source file 2025-08-26T20:22:02.4654437Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::allow_in_graph:0 2025-08-26T20:22:02.4655702Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0, line 181 <- wrt source file 2025-08-26T20:22:03.4006987Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::substitute_in_graph:0 2025-08-26T20:22:03.4008433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0, line 411 <- wrt source file 2025-08-26T20:22:03.4010143Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::wrap_numpy:0 2025-08-26T20:22:03.4011450Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0, line 443 <- wrt source file 2025-08-26T20:22:03.4012861Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_compiling:0 2025-08-26T20:22:03.4014311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0, line 464 <- wrt source file 2025-08-26T20:22:03.4015750Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_dynamo_compiling:0 2025-08-26T20:22:03.4017303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0, line 482 <- wrt source file 2025-08-26T20:22:03.4019275Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::is_exporting:0 2025-08-26T20:22:03.4020991Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0, line 497 <- wrt source file 2025-08-26T20:22:03.4022763Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::save_cache_artifacts:0 2025-08-26T20:22:03.4024711Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0, line 512 <- wrt source file 2025-08-26T20:22:03.4026588Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/compiler/__init__.py::load_cache_artifacts:0 2025-08-26T20:22:03.4028358Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py::_compile_kernel:0, line 1760 <- wrt source file 2025-08-26T20:22:03.4030012Z * SKIPPED: 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DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0, line 197 <- wrt source file 2025-08-26T20:22:03.4039694Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.add_done_callback:0 2025-08-26T20:22:03.4040993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0, line 261 <- wrt source file 2025-08-26T20:22:03.4042293Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::Future.set_exception:0 2025-08-26T20:22:03.4043743Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0, line 295 <- wrt source file 2025-08-26T20:22:03.4045157Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py::collect_all:0 2025-08-26T20:22:03.4046296Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0, line 147 <- wrt source file 2025-08-26T20:22:03.4047417Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/__init__.py::annotate:0 2025-08-26T20:22:03.4048646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0, line 22 <- wrt source file 2025-08-26T20:22:03.4050004Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/monitor/__init__.py::TensorboardEventHandler:0 2025-08-26T20:22:03.4051347Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0, line 61 <- wrt source file 2025-08-26T20:22:03.4071349Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::as_nested_tensor:0 2025-08-26T20:22:03.4072584Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0, line 240 <- wrt source file 2025-08-26T20:22:03.4076989Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor:0 2025-08-26T20:22:03.4078163Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0, line 315 <- wrt source file 2025-08-26T20:22:03.4143354Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::narrow:0 2025-08-26T20:22:03.4144764Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0, line 405 <- wrt source file 2025-08-26T20:22:03.4168122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::nested_tensor_from_jagged:0 2025-08-26T20:22:03.4169387Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0, line 481 <- wrt source file 2025-08-26T20:22:03.4190055Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py::masked_select:0 2025-08-26T20:22:03.4191345Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0, line 475 <- wrt source file 2025-08-26T20:22:03.4213380Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py::check_sparse_tensor_invariants:0 2025-08-26T20:22:03.4215044Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0, line 349 <- wrt source file 2025-08-26T20:22:03.4217406Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/decorators.py::substitute_in_graph:0 2025-08-26T20:22:03.4220033Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0, line 322 <- wrt source file 2025-08-26T20:22:03.4221906Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_dynamo/variables/base.py::VariableTracker.python_type:0 2025-08-26T20:22:03.4223328Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0, line 1410 <- wrt source file 2025-08-26T20:22:03.4224751Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/utils.py::register_module_as_pytree_input_node:0 2025-08-26T20:22:03.4226266Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0, line 158 <- wrt source file 2025-08-26T20:22:03.4228014Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_export/wrappers.py::mark_subclass_constructor_exportable_experimental:0 2025-08-26T20:22:03.4229415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0, line 768 <- wrt source file 2025-08-26T20:22:03.4625140Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/aot_autograd.py::aot_function:0 2025-08-26T20:22:03.4628044Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0, line 184 <- wrt source file 2025-08-26T20:22:03.4631324Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/benchmark_utils.py::benchmark_utilization:0 2025-08-26T20:22:03.4634037Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0, line 233 <- wrt source file 2025-08-26T20:22:03.4678098Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::vjp:0 2025-08-26T20:22:03.4679397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0, line 475 <- wrt source file 2025-08-26T20:22:03.4754358Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacrev:0 2025-08-26T20:22:03.4755639Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0, line 1023 <- wrt source file 2025-08-26T20:22:03.5453045Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jvp:0 2025-08-26T20:22:03.5455599Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0, line 1181 <- wrt source file 2025-08-26T20:22:03.5531946Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::jacfwd:0 2025-08-26T20:22:03.5534509Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0, line 1341 <- wrt source file 2025-08-26T20:22:03.5565936Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::hessian:0 2025-08-26T20:22:03.5568579Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0, line 1505 <- wrt source file 2025-08-26T20:22:03.5571368Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::functionalize:0 2025-08-26T20:22:03.5574017Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0, line 1704 <- wrt source file 2025-08-26T20:22:03.5810621Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/eager_transforms.py::linearize:0 2025-08-26T20:22:03.5813263Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0, line 36 <- wrt source file 2025-08-26T20:22:03.5815991Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/functional_call.py::functional_call:0 2025-08-26T20:22:03.5818564Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0, line 194 <- wrt source file 2025-08-26T20:22:03.5821128Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/fx_minifier.py::minifier:0 2025-08-26T20:22:03.5824183Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0, line 1131 <- wrt source file 2025-08-26T20:22:03.5827325Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::CompilerWrapper.post_compile:0 2025-08-26T20:22:03.5830405Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0, line 1186 <- wrt source file 2025-08-26T20:22:03.5833533Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/_aot_autograd/schemas.py::InductorWrapper.post_compile:0 2025-08-26T20:22:03.5836600Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0, line 186 <- wrt source file 2025-08-26T20:22:03.5839538Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::associative_scan:0 2025-08-26T20:22:03.5842478Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0, line 322 <- wrt source file 2025-08-26T20:22:03.5845544Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/associative_scan.py::generic_associative_scan:0 2025-08-26T20:22:03.5848185Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0, line 155 <- wrt source file 2025-08-26T20:22:03.5850580Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/cond.py::cond:0 2025-08-26T20:22:03.5853224Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0, line 80 <- wrt source file 2025-08-26T20:22:03.5856060Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/flat_apply.py::FlatApply.__call__:0 2025-08-26T20:22:03.5858568Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0, line 79 <- wrt source file 2025-08-26T20:22:03.5860989Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/map.py::map:0 2025-08-26T20:22:03.5863316Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0, line 156 <- wrt source file 2025-08-26T20:22:03.5865701Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::scan:0 2025-08-26T20:22:03.5868187Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0, line 474 <- wrt source file 2025-08-26T20:22:03.5870814Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_higher_order_ops/scan.py::ScanAutogradOp:0 2025-08-26T20:22:03.5873427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/codecache.py::WritableTempFile:0, line 372 <- wrt source file 2025-08-26T20:22:03.5876064Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/codecache.py::WritableTempFile:0 2025-08-26T20:22:03.5878837Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0, line 1721 <- wrt source file 2025-08-26T20:22:03.5881867Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/cpp_builder.py::get_name_and_dir_from_output_file_path:0 2025-08-26T20:22:03.5884768Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0, line 3418 <- wrt source file 2025-08-26T20:22:03.5887665Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/select_algorithm.py::add_preprocessing_fn:0 2025-08-26T20:22:03.5890539Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/template_registry.py::register_template_heuristic:0, line 45 <- wrt source file 2025-08-26T20:22:03.5893723Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_inductor/template_registry.py::register_template_heuristic:0 2025-08-26T20:22:03.5896357Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0, line 98 <- wrt source file 2025-08-26T20:22:03.6499788Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::custom_op:0 2025-08-26T20:22:03.6501194Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0, line 238 <- wrt source file 2025-08-26T20:22:03.6589360Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.set_kernel_enabled:0 2025-08-26T20:22:03.6590790Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0, line 307 <- wrt source file 2025-08-26T20:22:03.6592429Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_kernel:0 2025-08-26T20:22:03.6593842Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0, line 541 <- wrt source file 2025-08-26T20:22:03.6761628Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autograd:0 2025-08-26T20:22:03.6763035Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0, line 709 <- wrt source file 2025-08-26T20:22:03.6930940Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_vmap:0 2025-08-26T20:22:03.6932675Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0, line 795 <- wrt source file 2025-08-26T20:22:03.6934131Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py::CustomOpDef.register_autocast:0 2025-08-26T20:22:03.6935555Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0, line 230 <- wrt source file 2025-08-26T20:22:03.6936979Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_class_registry.py::register_fake_class:0 2025-08-26T20:22:03.6938362Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0, line 175 <- wrt source file 2025-08-26T20:22:03.7010903Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_impl.py::FakeImplCtx.new_dynamic_size:0 2025-08-26T20:22:03.7012218Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0, line 51 <- wrt source file 2025-08-26T20:22:03.7017260Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/infer_schema.py::infer_schema:0 2025-08-26T20:22:03.7018729Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0, line 459 <- wrt source file 2025-08-26T20:22:03.7020126Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_logging/_internal.py::set_logs:0 2025-08-26T20:22:03.7021429Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0, line 171 <- wrt source file 2025-08-26T20:22:03.7056489Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_equal:0 2025-08-26T20:22:03.7057772Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0, line 1008 <- wrt source file 2025-08-26T20:22:03.7113489Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_less:0 2025-08-26T20:22:03.7115103Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0, line 1073 <- wrt source file 2025-08-26T20:22:03.7116486Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_string_equal:0 2025-08-26T20:22:03.7117794Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0, line 1294 <- wrt source file 2025-08-26T20:22:03.7179343Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_allclose:0 2025-08-26T20:22:03.7180794Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0, line 1360 <- wrt source file 2025-08-26T20:22:03.7183207Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_almost_equal_nulp:0 2025-08-26T20:22:03.7184722Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0, line 1423 <- wrt source file 2025-08-26T20:22:03.7188446Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_array_max_ulp:0 2025-08-26T20:22:03.7189760Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0, line 1468 <- wrt source file 2025-08-26T20:22:03.7191068Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::nulp_diff:0 2025-08-26T20:22:03.7192459Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0, line 1578 <- wrt source file 2025-08-26T20:22:03.7194791Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py::assert_warns:0 2025-08-26T20:22:03.7196493Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0, line 95 <- wrt source file 2025-08-26T20:22:03.7198270Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims/context.py::TorchRefsMode:0 2025-08-26T20:22:03.7200150Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0, line 64 <- wrt source file 2025-08-26T20:22:03.7201818Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/amp/grad_scaler.py::GradScaler:0 2025-08-26T20:22:03.7203337Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0, line 30 <- wrt source file 2025-08-26T20:22:03.7204991Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/qat/modules/linear_relu.py::LinearReLU:0 2025-08-26T20:22:03.7206690Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0, line 24 <- wrt source file 2025-08-26T20:22:03.7208699Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py::LinearReLU:0 2025-08-26T20:22:03.7210424Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0, line 25 <- wrt source file 2025-08-26T20:22:03.7212158Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearReLU:0 2025-08-26T20:22:03.7213875Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0, line 67 <- wrt source file 2025-08-26T20:22:03.7215858Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearLeakyReLU:0 2025-08-26T20:22:03.7217583Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0, line 142 <- wrt source file 2025-08-26T20:22:03.7219229Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/intrinsic/quantized/modules/linear_relu.py::LinearTanh:0 2025-08-26T20:22:03.7220818Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0, line 30 <- wrt source file 2025-08-26T20:22:03.7224074Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTMCell:0 2025-08-26T20:22:03.7225606Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0, line 413 <- wrt source file 2025-08-26T20:22:03.7256091Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantizable/modules/rnn.py::LSTM:0 2025-08-26T20:22:03.7258974Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0, line 211 <- wrt source file 2025-08-26T20:22:03.7260985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv1d:0 2025-08-26T20:22:03.7262271Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0, line 283 <- wrt source file 2025-08-26T20:22:03.7263579Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv2d:0 2025-08-26T20:22:03.7264894Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0, line 359 <- wrt source file 2025-08-26T20:22:03.7266545Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/functional.py::conv3d:0 2025-08-26T20:22:03.7267863Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0, line 95 <- wrt source file 2025-08-26T20:22:03.7269238Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::Quantize:0 2025-08-26T20:22:03.7270862Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0, line 145 <- wrt source file 2025-08-26T20:22:03.7273045Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/__init__.py::DeQuantize:0 2025-08-26T20:22:03.7274655Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0, line 43 <- wrt source file 2025-08-26T20:22:03.7276633Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv1d:0 2025-08-26T20:22:03.7278930Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0, line 124 <- wrt source file 2025-08-26T20:22:03.7281214Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv2d:0 2025-08-26T20:22:03.7282638Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0, line 209 <- wrt source file 2025-08-26T20:22:03.7284059Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::Conv3d:0 2025-08-26T20:22:03.7286027Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0, line 296 <- wrt source file 2025-08-26T20:22:03.7288282Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose1d:0 2025-08-26T20:22:03.7290548Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0, line 378 <- wrt source file 2025-08-26T20:22:03.7292360Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose2d:0 2025-08-26T20:22:03.7293884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0, line 460 <- wrt source file 2025-08-26T20:22:03.7295540Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/conv.py::ConvTranspose3d:0 2025-08-26T20:22:03.7297008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0, line 30 <- wrt source file 2025-08-26T20:22:03.7298465Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/linear.py::Linear:0 2025-08-26T20:22:03.7299850Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0, line 515 <- wrt source file 2025-08-26T20:22:03.7301342Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTM:0 2025-08-26T20:22:03.7302695Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0, line 801 <- wrt source file 2025-08-26T20:22:03.7304071Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRU:0 2025-08-26T20:22:03.7305448Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0, line 1206 <- wrt source file 2025-08-26T20:22:03.7306890Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::RNNCell:0 2025-08-26T20:22:03.7308284Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0, line 1273 <- wrt source file 2025-08-26T20:22:03.7309720Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::LSTMCell:0 2025-08-26T20:22:03.7311129Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0, line 1326 <- wrt source file 2025-08-26T20:22:03.7312557Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/dynamic/modules/rnn.py::GRUCell:0 2025-08-26T20:22:03.7313980Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0, line 36 <- wrt source file 2025-08-26T20:22:03.7315362Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/activation.py::ReLU6:0 2025-08-26T20:22:03.7316683Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0, line 505 <- wrt source file 2025-08-26T20:22:03.7317996Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv2d:0 2025-08-26T20:22:03.7319370Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0, line 635 <- wrt source file 2025-08-26T20:22:03.7320695Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::Conv3d:0 2025-08-26T20:22:03.7322036Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0, line 892 <- wrt source file 2025-08-26T20:22:03.7323463Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose1d:0 2025-08-26T20:22:03.7324920Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0, line 1014 <- wrt source file 2025-08-26T20:22:03.7326353Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose2d:0 2025-08-26T20:22:03.7327825Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0, line 1140 <- wrt source file 2025-08-26T20:22:03.7329727Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py::ConvTranspose3d:0 2025-08-26T20:22:03.7331506Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0, line 111 <- wrt source file 2025-08-26T20:22:03.7332986Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::Embedding:0 2025-08-26T20:22:03.7334438Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0, line 275 <- wrt source file 2025-08-26T20:22:03.7345233Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/embedding_ops.py::EmbeddingBag:0 2025-08-26T20:22:03.7346794Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0, line 23 <- wrt source file 2025-08-26T20:22:03.7351345Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::FloatFunctional:0 2025-08-26T20:22:03.7352903Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0, line 176 <- wrt source file 2025-08-26T20:22:03.7356464Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/functional_modules.py::QFunctional:0 2025-08-26T20:22:03.7358684Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0, line 138 <- wrt source file 2025-08-26T20:22:03.7360298Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/linear.py::Linear:0 2025-08-26T20:22:03.7361857Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0, line 55 <- wrt source file 2025-08-26T20:22:03.7363733Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py::BaseDataSparsifier:0 2025-08-26T20:22:03.7365315Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0, line 24 <- wrt source file 2025-08-26T20:22:03.7391502Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/scheduler/lambda_scheduler.py::LambdaSL:0 2025-08-26T20:22:03.7394787Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0, line 47 <- wrt source file 2025-08-26T20:22:03.7396368Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py::BaseSparsifier:0 2025-08-26T20:22:03.7397788Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0, line 176 <- wrt source file 2025-08-26T20:22:03.7399161Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuse_modules.py::fuse_modules:0 2025-08-26T20:22:03.7400555Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0, line 31 <- wrt source file 2025-08-26T20:22:03.7405496Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn:0 2025-08-26T20:22:03.7407060Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0, line 76 <- wrt source file 2025-08-26T20:22:03.7414150Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_conv_bn_relu:0 2025-08-26T20:22:03.7416130Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0, line 130 <- wrt source file 2025-08-26T20:22:03.7420619Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_linear_bn:0 2025-08-26T20:22:03.7422152Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0, line 163 <- wrt source file 2025-08-26T20:22:03.7428610Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fuser_method_mappings.py::fuse_convtranspose_bn:0 2025-08-26T20:22:03.7431467Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0, line 110 <- wrt source file 2025-08-26T20:22:03.7434102Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_args:0 2025-08-26T20:22:03.7436767Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0, line 132 <- wrt source file 2025-08-26T20:22:03.7439592Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/observer.py::_with_callable_args:0 2025-08-26T20:22:03.7442254Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0, line 218 <- wrt source file 2025-08-26T20:22:03.7444881Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::fuse_fx:0 2025-08-26T20:22:03.7447658Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0, line 288 <- wrt source file 2025-08-26T20:22:03.7450359Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_fx:0 2025-08-26T20:22:03.7453035Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0, line 427 <- wrt source file 2025-08-26T20:22:03.7455788Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::prepare_qat_fx:0 2025-08-26T20:22:03.7458743Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0, line 608 <- wrt source file 2025-08-26T20:22:03.7461820Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_fx:0 2025-08-26T20:22:03.7464602Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0, line 668 <- wrt source file 2025-08-26T20:22:03.7467550Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::convert_to_reference_fx:0 2025-08-26T20:22:03.7470786Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0, line 720 <- wrt source file 2025-08-26T20:22:03.7474002Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_fx.py::_convert_to_reference_decomposed_fx:0 2025-08-26T20:22:03.7477009Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0, line 51 <- wrt source file 2025-08-26T20:22:03.7479806Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_pt2e:0 2025-08-26T20:22:03.7482582Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0, line 130 <- wrt source file 2025-08-26T20:22:03.7485448Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::prepare_qat_pt2e:0 2025-08-26T20:22:03.7488243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0, line 228 <- wrt source file 2025-08-26T20:22:03.7491022Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/quantize_pt2e.py::convert_pt2e:0 2025-08-26T20:22:03.7493876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0, line 172 <- wrt source file 2025-08-26T20:22:03.7496630Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::get_combined_dict:0 2025-08-26T20:22:03.7499291Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0, line 544 <- wrt source file 2025-08-26T20:22:03.7502075Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_path_of_module:0 2025-08-26T20:22:03.7504801Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0, line 566 <- wrt source file 2025-08-26T20:22:03.7507590Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_signature_locals:0 2025-08-26T20:22:03.7510329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0, line 580 <- wrt source file 2025-08-26T20:22:03.7513158Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_default_kwargs:0 2025-08-26T20:22:03.7515872Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0, line 602 <- wrt source file 2025-08-26T20:22:03.7518599Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_normalize_kwargs:0 2025-08-26T20:22:03.7521243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0, line 729 <- wrt source file 2025-08-26T20:22:03.7524028Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/utils.py::_get_num_pos_args:0 2025-08-26T20:22:03.7526932Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0, line 85 <- wrt source file 2025-08-26T20:22:03.7530166Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/onednn.py::_fuse_linear_bn_leaky_relu:0 2025-08-26T20:22:03.7533350Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0, line 102 <- wrt source file 2025-08-26T20:22:03.7536583Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_get_reduction_params:0 2025-08-26T20:22:03.7539743Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0, line 148 <- wrt source file 2025-08-26T20:22:03.7543036Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/_affine_quantization.py::_register_custom_op:0 2025-08-26T20:22:03.7546128Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0, line 188 <- wrt source file 2025-08-26T20:22:03.7549238Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/prepare.py::_get_edge_or_node_to_group_id:0 2025-08-26T20:22:03.7552414Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0, line 436 <- wrt source file 2025-08-26T20:22:03.7555710Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/pt2e/utils.py::_replace_literals_with_new_placeholders:0 2025-08-26T20:22:03.7558598Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0, line 28 <- wrt source file 2025-08-26T20:22:03.7561235Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/anomaly_mode.py::detect_anomaly:0 2025-08-26T20:22:03.7563715Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0, line 82 <- wrt source file 2025-08-26T20:22:03.7566202Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::make_dual:0 2025-08-26T20:22:03.7568644Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0, line 151 <- wrt source file 2025-08-26T20:22:03.7571154Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::unpack_dual:0 2025-08-26T20:22:03.7573622Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0, line 187 <- wrt source file 2025-08-26T20:22:03.7576156Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/forward_ad.py::dual_level:0 2025-08-26T20:22:03.7578796Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0, line 71 <- wrt source file 2025-08-26T20:22:03.7581731Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_backward:0 2025-08-26T20:22:03.7584542Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0, line 115 <- wrt source file 2025-08-26T20:22:03.7587408Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.save_for_forward:0 2025-08-26T20:22:03.7590243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0, line 167 <- wrt source file 2025-08-26T20:22:03.7593225Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_dirty:0 2025-08-26T20:22:03.7596056Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0, line 214 <- wrt source file 2025-08-26T20:22:03.7599061Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.mark_non_differentiable:0 2025-08-26T20:22:03.7602001Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0, line 243 <- wrt source file 2025-08-26T20:22:03.7605068Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::FunctionCtx.set_materialize_grads:0 2025-08-26T20:22:03.7607710Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0, line 485 <- wrt source file 2025-08-26T20:22:03.7610127Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/function.py::Function:0 2025-08-26T20:22:03.7612465Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0, line 293 <- wrt source file 2025-08-26T20:22:03.7614814Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vjp:0 2025-08-26T20:22:03.7617140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0, line 395 <- wrt source file 2025-08-26T20:22:03.7619524Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jvp:0 2025-08-26T20:22:03.7621970Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0, line 630 <- wrt source file 2025-08-26T20:22:03.7624446Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::jacobian:0 2025-08-26T20:22:03.7626871Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0, line 894 <- wrt source file 2025-08-26T20:22:03.7629311Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hessian:0 2025-08-26T20:22:03.7631683Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0, line 1010 <- wrt source file 2025-08-26T20:22:03.7634059Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::vhp:0 2025-08-26T20:22:03.7636383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0, line 1109 <- wrt source file 2025-08-26T20:22:03.7638882Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/functional.py::hvp:0 2025-08-26T20:22:03.7641196Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0, line 50 <- wrt source file 2025-08-26T20:22:03.7643561Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::no_grad:0 2025-08-26T20:22:03.7645952Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0, line 108 <- wrt source file 2025-08-26T20:22:03.7648456Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::enable_grad:0 2025-08-26T20:22:03.7651057Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0, line 166 <- wrt source file 2025-08-26T20:22:03.7653647Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::set_grad_enabled:0 2025-08-26T20:22:03.7656168Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0, line 238 <- wrt source file 2025-08-26T20:22:03.7658734Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/grad_mode.py::inference_mode:0 2025-08-26T20:22:03.7661192Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0, line 53 <- wrt source file 2025-08-26T20:22:03.7663544Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.name:0 2025-08-26T20:22:03.7666021Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0, line 110 <- wrt source file 2025-08-26T20:22:03.7668573Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_hook:0 2025-08-26T20:22:03.7671115Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0, line 147 <- wrt source file 2025-08-26T20:22:03.7673748Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::Node.register_prehook:0 2025-08-26T20:22:03.7676294Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0, line 283 <- wrt source file 2025-08-26T20:22:03.7678870Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::saved_tensors_hooks:0 2025-08-26T20:22:03.7681300Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0, line 353 <- wrt source file 2025-08-26T20:22:03.7683688Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::save_on_cpu:0 2025-08-26T20:22:03.7686217Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0, line 410 <- wrt source file 2025-08-26T20:22:03.7688955Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::disable_saved_tensors_hooks:0 2025-08-26T20:22:03.7691592Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0, line 487 <- wrt source file 2025-08-26T20:22:03.7694404Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::register_multi_grad_hook:0 2025-08-26T20:22:03.7697115Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0, line 753 <- wrt source file 2025-08-26T20:22:03.7700010Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/graph.py::allow_mutation_on_saved_tensors:0 2025-08-26T20:22:03.7702649Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0, line 182 <- wrt source file 2025-08-26T20:22:03.7705060Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::profile:0 2025-08-26T20:22:03.7707397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0, line 880 <- wrt source file 2025-08-26T20:22:03.7709800Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_itt:0 2025-08-26T20:22:03.7712271Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0, line 953 <- wrt source file 2025-08-26T20:22:03.7714714Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py::emit_nvtx:0 2025-08-26T20:22:03.7717069Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0, line 42 <- wrt source file 2025-08-26T20:22:03.7719479Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_register_buffer:0 2025-08-26T20:22:03.7721859Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0, line 58 <- wrt source file 2025-08-26T20:22:03.7724302Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::gds_deregister_buffer:0 2025-08-26T20:22:03.7726632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0, line 85 <- wrt source file 2025-08-26T20:22:03.7728822Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/gds.py::GdsFile:0 2025-08-26T20:22:03.7731118Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0, line 114 <- wrt source file 2025-08-26T20:22:03.7733558Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:0 2025-08-26T20:22:03.7735954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1, line 125 <- wrt source file 2025-08-26T20:22:03.7754536Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:1 2025-08-26T20:22:03.7757198Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2, line 140 <- wrt source file 2025-08-26T20:22:03.7759701Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_jit_fn:2 2025-08-26T20:22:03.7762248Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0, line 173 <- wrt source file 2025-08-26T20:22:03.7764977Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/jiterator.py::_create_multi_output_jit_fn:0 2025-08-26T20:22:03.7767454Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0, line 75 <- wrt source file 2025-08-26T20:22:03.7769770Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/profiler.py::profile:0 2025-08-26T20:22:03.7772191Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0, line 431 <- wrt source file 2025-08-26T20:22:03.7774844Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh:0 2025-08-26T20:22:03.7777662Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0, line 975 <- wrt source file 2025-08-26T20:22:03.7780669Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::DeviceMesh.get_local_rank:0 2025-08-26T20:22:03.7783444Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0, line 1121 <- wrt source file 2025-08-26T20:22:03.7786190Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py::init_device_mesh:0 2025-08-26T20:22:03.7789091Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0, line 2578 <- wrt source file 2025-08-26T20:22:03.7792153Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_coalescing_manager:0 2025-08-26T20:22:03.7794990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0, line 2680 <- wrt source file 2025-08-26T20:22:03.7797818Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::_time_estimator:0 2025-08-26T20:22:03.7800618Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0, line 3147 <- wrt source file 2025-08-26T20:22:03.7803485Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_object:0 2025-08-26T20:22:03.7806412Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0, line 3376 <- wrt source file 2025-08-26T20:22:03.7809259Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::send_object_list:0 2025-08-26T20:22:03.7812047Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0, line 3478 <- wrt source file 2025-08-26T20:22:03.7814890Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::recv_object_list:0 2025-08-26T20:22:03.7817729Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0, line 3592 <- wrt source file 2025-08-26T20:22:03.7820771Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::broadcast_object_list:0 2025-08-26T20:22:03.7823658Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0, line 3715 <- wrt source file 2025-08-26T20:22:03.7826562Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::scatter_object_list:0 2025-08-26T20:22:03.7829458Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0, line 3924 <- wrt source file 2025-08-26T20:22:03.7832424Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::all_gather_into_tensor:0 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DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0, line 4391 <- wrt source file 2025-08-26T20:22:03.7854818Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::reduce_scatter_tensor:0 2025-08-26T20:22:03.7857702Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0, line 4873 <- wrt source file 2025-08-26T20:22:03.7860636Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::monitored_barrier:0 2025-08-26T20:22:03.7863415Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0, line 5415 <- wrt source file 2025-08-26T20:22:03.7866208Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py::new_subgroups:0 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line 54 <- wrt source file 2025-08-26T20:22:03.8223087Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/sharded_grad_scaler.py::ShardedGradScaler:0 2025-08-26T20:22:03.8225881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0, line 224 <- wrt source file 2025-08-26T20:22:03.8228477Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/wrap.py::CustomPolicy:0 2025-08-26T20:22:03.8231123Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0, line 134 <- wrt source file 2025-08-26T20:22:03.8233933Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/functional.py::_all_gather_base:0 2025-08-26T20:22:03.8237093Z * DOCTEST : 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SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_tuple:0 2025-08-26T20:22:03.8274302Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0, line 102 <- wrt source file 2025-08-26T20:22:03.8277540Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py::TensorChunkSpec.from_dict:0 2025-08-26T20:22:03.8280311Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::_wait_all:0, line 174 <- wrt source file 2025-08-26T20:22:03.8282776Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::_wait_all:0 2025-08-26T20:22:03.8285190Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/api.py::shutdown:0, line 345 <- wrt source file 2025-08-26T20:22:03.8287891Z * SKIPPED: 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wrt source file 2025-08-26T20:22:03.8361707Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py::SequenceParallel:0 2025-08-26T20:22:03.8364452Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0, line 30 <- wrt source file 2025-08-26T20:22:03.8367041Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/bernoulli.py::Bernoulli:0 2025-08-26T20:22:03.8369446Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0, line 21 <- wrt source file 2025-08-26T20:22:03.8371848Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/beta.py::Beta:0 2025-08-26T20:22:03.8374254Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0, line 31 <- wrt source file 2025-08-26T20:22:03.8376788Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/binomial.py::Binomial:0 2025-08-26T20:22:03.8379369Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0, line 42 <- wrt source file 2025-08-26T20:22:03.8382233Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/categorical.py::Categorical:0 2025-08-26T20:22:03.8384750Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0, line 23 <- wrt source file 2025-08-26T20:22:03.8387207Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/cauchy.py::Cauchy:0 2025-08-26T20:22:03.8389542Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0, line 18 <- wrt source file 2025-08-26T20:22:03.8392005Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/chi2.py::Chi2:0 2025-08-26T20:22:03.8394499Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0, line 166 <- wrt source file 2025-08-26T20:22:03.8397304Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::is_dependent:0 2025-08-26T20:22:03.8400054Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0, line 187 <- wrt source file 2025-08-26T20:22:03.8402935Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/constraints.py::_DependentProperty:0 2025-08-26T20:22:03.8405876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0, line 35 <- wrt source file 2025-08-26T20:22:03.8408992Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/continuous_bernoulli.py::ContinuousBernoulli:0 2025-08-26T20:22:03.8411764Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0, line 42 <- wrt source file 2025-08-26T20:22:03.8414345Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/dirichlet.py::Dirichlet:0 2025-08-26T20:22:03.8416954Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0, line 20 <- wrt source file 2025-08-26T20:22:03.8419673Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/exponential.py::Exponential:0 2025-08-26T20:22:03.8422511Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0, line 21 <- wrt source file 2025-08-26T20:22:03.8425367Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/fishersnedecor.py::FisherSnedecor:0 2025-08-26T20:22:03.8427900Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0, line 24 <- wrt source file 2025-08-26T20:22:03.8430308Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gamma.py::Gamma:0 2025-08-26T20:22:03.8432973Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0, line 26 <- wrt source file 2025-08-26T20:22:03.8436059Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/generalized_pareto.py::GeneralizedPareto:0 2025-08-26T20:22:03.8438779Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0, line 36 <- wrt source file 2025-08-26T20:22:03.8441367Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/geometric.py::Geometric:0 2025-08-26T20:22:03.8443819Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0, line 23 <- wrt source file 2025-08-26T20:22:03.8446433Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/gumbel.py::Gumbel:0 2025-08-26T20:22:03.8448927Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0, line 24 <- wrt source file 2025-08-26T20:22:03.8451577Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_cauchy.py::HalfCauchy:0 2025-08-26T20:22:03.8454170Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0, line 24 <- wrt source file 2025-08-26T20:22:03.8456805Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/half_normal.py::HalfNormal:0 2025-08-26T20:22:03.8459407Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0, line 27 <- wrt source file 2025-08-26T20:22:03.8462189Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/independent.py::Independent:0 2025-08-26T20:22:03.8464862Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0, line 24 <- wrt source file 2025-08-26T20:22:03.8467611Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/inverse_gamma.py::InverseGamma:0 2025-08-26T20:22:03.8470291Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0, line 30 <- wrt source file 2025-08-26T20:22:03.8472989Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/kumaraswamy.py::Kumaraswamy:0 2025-08-26T20:22:03.8475501Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0, line 20 <- wrt source file 2025-08-26T20:22:03.8478004Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/laplace.py::Laplace:0 2025-08-26T20:22:03.8480540Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0, line 43 <- wrt source file 2025-08-26T20:22:03.8483230Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lkj_cholesky.py::LKJCholesky:0 2025-08-26T20:22:03.8485807Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0, line 23 <- wrt source file 2025-08-26T20:22:03.8488396Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/log_normal.py::LogNormal:0 2025-08-26T20:22:03.8491064Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0, line 28 <- wrt source file 2025-08-26T20:22:03.8494079Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/logistic_normal.py::LogisticNormal:0 2025-08-26T20:22:03.8496875Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0, line 38 <- wrt source file 2025-08-26T20:22:03.8499654Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multinomial.py::Multinomial:0 2025-08-26T20:22:03.8502566Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0, line 103 <- wrt source file 2025-08-26T20:22:03.8505639Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/multivariate_normal.py::MultivariateNormal:0 2025-08-26T20:22:03.8508428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0, line 22 <- wrt source file 2025-08-26T20:22:03.8510883Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/normal.py::Normal:0 2025-08-26T20:22:03.8513564Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0, line 34 <- wrt source file 2025-08-26T20:22:03.8516553Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/one_hot_categorical.py::OneHotCategorical:0 2025-08-26T20:22:03.8519210Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0, line 20 <- wrt source file 2025-08-26T20:22:03.8521664Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/pareto.py::Pareto:0 2025-08-26T20:22:03.8524155Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0, line 25 <- wrt source file 2025-08-26T20:22:03.8526658Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/poisson.py::Poisson:0 2025-08-26T20:22:03.8529129Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0, line 22 <- wrt source file 2025-08-26T20:22:03.8531646Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/studentT.py::StudentT:0 2025-08-26T20:22:03.8534246Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0, line 1065 <- wrt source file 2025-08-26T20:22:03.8536971Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CatTransform:0 2025-08-26T20:22:03.8539667Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0, line 1177 <- wrt source file 2025-08-26T20:22:03.8542497Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::StackTransform:0 2025-08-26T20:22:03.8545447Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0, line 1253 <- wrt source file 2025-08-26T20:22:03.8548634Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/transforms.py::CumulativeDistributionTransform:0 2025-08-26T20:22:03.8551383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0, line 21 <- wrt source file 2025-08-26T20:22:03.8553864Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/uniform.py::Uniform:0 2025-08-26T20:22:03.8556320Z * DOCTEST : 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0, line 39 <- wrt source file 2025-08-26T20:22:03.8573890Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/wishart.py::Wishart:0 2025-08-26T20:22:03.8576280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::Dim:0, line 103 <- wrt source file 2025-08-26T20:22:03.8578663Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::Dim:0 2025-08-26T20:22:03.8581221Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0, line 715 <- wrt source file 2025-08-26T20:22:03.8583930Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:0 2025-08-26T20:22:03.8586620Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1, line 731 <- wrt source file 2025-08-26T20:22:03.8589320Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::ShapesCollection:1 2025-08-26T20:22:03.8592027Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::AdditionalInputs:0, line 815 <- wrt source file 2025-08-26T20:22:03.8594738Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/export/dynamic_shapes.py::AdditionalInputs:0 2025-08-26T20:22:03.8597139Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0, line 102 <- wrt source file 2025-08-26T20:22:03.8599385Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::_snake_case:0 2025-08-26T20:22:03.8601773Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0, line 1873 <- wrt source file 2025-08-26T20:22:03.8604339Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.eliminate_dead_code:0 2025-08-26T20:22:03.8606798Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0, line 1967 <- wrt source file 2025-08-26T20:22:03.8609277Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/graph.py::Graph.on_generate_code:0 2025-08-26T20:22:03.8611684Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0, line 49 <- wrt source file 2025-08-26T20:22:03.8614093Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/interpreter.py::Interpreter:0 2025-08-26T20:22:03.8616465Z * DOCTEST : 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65 <- wrt source file 2025-08-26T20:22:03.8633835Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_consistent:0 2025-08-26T20:22:03.8636203Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0, line 93 <- wrt source file 2025-08-26T20:22:03.8638643Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/tensor_type.py::is_more_precise:0 2025-08-26T20:22:03.8641360Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0, line 96 <- wrt source file 2025-08-26T20:22:03.8644402Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/rewriter.py::AST_Rewriter.visit_AnnAssign:0 2025-08-26T20:22:03.8647221Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/core.py::reify:0, line 58 <- wrt source file 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0, line 122 <- wrt source file 2025-08-26T20:22:03.8703663Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::itemmap:0 2025-08-26T20:22:03.8706697Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0, line 138 <- wrt source file 2025-08-26T20:22:03.8709844Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::valfilter:0 2025-08-26T20:22:03.8712910Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::keyfilter:0, line 158 <- wrt source file 2025-08-26T20:22:03.8716134Z * SUCCESS: 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source file 2025-08-26T20:22:03.8734733Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::dissoc:0 2025-08-26T20:22:03.8737850Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0, line 416 <- wrt source file 2025-08-26T20:22:03.8740960Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py::first:0 2025-08-26T20:22:03.8743886Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0, line 15 <- wrt source file 2025-08-26T20:22:03.8746855Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::transitive_get:0 2025-08-26T20:22:03.8749701Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0, line 42 <- wrt source file 2025-08-26T20:22:03.8752640Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::_toposort:0 2025-08-26T20:22:03.8755453Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0, line 70 <- wrt source file 2025-08-26T20:22:03.8758373Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::reverse_dict:0 2025-08-26T20:22:03.8761148Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0, line 95 <- wrt source file 2025-08-26T20:22:03.8764021Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/utils.py::freeze:0 2025-08-26T20:22:03.8766830Z * DOCTEST : 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0, line 304 <- wrt source file 2025-08-26T20:22:03.8798348Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::Dispatcher.dispatch:0 2025-08-26T20:22:03.8800094Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0, line 434 <- wrt source file 2025-08-26T20:22:03.8801838Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/dispatcher.py::str_signature:0 2025-08-26T20:22:03.8803515Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::expand_tuples:0, line 18 <- wrt source file 2025-08-26T20:22:03.8805199Z * SUCCESS: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0, line 87 <- wrt source file 2025-08-26T20:22:03.8815118Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::groupby:0 2025-08-26T20:22:03.8816707Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0, line 117 <- wrt source file 2025-08-26T20:22:03.8818326Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/utils.py::typename:0 2025-08-26T20:22:03.8819928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/multipledispatch/variadic.py::isvariadic:0, line 47 <- wrt source file 2025-08-26T20:22:03.8821757Z * SKIPPED: 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file 2025-08-26T20:22:03.8830615Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/shape_prop.py::ShapeProp:0 2025-08-26T20:22:03.8831857Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0, line 89 <- wrt source file 2025-08-26T20:22:03.8833156Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/split_module.py::split_module:0 2025-08-26T20:22:03.8834685Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0, line 51 <- wrt source file 2025-08-26T20:22:03.8836463Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/passes/utils/matcher_with_name_node_map_utils.py::SubgraphMatcherWithNameNodeMap:0 2025-08-26T20:22:03.8838009Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0, line 36 <- wrt source file 2025-08-26T20:22:03.8839428Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/_check.py::AttributeTypeIsSupportedChecker:0 2025-08-26T20:22:03.8840777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0, line 22 <- wrt source file 2025-08-26T20:22:03.8842157Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_load_for_lite_interpreter:0 2025-08-26T20:22:03.8843546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0, line 122 <- wrt source file 2025-08-26T20:22:03.8845053Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_mobile_model_contained_types:0 2025-08-26T20:22:03.8846425Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0, line 214 <- wrt source file 2025-08-26T20:22:03.8847753Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/jit/mobile/__init__.py::_get_model_ops_and_info:0 2025-08-26T20:22:03.8848979Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/_ops.py::logaddexp:0, line 1530 <- wrt source file 2025-08-26T20:22:03.8850122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/_ops.py::logaddexp:0 2025-08-26T20:22:03.8851361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0, line 25 <- wrt source file 2025-08-26T20:22:03.8852755Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/masked/maskedtensor/core.py::is_masked_tensor:0 2025-08-26T20:22:03.8854185Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0, line 460 <- wrt source file 2025-08-26T20:22:03.8855601Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool2d_with_indices:0 2025-08-26T20:22:03.8856993Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0, line 579 <- wrt source file 2025-08-26T20:22:03.9800973Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::fractional_max_pool3d_with_indices:0 2025-08-26T20:22:03.9823975Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0, line 2174 <- wrt source file 2025-08-26T20:22:03.9834229Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::gumbel_softmax:0 2025-08-26T20:22:03.9836884Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0, line 2478 <- wrt source file 2025-08-26T20:22:03.9844498Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding:0 2025-08-26T20:22:03.9846879Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0, line 2618 <- wrt source file 2025-08-26T20:22:03.9855412Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::embedding_bag:0 2025-08-26T20:22:03.9857625Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0, line 3051 <- wrt source file 2025-08-26T20:22:03.9874103Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::ctc_loss:0 2025-08-26T20:22:03.9876580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0, line 3121 <- wrt source file 2025-08-26T20:22:03.9881138Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::nll_loss:0 2025-08-26T20:22:03.9883470Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0, line 3430 <- wrt source file 2025-08-26T20:22:03.9890375Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::cross_entropy:0 2025-08-26T20:22:03.9892968Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0, line 3495 <- wrt source file 2025-08-26T20:22:03.9898109Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy:0 2025-08-26T20:22:03.9900990Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0, line 3565 <- wrt source file 2025-08-26T20:22:03.9904668Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::binary_cross_entropy_with_logits:0 2025-08-26T20:22:03.9907468Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0, line 5263 <- wrt source file 2025-08-26T20:22:03.9914619Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/functional.py::pad:0 2025-08-26T20:22:03.9916781Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0, line 32 <- wrt source file 2025-08-26T20:22:03.9923104Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_input:0 2025-08-26T20:22:03.9928510Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0, line 79 <- wrt source file 2025-08-26T20:22:03.9930789Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv1d_weight:0 2025-08-26T20:22:03.9932989Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0, line 130 <- wrt source file 2025-08-26T20:22:03.9935584Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_input:0 2025-08-26T20:22:03.9937805Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0, line 177 <- wrt source file 2025-08-26T20:22:03.9940526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv2d_weight:0 2025-08-26T20:22:03.9942762Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0, line 228 <- wrt source file 2025-08-26T20:22:03.9973643Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_input:0 2025-08-26T20:22:03.9976651Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0, line 275 <- wrt source file 2025-08-26T20:22:04.0029953Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/grad.py::conv3d_weight:0 2025-08-26T20:22:04.0032600Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0, line 230 <- wrt source file 2025-08-26T20:22:04.0034795Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::uniform_:0 2025-08-26T20:22:04.0036917Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0, line 257 <- wrt source file 2025-08-26T20:22:04.0039085Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::normal_:0 2025-08-26T20:22:04.0041401Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0, line 292 <- wrt source file 2025-08-26T20:22:04.0060159Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::trunc_normal_:0 2025-08-26T20:22:04.0062478Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0, line 306 <- wrt source file 2025-08-26T20:22:04.0064689Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::constant_:0 2025-08-26T20:22:04.0066803Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0, line 323 <- wrt source file 2025-08-26T20:22:04.0069130Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::ones_:0 2025-08-26T20:22:04.0071197Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0, line 336 <- wrt source file 2025-08-26T20:22:04.0073333Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::zeros_:0 2025-08-26T20:22:04.0075397Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0, line 352 <- wrt source file 2025-08-26T20:22:04.0077497Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::eye_:0 2025-08-26T20:22:04.0079562Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0, line 374 <- wrt source file 2025-08-26T20:22:04.0081688Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::dirac_:0 2025-08-26T20:22:04.0083925Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0, line 460 <- wrt source file 2025-08-26T20:22:04.0086236Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_uniform_:0 2025-08-26T20:22:04.0088495Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0, line 492 <- wrt source file 2025-08-26T20:22:04.0090767Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::xavier_normal_:0 2025-08-26T20:22:04.0093242Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0, line 543 <- wrt source file 2025-08-26T20:22:04.0095579Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_uniform_:0 2025-08-26T20:22:04.0097855Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0, line 608 <- wrt source file 2025-08-26T20:22:04.0100138Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::kaiming_normal_:0 2025-08-26T20:22:04.0102443Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0, line 647 <- wrt source file 2025-08-26T20:22:04.0104697Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::orthogonal_:0 2025-08-26T20:22:04.0106850Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0, line 700 <- wrt source file 2025-08-26T20:22:04.0109005Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py::sparse_:0 2025-08-26T20:22:04.0111299Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0, line 120 <- wrt source file 2025-08-26T20:22:04.0113820Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/__init__.py::sdpa_kernel:0 2025-08-26T20:22:04.0116363Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0, line 95 <- wrt source file 2025-08-26T20:22:04.0118772Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/attention/bias.py::CausalBias:0 2025-08-26T20:22:04.0121219Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0, line 72 <- wrt source file 2025-08-26T20:22:04.0123747Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Threshold:0 2025-08-26T20:22:04.0126144Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0, line 120 <- wrt source file 2025-08-26T20:22:04.0128667Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU:0 2025-08-26T20:22:04.0131051Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0, line 185 <- wrt source file 2025-08-26T20:22:04.0133499Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::RReLU:0 2025-08-26T20:22:04.0135934Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0, line 247 <- wrt source file 2025-08-26T20:22:04.0138444Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardtanh:0 2025-08-26T20:22:04.0140913Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0, line 318 <- wrt source file 2025-08-26T20:22:04.0143441Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ReLU6:0 2025-08-26T20:22:04.0145810Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0, line 349 <- wrt source file 2025-08-26T20:22:04.0148309Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Sigmoid:0 2025-08-26T20:22:04.0150850Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0, line 384 <- wrt source file 2025-08-26T20:22:04.0153443Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardsigmoid:0 2025-08-26T20:22:04.0155871Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0, line 420 <- wrt source file 2025-08-26T20:22:04.0158274Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanh:0 2025-08-26T20:22:04.0160636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0, line 456 <- wrt source file 2025-08-26T20:22:04.0163055Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SiLU:0 2025-08-26T20:22:04.0165413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0, line 501 <- wrt source file 2025-08-26T20:22:04.0167819Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Mish:0 2025-08-26T20:22:04.0170234Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0, line 552 <- wrt source file 2025-08-26T20:22:04.0172766Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardswish:0 2025-08-26T20:22:04.0175173Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0, line 598 <- wrt source file 2025-08-26T20:22:04.0177615Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::ELU:0 2025-08-26T20:22:04.0179946Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0, line 646 <- wrt source file 2025-08-26T20:22:04.0182437Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::CELU:0 2025-08-26T20:22:04.0184774Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0, line 705 <- wrt source file 2025-08-26T20:22:04.0187195Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::SELU:0 2025-08-26T20:22:04.0189665Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0, line 751 <- wrt source file 2025-08-26T20:22:04.0192193Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GLU:0 2025-08-26T20:22:04.0194550Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0, line 799 <- wrt source file 2025-08-26T20:22:04.0196964Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::GELU:0 2025-08-26T20:22:04.0199396Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0, line 848 <- wrt source file 2025-08-26T20:22:04.0201987Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Hardshrink:0 2025-08-26T20:22:04.0204576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0, line 903 <- wrt source file 2025-08-26T20:22:04.0207072Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LeakyReLU:0 2025-08-26T20:22:04.0209576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0, line 945 <- wrt source file 2025-08-26T20:22:04.0212136Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSigmoid:0 2025-08-26T20:22:04.0214647Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0, line 981 <- wrt source file 2025-08-26T20:22:04.0217154Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softplus:0 2025-08-26T20:22:04.0219652Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0, line 1030 <- wrt source file 2025-08-26T20:22:04.0222290Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softshrink:0 2025-08-26T20:22:04.0224931Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0, line 1144 <- wrt source file 2025-08-26T20:22:04.0227668Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::MultiheadAttention:0 2025-08-26T20:22:04.0230326Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0, line 1609 <- wrt source file 2025-08-26T20:22:04.0233171Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::PReLU:0 2025-08-26T20:22:04.0236021Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0, line 1660 <- wrt source file 2025-08-26T20:22:04.0238749Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softsign:0 2025-08-26T20:22:04.0241348Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0, line 1686 <- wrt source file 2025-08-26T20:22:04.0243906Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Tanhshrink:0 2025-08-26T20:22:04.0246367Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0, line 1724 <- wrt source file 2025-08-26T20:22:04.0248840Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmin:0 2025-08-26T20:22:04.0251388Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0, line 1788 <- wrt source file 2025-08-26T20:22:04.0253876Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax:0 2025-08-26T20:22:04.0256348Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0, line 1835 <- wrt source file 2025-08-26T20:22:04.0258880Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::Softmax2d:0 2025-08-26T20:22:04.0261433Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0, line 1874 <- wrt source file 2025-08-26T20:22:04.0263968Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/activation.py::LogSoftmax:0 2025-08-26T20:22:04.0266472Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0, line 332 <- wrt source file 2025-08-26T20:22:04.0269079Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm1d:0 2025-08-26T20:22:04.0271588Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0, line 443 <- wrt source file 2025-08-26T20:22:04.0466152Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm2d:0 2025-08-26T20:22:04.0468800Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0, line 554 <- wrt source file 2025-08-26T20:22:04.2913406Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py::BatchNorm3d:0 2025-08-26T20:22:04.3056313Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0, line 21 <- wrt source file 2025-08-26T20:22:04.3078659Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/channelshuffle.py::ChannelShuffle:0 2025-08-26T20:22:04.3081313Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0, line 81 <- wrt source file 2025-08-26T20:22:04.3083853Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential:0 2025-08-26T20:22:04.3086413Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0, line 260 <- wrt source file 2025-08-26T20:22:04.3089082Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.append:0 2025-08-26T20:22:04.3091901Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0, line 283 <- wrt source file 2025-08-26T20:22:04.3097763Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.insert:0 2025-08-26T20:22:04.3100770Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0, line 314 <- wrt source file 2025-08-26T20:22:04.3107122Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::Sequential.extend:0 2025-08-26T20:22:04.3109722Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0, line 343 <- wrt source file 2025-08-26T20:22:04.3112263Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleList:0 2025-08-26T20:22:04.3114985Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0, line 523 <- wrt source file 2025-08-26T20:22:04.3117506Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ModuleDict:0 2025-08-26T20:22:04.3120069Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0, line 653 <- wrt source file 2025-08-26T20:22:04.3122704Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterList:0 2025-08-26T20:22:04.3125256Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0, line 808 <- wrt source file 2025-08-26T20:22:04.3127866Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/container.py::ParameterDict:0 2025-08-26T20:22:04.3130457Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0, line 38 <- wrt source file 2025-08-26T20:22:04.3133206Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::PairwiseDistance:0 2025-08-26T20:22:04.3135820Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0, line 81 <- wrt source file 2025-08-26T20:22:04.3138470Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/distance.py::CosineSimilarity:0 2025-08-26T20:22:04.3140986Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0, line 60 <- wrt source file 2025-08-26T20:22:04.3143391Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout:0 2025-08-26T20:22:04.3145795Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0, line 108 <- wrt source file 2025-08-26T20:22:04.3148260Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout1d:0 2025-08-26T20:22:04.3150666Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0, line 163 <- wrt source file 2025-08-26T20:22:04.3162224Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout2d:0 2025-08-26T20:22:04.3165080Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0, line 211 <- wrt source file 2025-08-26T20:22:04.3233466Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::Dropout3d:0 2025-08-26T20:22:04.3236330Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0, line 257 <- wrt source file 2025-08-26T20:22:04.3239012Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::AlphaDropout:0 2025-08-26T20:22:04.3241780Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0, line 309 <- wrt source file 2025-08-26T20:22:04.3310069Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/dropout.py::FeatureAlphaDropout:0 2025-08-26T20:22:04.3313067Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0, line 30 <- wrt source file 2025-08-26T20:22:04.3316398Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py::Flatten:0 2025-08-26T20:22:04.3318673Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0, line 224 <- wrt source file 2025-08-26T20:22:04.3322548Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Fold:0 2025-08-26T20:22:04.3324762Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0, line 395 <- wrt source file 2025-08-26T20:22:04.3338104Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/fold.py::Unfold:0 2025-08-26T20:22:04.3340621Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0, line 187 <- wrt source file 2025-08-26T20:22:04.3351754Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm1d:0 2025-08-26T20:22:04.3354383Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0, line 303 <- wrt source file 2025-08-26T20:22:04.3527323Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm2d:0 2025-08-26T20:22:04.3530259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0, line 419 <- wrt source file 2025-08-26T20:22:04.5917753Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/instancenorm.py::InstanceNorm3d:0 2025-08-26T20:22:04.6061277Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0, line 77 <- wrt source file 2025-08-26T20:22:04.6063638Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/lazy.py::LazyModuleMixin:0 2025-08-26T20:22:04.6065636Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0, line 34 <- wrt source file 2025-08-26T20:22:04.6072812Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Identity:0 2025-08-26T20:22:04.6075157Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0, line 83 <- wrt source file 2025-08-26T20:22:04.6083373Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Linear:0 2025-08-26T20:22:04.6085793Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0, line 191 <- wrt source file 2025-08-26T20:22:04.6106331Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/linear.py::Bilinear:0 2025-08-26T20:22:04.6108776Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0, line 115 <- wrt source file 2025-08-26T20:22:04.6115293Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::L1Loss:0 2025-08-26T20:22:04.6117603Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0, line 215 <- wrt source file 2025-08-26T20:22:04.6149260Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::NLLLoss:0 2025-08-26T20:22:04.6151955Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0, line 329 <- wrt source file 2025-08-26T20:22:04.6157526Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::PoissonNLLLoss:0 2025-08-26T20:22:04.6159887Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0, line 418 <- wrt source file 2025-08-26T20:22:04.6173936Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::GaussianNLLLoss:0 2025-08-26T20:22:04.6176837Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0, line 535 <- wrt source file 2025-08-26T20:22:04.6184332Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::KLDivLoss:0 2025-08-26T20:22:04.6186840Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0, line 617 <- wrt source file 2025-08-26T20:22:04.6192841Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MSELoss:0 2025-08-26T20:22:04.6195460Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0, line 703 <- wrt source file 2025-08-26T20:22:04.6202185Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCELoss:0 2025-08-26T20:22:04.6205040Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0, line 778 <- wrt source file 2025-08-26T20:22:04.6216271Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:0 2025-08-26T20:22:04.6218629Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1, line 826 <- wrt source file 2025-08-26T20:22:04.6224498Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::BCEWithLogitsLoss:1 2025-08-26T20:22:04.6227268Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0, line 974 <- wrt source file 2025-08-26T20:22:04.6235281Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiLabelMarginLoss:0 2025-08-26T20:22:04.6237777Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0, line 1306 <- wrt source file 2025-08-26T20:22:04.6246167Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:0 2025-08-26T20:22:04.6248983Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1, line 1333 <- wrt source file 2025-08-26T20:22:04.6251670Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CrossEntropyLoss:1 2025-08-26T20:22:04.6253843Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0, line 1495 <- wrt source file 2025-08-26T20:22:04.6261706Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::CosineEmbeddingLoss:0 2025-08-26T20:22:04.6264191Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0, line 1562 <- wrt source file 2025-08-26T20:22:04.6271090Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MarginRankingLoss:0 2025-08-26T20:22:04.6273689Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0, line 1643 <- wrt source file 2025-08-26T20:22:04.6281338Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::MultiMarginLoss:0 2025-08-26T20:22:04.6283704Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0, line 1745 <- wrt source file 2025-08-26T20:22:04.6295042Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py::TripletMarginLoss:0 2025-08-26T20:22:04.6297911Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0, line 551 <- wrt source file 2025-08-26T20:22:04.6300737Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.register_buffer:0 2025-08-26T20:22:04.6303303Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0, line 1039 <- wrt source file 2025-08-26T20:22:04.6312061Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.apply:0 2025-08-26T20:22:04.6314487Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0, line 1290 <- wrt source file 2025-08-26T20:22:04.6318672Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.to:0 2025-08-26T20:22:04.6321258Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0, line 2229 <- wrt source file 2025-08-26T20:22:04.6323879Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.state_dict:0 2025-08-26T20:22:04.6326451Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0, line 2670 <- wrt source file 2025-08-26T20:22:04.6329076Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.parameters:0 2025-08-26T20:22:04.6331731Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0, line 2698 <- wrt source file 2025-08-26T20:22:04.6334481Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_parameters:0 2025-08-26T20:22:04.6337073Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0, line 2725 <- wrt source file 2025-08-26T20:22:04.6339671Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.buffers:0 2025-08-26T20:22:04.6342326Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0, line 2752 <- wrt source file 2025-08-26T20:22:04.6344998Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_buffers:0 2025-08-26T20:22:04.6347639Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0, line 2783 <- wrt source file 2025-08-26T20:22:04.6350343Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_children:0 2025-08-26T20:22:04.6352861Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0, line 2807 <- wrt source file 2025-08-26T20:22:04.6355494Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.modules:0 2025-08-26T20:22:04.6358182Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0, line 2845 <- wrt source file 2025-08-26T20:22:04.6361479Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/module.py::Module.named_modules:0 2025-08-26T20:22:04.6364896Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0, line 38 <- wrt source file 2025-08-26T20:22:04.6379089Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LocalResponseNorm:0 2025-08-26T20:22:04.6382428Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0, line 163 <- wrt source file 2025-08-26T20:22:04.6388758Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::LayerNorm:0 2025-08-26T20:22:04.6391923Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0, line 274 <- wrt source file 2025-08-26T20:22:04.6397510Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::GroupNorm:0 2025-08-26T20:22:04.6400476Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0, line 367 <- wrt source file 2025-08-26T20:22:04.6403634Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/normalization.py::RMSNorm:0 2025-08-26T20:22:04.6406349Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0, line 70 <- wrt source file 2025-08-26T20:22:04.6410063Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad1d:0 2025-08-26T20:22:04.6412587Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0, line 122 <- wrt source file 2025-08-26T20:22:04.6432693Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad2d:0 2025-08-26T20:22:04.6435195Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0, line 187 <- wrt source file 2025-08-26T20:22:05.2554594Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::CircularPad3d:0 2025-08-26T20:22:05.2822466Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0, line 241 <- wrt source file 2025-08-26T20:22:05.2831698Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad1d:0 2025-08-26T20:22:05.2834228Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0, line 294 <- wrt source file 2025-08-26T20:22:05.2838300Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad2d:0 2025-08-26T20:22:05.2840816Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0, line 350 <- wrt source file 2025-08-26T20:22:05.2861867Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ConstantPad3d:0 2025-08-26T20:22:05.2864449Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0, line 395 <- wrt source file 2025-08-26T20:22:05.2868721Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad1d:0 2025-08-26T20:22:05.2871281Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0, line 439 <- wrt source file 2025-08-26T20:22:05.2875119Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad2d:0 2025-08-26T20:22:05.2877686Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0, line 497 <- wrt source file 2025-08-26T20:22:05.2880299Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReflectionPad3d:0 2025-08-26T20:22:05.2883140Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0, line 556 <- wrt source file 2025-08-26T20:22:05.2885776Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad1d:0 2025-08-26T20:22:05.2888358Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0, line 600 <- wrt source file 2025-08-26T20:22:05.2892640Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad2d:0 2025-08-26T20:22:05.2895249Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0, line 658 <- wrt source file 2025-08-26T20:22:05.8027026Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ReplicationPad3d:0 2025-08-26T20:22:05.8290161Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0, line 692 <- wrt source file 2025-08-26T20:22:05.8299962Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad1d:0 2025-08-26T20:22:05.8302473Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0, line 750 <- wrt source file 2025-08-26T20:22:05.8307388Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad2d:0 2025-08-26T20:22:05.8309853Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0, line 812 <- wrt source file 2025-08-26T20:22:05.8329472Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/padding.py::ZeroPad3d:0 2025-08-26T20:22:05.8331915Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0, line 40 <- wrt source file 2025-08-26T20:22:05.8336338Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelShuffle:0 2025-08-26T20:22:05.8338986Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0, line 99 <- wrt source file 2025-08-26T20:22:05.8343614Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pixelshuffle.py::PixelUnshuffle:0 2025-08-26T20:22:05.8346198Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0, line 129 <- wrt source file 2025-08-26T20:22:05.8350774Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool1d:0 2025-08-26T20:22:05.8353266Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0, line 207 <- wrt source file 2025-08-26T20:22:05.8403157Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool2d:0 2025-08-26T20:22:05.8405613Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0, line 291 <- wrt source file 2025-08-26T20:22:06.0634092Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxPool3d:0 2025-08-26T20:22:06.0691886Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0, line 366 <- wrt source file 2025-08-26T20:22:06.0704475Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool1d:0 2025-08-26T20:22:06.0707095Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0, line 550 <- wrt source file 2025-08-26T20:22:06.1504139Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::MaxUnpool3d:0 2025-08-26T20:22:06.1506276Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0, line 642 <- wrt source file 2025-08-26T20:22:06.1527213Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool1d:0 2025-08-26T20:22:06.1529380Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0, line 738 <- wrt source file 2025-08-26T20:22:06.1567923Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool2d:0 2025-08-26T20:22:06.1570552Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0, line 855 <- wrt source file 2025-08-26T20:22:06.3194601Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AvgPool3d:0 2025-08-26T20:22:06.3243758Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0, line 946 <- wrt source file 2025-08-26T20:22:06.3291073Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool2d:0 2025-08-26T20:22:06.3293934Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0, line 1033 <- wrt source file 2025-08-26T20:22:06.4017903Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::FractionalMaxPool3d:0 2025-08-26T20:22:06.4020576Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0, line 1152 <- wrt source file 2025-08-26T20:22:06.4026799Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool1d:0 2025-08-26T20:22:06.4029609Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0, line 1204 <- wrt source file 2025-08-26T20:22:06.4080106Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool2d:0 2025-08-26T20:22:06.4083187Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0, line 1264 <- wrt source file 2025-08-26T20:22:06.6241183Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::LPPool3d:0 2025-08-26T20:22:06.6289876Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0, line 1320 <- wrt source file 2025-08-26T20:22:06.6307194Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool1d:0 2025-08-26T20:22:06.6309929Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0, line 1355 <- wrt source file 2025-08-26T20:22:06.6318697Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool2d:0 2025-08-26T20:22:06.6321646Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0, line 1399 <- wrt source file 2025-08-26T20:22:06.6346331Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveMaxPool3d:0 2025-08-26T20:22:06.6349286Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0, line 1447 <- wrt source file 2025-08-26T20:22:06.6361554Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool1d:0 2025-08-26T20:22:06.6364213Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0, line 1481 <- wrt source file 2025-08-26T20:22:06.6369275Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool2d:0 2025-08-26T20:22:06.6371928Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0, line 1521 <- wrt source file 2025-08-26T20:22:06.6389097Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py::AdaptiveAvgPool3d:0 2025-08-26T20:22:06.6391674Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0, line 595 <- wrt source file 2025-08-26T20:22:06.6401859Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNN:0 2025-08-26T20:22:06.6404101Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0, line 953 <- wrt source file 2025-08-26T20:22:06.6752679Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTM:0 2025-08-26T20:22:06.6755118Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0, line 1288 <- wrt source file 2025-08-26T20:22:06.6769518Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRU:0 2025-08-26T20:22:06.6771783Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0, line 1537 <- wrt source file 2025-08-26T20:22:06.6783085Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::RNNCell:0 2025-08-26T20:22:06.6785427Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0, line 1659 <- wrt source file 2025-08-26T20:22:06.6794138Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::LSTMCell:0 2025-08-26T20:22:06.6796445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0, line 1773 <- wrt source file 2025-08-26T20:22:06.6808300Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/rnn.py::GRUCell:0 2025-08-26T20:22:06.6810757Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0, line 71 <- wrt source file 2025-08-26T20:22:06.6822805Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding:0 2025-08-26T20:22:06.6825402Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0, line 243 <- wrt source file 2025-08-26T20:22:06.6828455Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::Embedding.from_pretrained:0 2025-08-26T20:22:06.6831244Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0, line 521 <- wrt source file 2025-08-26T20:22:06.6835075Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py::EmbeddingBag.from_pretrained:0 2025-08-26T20:22:06.6837785Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0, line 90 <- wrt source file 2025-08-26T20:22:07.5082808Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::Transformer:0 2025-08-26T20:22:07.5099331Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0, line 336 <- wrt source file 2025-08-26T20:22:07.6249951Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoder:0 2025-08-26T20:22:07.6256580Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0, line 562 <- wrt source file 2025-08-26T20:22:07.8788352Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoder:0 2025-08-26T20:22:07.8815331Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0, line 686 <- wrt source file 2025-08-26T20:22:07.9099289Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerEncoderLayer:0 2025-08-26T20:22:07.9159700Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0, line 995 <- wrt source file 2025-08-26T20:22:07.9717796Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py::TransformerDecoderLayer:0 2025-08-26T20:22:07.9720255Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0, line 77 <- wrt source file 2025-08-26T20:22:07.9744440Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::Upsample:0 2025-08-26T20:22:07.9747133Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0, line 229 <- wrt source file 2025-08-26T20:22:07.9757588Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingNearest2d:0 2025-08-26T20:22:07.9760361Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0, line 279 <- wrt source file 2025-08-26T20:22:07.9766120Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/upsampling.py::UpsamplingBilinear2d:0 2025-08-26T20:22:07.9768799Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0, line 127 <- wrt source file 2025-08-26T20:22:07.9771501Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/data_parallel.py::DataParallel:0 2025-08-26T20:22:07.9774297Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0, line 642 <- wrt source file 2025-08-26T20:22:07.9777234Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel:0 2025-08-26T20:22:07.9780546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0, line 1446 <- wrt source file 2025-08-26T20:22:07.9783742Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.no_sync:0 2025-08-26T20:22:07.9786951Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0, line 1999 <- wrt source file 2025-08-26T20:22:07.9790299Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:0 2025-08-26T20:22:07.9793938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1, line 2009 <- wrt source file 2025-08-26T20:22:07.9797347Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel.register_comm_hook:1 2025-08-26T20:22:07.9800711Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0, line 2044 <- wrt source file 2025-08-26T20:22:07.9804270Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py::DistributedDataParallel._register_builtin_comm_hook:0 2025-08-26T20:22:07.9807329Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0, line 35 <- wrt source file 2025-08-26T20:22:07.9810245Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_per_sample_grad.py::call_for_per_sample_grads:0 2025-08-26T20:22:07.9812769Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0, line 33 <- wrt source file 2025-08-26T20:22:07.9815080Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/init.py::skip_init:0 2025-08-26T20:22:07.9817546Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0, line 265 <- wrt source file 2025-08-26T20:22:07.9820200Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::orthogonal:0 2025-08-26T20:22:07.9822895Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0, line 360 <- wrt source file 2025-08-26T20:22:07.9825564Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::weight_norm:0 2025-08-26T20:22:07.9828216Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0, line 591 <- wrt source file 2025-08-26T20:22:07.9830945Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrizations.py::spectral_norm:0 2025-08-26T20:22:07.9833414Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0, line 849 <- wrt source file 2025-08-26T20:22:07.9835730Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::identity:0 2025-08-26T20:22:07.9838143Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0, line 885 <- wrt source file 2025-08-26T20:22:07.9840700Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_unstructured:0 2025-08-26T20:22:07.9843259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0, line 928 <- wrt source file 2025-08-26T20:22:07.9845723Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::l1_unstructured:0 2025-08-26T20:22:07.9848174Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0, line 968 <- wrt source file 2025-08-26T20:22:07.9850662Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::random_structured:0 2025-08-26T20:22:07.9853013Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0, line 1197 <- wrt source file 2025-08-26T20:22:07.9855409Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::remove:0 2025-08-26T20:22:07.9857681Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0, line 1225 <- wrt source file 2025-08-26T20:22:07.9860015Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py::is_pruned:0 2025-08-26T20:22:07.9862372Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0, line 439 <- wrt source file 2025-08-26T20:22:07.9864694Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pad_sequence:0 2025-08-26T20:22:07.9867020Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0, line 500 <- wrt source file 2025-08-26T20:22:07.9869463Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpad_sequence:0 2025-08-26T20:22:07.9871792Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0, line 556 <- wrt source file 2025-08-26T20:22:07.9874170Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::pack_sequence:0 2025-08-26T20:22:07.9876514Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0, line 584 <- wrt source file 2025-08-26T20:22:07.9888844Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py::unpack_sequence:0 2025-08-26T20:22:07.9891328Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0, line 314 <- wrt source file 2025-08-26T20:22:07.9896436Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::spectral_norm:0 2025-08-26T20:22:07.9899071Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0, line 346 <- wrt source file 2025-08-26T20:22:07.9904604Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/spectral_norm.py::remove_spectral_norm:0 2025-08-26T20:22:07.9907293Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0, line 196 <- wrt source file 2025-08-26T20:22:07.9909905Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/stateless.py::functional_call:0 2025-08-26T20:22:07.9912377Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0, line 134 <- wrt source file 2025-08-26T20:22:07.9915349Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::weight_norm:0 2025-08-26T20:22:07.9917886Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0, line 156 <- wrt source file 2025-08-26T20:22:07.9921111Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py::remove_weight_norm:0 2025-08-26T20:22:07.9923812Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0, line 315 <- wrt source file 2025-08-26T20:22:07.9926630Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/conv_utils.py::unfold3d:0 2025-08-26T20:22:07.9929758Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0, line 178 <- wrt source file 2025-08-26T20:22:07.9933434Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/_expanded_weights/expanded_weights_utils.py::sum_over_all_but_batch_and_last_n:0 2025-08-26T20:22:07.9936379Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0, line 280 <- wrt source file 2025-08-26T20:22:07.9938812Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LambdaLR:0 2025-08-26T20:22:07.9941364Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0, line 388 <- wrt source file 2025-08-26T20:22:07.9943984Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiplicativeLR:0 2025-08-26T20:22:07.9946499Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0, line 491 <- wrt source file 2025-08-26T20:22:07.9948870Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::StepLR:0 2025-08-26T20:22:07.9951261Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0, line 547 <- wrt source file 2025-08-26T20:22:07.9953759Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::MultiStepLR:0 2025-08-26T20:22:07.9956184Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0, line 608 <- wrt source file 2025-08-26T20:22:07.9958641Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ConstantLR:0 2025-08-26T20:22:07.9961008Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0, line 683 <- wrt source file 2025-08-26T20:22:07.9963424Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::LinearLR:0 2025-08-26T20:22:07.9965963Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0, line 773 <- wrt source file 2025-08-26T20:22:07.9968528Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ExponentialLR:0 2025-08-26T20:22:07.9971006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0, line 971 <- wrt source file 2025-08-26T20:22:07.9973525Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::PolynomialLR:0 2025-08-26T20:22:07.9976149Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0, line 1062 <- wrt source file 2025-08-26T20:22:07.9978803Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingLR:0 2025-08-26T20:22:07.9981513Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0, line 1134 <- wrt source file 2025-08-26T20:22:07.9984137Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::ChainedScheduler:0 2025-08-26T20:22:07.9986938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0, line 1803 <- wrt source file 2025-08-26T20:22:07.9989945Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:0 2025-08-26T20:22:07.9993243Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1, line 1819 <- wrt source file 2025-08-26T20:22:07.9996267Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py::CosineAnnealingWarmRestarts.step:1 2025-08-26T20:22:07.9998867Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0, line 337 <- wrt source file 2025-08-26T20:22:08.0001234Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py::update_bn:0 2025-08-26T20:22:08.0003609Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0, line 22 <- wrt source file 2025-08-26T20:22:08.0006040Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/package/glob_group.py::GlobGroup:0 2025-08-26T20:22:08.0008874Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0, line 295 <- wrt source file 2025-08-26T20:22:08.0011985Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::_KinetoProfile.toggle_collection_dynamic:0 2025-08-26T20:22:08.0014673Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0, line 617 <- wrt source file 2025-08-26T20:22:08.0017079Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/profiler/profiler.py::profile:0 2025-08-26T20:22:08.0019661Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0, line 339 <- wrt source file 2025-08-26T20:22:08.0022576Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/semi_structured.py::to_sparse_semi_structured:0 2025-08-26T20:22:08.0025181Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0, line 114 <- wrt source file 2025-08-26T20:22:08.0027640Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_creation.py::make_tensor:0 2025-08-26T20:22:08.0030205Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0, line 615 <- wrt source file 2025-08-26T20:22:08.0032962Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::parametrize:0 2025-08-26T20:22:08.0035690Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0, line 736 <- wrt source file 2025-08-26T20:22:08.0038490Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::reparametrize:0 2025-08-26T20:22:08.0041257Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0, line 825 <- wrt source file 2025-08-26T20:22:08.0044072Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::decorateIf:0 2025-08-26T20:22:08.0046938Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0, line 4734 <- wrt source file 2025-08-26T20:22:08.0050020Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_symmetric_psd_matrix:0 2025-08-26T20:22:08.0053038Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0, line 4748 <- wrt source file 2025-08-26T20:22:08.0056196Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_psd_matrix:0 2025-08-26T20:22:08.0059217Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/common_utils.py::random_hermitian_pd_matrix:0, line 4778 <- wrt source file 2025-08-26T20:22:08.0062339Z * SKIPPED: 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<- wrt source file 2025-08-26T20:22:08.0080505Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/distributed/_tensor/common_dtensor.py::skip_unless_torch_gpu:0 2025-08-26T20:22:08.0083882Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0, line 29 <- wrt source file 2025-08-26T20:22:08.0087394Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_internal/optests/autograd_registration.py::autograd_registration_check:0 2025-08-26T20:22:08.0090259Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0, line 277 <- wrt source file 2025-08-26T20:22:08.0092883Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_is_leaf:0 2025-08-26T20:22:08.0095279Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0, line 320 <- wrt source file 2025-08-26T20:22:08.0097719Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_flatten:0 2025-08-26T20:22:08.0100139Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0, line 357 <- wrt source file 2025-08-26T20:22:08.0102654Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_unflatten:0 2025-08-26T20:22:08.0105057Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0, line 387 <- wrt source file 2025-08-26T20:22:08.0107536Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_iter:0 2025-08-26T20:22:08.0109882Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0, line 422 <- wrt source file 2025-08-26T20:22:08.0112291Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_leaves:0 2025-08-26T20:22:08.0114724Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0, line 457 <- wrt source file 2025-08-26T20:22:08.0117234Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_structure:0 2025-08-26T20:22:08.0119763Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0, line 494 <- wrt source file 2025-08-26T20:22:08.0122161Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::tree_map:0 2025-08-26T20:22:08.0124600Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0, line 893 <- wrt source file 2025-08-26T20:22:08.0127156Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py::broadcast_prefix:0 2025-08-26T20:22:08.0129669Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0, line 303 <- wrt source file 2025-08-26T20:22:08.0132175Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_dataclass:0 2025-08-26T20:22:08.0134730Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0, line 419 <- wrt source file 2025-08-26T20:22:08.0137230Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::register_constant:0 2025-08-26T20:22:08.0139602Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0, line 1026 <- wrt source file 2025-08-26T20:22:08.0142055Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_is_leaf:0 2025-08-26T20:22:08.0144339Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0, line 1345 <- wrt source file 2025-08-26T20:22:08.0146632Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_pytree.py::tree_map:0 2025-08-26T20:22:08.0149260Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0, line 69 <- wrt source file 2025-08-26T20:22:08.0152256Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::rename_privateuse1_backend:0 2025-08-26T20:22:08.0155334Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/backend_registration.py::generate_methods_for_privateuse1_backend:0, line 375 <- wrt source file 2025-08-26T20:22:08.0158610Z * SKIPPED: 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SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py::set_checkpoint_early_stop:0 2025-08-26T20:22:08.0177914Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0, line 93 <- wrt source file 2025-08-26T20:22:08.0180402Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/dlpack.py::from_dlpack:0 2025-08-26T20:22:08.0183053Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0, line 724 <- wrt source file 2025-08-26T20:22:08.0539598Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_sympy/functions.py::MinMaxBase._collapse_arguments:0 2025-08-26T20:22:08.0542445Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/dataset.py::IterableDataset:0, line 94 <- wrt source file 2025-08-26T20:22:08.0545066Z * SKIPPED: 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2025-08-26T20:22:08.0562568Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0, line 39 <- wrt source file 2025-08-26T20:22:08.0565296Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_convert:0 2025-08-26T20:22:08.0567881Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0, line 137 <- wrt source file 2025-08-26T20:22:08.0570437Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::collate:0 2025-08-26T20:22:08.0573006Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0, line 364 <- wrt source file 2025-08-26T20:22:08.0575722Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/_utils/collate.py::default_collate:0 2025-08-26T20:22:08.0578446Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0, line 268 <- wrt source file 2025-08-26T20:22:08.0581320Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py::MapDataPipe:0 2025-08-26T20:22:08.0584240Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0, line 53 <- wrt source file 2025-08-26T20:22:08.0587456Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::MapperIterDataPipe:0 2025-08-26T20:22:08.0590532Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0, line 201 <- wrt source file 2025-08-26T20:22:08.0593851Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/callable.py::CollatorIterDataPipe:0 2025-08-26T20:22:08.0597135Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0, line 90 <- wrt source file 2025-08-26T20:22:08.0600449Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combinatorics.py::ShufflerIterDataPipe:0 2025-08-26T20:22:08.0603601Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0, line 38 <- wrt source file 2025-08-26T20:22:08.0606806Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ConcaterIterDataPipe:0 2025-08-26T20:22:08.0609879Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py::ForkerIterDataPipe:0, line 88 <- wrt source file 2025-08-26T20:22:08.0612994Z * SKIPPED: 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/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0, line 25 <- wrt source file 2025-08-26T20:22:08.0650788Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/streamreader.py::StreamReaderIterDataPipe:0 2025-08-26T20:22:08.0654088Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0, line 29 <- wrt source file 2025-08-26T20:22:08.0657352Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/utils.py::IterableWrapperIterDataPipe:0 2025-08-26T20:22:08.0660489Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0, line 35 <- wrt source file 2025-08-26T20:22:08.0663564Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/callable.py::MapperMapDataPipe:0 2025-08-26T20:22:08.0666771Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0, line 34 <- wrt source file 2025-08-26T20:22:08.0670048Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combinatorics.py::ShufflerIterDataPipe:0 2025-08-26T20:22:08.0673184Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0, line 29 <- wrt source file 2025-08-26T20:22:08.0676299Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ConcaterMapDataPipe:0 2025-08-26T20:22:08.0679323Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0, line 73 <- wrt source file 2025-08-26T20:22:08.0682504Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/combining.py::ZipperMapDataPipe:0 2025-08-26T20:22:08.0685525Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0, line 29 <- wrt source file 2025-08-26T20:22:08.0688585Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/grouping.py::BatcherMapDataPipe:0 2025-08-26T20:22:08.0691665Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0, line 29 <- wrt source file 2025-08-26T20:22:08.0695010Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/map/utils.py::SequenceWrapperMapDataPipe:0 2025-08-26T20:22:08.0698049Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0, line 37 <- wrt source file 2025-08-26T20:22:08.0701128Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/common.py::validate_input_col:0 2025-08-26T20:22:08.0704080Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0, line 47 <- wrt source file 2025-08-26T20:22:08.0707819Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/utils/decoder.py::basichandlers:0 2025-08-26T20:22:08.0710632Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0, line 439 <- wrt source file 2025-08-26T20:22:08.0713451Z * SUCCESS: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::find_closure_group:0 2025-08-26T20:22:08.0716280Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0, line 535 <- wrt source file 2025-08-26T20:22:08.0719278Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/hipify/hipify_python.py::replace_extern_shared:0 2025-08-26T20:22:08.0722120Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0, line 216 <- wrt source file 2025-08-26T20:22:08.0725012Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.__init__:0 2025-08-26T20:22:08.0727899Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0, line 314 <- wrt source file 2025-08-26T20:22:08.0730880Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_hparams:0 2025-08-26T20:22:08.0733909Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0, line 362 <- wrt source file 2025-08-26T20:22:08.0736872Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalar:0 2025-08-26T20:22:08.0739767Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0, line 394 <- wrt source file 2025-08-26T20:22:08.0742794Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_scalars:0 2025-08-26T20:22:08.0745713Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0, line 441 <- wrt source file 2025-08-26T20:22:08.0748747Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_tensor:0 2025-08-26T20:22:08.0751681Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0, line 480 <- wrt source file 2025-08-26T20:22:08.0754689Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram:0 2025-08-26T20:22:08.0757692Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0, line 533 <- wrt source file 2025-08-26T20:22:08.0760772Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_histogram_raw:0 2025-08-26T20:22:08.0763734Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0, line 599 <- wrt source file 2025-08-26T20:22:08.0766659Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_image:0 2025-08-26T20:22:08.0769545Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0, line 648 <- wrt source file 2025-08-26T20:22:08.0772495Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_images:0 2025-08-26T20:22:08.0775375Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0, line 811 <- wrt source file 2025-08-26T20:22:08.0778269Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_text:0 2025-08-26T20:22:08.0781269Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0, line 878 <- wrt source file 2025-08-26T20:22:08.0784348Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_embedding:0 2025-08-26T20:22:08.0787285Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0, line 989 <- wrt source file 2025-08-26T20:22:08.0790260Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_pr_curve:0 2025-08-26T20:22:08.0793596Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0, line 1063 <- wrt source file 2025-08-26T20:22:08.0797190Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_multilinechart:0 2025-08-26T20:22:08.0800531Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0, line 1084 <- wrt source file 2025-08-26T20:22:08.0803916Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars_marginchart:0 2025-08-26T20:22:08.0807113Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0, line 1108 <- wrt source file 2025-08-26T20:22:08.0810253Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_custom_scalars:0 2025-08-26T20:22:08.0813226Z * DOCTEST : /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0, line 1154 <- wrt source file 2025-08-26T20:22:08.0816213Z * SKIPPED: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/tensorboard/writer.py::SummaryWriter.add_mesh:0 2025-08-26T20:22:08.0817706Z ============ 2025-08-26T20:22:08.0818228Z Finished doctests 2025-08-26T20:22:08.0818677Z 338 / 731 passed 2025-08-26T20:22:08.0819137Z  2025-08-26T20:22:08.0819693Z === Found 146 parse-time warnings === 2025-08-26T20:22:08.0820550Z --- Parse Warning: 1 / 146 --- 2025-08-26T20:22:08.0822732Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Tensor.dim_order in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py line=1493. 2025-08-26T20:22:08.0825220Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.0826142Z 2025-08-26T20:22:08.0826573Z dim_order(ambiguity_check=False) -> tuple 2025-08-26T20:22:08.0827188Z 2025-08-26T20:22:08.0827856Z Returns the uniquely determined tuple of int describing the dim order or 2025-08-26T20:22:08.0828773Z physical layout of :attr:`self`. 2025-08-26T20:22:08.0829327Z 2025-08-26T20:22:08.0830038Z The dim order represents how dimensions are laid out in memory of dense tensors, 2025-08-26T20:22:08.0831121Z starting from the outermost to the innermost dimension. 2025-08-26T20:22:08.0831834Z 2025-08-26T20:22:08.0832389Z Note that the dim order may not always be uniquely determined. 2025-08-26T20:22:08.0833768Z If `ambiguity_check` is True, this function raises a RuntimeError when the dim order cannot be uniquely determined; 2025-08-26T20:22:08.0835583Z If `ambiguity_check` is a list of memory formats, this function raises a RuntimeError when tensor can not be interpreted 2025-08-26T20:22:08.0837123Z into exactly one of the given memory formats, or it cannot be uniquely determined. 2025-08-26T20:22:08.0838515Z If `ambiguity_check` is False, it will return one of legal dim order(s) without checking its uniqueness. 2025-08-26T20:22:08.0839697Z Otherwise, it will raise TypeError. 2025-08-26T20:22:08.0840283Z 2025-08-26T20:22:08.0840658Z Args: 2025-08-26T20:22:08.0841520Z ambiguity_check (bool or List[torch.memory_format]): The check method for ambiguity of dim order. 2025-08-26T20:22:08.0842543Z 2025-08-26T20:22:08.0842929Z Examples:: 2025-08-26T20:22:08.0843340Z 2025-08-26T20:22:08.0843768Z >>> torch.empty((2, 3, 5, 7)).dim_order() 2025-08-26T20:22:08.0844357Z (0, 1, 2, 3) 2025-08-26T20:22:08.0844927Z >>> torch.empty((2, 3, 5, 7)).transpose(1, 2).dim_order() 2025-08-26T20:22:08.0845624Z (0, 2, 1, 3) 2025-08-26T20:22:08.0846299Z >>> torch.empty((2, 3, 5, 7), memory_format=torch.channels_last).dim_order() 2025-08-26T20:22:08.0847090Z (0, 2, 3, 1) 2025-08-26T20:22:08.0847579Z >>> torch.empty((1, 2, 3, 4)).dim_order() 2025-08-26T20:22:08.0848281Z (0, 1, 2, 3) 2025-08-26T20:22:08.0848718Z >>> try: 2025-08-26T20:22:08.0849286Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check=True) 2025-08-26T20:22:08.0850063Z ... except RuntimeError as e: 2025-08-26T20:22:08.0850648Z ... print(e) 2025-08-26T20:22:08.0851572Z The tensor does not have unique dim order, or cannot map to exact one of the given memory formats. 2025-08-26T20:22:08.0852634Z >>> torch.empty((1, 2, 3, 4)).dim_order( 2025-08-26T20:22:08.0853477Z ... ambiguity_check=[torch.contiguous_format, torch.channels_last] 2025-08-26T20:22:08.0854374Z ... ) # It can be mapped to contiguous format 2025-08-26T20:22:08.0855010Z (0, 1, 2, 3) 2025-08-26T20:22:08.0855436Z >>> try: 2025-08-26T20:22:08.0856038Z ... torch.empty((1, 2, 3, 4)).dim_order(ambiguity_check="ILLEGAL") 2025-08-26T20:22:08.0856901Z ... except TypeError as e: 2025-08-26T20:22:08.0857459Z ... print(e) 2025-08-26T20:22:08.0858209Z The ambiguity_check argument must be a bool or a list of memory formats. 2025-08-26T20:22:08.0859068Z 2025-08-26T20:22:08.0859449Z .. warning:: 2025-08-26T20:22:08.0860083Z The dim_order tensor API is experimental and subject to change. 2025-08-26T20:22:08.0860923Z 2025-08-26T20:22:08.0861616Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.0862508Z 2025-08-26T20:22:08.0862900Z warnings.warn(msg) 2025-08-26T20:22:08.0863358Z 2025-08-26T20:22:08.0863960Z --- Parse Warning: 2 / 146 --- 2025-08-26T20:22:08.0866114Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=meshgrid in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=397. 2025-08-26T20:22:08.0868564Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.0869798Z Creates grids of coordinates specified by the 1D inputs in `attr`:tensors. 2025-08-26T20:22:08.0870655Z 2025-08-26T20:22:08.0871195Z This is helpful when you want to visualize data over some 2025-08-26T20:22:08.0872062Z range of inputs. See below for a plotting example. 2025-08-26T20:22:08.0872741Z 2025-08-26T20:22:08.0873244Z Given :math:`N` 1D tensors :math:`T_0 \ldots T_{N-1}` as 2025-08-26T20:22:08.0874152Z inputs with corresponding sizes :math:`S_0 \ldots S_{N-1}`, 2025-08-26T20:22:08.0875118Z this creates :math:`N` N-dimensional tensors :math:`G_0 \ldots 2025-08-26T20:22:08.0876021Z G_{N-1}`, each with shape :math:`(S_0, ..., S_{N-1})` where 2025-08-26T20:22:08.0876907Z the output :math:`G_i` is constructed by expanding :math:`T_i` 2025-08-26T20:22:08.0877690Z to the result shape. 2025-08-26T20:22:08.0878220Z 2025-08-26T20:22:08.0878596Z .. note:: 2025-08-26T20:22:08.0879188Z 0D inputs are treated equivalently to 1D inputs of a 2025-08-26T20:22:08.0879906Z single element. 2025-08-26T20:22:08.0880523Z 2025-08-26T20:22:08.0880919Z .. warning:: 2025-08-26T20:22:08.0881584Z `torch.meshgrid(*tensors)` currently has the same behavior 2025-08-26T20:22:08.0882464Z as calling `numpy.meshgrid(*arrays, indexing='ij')`. 2025-08-26T20:22:08.0883170Z 2025-08-26T20:22:08.0883665Z In the future `torch.meshgrid` will transition to 2025-08-26T20:22:08.0884400Z `indexing='xy'` as the default. 2025-08-26T20:22:08.0884982Z 2025-08-26T20:22:08.0885546Z https://github.com/pytorch/pytorch/issues/50276 tracks 2025-08-26T20:22:08.0886467Z this issue with the goal of migrating to NumPy's behavior. 2025-08-26T20:22:08.0887213Z 2025-08-26T20:22:08.0887598Z .. seealso:: 2025-08-26T20:22:08.0888067Z 2025-08-26T20:22:08.0888680Z :func:`torch.cartesian_prod` has the same effect but it 2025-08-26T20:22:08.0889492Z collects the data in a tensor of vectors. 2025-08-26T20:22:08.0890121Z 2025-08-26T20:22:08.0890495Z Args: 2025-08-26T20:22:08.0891271Z tensors (list of Tensor): list of scalars or 1 dimensional tensors. Scalars will be 2025-08-26T20:22:08.0892499Z treated as tensors of size :math:`(1,)` automatically 2025-08-26T20:22:08.0893205Z 2025-08-26T20:22:08.0893760Z indexing: (str, optional): the indexing mode, either "xy" 2025-08-26T20:22:08.0894662Z or "ij", defaults to "ij". See warning for future changes. 2025-08-26T20:22:08.0895376Z 2025-08-26T20:22:08.0895857Z If "xy" is selected, the first dimension corresponds 2025-08-26T20:22:08.0896691Z to the cardinality of the second input and the second 2025-08-26T20:22:08.0897681Z dimension corresponds to the cardinality of the first 2025-08-26T20:22:08.0898413Z input. 2025-08-26T20:22:08.0898881Z 2025-08-26T20:22:08.0899356Z If "ij" is selected, the dimensions are in the same 2025-08-26T20:22:08.0900114Z order as the cardinality of the inputs. 2025-08-26T20:22:08.0900839Z 2025-08-26T20:22:08.0901218Z Returns: 2025-08-26T20:22:08.0901791Z seq (sequence of Tensors): If the input has :math:`N` 2025-08-26T20:22:08.0902629Z tensors of size :math:`S_0 \ldots S_{N-1}``, then the 2025-08-26T20:22:08.0903509Z output will also have :math:`N` tensors, where each tensor 2025-08-26T20:22:08.0904317Z is of shape :math:`(S_0, ..., S_{N-1})`. 2025-08-26T20:22:08.0904920Z 2025-08-26T20:22:08.0905304Z Example:: 2025-08-26T20:22:08.0905744Z 2025-08-26T20:22:08.0906164Z >>> x = torch.tensor([1, 2, 3]) 2025-08-26T20:22:08.0906803Z >>> y = torch.tensor([4, 5, 6]) 2025-08-26T20:22:08.0907382Z 2025-08-26T20:22:08.0907954Z Observe the element-wise pairings across the grid, (1, 4), 2025-08-26T20:22:08.0908803Z (1, 5), ..., (3, 6). This is the same thing as the 2025-08-26T20:22:08.0909464Z cartesian product. 2025-08-26T20:22:08.0910144Z >>> grid_x, grid_y = torch.meshgrid(x, y, indexing='ij') 2025-08-26T20:22:08.0910852Z >>> grid_x 2025-08-26T20:22:08.0911343Z tensor([[1, 1, 1], 2025-08-26T20:22:08.0911871Z [2, 2, 2], 2025-08-26T20:22:08.0912405Z [3, 3, 3]]) 2025-08-26T20:22:08.0912941Z >>> grid_y 2025-08-26T20:22:08.0913440Z tensor([[4, 5, 6], 2025-08-26T20:22:08.0913964Z [4, 5, 6], 2025-08-26T20:22:08.0914489Z [4, 5, 6]]) 2025-08-26T20:22:08.0915023Z 2025-08-26T20:22:08.0915572Z This correspondence can be seen when these grids are 2025-08-26T20:22:08.0916312Z stacked properly. 2025-08-26T20:22:08.0917193Z >>> torch.equal(torch.cat(tuple(torch.dstack([grid_x, grid_y]))), 2025-08-26T20:22:08.0918065Z ... torch.cartesian_prod(x, y)) 2025-08-26T20:22:08.0918707Z True 2025-08-26T20:22:08.0919132Z 2025-08-26T20:22:08.0919678Z `torch.meshgrid` is commonly used to produce a grid for 2025-08-26T20:22:08.0920424Z plotting. 2025-08-26T20:22:08.0920980Z >>> # xdoctest: +REQUIRES(module:matplotlib) 2025-08-26T20:22:08.0921687Z >>> # xdoctest: +REQUIRES(env:DOCTEST_SHOW) 2025-08-26T20:22:08.0922387Z >>> import matplotlib.pyplot as plt 2025-08-26T20:22:08.0923082Z >>> xs = torch.linspace(-5, 5, steps=100) 2025-08-26T20:22:08.0923778Z >>> ys = torch.linspace(-5, 5, steps=100) 2025-08-26T20:22:08.0924629Z >>> x, y = torch.meshgrid(xs, ys, indexing='xy') 2025-08-26T20:22:08.0925341Z >>> z = torch.sin(torch.sqrt(x * x + y * y)) 2025-08-26T20:22:08.0926032Z >>> ax = plt.axes(projection='3d') 2025-08-26T20:22:08.0926763Z >>> ax.plot_surface(x.numpy(), y.numpy(), z.numpy()) 2025-08-26T20:22:08.0927462Z >>> plt.show() 2025-08-26T20:22:08.0927948Z 2025-08-26T20:22:08.0928381Z .. image:: ../_static/img/meshgrid.png 2025-08-26T20:22:08.0929011Z :width: 512 2025-08-26T20:22:08.0929490Z 2025-08-26T20:22:08.0929851Z 2025-08-26T20:22:08.0930633Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.0931516Z 2025-08-26T20:22:08.0931915Z warnings.warn(msg) 2025-08-26T20:22:08.0932377Z 2025-08-26T20:22:08.0932993Z --- Parse Warning: 3 / 146 --- 2025-08-26T20:22:08.0935222Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_unique_impl in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/functional.py line=793. 2025-08-26T20:22:08.0937688Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.0939214Z unique(input, sorted=True, return_inverse=False, return_counts=False, dim=None) -> tuple[Tensor, Tensor, Tensor] 2025-08-26T20:22:08.0940430Z 2025-08-26T20:22:08.0940931Z Returns the unique elements of the input tensor. 2025-08-26T20:22:08.0941598Z 2025-08-26T20:22:08.0942383Z .. note:: This function is different from :func:`torch.unique_consecutive` in the sense that 2025-08-26T20:22:08.0943586Z this function also eliminates non-consecutive duplicate values. 2025-08-26T20:22:08.0944404Z 2025-08-26T20:22:08.0945058Z .. note:: Currently in the CUDA implementation and the CPU implementation, 2025-08-26T20:22:08.0946314Z `torch.unique` always sort the tensor at the beginning regardless of the `sort` argument. 2025-08-26T20:22:08.0947677Z Sorting could be slow, so if your input tensor is already sorted, it is recommended to use 2025-08-26T20:22:08.0948808Z :func:`torch.unique_consecutive` which avoids the sorting. 2025-08-26T20:22:08.0949543Z 2025-08-26T20:22:08.0949923Z Args: 2025-08-26T20:22:08.0950380Z input (Tensor): the input tensor 2025-08-26T20:22:08.0951212Z sorted (bool): Whether to sort the unique elements in ascending order 2025-08-26T20:22:08.0952132Z before returning as output. 2025-08-26T20:22:08.0952970Z return_inverse (bool): Whether to also return the indices for where 2025-08-26T20:22:08.0954047Z elements in the original input ended up in the returned unique list. 2025-08-26T20:22:08.0955140Z return_counts (bool): Whether to also return the counts for each unique 2025-08-26T20:22:08.0955999Z element. 2025-08-26T20:22:08.0956707Z dim (int, optional): the dimension to operate upon. If ``None``, the 2025-08-26T20:22:08.0957802Z unique of the flattened input is returned. Otherwise, each of the 2025-08-26T20:22:08.0958844Z tensors indexed by the given dimension is treated as one of the 2025-08-26T20:22:08.0959883Z elements to apply the unique operation upon. See examples for more 2025-08-26T20:22:08.0960746Z details. Default: ``None`` 2025-08-26T20:22:08.0961328Z 2025-08-26T20:22:08.0961705Z Returns: 2025-08-26T20:22:08.0962508Z (Tensor, Tensor (optional), Tensor (optional)): A tensor or a tuple of tensors containing 2025-08-26T20:22:08.0963483Z 2025-08-26T20:22:08.0964054Z - **output** (*Tensor*): the output list of unique scalar elements. 2025-08-26T20:22:08.0964931Z - **inverse_indices** (*Tensor*): (optional) if 2025-08-26T20:22:08.0965830Z :attr:`return_inverse` is True, there will be an additional 2025-08-26T20:22:08.0966812Z returned tensor (same shape as input) representing the indices 2025-08-26T20:22:08.0967822Z for where elements in the original input map to in the output; 2025-08-26T20:22:08.0968803Z otherwise, this function will only return a single tensor. 2025-08-26T20:22:08.0969642Z - **counts** (*Tensor*): (optional) if 2025-08-26T20:22:08.0970413Z :attr:`return_counts` is True, there will be an additional 2025-08-26T20:22:08.0971346Z returned tensor (same shape as output or output.size(dim), 2025-08-26T20:22:08.0972313Z if dim was specified) representing the number of occurrences 2025-08-26T20:22:08.0973131Z for each unique value or tensor. 2025-08-26T20:22:08.0973726Z 2025-08-26T20:22:08.0974150Z Example:: 2025-08-26T20:22:08.0974572Z 2025-08-26T20:22:08.0975194Z >>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long)) 2025-08-26T20:22:08.0976005Z >>> output 2025-08-26T20:22:08.0976474Z tensor([1, 2, 3]) 2025-08-26T20:22:08.0976970Z 2025-08-26T20:22:08.0977423Z >>> output, inverse_indices = torch.unique( 2025-08-26T20:22:08.0978331Z ... torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True) 2025-08-26T20:22:08.0979197Z >>> output 2025-08-26T20:22:08.0979658Z tensor([1, 2, 3]) 2025-08-26T20:22:08.0980183Z >>> inverse_indices 2025-08-26T20:22:08.0980759Z tensor([0, 2, 1, 2]) 2025-08-26T20:22:08.0981278Z 2025-08-26T20:22:08.0981735Z >>> output, inverse_indices = torch.unique( 2025-08-26T20:22:08.0982665Z ... torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True) 2025-08-26T20:22:08.0983520Z >>> output 2025-08-26T20:22:08.0983987Z tensor([1, 2, 3]) 2025-08-26T20:22:08.0984518Z >>> inverse_indices 2025-08-26T20:22:08.0985035Z tensor([[0, 2], 2025-08-26T20:22:08.0985516Z [1, 2]]) 2025-08-26T20:22:08.0985999Z 2025-08-26T20:22:08.0986395Z >>> a = torch.tensor([ 2025-08-26T20:22:08.0986927Z ... [ 2025-08-26T20:22:08.0987374Z ... [1, 1, 0, 0], 2025-08-26T20:22:08.0987929Z ... [1, 1, 0, 0], 2025-08-26T20:22:08.0988483Z ... [0, 0, 1, 1], 2025-08-26T20:22:08.0989020Z ... ], 2025-08-26T20:22:08.0989447Z ... [ 2025-08-26T20:22:08.0989892Z ... [0, 0, 1, 1], 2025-08-26T20:22:08.0990442Z ... [0, 0, 1, 1], 2025-08-26T20:22:08.0990982Z ... [1, 1, 1, 1], 2025-08-26T20:22:08.0991500Z ... ], 2025-08-26T20:22:08.0992078Z ... [ 2025-08-26T20:22:08.0992532Z ... [1, 1, 0, 0], 2025-08-26T20:22:08.0993079Z ... [1, 1, 0, 0], 2025-08-26T20:22:08.0993611Z ... [0, 0, 1, 1], 2025-08-26T20:22:08.0994257Z ... ], 2025-08-26T20:22:08.0994699Z ... ]) 2025-08-26T20:22:08.0995119Z 2025-08-26T20:22:08.0995722Z >>> # If we call `torch.unique(a, dim=0)`, each of the tensors `a[idx, :, :]` 2025-08-26T20:22:08.0996770Z >>> # will be compared. We can see that `a[0, :, :]` and `a[2, :, :]` match 2025-08-26T20:22:08.0997661Z >>> # each other, so one of them will be removed. 2025-08-26T20:22:08.0998374Z >>> (a[0, :, :] == a[2, :, :]).all() 2025-08-26T20:22:08.0998960Z tensor(True) 2025-08-26T20:22:08.0999506Z >>> a_unique_dim0 = torch.unique(a, dim=0) 2025-08-26T20:22:08.1000141Z >>> a_unique_dim0 2025-08-26T20:22:08.1000673Z tensor([[[0, 0, 1, 1], 2025-08-26T20:22:08.1001203Z [0, 0, 1, 1], 2025-08-26T20:22:08.1001737Z [1, 1, 1, 1]], 2025-08-26T20:22:08.1002384Z [[1, 1, 0, 0], 2025-08-26T20:22:08.1002917Z [1, 1, 0, 0], 2025-08-26T20:22:08.1003437Z [0, 0, 1, 1]]]) 2025-08-26T20:22:08.1003967Z 2025-08-26T20:22:08.1004588Z >>> # Notice which sub-tensors from `a` match with the sub-tensors from 2025-08-26T20:22:08.1005426Z >>> # `a_unique_dim0`: 2025-08-26T20:22:08.1006010Z >>> (a_unique_dim0[0, :, :] == a[1, :, :]).all() 2025-08-26T20:22:08.1006647Z tensor(True) 2025-08-26T20:22:08.1007182Z >>> (a_unique_dim0[1, :, :] == a[0, :, :]).all() 2025-08-26T20:22:08.1007818Z tensor(True) 2025-08-26T20:22:08.1008268Z 2025-08-26T20:22:08.1008863Z >>> # For `torch.unique(a, dim=1)`, each of the tensors `a[:, idx, :]` are 2025-08-26T20:22:08.1009853Z >>> # compared. `a[:, 0, :]` and `a[:, 1, :]` match each other, so one of 2025-08-26T20:22:08.1010708Z >>> # them will be removed. 2025-08-26T20:22:08.1011308Z >>> (a[:, 0, :] == a[:, 1, :]).all() 2025-08-26T20:22:08.1011900Z tensor(True) 2025-08-26T20:22:08.1012412Z >>> torch.unique(a, dim=1) 2025-08-26T20:22:08.1012998Z tensor([[[0, 0, 1, 1], 2025-08-26T20:22:08.1013542Z [1, 1, 0, 0]], 2025-08-26T20:22:08.1014059Z [[1, 1, 1, 1], 2025-08-26T20:22:08.1014588Z [0, 0, 1, 1]], 2025-08-26T20:22:08.1015125Z [[0, 0, 1, 1], 2025-08-26T20:22:08.1015658Z [1, 1, 0, 0]]]) 2025-08-26T20:22:08.1016194Z 2025-08-26T20:22:08.1016803Z >>> # For `torch.unique(a, dim=2)`, the tensors `a[:, :, idx]` are compared. 2025-08-26T20:22:08.1017747Z >>> # `a[:, :, 0]` and `a[:, :, 1]` match each other. Also, `a[:, :, 2]` and 2025-08-26T20:22:08.1018616Z >>> # `a[:, :, 3]` match each other as well. So in this case, two of the 2025-08-26T20:22:08.1019405Z >>> # sub-tensors will be removed. 2025-08-26T20:22:08.1020035Z >>> (a[:, :, 0] == a[:, :, 1]).all() 2025-08-26T20:22:08.1020691Z tensor(True) 2025-08-26T20:22:08.1021192Z >>> (a[:, :, 2] == a[:, :, 3]).all() 2025-08-26T20:22:08.1021773Z tensor(True) 2025-08-26T20:22:08.1022271Z >>> torch.unique(a, dim=2) 2025-08-26T20:22:08.1022854Z tensor([[[0, 1], 2025-08-26T20:22:08.1023356Z [0, 1], 2025-08-26T20:22:08.1023830Z [1, 0]], 2025-08-26T20:22:08.1024330Z [[1, 0], 2025-08-26T20:22:08.1024821Z [1, 0], 2025-08-26T20:22:08.1025316Z [1, 1]], 2025-08-26T20:22:08.1025799Z [[0, 1], 2025-08-26T20:22:08.1026288Z [0, 1], 2025-08-26T20:22:08.1026782Z [1, 0]]]) 2025-08-26T20:22:08.1027281Z 2025-08-26T20:22:08.1027983Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1028879Z 2025-08-26T20:22:08.1029274Z warnings.warn(msg) 2025-08-26T20:22:08.1029805Z 2025-08-26T20:22:08.1030415Z --- Parse Warning: 4 / 146 --- 2025-08-26T20:22:08.1032458Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=565. 2025-08-26T20:22:08.1034802Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1035721Z 2025-08-26T20:22:08.1036231Z Load a model from a github repo or a local directory. 2025-08-26T20:22:08.1036904Z 2025-08-26T20:22:08.1037560Z Note: Loading a model is the typical use case, but this can also be used to 2025-08-26T20:22:08.1038658Z for loading other objects such as tokenizers, loss functions, etc. 2025-08-26T20:22:08.1039470Z 2025-08-26T20:22:08.1040056Z If ``source`` is 'github', ``repo_or_dir`` is expected to be 2025-08-26T20:22:08.1040928Z of the form ``repo_owner/repo_name[:ref]`` with an optional 2025-08-26T20:22:08.1041676Z ref (a tag or a branch). 2025-08-26T20:22:08.1042187Z 2025-08-26T20:22:08.1042705Z If ``source`` is 'local', ``repo_or_dir`` is expected to be a 2025-08-26T20:22:08.1043454Z path to a local directory. 2025-08-26T20:22:08.1043987Z 2025-08-26T20:22:08.1044359Z Args: 2025-08-26T20:22:08.1044817Z repo_or_dir (str): If ``source`` is 'github', 2025-08-26T20:22:08.1045862Z this should correspond to a github repo with format ``repo_owner/repo_name[:ref]`` with 2025-08-26T20:22:08.1047269Z an optional ref (tag or branch), for example 'pytorch/vision:0.10'. If ``ref`` is not specified, 2025-08-26T20:22:08.1048616Z the default branch is assumed to be ``main`` if it exists, and otherwise ``master``. 2025-08-26T20:22:08.1049815Z If ``source`` is 'local' then it should be a path to a local directory. 2025-08-26T20:22:08.1050823Z model (str): the name of a callable (entrypoint) defined in the 2025-08-26T20:22:08.1051631Z repo/dir's ``hubconf.py``. 2025-08-26T20:22:08.1052420Z *args (optional): the corresponding args for callable ``model``. 2025-08-26T20:22:08.1053403Z source (str, optional): 'github' or 'local'. Specifies how 2025-08-26T20:22:08.1054277Z ``repo_or_dir`` is to be interpreted. Default is 'github'. 2025-08-26T20:22:08.1055245Z trust_repo (bool, str or None): ``"check"``, ``True``, ``False`` or ``None``. 2025-08-26T20:22:08.1056343Z This parameter was introduced in v1.12 and helps ensuring that users 2025-08-26T20:22:08.1057265Z only run code from repos that they trust. 2025-08-26T20:22:08.1057882Z 2025-08-26T20:22:08.1058455Z - If ``False``, a prompt will ask the user whether the repo should 2025-08-26T20:22:08.1059236Z be trusted. 2025-08-26T20:22:08.1059915Z - If ``True``, the repo will be added to the trusted list and loaded 2025-08-26T20:22:08.1060821Z without requiring explicit confirmation. 2025-08-26T20:22:08.1061625Z - If ``"check"``, the repo will be checked against the list of 2025-08-26T20:22:08.1062589Z trusted repos in the cache. If it is not present in that list, the 2025-08-26T20:22:08.1063618Z behaviour will fall back onto the ``trust_repo=False`` option. 2025-08-26T20:22:08.1064588Z - If ``None``: this will raise a warning, inviting the user to set 2025-08-26T20:22:08.1065525Z ``trust_repo`` to either ``False``, ``True`` or ``"check"``. This 2025-08-26T20:22:08.1066519Z is only present for backward compatibility and will be removed in 2025-08-26T20:22:08.1067326Z v2.0. 2025-08-26T20:22:08.1067743Z 2025-08-26T20:22:08.1068336Z Default is ``None`` and will eventually change to ``"check"`` in v2.0. 2025-08-26T20:22:08.1069394Z force_reload (bool, optional): whether to force a fresh download of 2025-08-26T20:22:08.1070403Z the github repo unconditionally. Does not have any effect if 2025-08-26T20:22:08.1071292Z ``source = 'local'``. Default is ``False``. 2025-08-26T20:22:08.1072147Z verbose (bool, optional): If ``False``, mute messages about hitting 2025-08-26T20:22:08.1073166Z local caches. Note that the message about first download cannot be 2025-08-26T20:22:08.1074135Z muted. Does not have any effect if ``source = 'local'``. 2025-08-26T20:22:08.1074878Z Default is ``True``. 2025-08-26T20:22:08.1075811Z skip_validation (bool, optional): if ``False``, torchhub will check that the branch or commit 2025-08-26T20:22:08.1077174Z specified by the ``github`` argument properly belongs to the repo owner. This will make 2025-08-26T20:22:08.1078520Z requests to the GitHub API; you can specify a non-default GitHub token by setting the 2025-08-26T20:22:08.1079715Z ``GITHUB_TOKEN`` environment variable. Default is ``False``. 2025-08-26T20:22:08.1080717Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-08-26T20:22:08.1081535Z 2025-08-26T20:22:08.1081902Z Returns: 2025-08-26T20:22:08.1082506Z The output of the ``model`` callable when called with the given 2025-08-26T20:22:08.1083292Z ``*args`` and ``**kwargs``. 2025-08-26T20:22:08.1083834Z 2025-08-26T20:22:08.1084196Z Example: 2025-08-26T20:22:08.1084688Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-26T20:22:08.1085358Z >>> # from a github repo 2025-08-26T20:22:08.1085914Z >>> repo = "pytorch/vision" 2025-08-26T20:22:08.1086484Z >>> model = torch.hub.load( 2025-08-26T20:22:08.1087217Z ... repo, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1" 2025-08-26T20:22:08.1088013Z ... ) 2025-08-26T20:22:08.1088445Z >>> # from a local directory 2025-08-26T20:22:08.1089072Z >>> path = "/some/local/path/pytorch/vision" 2025-08-26T20:22:08.1089726Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1090537Z >>> model = torch.hub.load(path, "resnet50", weights="ResNet50_Weights.DEFAULT") 2025-08-26T20:22:08.1091415Z 2025-08-26T20:22:08.1092301Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1093206Z 2025-08-26T20:22:08.1093606Z warnings.warn(msg) 2025-08-26T20:22:08.1094082Z 2025-08-26T20:22:08.1094665Z --- Parse Warning: 5 / 146 --- 2025-08-26T20:22:08.1096757Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_load_local in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=657. 2025-08-26T20:22:08.1099153Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1100067Z 2025-08-26T20:22:08.1100654Z Load a model from a local directory with a ``hubconf.py``. 2025-08-26T20:22:08.1101373Z 2025-08-26T20:22:08.1101748Z Args: 2025-08-26T20:22:08.1102326Z hubconf_dir (str): path to a local directory that contains a 2025-08-26T20:22:08.1103086Z ``hubconf.py``. 2025-08-26T20:22:08.1103762Z model (str): name of an entrypoint defined in the directory's 2025-08-26T20:22:08.1104533Z ``hubconf.py``. 2025-08-26T20:22:08.1105235Z *args (optional): the corresponding args for callable ``model``. 2025-08-26T20:22:08.1106266Z **kwargs (optional): the corresponding kwargs for callable ``model``. 2025-08-26T20:22:08.1107064Z 2025-08-26T20:22:08.1107436Z Returns: 2025-08-26T20:22:08.1107982Z a single model with corresponding pretrained weights. 2025-08-26T20:22:08.1108684Z 2025-08-26T20:22:08.1109043Z Example: 2025-08-26T20:22:08.1109509Z >>> # xdoctest: +SKIP("stub local path") 2025-08-26T20:22:08.1110209Z >>> path = "/some/local/path/pytorch/vision" 2025-08-26T20:22:08.1110858Z >>> model = _load_local( 2025-08-26T20:22:08.1111375Z ... path, 2025-08-26T20:22:08.1111939Z ... "resnet50", 2025-08-26T20:22:08.1112525Z ... weights="ResNet50_Weights.IMAGENET1K_V1", 2025-08-26T20:22:08.1113182Z ... ) 2025-08-26T20:22:08.1113573Z 2025-08-26T20:22:08.1114283Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1115183Z 2025-08-26T20:22:08.1115569Z warnings.warn(msg) 2025-08-26T20:22:08.1116042Z 2025-08-26T20:22:08.1116603Z --- Parse Warning: 6 / 146 --- 2025-08-26T20:22:08.1118769Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=download_url_to_file in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=696. 2025-08-26T20:22:08.1121244Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1122391Z Download object at the given URL to a local path. 2025-08-26T20:22:08.1123070Z 2025-08-26T20:22:08.1123448Z Args: 2025-08-26T20:22:08.1123929Z url (str): URL of the object to download 2025-08-26T20:22:08.1124825Z dst (str): Full path where object will be saved, e.g. ``/tmp/temporary_file`` 2025-08-26T20:22:08.1126169Z hash_prefix (str, optional): If not None, the SHA256 downloaded file should start with ``hash_prefix``. 2025-08-26T20:22:08.1127243Z Default: None 2025-08-26T20:22:08.1128072Z progress (bool, optional): whether or not to display a progress bar to stderr 2025-08-26T20:22:08.1128991Z Default: True 2025-08-26T20:22:08.1129474Z 2025-08-26T20:22:08.1129845Z Example: 2025-08-26T20:22:08.1130356Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-26T20:22:08.1131112Z >>> # xdoctest: +REQUIRES(POSIX) 2025-08-26T20:22:08.1131753Z >>> torch.hub.download_url_to_file( 2025-08-26T20:22:08.1132623Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth", 2025-08-26T20:22:08.1133507Z ... "/tmp/temporary_file", 2025-08-26T20:22:08.1134080Z ... ) 2025-08-26T20:22:08.1134478Z 2025-08-26T20:22:08.1134843Z 2025-08-26T20:22:08.1135550Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1136436Z 2025-08-26T20:22:08.1136822Z warnings.warn(msg) 2025-08-26T20:22:08.1137293Z 2025-08-26T20:22:08.1137838Z --- Parse Warning: 7 / 146 --- 2025-08-26T20:22:08.1140027Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_state_dict_from_url in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/hub.py line=821. 2025-08-26T20:22:08.1142628Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1143667Z Loads the Torch serialized object at the given URL. 2025-08-26T20:22:08.1144345Z 2025-08-26T20:22:08.1144901Z If downloaded file is a zip file, it will be automatically 2025-08-26T20:22:08.1145663Z decompressed. 2025-08-26T20:22:08.1146091Z 2025-08-26T20:22:08.1146714Z If the object is already present in `model_dir`, it's deserialized and 2025-08-26T20:22:08.1147537Z returned. 2025-08-26T20:22:08.1148198Z The default value of ``model_dir`` is ``/checkpoints`` where 2025-08-26T20:22:08.1149213Z ``hub_dir`` is the directory returned by :func:`~torch.hub.get_dir`. 2025-08-26T20:22:08.1149994Z 2025-08-26T20:22:08.1150361Z Args: 2025-08-26T20:22:08.1150843Z url (str): URL of the object to download 2025-08-26T20:22:08.1151667Z model_dir (str, optional): directory in which to save the object 2025-08-26T20:22:08.1152989Z map_location (optional): a function or a dict specifying how to remap storage locations (see torch.load) 2025-08-26T20:22:08.1154427Z progress (bool, optional): whether or not to display a progress bar to stderr. 2025-08-26T20:22:08.1155409Z Default: True 2025-08-26T20:22:08.1156393Z check_hash(bool, optional): If True, the filename part of the URL should follow the naming convention 2025-08-26T20:22:08.1157690Z ``filename-.ext`` where ```` is the first eight or more 2025-08-26T20:22:08.1158792Z digits of the SHA256 hash of the contents of the file. The hash is used to 2025-08-26T20:22:08.1159853Z ensure unique names and to verify the contents of the file. 2025-08-26T20:22:08.1160644Z Default: False 2025-08-26T20:22:08.1204659Z file_name (str, optional): name for the downloaded file. Filename from ``url`` will be used if not set. 2025-08-26T20:22:08.1206593Z weights_only(bool, optional): If True, only weights will be loaded and no complex pickled objects. 2025-08-26T20:22:08.1207974Z Recommended for untrusted sources. See :func:`~torch.load` for more details. 2025-08-26T20:22:08.1208878Z 2025-08-26T20:22:08.1209241Z Example: 2025-08-26T20:22:08.1209738Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_HUB) 2025-08-26T20:22:08.1210498Z >>> state_dict = torch.hub.load_state_dict_from_url( 2025-08-26T20:22:08.1211440Z ... "https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth" 2025-08-26T20:22:08.1212258Z ... ) 2025-08-26T20:22:08.1212672Z 2025-08-26T20:22:08.1213031Z 2025-08-26T20:22:08.1213754Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1214644Z 2025-08-26T20:22:08.1215047Z warnings.warn(msg) 2025-08-26T20:22:08.1215512Z 2025-08-26T20:22:08.1216225Z --- Parse Warning: 8 / 146 --- 2025-08-26T20:22:08.1218435Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Library.fallback in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=375. 2025-08-26T20:22:08.1220974Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.1222150Z Registers the function implementation as the fallback for the given key. 2025-08-26T20:22:08.1223030Z 2025-08-26T20:22:08.1223659Z This function only works for a library with global namespace ("_"). 2025-08-26T20:22:08.1224486Z 2025-08-26T20:22:08.1224846Z Args: 2025-08-26T20:22:08.1225654Z fn: function used as fallback for the given dispatch key or :func:`~fallthrough_kernel` 2025-08-26T20:22:08.1226668Z to register a fallthrough. 2025-08-26T20:22:08.1227747Z dispatch_key: dispatch key that the input function should be registered for. By default, it uses 2025-08-26T20:22:08.1228963Z the dispatch key that the library was created with. 2025-08-26T20:22:08.1230236Z with_keyset: flag controlling if the current dispatcher call keyset should be passed as the first argument 2025-08-26T20:22:08.1231833Z to :attr:`fn` when calling. This should be used to create the appropriate keyset for redispatch calls. 2025-08-26T20:22:08.1232856Z 2025-08-26T20:22:08.1233260Z Example:: 2025-08-26T20:22:08.1233700Z 2025-08-26T20:22:08.1234112Z >>> my_lib = Library("_", "IMPL") 2025-08-26T20:22:08.1234787Z >>> def fallback_kernel(op, *args, **kwargs): 2025-08-26T20:22:08.1235503Z >>> # Handle all autocast ops generically 2025-08-26T20:22:08.1236133Z >>> # ... 2025-08-26T20:22:08.1236723Z >>> my_lib.fallback(fallback_kernel, "Autocast") 2025-08-26T20:22:08.1237389Z 2025-08-26T20:22:08.1238907Z Original Error: IndentationError('expected an indented block after function definition on line 2', ('', 5, 1, 'my_lib.fallback(fallback_kernel, "Autocast")\n', 5, 7)) 2025-08-26T20:22:08.1240627Z 2025-08-26T20:22:08.1241094Z my_lib.fallback(fallback_kernel, "Autocast") 2025-08-26T20:22:08.1241733Z ^ 2025-08-26T20:22:08.1242138Z warnings.warn(msg) 2025-08-26T20:22:08.1242598Z 2025-08-26T20:22:08.1243164Z --- Parse Warning: 9 / 146 --- 2025-08-26T20:22:08.1245328Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=948. 2025-08-26T20:22:08.1247759Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.1248936Z Register a FakeTensor implementation ("fake impl") for this operator. 2025-08-26T20:22:08.1249774Z 2025-08-26T20:22:08.1250434Z Also sometimes known as a "meta kernel", "abstract impl". 2025-08-26T20:22:08.1251167Z 2025-08-26T20:22:08.1251849Z An "FakeTensor implementation" specifies the behavior of this operator on 2025-08-26T20:22:08.1252987Z Tensors that carry no data ("FakeTensor"). Given some input Tensors with 2025-08-26T20:22:08.1254121Z certain properties (sizes/strides/storage_offset/device), it specifies 2025-08-26T20:22:08.1255093Z what the properties of the output Tensors are. 2025-08-26T20:22:08.1255738Z 2025-08-26T20:22:08.1256382Z The FakeTensor implementation has the same signature as the operator. 2025-08-26T20:22:08.1257482Z It is run for both FakeTensors and meta tensors. To write a FakeTensor 2025-08-26T20:22:08.1258544Z implementation, assume that all Tensor inputs to the operator are 2025-08-26T20:22:08.1259601Z regular CPU/CUDA/Meta tensors, but they do not have storage, and 2025-08-26T20:22:08.1260739Z you are trying to return regular CPU/CUDA/Meta tensor(s) as output. 2025-08-26T20:22:08.1261837Z The FakeTensor implementation must consist of only PyTorch operations 2025-08-26T20:22:08.1262902Z (and may not directly access the storage or data of any input or 2025-08-26T20:22:08.1263708Z intermediate Tensors). 2025-08-26T20:22:08.1264222Z 2025-08-26T20:22:08.1264706Z This API may be used as a decorator (see examples). 2025-08-26T20:22:08.1265384Z 2025-08-26T20:22:08.1265859Z For a detailed guide on custom ops, please see 2025-08-26T20:22:08.1266806Z https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html 2025-08-26T20:22:08.1267633Z 2025-08-26T20:22:08.1268003Z Args: 2025-08-26T20:22:08.1268676Z op_name: Operator name (along with the overload) or OpOverload object. 2025-08-26T20:22:08.1269571Z func: Fake tensor implementation. 2025-08-26T20:22:08.1270388Z lib (Optional[Library]): Library to register the fake tensor to. 2025-08-26T20:22:08.1271370Z allow_override: Flag controlling if we want to override an 2025-08-26T20:22:08.1272296Z existing registered fake impl. This is by default off, 2025-08-26T20:22:08.1273213Z and will error you're trying to register a fake impl to 2025-08-26T20:22:08.1274107Z an operator that already has a fake impl. This also only 2025-08-26T20:22:08.1275000Z applies if the custom operator was not created via 2025-08-26T20:22:08.1275909Z torch.library.custom_op, as overriding and existing fake 2025-08-26T20:22:08.1276732Z impl is already allowed. 2025-08-26T20:22:08.1277332Z 2025-08-26T20:22:08.1277697Z Examples: 2025-08-26T20:22:08.1278137Z >>> import torch 2025-08-26T20:22:08.1278672Z >>> import numpy as np 2025-08-26T20:22:08.1279248Z >>> from torch import Tensor 2025-08-26T20:22:08.1279812Z >>> 2025-08-26T20:22:08.1280417Z >>> # Example 1: an operator without data-dependent output shape 2025-08-26T20:22:08.1281501Z >>> @torch.library.custom_op("mylib::custom_linear", mutates_args=()) 2025-08-26T20:22:08.1282581Z >>> def custom_linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-08-26T20:22:08.1283578Z >>> raise NotImplementedError("Implementation goes here") 2025-08-26T20:22:08.1284320Z >>> 2025-08-26T20:22:08.1284888Z >>> @torch.library.register_fake("mylib::custom_linear") 2025-08-26T20:22:08.1285646Z >>> def _(x, weight, bias): 2025-08-26T20:22:08.1286233Z >>> assert x.dim() == 2 2025-08-26T20:22:08.1286838Z >>> assert weight.dim() == 2 2025-08-26T20:22:08.1287459Z >>> assert bias.dim() == 1 2025-08-26T20:22:08.1288105Z >>> assert x.shape[1] == weight.shape[1] 2025-08-26T20:22:08.1288873Z >>> assert weight.shape[0] == bias.shape[0] 2025-08-26T20:22:08.1289556Z >>> assert x.device == weight.device 2025-08-26T20:22:08.1290173Z >>> 2025-08-26T20:22:08.1290627Z >>> return (x @ weight.t()) + bias 2025-08-26T20:22:08.1291229Z >>> 2025-08-26T20:22:08.1291951Z >>> with torch._subclasses.fake_tensor.FakeTensorMode(): 2025-08-26T20:22:08.1292708Z >>> x = torch.randn(2, 3) 2025-08-26T20:22:08.1293312Z >>> w = torch.randn(3, 3) 2025-08-26T20:22:08.1293914Z >>> b = torch.randn(3) 2025-08-26T20:22:08.1294547Z >>> y = torch.ops.mylib.custom_linear(x, w, b) 2025-08-26T20:22:08.1295200Z >>> 2025-08-26T20:22:08.1295644Z >>> assert y.shape == (2, 3) 2025-08-26T20:22:08.1296213Z >>> 2025-08-26T20:22:08.1296777Z >>> # Example 2: an operator with data-dependent output shape 2025-08-26T20:22:08.1297867Z >>> @torch.library.custom_op("mylib::custom_nonzero", mutates_args=()) 2025-08-26T20:22:08.1298771Z >>> def custom_nonzero(x: Tensor) -> Tensor: 2025-08-26T20:22:08.1299451Z >>> x_np = x.numpy(force=True) 2025-08-26T20:22:08.1300113Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-08-26T20:22:08.1300898Z >>> return torch.tensor(res, device=x.device) 2025-08-26T20:22:08.1301550Z >>> 2025-08-26T20:22:08.1302124Z >>> @torch.library.register_fake("mylib::custom_nonzero") 2025-08-26T20:22:08.1302863Z >>> def _(x): 2025-08-26T20:22:08.1303444Z >>> # Number of nonzero-elements is data-dependent. 2025-08-26T20:22:08.1304250Z >>> # Since we cannot peek at the data in an fake impl, 2025-08-26T20:22:08.1305070Z >>> # we use the ctx object to construct a new symint that 2025-08-26T20:22:08.1305842Z >>> # represents the data-dependent size. 2025-08-26T20:22:08.1306514Z >>> ctx = torch.library.get_ctx() 2025-08-26T20:22:08.1307201Z >>> nnz = ctx.new_dynamic_size() 2025-08-26T20:22:08.1307843Z >>> shape = [nnz, x.dim()] 2025-08-26T20:22:08.1308527Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-08-26T20:22:08.1309210Z >>> return result 2025-08-26T20:22:08.1309737Z >>> 2025-08-26T20:22:08.1310321Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-08-26T20:22:08.1311060Z >>> 2025-08-26T20:22:08.1311510Z >>> x = torch.tensor([0, 1, 2, 3, 4, 0]) 2025-08-26T20:22:08.1312415Z >>> trace = make_fx(torch.ops.mylib.custom_nonzero, tracing_mode="symbolic")(x) 2025-08-26T20:22:08.1313340Z >>> trace.print_readable() 2025-08-26T20:22:08.1313909Z >>> 2025-08-26T20:22:08.1314575Z >>> assert torch.allclose(trace(x), torch.ops.mylib.custom_nonzero(x)) 2025-08-26T20:22:08.1315417Z 2025-08-26T20:22:08.1315785Z 2025-08-26T20:22:08.1317077Z Original Error: IndentationError('expected an indented block after function definition on line 37', ('', 38, 1, '_._ = None\n', 38, 2)) 2025-08-26T20:22:08.1318618Z 2025-08-26T20:22:08.1318977Z _._ = None 2025-08-26T20:22:08.1319385Z ^ 2025-08-26T20:22:08.1319786Z warnings.warn(msg) 2025-08-26T20:22:08.1320247Z 2025-08-26T20:22:08.1320854Z --- Parse Warning: 10 / 146 --- 2025-08-26T20:22:08.1323083Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_autograd in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1083. 2025-08-26T20:22:08.1325584Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1326613Z Register a backward formula for this custom op. 2025-08-26T20:22:08.1327255Z 2025-08-26T20:22:08.1327855Z In order for an operator to work with autograd, you need to register 2025-08-26T20:22:08.1328777Z a backward formula: 2025-08-26T20:22:08.1329503Z 1. You must tell us how to compute gradients during the backward pass 2025-08-26T20:22:08.1330358Z by providing us a "backward" function. 2025-08-26T20:22:08.1331214Z 2. If you need any values from the forward to compute gradients, you can 2025-08-26T20:22:08.1332136Z use `setup_context` to save values for backward. 2025-08-26T20:22:08.1332790Z 2025-08-26T20:22:08.1333421Z ``backward`` runs during the backward pass. It accepts ``(ctx, *grads)``: 2025-08-26T20:22:08.1334468Z - ``grads`` is one or more gradients. The number of gradients matches 2025-08-26T20:22:08.1335307Z the number of outputs of the operator. 2025-08-26T20:22:08.1336175Z The ``ctx`` object is `the same ctx object `_ used by 2025-08-26T20:22:08.1337314Z :class:`torch.autograd.Function`. The semantics of ``backward_fn`` are the 2025-08-26T20:22:08.1338384Z same as :meth:`torch.autograd.Function.backward`. 2025-08-26T20:22:08.1339061Z 2025-08-26T20:22:08.1339678Z ``setup_context(ctx, inputs, output)`` runs during the forward pass. 2025-08-26T20:22:08.1340843Z Please save quantities needed for backward onto the ``ctx`` object via 2025-08-26T20:22:08.1341972Z either :meth:`torch.autograd.function.FunctionCtx.save_for_backward` 2025-08-26T20:22:08.1343050Z or assigning them as attributes of ``ctx``. If your custom op has 2025-08-26T20:22:08.1344109Z kwarg-only arguments, we expect the signature of ``setup_context`` 2025-08-26T20:22:08.1345137Z to be ``setup_context(ctx, inputs, keyword_only_inputs, output)``. 2025-08-26T20:22:08.1345909Z 2025-08-26T20:22:08.1346526Z Both ``setup_context_fn`` and ``backward_fn`` must be traceable. That is, 2025-08-26T20:22:08.1347636Z they may not directly access :meth:`torch.Tensor.data_ptr` and they must 2025-08-26T20:22:08.1348775Z not depend on or mutate global state. If you need a non-traceable backward, 2025-08-26T20:22:08.1349906Z you can make it a separate custom_op that you call inside ``backward_fn``. 2025-08-26T20:22:08.1350735Z 2025-08-26T20:22:08.1351365Z If you need different autograd behavior on different devices, then we 2025-08-26T20:22:08.1352490Z recommend creating two different custom operators, one for each device 2025-08-26T20:22:08.1353616Z that needs different behavior, and switching between them at runtime. 2025-08-26T20:22:08.1354440Z 2025-08-26T20:22:08.1354817Z Examples: 2025-08-26T20:22:08.1355264Z >>> import torch 2025-08-26T20:22:08.1355798Z >>> import numpy as np 2025-08-26T20:22:08.1356364Z >>> from torch import Tensor 2025-08-26T20:22:08.1356938Z >>> 2025-08-26T20:22:08.1357578Z >>> @torch.library.custom_op("mylib::numpy_sin", mutates_args=()) 2025-08-26T20:22:08.1358430Z >>> def numpy_sin(x: Tensor) -> Tensor: 2025-08-26T20:22:08.1359082Z >>> x_np = x.cpu().numpy() 2025-08-26T20:22:08.1359673Z >>> y_np = np.sin(x_np) 2025-08-26T20:22:08.1360416Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-08-26T20:22:08.1361100Z >>> 2025-08-26T20:22:08.1361638Z >>> def setup_context(ctx, inputs, output) -> Tensor: 2025-08-26T20:22:08.1362324Z >>> x, = inputs 2025-08-26T20:22:08.1362867Z >>> ctx.save_for_backward(x) 2025-08-26T20:22:08.1363444Z >>> 2025-08-26T20:22:08.1363892Z >>> def backward(ctx, grad): 2025-08-26T20:22:08.1364485Z >>> x, = ctx.saved_tensors 2025-08-26T20:22:08.1365088Z >>> return grad * x.cos() 2025-08-26T20:22:08.1365657Z >>> 2025-08-26T20:22:08.1366133Z >>> torch.library.register_autograd( 2025-08-26T20:22:08.1366914Z ... "mylib::numpy_sin", backward, setup_context=setup_context 2025-08-26T20:22:08.1367731Z ... ) 2025-08-26T20:22:08.1368144Z >>> 2025-08-26T20:22:08.1368621Z >>> x = torch.randn(3, requires_grad=True) 2025-08-26T20:22:08.1369256Z >>> y = numpy_sin(x) 2025-08-26T20:22:08.1369949Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-08-26T20:22:08.1370734Z >>> assert torch.allclose(grad_x, x.cos()) 2025-08-26T20:22:08.1371360Z >>> 2025-08-26T20:22:08.1371830Z >>> # Example with a keyword-only arg 2025-08-26T20:22:08.1372668Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-08-26T20:22:08.1373559Z >>> def numpy_mul(x: Tensor, *, val: float) -> Tensor: 2025-08-26T20:22:08.1374265Z >>> x_np = x.cpu().numpy() 2025-08-26T20:22:08.1374865Z >>> y_np = x_np * val 2025-08-26T20:22:08.1375549Z >>> return torch.from_numpy(y_np).to(device=x.device) 2025-08-26T20:22:08.1376268Z >>> 2025-08-26T20:22:08.1376958Z >>> def setup_context(ctx, inputs, keyword_only_inputs, output) -> Tensor: 2025-08-26T20:22:08.1377882Z >>> ctx.val = keyword_only_inputs["val"] 2025-08-26T20:22:08.1378507Z >>> 2025-08-26T20:22:08.1378946Z >>> def backward(ctx, grad): 2025-08-26T20:22:08.1379554Z >>> return grad * ctx.val 2025-08-26T20:22:08.1380128Z >>> 2025-08-26T20:22:08.1380661Z >>> torch.library.register_autograd( 2025-08-26T20:22:08.1381443Z ... "mylib::numpy_mul", backward, setup_context=setup_context 2025-08-26T20:22:08.1382194Z ... ) 2025-08-26T20:22:08.1382615Z >>> 2025-08-26T20:22:08.1383099Z >>> x = torch.randn(3, requires_grad=True) 2025-08-26T20:22:08.1383744Z >>> y = numpy_mul(x, val=3.14) 2025-08-26T20:22:08.1384476Z >>> (grad_x,) = torch.autograd.grad(y, x, torch.ones_like(y)) 2025-08-26T20:22:08.1385379Z >>> assert torch.allclose(grad_x, torch.full_like(x, 3.14)) 2025-08-26T20:22:08.1386107Z 2025-08-26T20:22:08.1386473Z 2025-08-26T20:22:08.1387170Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1388061Z 2025-08-26T20:22:08.1388448Z warnings.warn(msg) 2025-08-26T20:22:08.1388918Z 2025-08-26T20:22:08.1389496Z --- Parse Warning: 11 / 146 --- 2025-08-26T20:22:08.1391632Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_kernel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1482. 2025-08-26T20:22:08.1394183Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.1395325Z Returns the computed kernel for a given operator and dispatch key. 2025-08-26T20:22:08.1396113Z 2025-08-26T20:22:08.1396755Z This function retrieves the kernel that would be executed for a given 2025-08-26T20:22:08.1397889Z operator and dispatch key combination. The returned SafeKernelFunction 2025-08-26T20:22:08.1399050Z can be used to call the kernel in a boxed fashion. The intended use 2025-08-26T20:22:08.1400073Z case for this function is to retrieve the original kernel for a given 2025-08-26T20:22:08.1401141Z dispatch key and then register another kernel to the same dispatch key 2025-08-26T20:22:08.1402129Z that calls into the original kernel for certain cases. 2025-08-26T20:22:08.1402828Z 2025-08-26T20:22:08.1403226Z Args: 2025-08-26T20:22:08.1403835Z op: Operator name (along with the overload) or OpOverload object 2025-08-26T20:22:08.1404884Z Can be a string (e.g., "aten::add.Tensor"), an OpOverload, or a CustomOpDef. 2025-08-26T20:22:08.1406048Z dispatch_key (str | torch.DispatchKey): The dispatch key to get the kernel for. 2025-08-26T20:22:08.1407250Z Can be a string (e.g., "CPU", "CUDA") or a DispatchKey enum value. 2025-08-26T20:22:08.1408002Z 2025-08-26T20:22:08.1408378Z Returns: 2025-08-26T20:22:08.1409109Z torch._C._SafeKernelFunction: A safe kernel function that can be used to 2025-08-26T20:22:08.1410007Z call the kernel. 2025-08-26T20:22:08.1410509Z 2025-08-26T20:22:08.1410882Z Raises: 2025-08-26T20:22:08.1411396Z RuntimeError: If the operator does not exist. 2025-08-26T20:22:08.1412051Z 2025-08-26T20:22:08.1412405Z Example: 2025-08-26T20:22:08.1412888Z >>> # Get the CPU kernel for torch.add 2025-08-26T20:22:08.1413696Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", "CPU") 2025-08-26T20:22:08.1414458Z >>> 2025-08-26T20:22:08.1414915Z >>> # You can also use DispatchKey enum 2025-08-26T20:22:08.1415865Z >>> kernel = torch.library.get_kernel("aten::add.Tensor", torch.DispatchKey.CPU) 2025-08-26T20:22:08.1416855Z >>> 2025-08-26T20:22:08.1417323Z >>> # Or use an OpOverload directly 2025-08-26T20:22:08.1418185Z >>> kernel = torch.library.get_kernel(torch.ops.aten.add.Tensor, "CPU") 2025-08-26T20:22:08.1419009Z >>> 2025-08-26T20:22:08.1419676Z >>> # Example: Using get_kernel in a custom op with conditional dispatch 2025-08-26T20:22:08.1420635Z >>> # Get the original kernel for torch.sin 2025-08-26T20:22:08.1421513Z >>> original_sin_kernel = torch.library.get_kernel("aten::sin", "CPU") 2025-08-26T20:22:08.1422316Z >>> 2025-08-26T20:22:08.1422985Z >>> # If input has negative values, use original sin, otherwise return zeros 2025-08-26T20:22:08.1423935Z >>> def conditional_sin_impl(dispatch_keys, x): 2025-08-26T20:22:08.1424603Z >>> if (x < 0).any(): 2025-08-26T20:22:08.1425330Z >>> return original_sin_kernel.call_boxed(dispatch_keys, x) 2025-08-26T20:22:08.1426076Z >>> else: 2025-08-26T20:22:08.1426589Z >>> return torch.zeros_like(x) 2025-08-26T20:22:08.1427191Z >>> 2025-08-26T20:22:08.1427689Z >>> lib = torch.library.Library("aten", "IMPL") 2025-08-26T20:22:08.1428656Z >>> # with_keyset=True so the first argument to the impl is the current DispatchKeySet 2025-08-26T20:22:08.1429760Z >>> which needs to be the first argument to ``kernel.call_boxed`` 2025-08-26T20:22:08.1430715Z >>> lib.impl("sin", conditional_sin_impl, "CPU", with_keyset=True) 2025-08-26T20:22:08.1431480Z >>> 2025-08-26T20:22:08.1431929Z >>> # Test the conditional behavior 2025-08-26T20:22:08.1432589Z >>> x_positive = torch.tensor([1.0, 2.0]) 2025-08-26T20:22:08.1433255Z >>> x_mixed = torch.tensor([-1.0, 2.0]) 2025-08-26T20:22:08.1433898Z >>> torch.sin(x_positive) 2025-08-26T20:22:08.1434464Z tensor([0., 0.]) 2025-08-26T20:22:08.1434980Z >>> torch.sin(x_mixed) 2025-08-26T20:22:08.1435539Z tensor([-0.8415, 0.9093]) 2025-08-26T20:22:08.1436086Z 2025-08-26T20:22:08.1437293Z Original Error: SyntaxError('invalid syntax', ('', 23, 7, 'which needs to be the first argument to ``kernel.call_boxed``\n', 23, 12)) 2025-08-26T20:22:08.1438644Z 2025-08-26T20:22:08.1439193Z which needs to be the first argument to ``kernel.call_boxed`` 2025-08-26T20:22:08.1439938Z ^ 2025-08-26T20:22:08.1440338Z warnings.warn(msg) 2025-08-26T20:22:08.1440808Z 2025-08-26T20:22:08.1441407Z --- Parse Warning: 12 / 146 --- 2025-08-26T20:22:08.1443537Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=opcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py line=1571. 2025-08-26T20:22:08.1445949Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1447199Z Given an operator and some sample arguments, tests if the operator is 2025-08-26T20:22:08.1448054Z registered correctly. 2025-08-26T20:22:08.1448573Z 2025-08-26T20:22:08.1449182Z That is, when you use the torch.library/TORCH_LIBRARY APIs to create a 2025-08-26T20:22:08.1450295Z custom op, you specified metadata (e.g. mutability info) about the custom op 2025-08-26T20:22:08.1451458Z and these APIs require that the functions you pass them satisfy certain 2025-08-26T20:22:08.1452580Z properties (e.g. no data pointer access in the fake/meta/abstract kernel) 2025-08-26T20:22:08.1453547Z ``opcheck`` tests these metadata and properties. 2025-08-26T20:22:08.1454204Z 2025-08-26T20:22:08.1454622Z Concretely, we test the following: 2025-08-26T20:22:08.1455209Z 2025-08-26T20:22:08.1455746Z - test_schema: If the schema matches the implementation of 2025-08-26T20:22:08.1456784Z the operator. For example: if the schema specifies a Tensor is mutated, 2025-08-26T20:22:08.1457853Z then we check the implementation mutates the Tensor. If the schema 2025-08-26T20:22:08.1458862Z specifies that we return a new Tensor, then we check that the 2025-08-26T20:22:08.1459892Z implementation returns a new Tensor (instead of an existing one or 2025-08-26T20:22:08.1460846Z a view of an existing one). 2025-08-26T20:22:08.1461622Z - test_autograd_registration: If the operator supports training 2025-08-26T20:22:08.1462624Z (autograd): we check that its autograd formula is registered via 2025-08-26T20:22:08.1463648Z torch.library.register_autograd or a manual registration to one 2025-08-26T20:22:08.1464687Z or more DispatchKey::Autograd keys. Any other DispatchKey-based 2025-08-26T20:22:08.1465574Z registrations may lead to undefined behavior. 2025-08-26T20:22:08.1466400Z - test_faketensor: If the operator has a FakeTensor kernel 2025-08-26T20:22:08.1467308Z (and if it is correct). The FakeTensor kernel is necessary ( 2025-08-26T20:22:08.1468299Z but not sufficient) for the operator to work with PyTorch compilation 2025-08-26T20:22:08.1469365Z APIs (torch.compile/export/FX). We check that a FakeTensor kernel 2025-08-26T20:22:08.1470371Z (also sometimes known as a meta kernel) was registered for the 2025-08-26T20:22:08.1471357Z operator and that it is correct. This test takes the result of 2025-08-26T20:22:08.1472341Z running the operator on real tensors and the result of running 2025-08-26T20:22:08.1473329Z the operator on FakeTensors and checks that they have the same 2025-08-26T20:22:08.1474227Z Tensor metadata (sizes/strides/dtype/device/etc). 2025-08-26T20:22:08.1475115Z - test_aot_dispatch_dynamic: If the operator has correct behavior 2025-08-26T20:22:08.1476087Z with PyTorch compilation APIs (torch.compile/export/FX). 2025-08-26T20:22:08.1477075Z This checks that the outputs (and gradients, if applicable) are the 2025-08-26T20:22:08.1478006Z same under eager-mode PyTorch and torch.compile. 2025-08-26T20:22:08.1478925Z This test is a superset of ``test_faketensor`` and is an e2e test; 2025-08-26T20:22:08.1479845Z other things it tests are that the operator supports 2025-08-26T20:22:08.1480811Z functionalization and that the backward pass (if it exists) also 2025-08-26T20:22:08.1481731Z supports FakeTensor and functionalization. 2025-08-26T20:22:08.1482359Z 2025-08-26T20:22:08.1482943Z For best results, please call ``opcheck`` multiple times with a 2025-08-26T20:22:08.1483898Z representative set of inputs. If your operator supports 2025-08-26T20:22:08.1484932Z autograd, please use ``opcheck`` with inputs with ``requires_grad = True``; 2025-08-26T20:22:08.1486063Z if your operator supports multiple devices (e.g. CPU and CUDA), please 2025-08-26T20:22:08.1487114Z use ``opcheck`` with inputs on all supported devices. 2025-08-26T20:22:08.1487808Z 2025-08-26T20:22:08.1488181Z Args: 2025-08-26T20:22:08.1488724Z op: The operator. Must either be a function decorated with 2025-08-26T20:22:08.1489688Z :func:`torch.library.custom_op` or an OpOverload/OpOverloadPacket 2025-08-26T20:22:08.1490744Z found in torch.ops.* (e.g. torch.ops.aten.sin, torch.ops.mylib.foo) 2025-08-26T20:22:08.1491603Z args: The args to the operator 2025-08-26T20:22:08.1492378Z kwargs: The kwargs to the operator 2025-08-26T20:22:08.1493150Z test_utils: Tests that we should run. Default: all of them. 2025-08-26T20:22:08.1493992Z Example: ("test_schema", "test_faketensor") 2025-08-26T20:22:08.1494851Z raise_exception: If we should raise an exception on the first 2025-08-26T20:22:08.1495778Z error. If False, we will return a dict with information 2025-08-26T20:22:08.1496642Z on if each test passed or not. 2025-08-26T20:22:08.1497534Z rtol (Optional[float]): Relative tolerance for floating point comparisons. 2025-08-26T20:22:08.1498510Z If specified ``atol`` must also be specified. 2025-08-26T20:22:08.1499373Z If omitted, default values based on the ``dtype`` are selected 2025-08-26T20:22:08.1500299Z (see the table in :func:`torch.testing.assert_close`). 2025-08-26T20:22:08.1501401Z atol (Optional[float]): Absolute tolerance for floating point comparisons. 2025-08-26T20:22:08.1502374Z If specified ``rtol`` must also be specified. 2025-08-26T20:22:08.1503234Z If omitted, default values based on the ``dtype`` are selected 2025-08-26T20:22:08.1504151Z (see the table in :func:`torch.testing.assert_close`). 2025-08-26T20:22:08.1504849Z 2025-08-26T20:22:08.1505252Z .. warning:: 2025-08-26T20:22:08.1505687Z 2025-08-26T20:22:08.1506318Z opcheck and :func:`torch.autograd.gradcheck` test different things; 2025-08-26T20:22:08.1507372Z opcheck tests if your usage of torch.library APIs is correct while 2025-08-26T20:22:08.1508435Z :func:`torch.autograd.gradcheck` tests if your autograd formula is 2025-08-26T20:22:08.1509516Z mathematically correct. Use both to test custom ops that support 2025-08-26T20:22:08.1510390Z gradient computation. 2025-08-26T20:22:08.1510916Z 2025-08-26T20:22:08.1511299Z Example: 2025-08-26T20:22:08.1511724Z 2025-08-26T20:22:08.1512196Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:22:08.1513062Z >>> @torch.library.custom_op("mylib::numpy_mul", mutates_args=()) 2025-08-26T20:22:08.1513963Z >>> def numpy_mul(x: Tensor, y: float) -> Tensor: 2025-08-26T20:22:08.1514680Z >>> x_np = x.numpy(force=True) 2025-08-26T20:22:08.1515295Z >>> z_np = x_np * y 2025-08-26T20:22:08.1515918Z >>> return torch.from_numpy(z_np).to(x.device) 2025-08-26T20:22:08.1516576Z >>> 2025-08-26T20:22:08.1517038Z >>> @numpy_mul.register_fake 2025-08-26T20:22:08.1517713Z >>> def _(x, y): 2025-08-26T20:22:08.1518248Z >>> return torch.empty_like(x) 2025-08-26T20:22:08.1518844Z >>> 2025-08-26T20:22:08.1519337Z >>> def setup_context(ctx, inputs, output): 2025-08-26T20:22:08.1519984Z >>> y, = inputs 2025-08-26T20:22:08.1520480Z >>> ctx.y = y 2025-08-26T20:22:08.1520974Z >>> 2025-08-26T20:22:08.1521419Z >>> def backward(ctx, grad): 2025-08-26T20:22:08.1522033Z >>> return grad * ctx.y, None 2025-08-26T20:22:08.1522601Z >>> 2025-08-26T20:22:08.1523274Z >>> numpy_mul.register_autograd(backward, setup_context=setup_context) 2025-08-26T20:22:08.1524111Z >>> 2025-08-26T20:22:08.1524546Z >>> sample_inputs = [ 2025-08-26T20:22:08.1525197Z >>> (torch.randn(3), 3.14), 2025-08-26T20:22:08.1525853Z >>> (torch.randn(2, 3, device='cuda'), 2.718), 2025-08-26T20:22:08.1526611Z >>> (torch.randn(1, 10, requires_grad=True), 1.234), 2025-08-26T20:22:08.1527481Z >>> (torch.randn(64, 64, device='cuda', requires_grad=True), 90.18), 2025-08-26T20:22:08.1528264Z >>> ] 2025-08-26T20:22:08.1528669Z >>> 2025-08-26T20:22:08.1529121Z >>> for args in sample_inputs: 2025-08-26T20:22:08.1529793Z >>> torch.library.opcheck(numpy_mul, args) 2025-08-26T20:22:08.1530432Z 2025-08-26T20:22:08.1530785Z 2025-08-26T20:22:08.1531497Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1532384Z 2025-08-26T20:22:08.1532774Z warnings.warn(msg) 2025-08-26T20:22:08.1533240Z 2025-08-26T20:22:08.1533873Z --- Parse Warning: 13 / 146 --- 2025-08-26T20:22:08.1536031Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/serialization.py line=1285. 2025-08-26T20:22:08.1538484Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1539881Z load(f, map_location=None, pickle_module=pickle, *, weights_only=True, mmap=None, **pickle_load_args) 2025-08-26T20:22:08.1540992Z 2025-08-26T20:22:08.1541547Z Loads an object saved with :func:`torch.save` from a file. 2025-08-26T20:22:08.1542279Z 2025-08-26T20:22:08.1542922Z :func:`torch.load` uses Python's unpickling facilities but treats storages, 2025-08-26T20:22:08.1544075Z which underlie tensors, specially. They are first deserialized on the 2025-08-26T20:22:08.1545186Z CPU and are then moved to the device they were saved from. If this fails 2025-08-26T20:22:08.1546305Z (e.g. because the run time system doesn't have certain devices), an exception 2025-08-26T20:22:08.1547464Z is raised. However, storages can be dynamically remapped to an alternative 2025-08-26T20:22:08.1548468Z set of devices using the :attr:`map_location` argument. 2025-08-26T20:22:08.1549175Z 2025-08-26T20:22:08.1549849Z If :attr:`map_location` is a callable, it will be called once for each serialized 2025-08-26T20:22:08.1551005Z storage with two arguments: storage and location. The storage argument 2025-08-26T20:22:08.1552140Z will be the initial deserialization of the storage, residing on the CPU. 2025-08-26T20:22:08.1553258Z Each serialized storage has a location tag associated with it which 2025-08-26T20:22:08.1554328Z identifies the device it was saved from, and this tag is the second 2025-08-26T20:22:08.1555472Z argument passed to :attr:`map_location`. The builtin location tags are ``'cpu'`` 2025-08-26T20:22:08.1556656Z for CPU tensors and ``'cuda:device_id'`` (e.g. ``'cuda:2'``) for CUDA tensors. 2025-08-26T20:22:08.1557718Z :attr:`map_location` should return either ``None`` or a storage. If 2025-08-26T20:22:08.1558902Z :attr:`map_location` returns a storage, it will be used as the final deserialized 2025-08-26T20:22:08.1560111Z object, already moved to the right device. Otherwise, :func:`torch.load` will 2025-08-26T20:22:08.1561309Z fall back to the default behavior, as if :attr:`map_location` wasn't specified. 2025-08-26T20:22:08.1562196Z 2025-08-26T20:22:08.1562842Z If :attr:`map_location` is a :class:`torch.device` object or a string containing 2025-08-26T20:22:08.1563997Z a device tag, it indicates the location where all tensors should be loaded. 2025-08-26T20:22:08.1564882Z 2025-08-26T20:22:08.1565597Z Otherwise, if :attr:`map_location` is a dict, it will be used to remap location tags 2025-08-26T20:22:08.1566726Z appearing in the file (keys), to ones that specify where to put the 2025-08-26T20:22:08.1567621Z storages (values). 2025-08-26T20:22:08.1568098Z 2025-08-26T20:22:08.1568737Z User extensions can register their own location tags and tagging and 2025-08-26T20:22:08.1569912Z deserialization methods using :func:`torch.serialization.register_package`. 2025-08-26T20:22:08.1570829Z 2025-08-26T20:22:08.1571511Z See :ref:`layout-control` for more advanced tools to manipulate a checkpoint. 2025-08-26T20:22:08.1572391Z 2025-08-26T20:22:08.1572745Z Args: 2025-08-26T20:22:08.1573614Z f: a file-like object (has to implement :meth:`read`, :meth:`readline`, :meth:`tell`, and :meth:`seek`), 2025-08-26T20:22:08.1574809Z or a string or os.PathLike object containing a file name 2025-08-26T20:22:08.1576004Z map_location: a function, :class:`torch.device`, string or a dict specifying how to remap storage 2025-08-26T20:22:08.1577097Z locations 2025-08-26T20:22:08.1577839Z pickle_module: module used for unpickling metadata and objects (has to 2025-08-26T20:22:08.1578844Z match the :attr:`pickle_module` used to serialize file) 2025-08-26T20:22:08.1579816Z weights_only: Indicates whether unpickler should be restricted to 2025-08-26T20:22:08.1580846Z loading only tensors, primitive types, dictionaries 2025-08-26T20:22:08.1581791Z and any types added via :func:`torch.serialization.add_safe_globals`. 2025-08-26T20:22:08.1582711Z See :ref:`weights-only` for more details. 2025-08-26T20:22:08.1583850Z mmap: Indicates whether the file should be mapped rather than loading all the storages into memory. 2025-08-26T20:22:08.1585433Z Typically, tensor storages in the file will first be moved from disk to CPU memory, after which they 2025-08-26T20:22:08.1587018Z are moved to the location that they were tagged with when saving, or specified by ``map_location``. This 2025-08-26T20:22:08.1588575Z second step is a no-op if the final location is CPU. When the ``mmap`` flag is set, instead of copying the 2025-08-26T20:22:08.1590122Z tensor storages from disk to CPU memory in the first step, ``f`` is mapped, which means tensor storages 2025-08-26T20:22:08.1591314Z will be lazily loaded when their data is accessed. 2025-08-26T20:22:08.1592480Z pickle_load_args: (Python 3 only) optional keyword arguments passed over to 2025-08-26T20:22:08.1593628Z :func:`pickle_module.load` and :func:`pickle_module.Unpickler`, e.g., 2025-08-26T20:22:08.1594487Z :attr:`errors=...`. 2025-08-26T20:22:08.1595030Z 2025-08-26T20:22:08.1595433Z .. warning:: 2025-08-26T20:22:08.1596122Z :func:`torch.load()` unless `weights_only` parameter is set to `True`, 2025-08-26T20:22:08.1597147Z uses ``pickle`` module implicitly, which is known to be insecure. 2025-08-26T20:22:08.1597704Z It is possible to construct malicious pickle data which will execute arbitrary code 2025-08-26T20:22:08.1598172Z during unpickling. Never load data that could have come from an untrusted 2025-08-26T20:22:08.1598860Z source in an unsafe mode, or that could have been tampered with. **Only load data you trust**. 2025-08-26T20:22:08.1599019Z 2025-08-26T20:22:08.1599182Z .. note:: 2025-08-26T20:22:08.1599696Z When you call :func:`torch.load()` on a file which contains GPU tensors, those tensors 2025-08-26T20:22:08.1600191Z will be loaded to GPU by default. You can call ``torch.load(.., map_location='cpu')`` 2025-08-26T20:22:08.1600728Z and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint. 2025-08-26T20:22:08.1600882Z 2025-08-26T20:22:08.1601047Z .. note:: 2025-08-26T20:22:08.1601541Z By default, we decode byte strings as ``utf-8``. This is to avoid a common error 2025-08-26T20:22:08.1602055Z case ``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...`` 2025-08-26T20:22:08.1602467Z when loading files saved by Python 2 in Python 3. If this default 2025-08-26T20:22:08.1602965Z is incorrect, you may use an extra :attr:`encoding` keyword argument to specify how 2025-08-26T20:22:08.1603443Z these objects should be loaded, e.g., :attr:`encoding='latin1'` decodes them 2025-08-26T20:22:08.1603898Z to strings using ``latin1`` encoding, and :attr:`encoding='bytes'` keeps them 2025-08-26T20:22:08.1604335Z as byte arrays which can be decoded later with ``byte_array.decode(...)``. 2025-08-26T20:22:08.1604500Z 2025-08-26T20:22:08.1604663Z Example: 2025-08-26T20:22:08.1604917Z >>> # xdoctest: +SKIP("undefined filepaths") 2025-08-26T20:22:08.1605172Z >>> torch.load("tensors.pt", weights_only=True) 2025-08-26T20:22:08.1605426Z # Load all tensors onto the CPU 2025-08-26T20:22:08.1605624Z >>> torch.load( 2025-08-26T20:22:08.1605801Z ... "tensors.pt", 2025-08-26T20:22:08.1606038Z ... map_location=torch.device("cpu"), 2025-08-26T20:22:08.1606239Z ... weights_only=True, 2025-08-26T20:22:08.1606396Z ... ) 2025-08-26T20:22:08.1606676Z # Load all tensors onto the CPU, using a function 2025-08-26T20:22:08.1606845Z >>> torch.load( 2025-08-26T20:22:08.1607024Z ... "tensors.pt", 2025-08-26T20:22:08.1607290Z ... map_location=lambda storage, loc: storage, 2025-08-26T20:22:08.1607475Z ... weights_only=True, 2025-08-26T20:22:08.1607642Z ... ) 2025-08-26T20:22:08.1607845Z # Load all tensors onto GPU 1 2025-08-26T20:22:08.1608016Z >>> torch.load( 2025-08-26T20:22:08.1608206Z ... "tensors.pt", 2025-08-26T20:22:08.1608506Z ... map_location=lambda storage, loc: storage.cuda(1), 2025-08-26T20:22:08.1608714Z ... weights_only=True, 2025-08-26T20:22:08.1608923Z ... ) # type: ignore[attr-defined] 2025-08-26T20:22:08.1609136Z # Map tensors from GPU 1 to GPU 0 2025-08-26T20:22:08.1609319Z >>> torch.load( 2025-08-26T20:22:08.1609499Z ... "tensors.pt", 2025-08-26T20:22:08.1609738Z ... map_location={"cuda:1": "cuda:0"}, 2025-08-26T20:22:08.1609926Z ... weights_only=True, 2025-08-26T20:22:08.1610081Z ... ) 2025-08-26T20:22:08.1610312Z # Load tensor from io.BytesIO object 2025-08-26T20:22:08.1610799Z # Loading from a buffer setting weights_only=False, warning this can be unsafe 2025-08-26T20:22:08.1611033Z >>> with open("tensor.pt", "rb") as f: 2025-08-26T20:22:08.1611243Z ... buffer = io.BytesIO(f.read()) 2025-08-26T20:22:08.1611474Z >>> torch.load(buffer, weights_only=False) 2025-08-26T20:22:08.1611775Z # Load a module with 'ascii' encoding for unpickling 2025-08-26T20:22:08.1612256Z # Loading from a module setting weights_only=False, warning this can be unsafe 2025-08-26T20:22:08.1612674Z >>> torch.load("module.pt", encoding="ascii", weights_only=False) 2025-08-26T20:22:08.1612826Z 2025-08-26T20:22:08.1613319Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1613481Z 2025-08-26T20:22:08.1613663Z warnings.warn(msg) 2025-08-26T20:22:08.1613833Z 2025-08-26T20:22:08.1614229Z --- Parse Warning: 14 / 146 --- 2025-08-26T20:22:08.1616250Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compute_required_storage_length in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_prims_common/__init__.py line=1877. 2025-08-26T20:22:08.1616779Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1617295Z Computes the minimum storage size to hold the given tensor geometry. 2025-08-26T20:22:08.1617465Z 2025-08-26T20:22:08.1617630Z Example 2025-08-26T20:22:08.1617785Z ======= 2025-08-26T20:22:08.1617954Z 2025-08-26T20:22:08.1618389Z This is the size of a newly allocated tensor's storage, in units of elements 2025-08-26T20:22:08.1618559Z 2025-08-26T20:22:08.1618754Z >>> t = torch.empty((10, 20)) 2025-08-26T20:22:08.1619229Z >>> compute_required_storage_length(t.shape, t.stride(), t.storage_offset()) 2025-08-26T20:22:08.1619400Z 200 2025-08-26T20:22:08.1619552Z 2025-08-26T20:22:08.1619749Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:22:08.1620004Z >>> t2 = torch.empty_strided((1, 2, 3), (5, 7, 11)) 2025-08-26T20:22:08.1620238Z >>> size = compute_required_storage_length( 2025-08-26T20:22:08.1620565Z ... t2.shape, t2.stride(), t2.storage_offset() 2025-08-26T20:22:08.1620756Z ... ) 2025-08-26T20:22:08.1620959Z >>> size == t.storage().size() 2025-08-26T20:22:08.1621128Z True 2025-08-26T20:22:08.1621278Z 2025-08-26T20:22:08.1621672Z A valid tensor may have a larger storage size, but never smaller 2025-08-26T20:22:08.1621825Z 2025-08-26T20:22:08.1622028Z >>> slice = torch.empty(100)[20:40] 2025-08-26T20:22:08.1622234Z >>> slice.storage().size() 2025-08-26T20:22:08.1622384Z 100 2025-08-26T20:22:08.1622551Z 2025-08-26T20:22:08.1622766Z >>> compute_required_storage_length( 2025-08-26T20:22:08.1623071Z ... slice.shape, slice.stride(), slice.storage_offset() 2025-08-26T20:22:08.1623239Z ... ) 2025-08-26T20:22:08.1623396Z 40 2025-08-26T20:22:08.1623550Z 2025-08-26T20:22:08.1623722Z 2025-08-26T20:22:08.1624211Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1624381Z 2025-08-26T20:22:08.1624563Z warnings.warn(msg) 2025-08-26T20:22:08.1624721Z 2025-08-26T20:22:08.1625085Z --- Parse Warning: 15 / 146 --- 2025-08-26T20:22:08.1626890Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=is_available in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=66. 2025-08-26T20:22:08.1627381Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.1627867Z Check if the current accelerator is available at runtime: it was build, all the 2025-08-26T20:22:08.1628280Z required drivers are available and at least one device is visible. 2025-08-26T20:22:08.1628561Z See :ref:`accelerator` for details. 2025-08-26T20:22:08.1628716Z 2025-08-26T20:22:08.1628890Z Returns: 2025-08-26T20:22:08.1629421Z bool: A boolean indicating if there is an available :ref:`accelerator`. 2025-08-26T20:22:08.1629579Z 2025-08-26T20:22:08.1630068Z .. note:: This API delegates to the device-specific version of `is_available`. 2025-08-26T20:22:08.1630576Z On CUDA, when the environment variable ``PYTORCH_NVML_BASED_CUDA_CHECK=1`` is set, 2025-08-26T20:22:08.1631114Z this function will NOT poison fork. Otherwise, it will. For more details, see 2025-08-26T20:22:08.1631378Z :ref:`multiprocessing-poison-fork-note`. 2025-08-26T20:22:08.1631546Z 2025-08-26T20:22:08.1631712Z Example:: 2025-08-26T20:22:08.1631864Z 2025-08-26T20:22:08.1632389Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-26T20:22:08.1632544Z 2025-08-26T20:22:08.1633657Z Original Error: SyntaxError('invalid syntax', ('', 1, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 1, 78)) 2025-08-26T20:22:08.1633822Z 2025-08-26T20:22:08.1634326Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-26T20:22:08.1634594Z ^ 2025-08-26T20:22:08.1634781Z warnings.warn(msg) 2025-08-26T20:22:08.1634938Z 2025-08-26T20:22:08.1635288Z --- Parse Warning: 16 / 146 --- 2025-08-26T20:22:08.1637105Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=synchronize in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/accelerator/__init__.py line=212. 2025-08-26T20:22:08.1637591Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.1637986Z Wait for all kernels in all streams on the given device to complete. 2025-08-26T20:22:08.1638151Z 2025-08-26T20:22:08.1638310Z Args: 2025-08-26T20:22:08.1638928Z device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match 2025-08-26T20:22:08.1639417Z the current :ref:`accelerator` device type. If not given, 2025-08-26T20:22:08.1639780Z use :func:`torch.accelerator.current_device_index` by default. 2025-08-26T20:22:08.1639946Z 2025-08-26T20:22:08.1640548Z .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. 2025-08-26T20:22:08.1640701Z 2025-08-26T20:22:08.1640882Z Example:: 2025-08-26T20:22:08.1641040Z 2025-08-26T20:22:08.1641311Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:22:08.1641828Z >>> assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-26T20:22:08.1642092Z >>> start_event = torch.Event(enable_timing=True) 2025-08-26T20:22:08.1642363Z >>> end_event = torch.Event(enable_timing=True) 2025-08-26T20:22:08.1642554Z >>> start_event.record() 2025-08-26T20:22:08.1643029Z >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) 2025-08-26T20:22:08.1643223Z >>> sum = torch.sum(tensor) 2025-08-26T20:22:08.1643412Z >>> end_event.record() 2025-08-26T20:22:08.1643659Z >>> torch.accelerator.synchronize() 2025-08-26T20:22:08.1643981Z >>> elapsed_time_ms = start_event.elapsed_time(end_event) 2025-08-26T20:22:08.1644151Z 2025-08-26T20:22:08.1645273Z Original Error: SyntaxError('invalid syntax', ('', 2, 41, 'assert torch.accelerator.is_available() "No available accelerators detected."\n', 2, 78)) 2025-08-26T20:22:08.1645426Z 2025-08-26T20:22:08.1645944Z assert torch.accelerator.is_available() "No available accelerators detected." 2025-08-26T20:22:08.1646123Z ^ 2025-08-26T20:22:08.1646319Z warnings.warn(msg) 2025-08-26T20:22:08.1646471Z 2025-08-26T20:22:08.1646811Z --- Parse Warning: 17 / 146 --- 2025-08-26T20:22:08.1648542Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=cudart in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/cuda/__init__.py line=434. 2025-08-26T20:22:08.1649070Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.1649307Z Retrieves the CUDA runtime API module. 2025-08-26T20:22:08.1649461Z 2025-08-26T20:22:08.1649615Z 2025-08-26T20:22:08.1650116Z This function initializes the CUDA runtime environment if it is not already 2025-08-26T20:22:08.1650563Z initialized and returns the CUDA runtime API module (_cudart). The CUDA 2025-08-26T20:22:08.1651014Z runtime API module provides access to various CUDA runtime functions. 2025-08-26T20:22:08.1651169Z 2025-08-26T20:22:08.1651329Z Args: 2025-08-26T20:22:08.1651506Z ``None`` 2025-08-26T20:22:08.1651660Z 2025-08-26T20:22:08.1651836Z Returns: 2025-08-26T20:22:08.1652108Z module: The CUDA runtime API module (_cudart). 2025-08-26T20:22:08.1652264Z 2025-08-26T20:22:08.1652534Z Raises: 2025-08-26T20:22:08.1652981Z RuntimeError: If CUDA cannot be re-initialized in a forked subprocess. 2025-08-26T20:22:08.1653692Z AssertionError: If PyTorch is not compiled with CUDA support or if libcudart functions are unavailable. 2025-08-26T20:22:08.1653860Z 2025-08-26T20:22:08.1654108Z Example of CUDA operations with profiling: 2025-08-26T20:22:08.1654299Z >>> import torch 2025-08-26T20:22:08.1654548Z >>> from torch.cuda import cudart, check_error 2025-08-26T20:22:08.1654717Z >>> import os 2025-08-26T20:22:08.1654891Z >>> 2025-08-26T20:22:08.1655111Z >>> os.environ["CUDA_PROFILE"] = "1" 2025-08-26T20:22:08.1655281Z >>> 2025-08-26T20:22:08.1655537Z >>> def perform_cuda_operations_with_streams(): 2025-08-26T20:22:08.1655786Z >>> stream = torch.cuda.Stream() 2025-08-26T20:22:08.1656026Z >>> with torch.cuda.stream(stream): 2025-08-26T20:22:08.1656259Z >>> x = torch.randn(100, 100, device='cuda') 2025-08-26T20:22:08.1656508Z >>> y = torch.randn(100, 100, device='cuda') 2025-08-26T20:22:08.1656703Z >>> z = torch.mul(x, y) 2025-08-26T20:22:08.1656875Z >>> return z 2025-08-26T20:22:08.1657049Z >>> 2025-08-26T20:22:08.1657258Z >>> torch.cuda.synchronize() 2025-08-26T20:22:08.1657517Z >>> print("====== Start nsys profiling ======") 2025-08-26T20:22:08.1657768Z >>> check_error(cudart().cudaProfilerStart()) 2025-08-26T20:22:08.1658026Z >>> with torch.autograd.profiler.emit_nvtx(): 2025-08-26T20:22:08.1658368Z >>> result = perform_cuda_operations_with_streams() 2025-08-26T20:22:08.1658602Z >>> print("CUDA operations completed.") 2025-08-26T20:22:08.1658919Z >>> check_error(torch.cuda.cudart().cudaProfilerStop()) 2025-08-26T20:22:08.1659157Z >>> print("====== End nsys profiling ======") 2025-08-26T20:22:08.1659315Z 2025-08-26T20:22:08.1659711Z To run this example and save the profiling information, execute: 2025-08-26T20:22:08.1660484Z >>> $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-08-26T20:22:08.1660650Z 2025-08-26T20:22:08.1661121Z This command profiles the CUDA operations in the provided script and saves 2025-08-26T20:22:08.1661489Z the profiling information to a file named `trace_name.prof`. 2025-08-26T20:22:08.1661947Z The `--profile-from-start off` option ensures that profiling starts only 2025-08-26T20:22:08.1662228Z after the `cudaProfilerStart` call in the script. 2025-08-26T20:22:08.1662663Z The `--csv` and `--print-summary` options format the profiling output as a 2025-08-26T20:22:08.1662908Z CSV file and print a summary, respectively. 2025-08-26T20:22:08.1663386Z The `-o` option specifies the output file name, and the `-f` option forces the 2025-08-26T20:22:08.1663683Z overwrite of the output file if it already exists. 2025-08-26T20:22:08.1663883Z 2025-08-26T20:22:08.1665183Z Original Error: SyntaxError('invalid syntax', ('', 1, 1, '$ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py\n', 1, 2)) 2025-08-26T20:22:08.1665340Z 2025-08-26T20:22:08.1666018Z $ nvprof --profile-from-start off --csv --print-summary -o trace_name.prof -f -- python cudart_test.py 2025-08-26T20:22:08.1666189Z ^ 2025-08-26T20:22:08.1666368Z warnings.warn(msg) 2025-08-26T20:22:08.1666533Z 2025-08-26T20:22:08.1666878Z --- Parse Warning: 18 / 146 --- 2025-08-26T20:22:08.1669299Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.then in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=101. 2025-08-26T20:22:08.1669834Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1669989Z 2025-08-26T20:22:08.1670445Z Append the given callback function to this ``Future``, which will be run 2025-08-26T20:22:08.1670837Z when the ``Future`` is completed. Multiple callbacks can be added to 2025-08-26T20:22:08.1671227Z the same ``Future``, but the order in which they will be executed cannot 2025-08-26T20:22:08.1671586Z be guaranteed (to enforce a certain order consider chaining: 2025-08-26T20:22:08.1671969Z ``fut.then(cb1).then(cb2)``). The callback must take one argument, which 2025-08-26T20:22:08.1672381Z is the reference to this ``Future``. The callback function can use the 2025-08-26T20:22:08.1672771Z :meth:`value` method to get the value. Note that if this ``Future`` is 2025-08-26T20:22:08.1673257Z already completed, the given callback will be run immediately inline. 2025-08-26T20:22:08.1673409Z 2025-08-26T20:22:08.1673778Z If the ``Future``'s value contains tensors that reside on GPUs, the 2025-08-26T20:22:08.1674215Z callback might be invoked while the async kernels that are populating 2025-08-26T20:22:08.1674652Z those tensors haven't yet finished executing on the device. However, the 2025-08-26T20:22:08.1675065Z callback will be invoked with some dedicated streams set as current 2025-08-26T20:22:08.1675458Z (fetched from a global pool) which will be synchronized with those 2025-08-26T20:22:08.1675893Z kernels. Hence any operation performed by the callback on these tensors 2025-08-26T20:22:08.1676313Z will be scheduled on the device after the kernels complete. In other 2025-08-26T20:22:08.1676685Z words, as long as the callback doesn't switch streams, it can safely 2025-08-26T20:22:08.1677137Z manipulate the result without any additional synchronization. This is 2025-08-26T20:22:08.1677442Z similar to the non-blocking behavior of :meth:`wait`. 2025-08-26T20:22:08.1677597Z 2025-08-26T20:22:08.1678020Z Similarly, if the callback returns a value that contains tensors that 2025-08-26T20:22:08.1678389Z reside on a GPU, it can do so even if the kernels that are producing 2025-08-26T20:22:08.1678823Z these tensors are still running on the device, as long as the callback 2025-08-26T20:22:08.1679215Z didn't change streams during its execution. If one wants to change 2025-08-26T20:22:08.1679619Z streams, one must be careful to re-synchronize them with the original 2025-08-26T20:22:08.1680046Z streams, that is, those that were current when the callback was invoked. 2025-08-26T20:22:08.1680202Z 2025-08-26T20:22:08.1680367Z Args: 2025-08-26T20:22:08.1680757Z callback(``Callable``): a ``Callable`` that takes this ``Future`` as 2025-08-26T20:22:08.1680955Z the only argument. 2025-08-26T20:22:08.1681129Z 2025-08-26T20:22:08.1681288Z Returns: 2025-08-26T20:22:08.1681616Z A new ``Future`` object that holds the return value of the 2025-08-26T20:22:08.1681949Z ``callback`` and will be marked as completed when the given 2025-08-26T20:22:08.1682190Z ``callback`` finishes. 2025-08-26T20:22:08.1682359Z 2025-08-26T20:22:08.1682686Z .. note:: Note that if the callback function throws, either 2025-08-26T20:22:08.1683099Z through the original future being completed with an exception and 2025-08-26T20:22:08.1683464Z calling ``fut.wait()``, or through other code in the callback, the 2025-08-26T20:22:08.1683854Z future returned by ``then`` will be marked appropriately with the 2025-08-26T20:22:08.1684252Z encountered error. However, if this callback later completes 2025-08-26T20:22:08.1684672Z additional futures, those futures are not marked as completed with 2025-08-26T20:22:08.1685074Z an error and the user is responsible for handling completion/waiting 2025-08-26T20:22:08.1685288Z on those futures independently. 2025-08-26T20:22:08.1685501Z 2025-08-26T20:22:08.1685687Z Example:: 2025-08-26T20:22:08.1685844Z 2025-08-26T20:22:08.1686138Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-08-26T20:22:08.1686327Z >>> def callback(fut): 2025-08-26T20:22:08.1686582Z ... print(f"RPC return value is {fut.wait()}.") 2025-08-26T20:22:08.1686806Z >>> fut = torch.futures.Future() 2025-08-26T20:22:08.1687129Z >>> # The inserted callback will print the return value when 2025-08-26T20:22:08.1687373Z >>> # receiving the response from "worker1" 2025-08-26T20:22:08.1687568Z >>> cb_fut = fut.then(callback) 2025-08-26T20:22:08.1687761Z >>> chain_cb_fut = cb_fut.then( 2025-08-26T20:22:08.1688048Z ... lambda x : print(f"Chained cb done. {x.wait()}") 2025-08-26T20:22:08.1688206Z ... ) 2025-08-26T20:22:08.1688390Z >>> fut.set_result(5) 2025-08-26T20:22:08.1688625Z RPC return value is 5. 2025-08-26T20:22:08.1688815Z Chained cb done. None 2025-08-26T20:22:08.1688977Z 2025-08-26T20:22:08.1689475Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1689631Z 2025-08-26T20:22:08.1689825Z warnings.warn(msg) 2025-08-26T20:22:08.1689983Z 2025-08-26T20:22:08.1690347Z --- Parse Warning: 19 / 146 --- 2025-08-26T20:22:08.1692337Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Future.set_result in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/futures/__init__.py line=211. 2025-08-26T20:22:08.1692852Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1693023Z 2025-08-26T20:22:08.1693414Z Set the result for this ``Future``, which will mark this ``Future`` as 2025-08-26T20:22:08.1693844Z completed and trigger all attached callbacks. Note that a ``Future`` 2025-08-26T20:22:08.1694066Z cannot be marked completed twice. 2025-08-26T20:22:08.1694216Z 2025-08-26T20:22:08.1694646Z If the result contains tensors that reside on GPUs, this method can be 2025-08-26T20:22:08.1695036Z called even if the asynchronous kernels that are populating those 2025-08-26T20:22:08.1695475Z tensors haven't yet completed running on the device, provided that the 2025-08-26T20:22:08.1695913Z streams on which those kernels were enqueued are set as the current ones 2025-08-26T20:22:08.1696313Z when this method is called. Put simply, it's safe to call this method 2025-08-26T20:22:08.1696737Z immediately after launching those kernels, without any additional 2025-08-26T20:22:08.1697178Z synchronization, as long as one doesn't change streams in between. This 2025-08-26T20:22:08.1697608Z method will record events on all the relevant current streams and will 2025-08-26T20:22:08.1697992Z use them to ensure proper scheduling for all the consumers of this 2025-08-26T20:22:08.1698151Z ``Future``. 2025-08-26T20:22:08.1698326Z 2025-08-26T20:22:08.1698487Z Args: 2025-08-26T20:22:08.1698801Z result (object): the result object of this ``Future``. 2025-08-26T20:22:08.1699056Z 2025-08-26T20:22:08.1699224Z Example:: 2025-08-26T20:22:08.1699386Z 2025-08-26T20:22:08.1699657Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_FUTURES) 2025-08-26T20:22:08.1699853Z >>> import threading 2025-08-26T20:22:08.1700023Z >>> import time 2025-08-26T20:22:08.1700230Z >>> def slow_set_future(fut, value): 2025-08-26T20:22:08.1700493Z ... time.sleep(0.5) 2025-08-26T20:22:08.1700685Z ... fut.set_result(value) 2025-08-26T20:22:08.1700903Z >>> fut = torch.futures.Future() 2025-08-26T20:22:08.1701090Z >>> t = threading.Thread( 2025-08-26T20:22:08.1701283Z ... target=slow_set_future, 2025-08-26T20:22:08.1701497Z ... args=(fut, torch.ones(2) * 3) 2025-08-26T20:22:08.1701655Z ... ) 2025-08-26T20:22:08.1701921Z >>> t.start() 2025-08-26T20:22:08.1702120Z >>> print(fut.wait()) 2025-08-26T20:22:08.1702295Z tensor([3., 3.]) 2025-08-26T20:22:08.1702521Z >>> t.join() 2025-08-26T20:22:08.1702674Z 2025-08-26T20:22:08.1703164Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1703329Z 2025-08-26T20:22:08.1703513Z warnings.warn(msg) 2025-08-26T20:22:08.1703682Z 2025-08-26T20:22:08.1704040Z --- Parse Warning: 20 / 146 --- 2025-08-26T20:22:08.1705800Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=compile_shader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/mps/__init__.py line=145. 2025-08-26T20:22:08.1706326Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1706806Z Compiles compute shader from source and allows one to invoke kernels 2025-08-26T20:22:08.1707101Z defined there from the comfort of Python runtime 2025-08-26T20:22:08.1707273Z Example:: 2025-08-26T20:22:08.1707429Z 2025-08-26T20:22:08.1707698Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_MPS) 2025-08-26T20:22:08.1707914Z >>> lib = torch.mps.compile_shader( 2025-08-26T20:22:08.1708682Z ... "kernel void full(device float* out, constant float& val, uint idx [[thread_position_in_grid]]) { out[idx] = val; }" 2025-08-26T20:22:08.1708839Z ... ) 2025-08-26T20:22:08.1709057Z >>> x = torch.zeros(16, device="mps") 2025-08-26T20:22:08.1709248Z >>> lib.full(x, 3.14) 2025-08-26T20:22:08.1709406Z 2025-08-26T20:22:08.1709910Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1710064Z 2025-08-26T20:22:08.1710248Z warnings.warn(msg) 2025-08-26T20:22:08.1710419Z 2025-08-26T20:22:08.1710757Z --- Parse Warning: 21 / 146 --- 2025-08-26T20:22:08.1712435Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=sum in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=202. 2025-08-26T20:22:08.1712950Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1713254Z Return the sum of each row of the given sparse tensor. 2025-08-26T20:22:08.1713425Z 2025-08-26T20:22:08.1713855Z Returns the sum of each row of the sparse tensor :attr:`input` in the given 2025-08-26T20:22:08.1714245Z dimensions :attr:`dim`. If :attr:`dim` is a list of dimensions, 2025-08-26T20:22:08.1714645Z reduce over all of them. When sum over all ``sparse_dim``, this method 2025-08-26T20:22:08.1714933Z returns a dense tensor instead of a sparse tensor. 2025-08-26T20:22:08.1715104Z 2025-08-26T20:22:08.1715607Z All summed :attr:`dim` are squeezed (see :func:`torch.squeeze`), resulting an output 2025-08-26T20:22:08.1715995Z tensor having :attr:`dim` fewer dimensions than :attr:`input`. 2025-08-26T20:22:08.1716186Z 2025-08-26T20:22:08.1716612Z During backward, only gradients at ``nnz`` locations of :attr:`input` 2025-08-26T20:22:08.1717091Z will propagate back. Note that the gradients of :attr:`input` is coalesced. 2025-08-26T20:22:08.1717243Z 2025-08-26T20:22:08.1717412Z Args: 2025-08-26T20:22:08.1717643Z input (Tensor): the input sparse tensor 2025-08-26T20:22:08.1718165Z dim (int or tuple of ints): a dimension or a list of dimensions to reduce. Default: reduce 2025-08-26T20:22:08.1718356Z over all dims. 2025-08-26T20:22:08.1718845Z dtype (:class:`torch.dtype`, optional): the desired data type of returned Tensor. 2025-08-26T20:22:08.1719079Z Default: dtype of :attr:`input`. 2025-08-26T20:22:08.1719239Z 2025-08-26T20:22:08.1719409Z Example:: 2025-08-26T20:22:08.1719572Z 2025-08-26T20:22:08.1719851Z >>> nnz = 3 2025-08-26T20:22:08.1720041Z >>> dims = [5, 5, 2, 3] 2025-08-26T20:22:08.1720342Z >>> I = torch.cat([torch.randint(0, dims[0], size=(nnz,)), 2025-08-26T20:22:08.1720691Z torch.randint(0, dims[1], size=(nnz,))], 0).reshape(2, nnz) 2025-08-26T20:22:08.1720931Z >>> V = torch.randn(nnz, dims[2], dims[3]) 2025-08-26T20:22:08.1721126Z >>> size = torch.Size(dims) 2025-08-26T20:22:08.1721400Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:22:08.1721632Z >>> S = torch.sparse_coo_tensor(I, V, size) 2025-08-26T20:22:08.1721791Z >>> S 2025-08-26T20:22:08.1722015Z tensor(indices=tensor([[2, 0, 3], 2025-08-26T20:22:08.1722195Z [2, 4, 1]]), 2025-08-26T20:22:08.1722443Z values=tensor([[[-0.6438, -1.6467, 1.4004], 2025-08-26T20:22:08.1722681Z [ 0.3411, 0.0918, -0.2312]], 2025-08-26T20:22:08.1722834Z 2025-08-26T20:22:08.1723046Z [[ 0.5348, 0.0634, -2.0494], 2025-08-26T20:22:08.1723249Z [-0.7125, -1.0646, 2.1844]], 2025-08-26T20:22:08.1723398Z 2025-08-26T20:22:08.1723612Z [[ 0.1276, 0.1874, -0.6334], 2025-08-26T20:22:08.1723813Z [-1.9682, -0.5340, 0.7483]]]), 2025-08-26T20:22:08.1724087Z size=(5, 5, 2, 3), nnz=3, layout=torch.sparse_coo) 2025-08-26T20:22:08.1724238Z 2025-08-26T20:22:08.1724599Z # when sum over only part of sparse_dims, return a sparse tensor 2025-08-26T20:22:08.1724814Z >>> torch.sparse.sum(S, [1, 3]) 2025-08-26T20:22:08.1725024Z tensor(indices=tensor([[0, 2, 3]]), 2025-08-26T20:22:08.1725244Z values=tensor([[-1.4512, 0.4073], 2025-08-26T20:22:08.1725434Z [-0.8901, 0.2017], 2025-08-26T20:22:08.1725626Z [-0.3183, -1.7539]]), 2025-08-26T20:22:08.1725887Z size=(5, 2), nnz=3, layout=torch.sparse_coo) 2025-08-26T20:22:08.1726039Z 2025-08-26T20:22:08.1726334Z # when sum over all sparse dim, return a dense tensor 2025-08-26T20:22:08.1726529Z # with summed dims squeezed 2025-08-26T20:22:08.1726734Z >>> torch.sparse.sum(S, [0, 1, 3]) 2025-08-26T20:22:08.1726933Z tensor([-2.6596, -1.1450]) 2025-08-26T20:22:08.1727085Z 2025-08-26T20:22:08.1727591Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1727742Z 2025-08-26T20:22:08.1727922Z warnings.warn(msg) 2025-08-26T20:22:08.1728095Z 2025-08-26T20:22:08.1728441Z --- Parse Warning: 22 / 146 --- 2025-08-26T20:22:08.1730267Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=as_sparse_gradcheck in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/sparse/__init__.py line=550. 2025-08-26T20:22:08.1730819Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1731174Z Decorate function, to extend gradcheck for sparse tensors. 2025-08-26T20:22:08.1731339Z 2025-08-26T20:22:08.1731742Z Decorator for torch.autograd.gradcheck or its functools.partial 2025-08-26T20:22:08.1732166Z variants that extends the gradcheck function with support to input 2025-08-26T20:22:08.1732490Z functions that operate on or/and return sparse tensors. 2025-08-26T20:22:08.1732640Z 2025-08-26T20:22:08.1733054Z The specified gradcheck function itself is guaranteed to operate 2025-08-26T20:22:08.1733246Z on strided tensors only. 2025-08-26T20:22:08.1733415Z 2025-08-26T20:22:08.1733587Z For example: 2025-08-26T20:22:08.1733736Z 2025-08-26T20:22:08.1734262Z >>> gradcheck = torch.sparse.as_sparse_gradcheck(torch.autograd.gradcheck) 2025-08-26T20:22:08.1734422Z >>> x = ( 2025-08-26T20:22:08.1734724Z ... torch.tensor([[0, 1], [2, 3]], dtype=torch.float64) 2025-08-26T20:22:08.1734904Z ... .to_sparse_coo() 2025-08-26T20:22:08.1735095Z ... .requires_grad_(True) 2025-08-26T20:22:08.1735265Z ... ) 2025-08-26T20:22:08.1735508Z >>> gradcheck(lambda x: x.to_sparse_csr(), x) 2025-08-26T20:22:08.1735684Z True 2025-08-26T20:22:08.1735840Z 2025-08-26T20:22:08.1736331Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1736496Z 2025-08-26T20:22:08.1736680Z warnings.warn(msg) 2025-08-26T20:22:08.1736837Z 2025-08-26T20:22:08.1737188Z --- Parse Warning: 23 / 146 --- 2025-08-26T20:22:08.1738869Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=vmap in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=39. 2025-08-26T20:22:08.1739454Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1739608Z 2025-08-26T20:22:08.1740025Z vmap is the vectorizing map; ``vmap(func)`` returns a new function that 2025-08-26T20:22:08.1740486Z maps ``func`` over some dimension of the inputs. Semantically, vmap 2025-08-26T20:22:08.1740907Z pushes the map into PyTorch operations called by ``func``, effectively 2025-08-26T20:22:08.1741133Z vectorizing those operations. 2025-08-26T20:22:08.1741287Z 2025-08-26T20:22:08.1741701Z vmap is useful for handling batch dimensions: one can write a function 2025-08-26T20:22:08.1742106Z ``func`` that runs on examples and then lift it to a function that can 2025-08-26T20:22:08.1742516Z take batches of examples with ``vmap(func)``. vmap can also be used to 2025-08-26T20:22:08.1742857Z compute batched gradients when composed with autograd. 2025-08-26T20:22:08.1743017Z 2025-08-26T20:22:08.1743180Z .. note:: 2025-08-26T20:22:08.1743539Z :func:`torch.vmap` is aliased to :func:`torch.func.vmap` for 2025-08-26T20:22:08.1743783Z convenience. Use whichever one you'd like. 2025-08-26T20:22:08.1743971Z 2025-08-26T20:22:08.1744126Z Args: 2025-08-26T20:22:08.1744523Z func (function): A Python function that takes one or more arguments. 2025-08-26T20:22:08.1744750Z Must return one or more Tensors. 2025-08-26T20:22:08.1745131Z in_dims (int or nested structure): Specifies which dimension of the 2025-08-26T20:22:08.1745470Z inputs should be mapped over. ``in_dims`` should have a 2025-08-26T20:22:08.1745830Z structure like the inputs. If the ``in_dim`` for a particular 2025-08-26T20:22:08.1746181Z input is None, then that indicates there is no map dimension. 2025-08-26T20:22:08.1746363Z Default: 0. 2025-08-26T20:22:08.1746733Z out_dims (int or Tuple[int]): Specifies where the mapped dimension 2025-08-26T20:22:08.1747119Z should appear in the outputs. If ``out_dims`` is a Tuple, then 2025-08-26T20:22:08.1747457Z it should have one element per output. Default: 0. 2025-08-26T20:22:08.1747802Z randomness (str): Specifies whether the randomness in this 2025-08-26T20:22:08.1748213Z vmap should be the same or different across batches. If 'different', 2025-08-26T20:22:08.1748592Z the randomness for each batch will be different. If 'same', the 2025-08-26T20:22:08.1749019Z randomness will be the same across batches. If 'error', any calls to 2025-08-26T20:22:08.1749415Z random functions will error. Default: 'error'. WARNING: this flag 2025-08-26T20:22:08.1749812Z only applies to random PyTorch operations and does not apply to 2025-08-26T20:22:08.1750060Z Python's random module or numpy randomness. 2025-08-26T20:22:08.1750557Z chunk_size (None or int): If None (default), apply a single vmap over inputs. 2025-08-26T20:22:08.1750989Z If not None, then compute the vmap :attr:`chunk_size` samples at a time. 2025-08-26T20:22:08.1751486Z Note that :attr:`chunk_size=1` is equivalent to computing the vmap with a for-loop. 2025-08-26T20:22:08.1752008Z If you run into memory issues computing the vmap, please try a non-None chunk_size. 2025-08-26T20:22:08.1752161Z 2025-08-26T20:22:08.1752319Z Returns: 2025-08-26T20:22:08.1752680Z Returns a new "batched" function. It takes the same inputs as 2025-08-26T20:22:08.1753021Z ``func``, except each input has an extra dimension at the index 2025-08-26T20:22:08.1753391Z specified by ``in_dims``. It takes returns the same outputs as 2025-08-26T20:22:08.1753741Z ``func``, except each output has an extra dimension at the index 2025-08-26T20:22:08.1753971Z specified by ``out_dims``. 2025-08-26T20:22:08.1754132Z 2025-08-26T20:22:08.1754284Z .. warning: 2025-08-26T20:22:08.1754673Z :func:`vmap` works best with functional-style code. Please do not 2025-08-26T20:22:08.1755026Z perform any side-effects in ``func``, with the exception of 2025-08-26T20:22:08.1755473Z in-place PyTorch operations. Examples of side-effects include mutating 2025-08-26T20:22:08.1755918Z Python data structures and assigning values to variables not captured 2025-08-26T20:22:08.1756074Z in ``func``. 2025-08-26T20:22:08.1756238Z 2025-08-26T20:22:08.1756684Z One example of using :func:`vmap` is to compute batched dot products. PyTorch 2025-08-26T20:22:08.1757092Z doesn't provide a batched ``torch.dot`` API; instead of unsuccessfully 2025-08-26T20:22:08.1757531Z rummaging through docs, use :func:`vmap` to construct a new function. 2025-08-26T20:22:08.1757687Z 2025-08-26T20:22:08.1757887Z >>> torch.dot # [D], [D] -> [] 2025-08-26T20:22:08.1758269Z >>> batched_dot = torch.func.vmap(torch.dot) # [N, D], [N, D] -> [N] 2025-08-26T20:22:08.1758505Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-08-26T20:22:08.1758708Z >>> batched_dot(x, y) 2025-08-26T20:22:08.1758864Z 2025-08-26T20:22:08.1759302Z :func:`vmap` can be helpful in hiding batch dimensions, leading to a simpler 2025-08-26T20:22:08.1759502Z model authoring experience. 2025-08-26T20:22:08.1759655Z 2025-08-26T20:22:08.1759876Z >>> batch_size, feature_size = 3, 5 2025-08-26T20:22:08.1760202Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-08-26T20:22:08.1760354Z >>> 2025-08-26T20:22:08.1760557Z >>> def model(feature_vec): 2025-08-26T20:22:08.1760797Z >>> # Very simple linear model with activation 2025-08-26T20:22:08.1761042Z >>> return feature_vec.dot(weights).relu() 2025-08-26T20:22:08.1761197Z >>> 2025-08-26T20:22:08.1761474Z >>> examples = torch.randn(batch_size, feature_size) 2025-08-26T20:22:08.1761714Z >>> result = torch.vmap(model)(examples) 2025-08-26T20:22:08.1761869Z 2025-08-26T20:22:08.1762363Z :func:`vmap` can also help vectorize computations that were previously difficult 2025-08-26T20:22:08.1762847Z or impossible to batch. One example is higher-order gradient computation. 2025-08-26T20:22:08.1763288Z The PyTorch autograd engine computes vjps (vector-Jacobian products). 2025-08-26T20:22:08.1763738Z Computing a full Jacobian matrix for some function f: R^N -> R^N usually 2025-08-26T20:22:08.1764218Z requires N calls to ``autograd.grad``, one per Jacobian row. Using :func:`vmap`, 2025-08-26T20:22:08.1764694Z we can vectorize the whole computation, computing the Jacobian in a single 2025-08-26T20:22:08.1764887Z call to ``autograd.grad``. 2025-08-26T20:22:08.1765044Z 2025-08-26T20:22:08.1765218Z >>> # Setup 2025-08-26T20:22:08.1765377Z >>> N = 5 2025-08-26T20:22:08.1765575Z >>> f = lambda x: x**2 2025-08-26T20:22:08.1765863Z >>> x = torch.randn(N, requires_grad=True) 2025-08-26T20:22:08.1766029Z >>> y = f(x) 2025-08-26T20:22:08.1766222Z >>> I_N = torch.eye(N) 2025-08-26T20:22:08.1766381Z >>> 2025-08-26T20:22:08.1766584Z >>> # Sequential approach 2025-08-26T20:22:08.1766993Z >>> jacobian_rows = [torch.autograd.grad(y, x, v, retain_graph=True)[0] 2025-08-26T20:22:08.1767191Z >>> for v in I_N.unbind()] 2025-08-26T20:22:08.1767425Z >>> jacobian = torch.stack(jacobian_rows) 2025-08-26T20:22:08.1767578Z >>> 2025-08-26T20:22:08.1767798Z >>> # vectorized gradient computation 2025-08-26T20:22:08.1767990Z >>> def get_vjp(v): 2025-08-26T20:22:08.1768210Z >>> return torch.autograd.grad(y, x, v) 2025-08-26T20:22:08.1768440Z >>> jacobian = torch.vmap(get_vjp)(I_N) 2025-08-26T20:22:08.1768591Z 2025-08-26T20:22:08.1769101Z :func:`vmap` can also be nested, producing an output with multiple batched dimensions 2025-08-26T20:22:08.1769312Z 2025-08-26T20:22:08.1769514Z >>> torch.dot # [D], [D] -> [] 2025-08-26T20:22:08.1769726Z >>> batched_dot = torch.vmap( 2025-08-26T20:22:08.1769926Z ... torch.vmap(torch.dot) 2025-08-26T20:22:08.1770140Z ... ) # [N1, N0, D], [N1, N0, D] -> [N1, N0] 2025-08-26T20:22:08.1770405Z >>> x, y = torch.randn(2, 3, 5), torch.randn(2, 3, 5) 2025-08-26T20:22:08.1770642Z >>> batched_dot(x, y) # tensor of size [2, 3] 2025-08-26T20:22:08.1770809Z 2025-08-26T20:22:08.1771274Z If the inputs are not batched along the first dimension, ``in_dims`` specifies 2025-08-26T20:22:08.1771562Z the dimension that each inputs are batched along as 2025-08-26T20:22:08.1771731Z 2025-08-26T20:22:08.1771926Z >>> torch.dot # [N], [N] -> [] 2025-08-26T20:22:08.1772358Z >>> batched_dot = torch.vmap(torch.dot, in_dims=1) # [N, D], [N, D] -> [D] 2025-08-26T20:22:08.1772592Z >>> x, y = torch.randn(2, 5), torch.randn(2, 5) 2025-08-26T20:22:08.1772772Z >>> batched_dot( 2025-08-26T20:22:08.1772949Z ... x, y 2025-08-26T20:22:08.1773306Z ... ) # output is [5] instead of [2] if batched along the 0th dimension 2025-08-26T20:22:08.1773481Z 2025-08-26T20:22:08.1773981Z If there are multiple inputs each of which is batched along different dimensions, 2025-08-26T20:22:08.1774358Z ``in_dims`` must be a tuple with the batch dimension for each input as 2025-08-26T20:22:08.1774527Z 2025-08-26T20:22:08.1774720Z >>> torch.dot # [D], [D] -> [] 2025-08-26T20:22:08.1775184Z >>> batched_dot = torch.vmap(torch.dot, in_dims=(0, None)) # [N, D], [D] -> [N] 2025-08-26T20:22:08.1775409Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-08-26T20:22:08.1775583Z >>> batched_dot( 2025-08-26T20:22:08.1775756Z ... x, y 2025-08-26T20:22:08.1776122Z ... ) # second arg doesn't have a batch dim because in_dim[1] was None 2025-08-26T20:22:08.1776288Z 2025-08-26T20:22:08.1776746Z If the input is a Python struct, ``in_dims`` must be a tuple containing a struct 2025-08-26T20:22:08.1776952Z matching the shape of the input: 2025-08-26T20:22:08.1777156Z 2025-08-26T20:22:08.1777411Z >>> f = lambda dict: torch.dot(dict["x"], dict["y"]) 2025-08-26T20:22:08.1777634Z >>> x, y = torch.randn(2, 5), torch.randn(5) 2025-08-26T20:22:08.1777828Z >>> input = {"x": x, "y": y} 2025-08-26T20:22:08.1778160Z >>> batched_dot = torch.vmap(f, in_dims=({"x": 0, "y": None},)) 2025-08-26T20:22:08.1778359Z >>> batched_dot(input) 2025-08-26T20:22:08.1778514Z 2025-08-26T20:22:08.1779038Z By default, the output is batched along the first dimension. However, it can be batched 2025-08-26T20:22:08.1779280Z along any dimension by using ``out_dims`` 2025-08-26T20:22:08.1779432Z 2025-08-26T20:22:08.1779621Z >>> f = lambda x: x**2 2025-08-26T20:22:08.1779808Z >>> x = torch.randn(2, 5) 2025-08-26T20:22:08.1780034Z >>> batched_pow = torch.vmap(f, out_dims=1) 2025-08-26T20:22:08.1780294Z >>> batched_pow(x) # [5, 2] 2025-08-26T20:22:08.1780523Z 2025-08-26T20:22:08.1781108Z For any function that uses kwargs, the returned function will not batch the kwargs but will 2025-08-26T20:22:08.1781279Z accept kwargs 2025-08-26T20:22:08.1781434Z 2025-08-26T20:22:08.1781636Z >>> x = torch.randn([2, 5]) 2025-08-26T20:22:08.1781819Z >>> def fn(x, scale=4.): 2025-08-26T20:22:08.1782010Z >>> return x * scale 2025-08-26T20:22:08.1782166Z >>> 2025-08-26T20:22:08.1782367Z >>> batched_pow = torch.vmap(fn) 2025-08-26T20:22:08.1782640Z >>> assert torch.allclose(batched_pow(x), x * 4) 2025-08-26T20:22:08.1783066Z >>> batched_pow(x, scale=x) # scale is not batched, output has shape [2, 2, 5] 2025-08-26T20:22:08.1783219Z 2025-08-26T20:22:08.1783400Z .. note:: 2025-08-26T20:22:08.1783890Z vmap does not provide general autobatching or handle variable-length 2025-08-26T20:22:08.1784099Z sequences out of the box. 2025-08-26T20:22:08.1784251Z 2025-08-26T20:22:08.1784740Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1784908Z 2025-08-26T20:22:08.1785086Z warnings.warn(msg) 2025-08-26T20:22:08.1785253Z 2025-08-26T20:22:08.1785617Z --- Parse Warning: 24 / 146 --- 2025-08-26T20:22:08.1787323Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=grad in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_functorch/apis.py line=306. 2025-08-26T20:22:08.1787847Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1788301Z ``grad`` operator helps computing gradients of ``func`` with respect to the 2025-08-26T20:22:08.1788690Z input(s) specified by ``argnums``. This operator can be nested to 2025-08-26T20:22:08.1788903Z compute higher-order gradients. 2025-08-26T20:22:08.1789062Z 2025-08-26T20:22:08.1789234Z Args: 2025-08-26T20:22:08.1789634Z func (Callable): A Python function that takes one or more arguments. 2025-08-26T20:22:08.1790148Z Must return a single-element Tensor. If specified ``has_aux`` equals ``True``, 2025-08-26T20:22:08.1790665Z function can return a tuple of single-element Tensor and other auxiliary objects: 2025-08-26T20:22:08.1790850Z ``(output, aux)``. 2025-08-26T20:22:08.1791396Z argnums (int or Tuple[int]): Specifies arguments to compute gradients with respect to. 2025-08-26T20:22:08.1791928Z ``argnums`` can be single integer or tuple of integers. Default: 0. 2025-08-26T20:22:08.1792362Z has_aux (bool): Flag indicating that ``func`` returns a tensor and other 2025-08-26T20:22:08.1792676Z auxiliary objects: ``(output, aux)``. Default: False. 2025-08-26T20:22:08.1792851Z 2025-08-26T20:22:08.1793018Z Returns: 2025-08-26T20:22:08.1793575Z Function to compute gradients with respect to its inputs. By default, the output of 2025-08-26T20:22:08.1794139Z the function is the gradient tensor(s) with respect to the first argument. 2025-08-26T20:22:08.1794660Z If specified ``has_aux`` equals ``True``, tuple of gradients and output auxiliary objects 2025-08-26T20:22:08.1795155Z is returned. If ``argnums`` is a tuple of integers, a tuple of output gradients with 2025-08-26T20:22:08.1795420Z respect to each ``argnums`` value is returned. 2025-08-26T20:22:08.1795582Z 2025-08-26T20:22:08.1795784Z Example of using ``grad``: 2025-08-26T20:22:08.1795938Z 2025-08-26T20:22:08.1796134Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1796336Z >>> from torch.func import grad 2025-08-26T20:22:08.1796523Z >>> x = torch.randn([]) 2025-08-26T20:22:08.1796769Z >>> cos_x = grad(lambda x: torch.sin(x))(x) 2025-08-26T20:22:08.1797134Z >>> assert torch.allclose(cos_x, x.cos()) 2025-08-26T20:22:08.1797296Z >>> 2025-08-26T20:22:08.1797516Z >>> # Second-order gradients 2025-08-26T20:22:08.1797793Z >>> neg_sin_x = grad(grad(lambda x: torch.sin(x)))(x) 2025-08-26T20:22:08.1798058Z >>> assert torch.allclose(neg_sin_x, -x.sin()) 2025-08-26T20:22:08.1798212Z 2025-08-26T20:22:08.1798690Z When composed with ``vmap``, ``grad`` can be used to compute per-sample-gradients: 2025-08-26T20:22:08.1798863Z 2025-08-26T20:22:08.1799050Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1799290Z >>> from torch.func import grad, vmap 2025-08-26T20:22:08.1799501Z >>> batch_size, feature_size = 3, 5 2025-08-26T20:22:08.1799659Z >>> 2025-08-26T20:22:08.1799889Z >>> def model(weights, feature_vec): 2025-08-26T20:22:08.1800184Z >>> # Very simple linear model with activation 2025-08-26T20:22:08.1800413Z >>> assert feature_vec.dim() == 1 2025-08-26T20:22:08.1800645Z >>> return feature_vec.dot(weights).relu() 2025-08-26T20:22:08.1800807Z >>> 2025-08-26T20:22:08.1801075Z >>> def compute_loss(weights, example, target): 2025-08-26T20:22:08.1801283Z >>> y = model(weights, example) 2025-08-26T20:22:08.1801538Z >>> return ((y - target) ** 2).mean() # MSELoss 2025-08-26T20:22:08.1801691Z >>> 2025-08-26T20:22:08.1802022Z >>> weights = torch.randn(feature_size, requires_grad=True) 2025-08-26T20:22:08.1802316Z >>> examples = torch.randn(batch_size, feature_size) 2025-08-26T20:22:08.1802537Z >>> targets = torch.randn(batch_size) 2025-08-26T20:22:08.1802805Z >>> inputs = (weights, examples, targets) 2025-08-26T20:22:08.1803253Z >>> grad_weight_per_example = vmap(grad(compute_loss), in_dims=(None, 0, 0))( 2025-08-26T20:22:08.1803421Z ... *inputs 2025-08-26T20:22:08.1803591Z ... ) 2025-08-26T20:22:08.1803740Z 2025-08-26T20:22:08.1804084Z Example of using ``grad`` with ``has_aux`` and ``argnums``: 2025-08-26T20:22:08.1804236Z 2025-08-26T20:22:08.1804418Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1804632Z >>> from torch.func import grad 2025-08-26T20:22:08.1804833Z >>> def my_loss_func(y, y_pred): 2025-08-26T20:22:08.1805064Z >>> loss_per_sample = (0.5 * y_pred - y) ** 2 2025-08-26T20:22:08.1805291Z >>> loss = loss_per_sample.mean() 2025-08-26T20:22:08.1805519Z >>> return loss, (y_pred, loss_per_sample) 2025-08-26T20:22:08.1805686Z >>> 2025-08-26T20:22:08.1805959Z >>> fn = grad(my_loss_func, argnums=(0, 1), has_aux=True) 2025-08-26T20:22:08.1806147Z >>> y_true = torch.rand(4) 2025-08-26T20:22:08.1806408Z >>> y_preds = torch.rand(4, requires_grad=True) 2025-08-26T20:22:08.1806608Z >>> out = fn(y_true, y_preds) 2025-08-26T20:22:08.1807084Z >>> # > output is ((grads w.r.t y_true, grads w.r.t y_preds), (y_pred, loss_per_sample)) 2025-08-26T20:22:08.1807272Z 2025-08-26T20:22:08.1807439Z .. note:: 2025-08-26T20:22:08.1807767Z Using PyTorch ``torch.no_grad`` together with ``grad``. 2025-08-26T20:22:08.1807918Z 2025-08-26T20:22:08.1808204Z Case 1: Using ``torch.no_grad`` inside a function: 2025-08-26T20:22:08.1808355Z 2025-08-26T20:22:08.1808544Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1808729Z >>> def f(x): 2025-08-26T20:22:08.1808930Z >>> with torch.no_grad(): 2025-08-26T20:22:08.1809117Z >>> c = x ** 2 2025-08-26T20:22:08.1809319Z >>> return x - c 2025-08-26T20:22:08.1809476Z 2025-08-26T20:22:08.1809862Z In this case, ``grad(f)(x)`` will respect the inner ``torch.no_grad``. 2025-08-26T20:22:08.1810019Z 2025-08-26T20:22:08.1810425Z Case 2: Using ``grad`` inside ``torch.no_grad`` context manager: 2025-08-26T20:22:08.1825605Z 2025-08-26T20:22:08.1825862Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1826072Z >>> with torch.no_grad(): 2025-08-26T20:22:08.1826246Z >>> grad(f)(x) 2025-08-26T20:22:08.1826403Z 2025-08-26T20:22:08.1826854Z In this case, ``grad`` will respect the inner ``torch.no_grad``, but not the 2025-08-26T20:22:08.1827273Z outer one. This is because ``grad`` is a "function transform": its result 2025-08-26T20:22:08.1827722Z should not depend on the result of a context manager outside of ``f``. 2025-08-26T20:22:08.1827879Z 2025-08-26T20:22:08.1828035Z 2025-08-26T20:22:08.1828536Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1828707Z 2025-08-26T20:22:08.1829028Z warnings.warn(msg) 2025-08-26T20:22:08.1829183Z 2025-08-26T20:22:08.1829620Z --- Parse Warning: 25 / 146 --- 2025-08-26T20:22:08.1831570Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CustomOpDef.register_fake in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/custom_ops.py line=397. 2025-08-26T20:22:08.1832065Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.1832406Z Register a FakeTensor implementation for this custom op. 2025-08-26T20:22:08.1832558Z 2025-08-26T20:22:08.1833049Z This is necessary to get the operator to work efficiently with torch.compile. 2025-08-26T20:22:08.1833199Z 2025-08-26T20:22:08.1833633Z The Fake impl (sometimes also known as a meta kernel or abstract impl) 2025-08-26T20:22:08.1834076Z specifies the behavior of this operator on Tensors that carry no data. 2025-08-26T20:22:08.1834359Z Given some input Tensors with certain properties 2025-08-26T20:22:08.1834877Z (sizes/strides/storage_offset/device), it specifies what the properties of 2025-08-26T20:22:08.1835081Z the output Tensors are. 2025-08-26T20:22:08.1835254Z 2025-08-26T20:22:08.1835638Z Please see :func:`torch.library.register_fake` for more details. 2025-08-26T20:22:08.1835793Z 2025-08-26T20:22:08.1836021Z Args: 2025-08-26T20:22:08.1836339Z fn (Callable): The function to register as the FakeTensor 2025-08-26T20:22:08.1836544Z implementation. 2025-08-26T20:22:08.1836701Z 2025-08-26T20:22:08.1836864Z Examples: 2025-08-26T20:22:08.1837057Z >>> import torch 2025-08-26T20:22:08.1837256Z >>> import numpy as np 2025-08-26T20:22:08.1837473Z >>> from torch import Tensor 2025-08-26T20:22:08.1837631Z >>> 2025-08-26T20:22:08.1837997Z >>> # Example 1: an operator without data-dependent output shape 2025-08-26T20:22:08.1838385Z >>> @torch.library.custom_op("mylib::linear", mutates_args=()) 2025-08-26T20:22:08.1838809Z >>> def linear(x: Tensor, weight: Tensor, bias: Tensor) -> Tensor: 2025-08-26T20:22:08.1839040Z >>> return (x @ weight.t()) + bias 2025-08-26T20:22:08.1839203Z >>> 2025-08-26T20:22:08.1839404Z >>> @linear.register_fake 2025-08-26T20:22:08.1839611Z >>> def _(x, weight, bias): 2025-08-26T20:22:08.1839809Z >>> assert x.dim() == 2 2025-08-26T20:22:08.1840034Z >>> assert weight.dim() == 2 2025-08-26T20:22:08.1840234Z >>> assert bias.dim() == 1 2025-08-26T20:22:08.1840465Z >>> assert x.shape[1] == weight.shape[1] 2025-08-26T20:22:08.1840724Z >>> assert weight.shape[0] == bias.shape[0] 2025-08-26T20:22:08.1840955Z >>> assert x.device == weight.device 2025-08-26T20:22:08.1841299Z >>> return x.new_empty(x.size(0), weight.size(0)) 2025-08-26T20:22:08.1841478Z >>> 2025-08-26T20:22:08.1841677Z >>> x = torch.randn(2, 2) 2025-08-26T20:22:08.1841881Z >>> weight = torch.randn(2, 2) 2025-08-26T20:22:08.1842083Z >>> bias = torch.randn(2) 2025-08-26T20:22:08.1842335Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-26T20:22:08.1842706Z >>> out = torch.compile(linear, fullgraph=True)(x, weight, bias) 2025-08-26T20:22:08.1842961Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-26T20:22:08.1843434Z >>> assert torch.allclose(out, torch.nn.functional.linear(x, weight, bias)) 2025-08-26T20:22:08.1843668Z >>> 2025-08-26T20:22:08.1844027Z >>> # Example 2: an operator with data-dependent output shape 2025-08-26T20:22:08.1844441Z >>> @torch.library.custom_op("mylib::nonzero", mutates_args=()) 2025-08-26T20:22:08.1844666Z >>> def nonzero(x: Tensor) -> Tensor: 2025-08-26T20:22:08.1844882Z >>> x_np = x.cpu().numpy() 2025-08-26T20:22:08.1845128Z >>> res = np.stack(np.nonzero(x_np), axis=1) 2025-08-26T20:22:08.1845377Z >>> return torch.tensor(res, device=x.device) 2025-08-26T20:22:08.1845550Z >>> 2025-08-26T20:22:08.1845749Z >>> @nonzero.register_fake 2025-08-26T20:22:08.1845922Z >>> def _(x): 2025-08-26T20:22:08.1846213Z >>> # Number of nonzero-elements is data-dependent. 2025-08-26T20:22:08.1846523Z >>> # Since we cannot peek at the data in an abstract impl, 2025-08-26T20:22:08.1846826Z >>> # we use the ctx object to construct a new symint that 2025-08-26T20:22:08.1847064Z >>> # represents the data-dependent size. 2025-08-26T20:22:08.1847300Z >>> ctx = torch.library.get_ctx() 2025-08-26T20:22:08.1847521Z >>> nnz = ctx.new_dynamic_size() 2025-08-26T20:22:08.1847719Z >>> shape = [nnz, x.dim()] 2025-08-26T20:22:08.1848010Z >>> result = x.new_empty(shape, dtype=torch.int64) 2025-08-26T20:22:08.1848191Z >>> return result 2025-08-26T20:22:08.1848361Z >>> 2025-08-26T20:22:08.1848577Z >>> x = torch.tensor([0, 1, 2, 0, 0, 1]) 2025-08-26T20:22:08.1848828Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-26T20:22:08.1849116Z >>> out = torch.compile(nonzero, fullgraph=True)(x) 2025-08-26T20:22:08.1849363Z >>> # xdoctest: +SKIP("Requires Python <= 3.11") 2025-08-26T20:22:08.1849618Z >>> assert torch.allclose(out, x.nonzero()) 2025-08-26T20:22:08.1849774Z 2025-08-26T20:22:08.1849929Z 2025-08-26T20:22:08.1851014Z Original Error: IndentationError('expected an indented block after function definition on line 36', ('', 37, 1, '_._ = None\n', 37, 2)) 2025-08-26T20:22:08.1851170Z 2025-08-26T20:22:08.1851323Z _._ = None 2025-08-26T20:22:08.1851523Z ^ 2025-08-26T20:22:08.1851710Z warnings.warn(msg) 2025-08-26T20:22:08.1851881Z 2025-08-26T20:22:08.1852236Z --- Parse Warning: 26 / 146 --- 2025-08-26T20:22:08.1854199Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=unsafe_generate_fake_kernels in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/fake_profile.py line=94. 2025-08-26T20:22:08.1854728Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1854883Z 2025-08-26T20:22:08.1855336Z Registers a fake kernel based on the given operator profiles. This fake 2025-08-26T20:22:08.1855814Z kernel registration will override any existing fake kernel registrations. 2025-08-26T20:22:08.1855970Z 2025-08-26T20:22:08.1856451Z The input is a dictionary mapping operator names to a set of operator 2025-08-26T20:22:08.1856899Z profiles, which we will use to generate fake kernels. The operator profiles 2025-08-26T20:22:08.1857293Z are a record of the input and output tensor metadata. Based on this 2025-08-26T20:22:08.1857759Z information we will match a given input to the recorded profile, and return 2025-08-26T20:22:08.1858202Z an output with the same metadata as in the recorded profile. If a profile 2025-08-26T20:22:08.1858458Z doesn't exist then an exception will be thrown. 2025-08-26T20:22:08.1858609Z 2025-08-26T20:22:08.1859063Z The fake kernel generation is considered unsafe because it relies on the 2025-08-26T20:22:08.1859540Z rigid, pre-defined operator profiles that do not account for potential 2025-08-26T20:22:08.1860030Z variations in output behavior. Specifically, the generated kernels assume a 2025-08-26T20:22:08.1860648Z fixed relationship between input and output ranks. However, in reality, it's 2025-08-26T20:22:08.1861113Z possible that data-dependent operations may produce outputs of different 2025-08-26T20:22:08.1861556Z ranks even when given inputs of the same rank. The generated fake kernels 2025-08-26T20:22:08.1861968Z are inflexible and unable to accommodate these nuances, making them 2025-08-26T20:22:08.1862168Z potentially unsafe. 2025-08-26T20:22:08.1862318Z 2025-08-26T20:22:08.1862472Z Args: 2025-08-26T20:22:08.1862897Z op_profiles (dict[str, set[OpProfile]]): A dictionary mapping operator 2025-08-26T20:22:08.1863278Z name to a set of operator profiles from which we will generate fake 2025-08-26T20:22:08.1863454Z kernels. 2025-08-26T20:22:08.1863606Z 2025-08-26T20:22:08.1863767Z Examples: 2025-08-26T20:22:08.1863937Z 2025-08-26T20:22:08.1864250Z >>> # Example: Registering an op-profile from draft-export 2025-08-26T20:22:08.1864424Z >>> import torch 2025-08-26T20:22:08.1864745Z >>> from torch.export._draft_export import draft_export 2025-08-26T20:22:08.1864905Z >>> 2025-08-26T20:22:08.1865254Z >>> @torch.library.custom_op("mylib::foo", mutates_args=()) 2025-08-26T20:22:08.1865487Z >>> def foo(x: Tensor, y: Tensor) -> Tensor: 2025-08-26T20:22:08.1865660Z >>> return x + y 2025-08-26T20:22:08.1865833Z >>> 2025-08-26T20:22:08.1866030Z >>> class M(torch.nn.Module): 2025-08-26T20:22:08.1866235Z >>> def forward(self, a, b): 2025-08-26T20:22:08.1866500Z >>> res = torch.ops.mylib.foo(a, b) # no fake impl 2025-08-26T20:22:08.1866676Z >>> return res 2025-08-26T20:22:08.1866847Z >>> 2025-08-26T20:22:08.1867160Z >>> ep = draft_export(M(), (torch.ones(3, 4), torch.ones(3, 4)) 2025-08-26T20:22:08.1867331Z >>> 2025-08-26T20:22:08.1867908Z >>> with torch._library.fake_profile.unsafe_generate_fake_kernels(ep._report.op_profiles): 2025-08-26T20:22:08.1868139Z >>> decomp = ep.run_decompositions() 2025-08-26T20:22:08.1868303Z 2025-08-26T20:22:08.1868455Z 2025-08-26T20:22:08.1869001Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1869159Z 2025-08-26T20:22:08.1869343Z warnings.warn(msg) 2025-08-26T20:22:08.1869507Z 2025-08-26T20:22:08.1869860Z --- Parse Warning: 27 / 146 --- 2025-08-26T20:22:08.1871583Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=triton_op in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=96. 2025-08-26T20:22:08.1872102Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1872593Z Create a custom operator whose implementation is backed by 1+ triton kernels. 2025-08-26T20:22:08.1872765Z 2025-08-26T20:22:08.1873162Z This is a more structured way of using triton kernels with PyTorch. 2025-08-26T20:22:08.1873759Z Prefer using triton kernels with no ``torch.library`` custom operator wrappers 2025-08-26T20:22:08.1874243Z (like :func:`torch.library.custom_op`, :func:`torch.library.triton_op`) because 2025-08-26T20:22:08.1874421Z that is simpler; 2025-08-26T20:22:08.1874918Z only use :func:`torch.library.custom_op`/:func:`torch.library.triton_op` if you 2025-08-26T20:22:08.1875353Z want to create an operator that behaves like PyTorch built-in operators. 2025-08-26T20:22:08.1875769Z For example, you may use a ``torch.library`` wrapper API to define the 2025-08-26T20:22:08.1876179Z behavior of the triton kernel when passed a tensor subclass or under 2025-08-26T20:22:08.1876377Z a TorchDispatchMode. 2025-08-26T20:22:08.1876544Z 2025-08-26T20:22:08.1877032Z Use :func:`torch.library.triton_op` instead of :func:`torch.library.custom_op` 2025-08-26T20:22:08.1877270Z when the implementation 2025-08-26T20:22:08.1877687Z consists of 1+ triton kernels. :func:`torch.library.custom_op` treats 2025-08-26T20:22:08.1878006Z custom operators as opaque (:func:`torch.compile` and 2025-08-26T20:22:08.1878471Z :func:`torch.export.export` will never trace into them), but ``triton_op`` 2025-08-26T20:22:08.1878896Z makes the implementation visible to these subsystems, allowing them 2025-08-26T20:22:08.1879123Z to optimize the triton kernel(s). 2025-08-26T20:22:08.1879272Z 2025-08-26T20:22:08.1879628Z Note that ``fn`` must only consist of calls to PyTorch-understood 2025-08-26T20:22:08.1880076Z operators and triton kernels. Any triton kernels called inside ``fn`` 2025-08-26T20:22:08.1880441Z must be wrapped in a call to :func:`torch.library.wrap_triton`. 2025-08-26T20:22:08.1880606Z 2025-08-26T20:22:08.1880761Z Args: 2025-08-26T20:22:08.1881190Z name (str): A name for the custom op that looks like "{namespace}::{name}", 2025-08-26T20:22:08.1881613Z e.g. "mylib::my_linear". The name is used as the op's stable identifier 2025-08-26T20:22:08.1881917Z in PyTorch subsystems (e.g. torch.export, FX graphs). 2025-08-26T20:22:08.1882393Z To avoid name collisions, please use your project name as the namespace; 2025-08-26T20:22:08.1882791Z e.g. all custom ops in pytorch/fbgemm use "fbgemm" as the namespace. 2025-08-26T20:22:08.1883319Z mutates_args (Iterable[str] or "unknown"): The names of args that the function mutates. 2025-08-26T20:22:08.1883788Z This MUST be accurate, otherwise, the behavior is undefined. If "unknown", 2025-08-26T20:22:08.1884305Z it pessimistically assumes that all inputs to the operator are being mutated. 2025-08-26T20:22:08.1884665Z schema (None | str): A schema string for the operator. If None 2025-08-26T20:22:08.1885061Z (recommended) we'll infer a schema for the operator from its type 2025-08-26T20:22:08.1885471Z annotations. We recommend letting us infer a schema unless you 2025-08-26T20:22:08.1885682Z have a specific reason not to. 2025-08-26T20:22:08.1885990Z Example: "(Tensor x, int y) -> (Tensor, Tensor)". 2025-08-26T20:22:08.1886158Z 2025-08-26T20:22:08.1886326Z Example:: 2025-08-26T20:22:08.1886489Z 2025-08-26T20:22:08.1886739Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:22:08.1886912Z >>> import torch 2025-08-26T20:22:08.1887203Z >>> from torch.library import triton_op, wrap_triton 2025-08-26T20:22:08.1887360Z >>> 2025-08-26T20:22:08.1887535Z >>> import triton 2025-08-26T20:22:08.1887773Z >>> from triton import language as tl 2025-08-26T20:22:08.1887924Z >>> 2025-08-26T20:22:08.1888105Z >>> @triton.jit 2025-08-26T20:22:08.1888285Z >>> def add_kernel( 2025-08-26T20:22:08.1888454Z >>> in_ptr0, 2025-08-26T20:22:08.1888698Z >>> in_ptr1, 2025-08-26T20:22:08.1888860Z >>> out_ptr, 2025-08-26T20:22:08.1889044Z >>> n_elements, 2025-08-26T20:22:08.1889254Z >>> BLOCK_SIZE: "tl.constexpr", 2025-08-26T20:22:08.1889412Z >>> ): 2025-08-26T20:22:08.1889631Z >>> pid = tl.program_id(axis=0) 2025-08-26T20:22:08.1889841Z >>> block_start = pid * BLOCK_SIZE 2025-08-26T20:22:08.1890129Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-08-26T20:22:08.1890329Z >>> mask = offsets < n_elements 2025-08-26T20:22:08.1890567Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-08-26T20:22:08.1890812Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-08-26T20:22:08.1890993Z >>> output = x + y 2025-08-26T20:22:08.1891270Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-08-26T20:22:08.1891459Z >>> 2025-08-26T20:22:08.1891913Z >>> @triton_op("mylib::add", mutates_args={}) 2025-08-26T20:22:08.1892267Z >>> def add(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor: 2025-08-26T20:22:08.1892480Z >>> output = torch.empty_like(x) 2025-08-26T20:22:08.1892704Z >>> n_elements = output.numel() 2025-08-26T20:22:08.1892861Z >>> 2025-08-26T20:22:08.1893042Z >>> def grid(meta): 2025-08-26T20:22:08.1893375Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-08-26T20:22:08.1893535Z >>> 2025-08-26T20:22:08.1893892Z >>> # NB: we need to wrap the triton kernel in a call to wrap_triton 2025-08-26T20:22:08.1894244Z >>> wrap_triton(add_kernel)[grid](x, y, output, n_elements, 16) 2025-08-26T20:22:08.1894423Z >>> return output 2025-08-26T20:22:08.1894595Z >>> 2025-08-26T20:22:08.1894775Z >>> @torch.compile 2025-08-26T20:22:08.1894950Z >>> def f(x, y): 2025-08-26T20:22:08.1895146Z >>> return add(x, y) 2025-08-26T20:22:08.1895307Z >>> 2025-08-26T20:22:08.1895533Z >>> x = torch.randn(3, device="cuda") 2025-08-26T20:22:08.1895743Z >>> y = torch.randn(3, device="cuda") 2025-08-26T20:22:08.1895899Z >>> 2025-08-26T20:22:08.1896076Z >>> z = f(x, y) 2025-08-26T20:22:08.1896294Z >>> assert torch.allclose(z, x + y) 2025-08-26T20:22:08.1896458Z 2025-08-26T20:22:08.1896609Z 2025-08-26T20:22:08.1897103Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1897267Z 2025-08-26T20:22:08.1897446Z warnings.warn(msg) 2025-08-26T20:22:08.1897611Z 2025-08-26T20:22:08.1897984Z --- Parse Warning: 28 / 146 --- 2025-08-26T20:22:08.1899750Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=wrap_triton in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_library/triton.py line=296. 2025-08-26T20:22:08.1900283Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1900821Z Allows capture of a triton kernel into a graph via make_fx or 2025-08-26T20:22:08.1901033Z non-strict ``torch.export``. 2025-08-26T20:22:08.1901192Z 2025-08-26T20:22:08.1901540Z These technologies perform Dispatcher-based tracing (via 2025-08-26T20:22:08.1901925Z ``__torch_dispatch__``) and cannot see calls to raw triton kernels. 2025-08-26T20:22:08.1902298Z The ``wrap_triton`` API wraps a triton kernel into a callable that 2025-08-26T20:22:08.1902537Z can actually be traced into a graph. 2025-08-26T20:22:08.1902691Z 2025-08-26T20:22:08.1903088Z Please use this API together with :func:`torch.library.triton_op`. 2025-08-26T20:22:08.1903256Z 2025-08-26T20:22:08.1903419Z Examples: 2025-08-26T20:22:08.1903587Z 2025-08-26T20:22:08.1903866Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1904040Z >>> import torch 2025-08-26T20:22:08.1904240Z >>> import triton 2025-08-26T20:22:08.1904461Z >>> from triton import language as tl 2025-08-26T20:22:08.1904797Z >>> from torch.fx.experimental.proxy_tensor import make_fx 2025-08-26T20:22:08.1905052Z >>> from torch.library import wrap_triton 2025-08-26T20:22:08.1905209Z >>> 2025-08-26T20:22:08.1905398Z >>> @triton.jit 2025-08-26T20:22:08.1905577Z >>> def add_kernel( 2025-08-26T20:22:08.1905745Z >>> in_ptr0, 2025-08-26T20:22:08.1905929Z >>> in_ptr1, 2025-08-26T20:22:08.1906102Z >>> out_ptr, 2025-08-26T20:22:08.1906285Z >>> n_elements, 2025-08-26T20:22:08.1906495Z >>> BLOCK_SIZE: "tl.constexpr", 2025-08-26T20:22:08.1906710Z >>> ): 2025-08-26T20:22:08.1906938Z >>> pid = tl.program_id(axis=0) 2025-08-26T20:22:08.1907149Z >>> block_start = pid * BLOCK_SIZE 2025-08-26T20:22:08.1907443Z >>> offsets = block_start + tl.arange(0, BLOCK_SIZE) 2025-08-26T20:22:08.1907645Z >>> mask = offsets < n_elements 2025-08-26T20:22:08.1907880Z >>> x = tl.load(in_ptr0 + offsets, mask=mask) 2025-08-26T20:22:08.1908126Z >>> y = tl.load(in_ptr1 + offsets, mask=mask) 2025-08-26T20:22:08.1908307Z >>> output = x + y 2025-08-26T20:22:08.1908586Z >>> tl.store(out_ptr + offsets, output, mask=mask) 2025-08-26T20:22:08.1908743Z >>> 2025-08-26T20:22:08.1908921Z >>> def add(x, y): 2025-08-26T20:22:08.1909145Z >>> output = torch.empty_like(x) 2025-08-26T20:22:08.1909350Z >>> n_elements = output.numel() 2025-08-26T20:22:08.1909524Z >>> 2025-08-26T20:22:08.1909722Z >>> def grid_fn(meta): 2025-08-26T20:22:08.1910046Z >>> return (triton.cdiv(n_elements, meta["BLOCK_SIZE"]),) 2025-08-26T20:22:08.1910214Z >>> 2025-08-26T20:22:08.1910590Z >>> wrap_triton(add_kernel)[grid_fn](x, y, output, n_elements, 16) 2025-08-26T20:22:08.1910782Z >>> return output 2025-08-26T20:22:08.1910938Z >>> 2025-08-26T20:22:08.1911150Z >>> x = torch.randn(3, device="cuda") 2025-08-26T20:22:08.1911372Z >>> y = torch.randn(3, device="cuda") 2025-08-26T20:22:08.1911563Z >>> gm = make_fx(add)(x, y) 2025-08-26T20:22:08.1911738Z >>> print(gm.code) 2025-08-26T20:22:08.1911953Z >>> # def forward(self, x_1, y_1): 2025-08-26T20:22:08.1912440Z >>> # empty_like = torch.ops.aten.empty_like.default(x_1, pin_memory = False) 2025-08-26T20:22:08.1912919Z >>> # triton_kernel_wrapper_mutation_proxy = triton_kernel_wrapper_mutation( 2025-08-26T20:22:08.1913166Z >>> # kernel_idx = 0, constant_args_idx = 0, 2025-08-26T20:22:08.1913372Z >>> # grid = [(1, 1, 1)], kwargs = { 2025-08-26T20:22:08.1913704Z >>> # 'in_ptr0': x_1, 'in_ptr1': y_1, 'out_ptr': empty_like, 2025-08-26T20:22:08.1913930Z >>> # 'n_elements': 3, 'BLOCK_SIZE': 16 2025-08-26T20:22:08.1914108Z >>> # }) 2025-08-26T20:22:08.1914298Z >>> # return empty_like 2025-08-26T20:22:08.1914450Z 2025-08-26T20:22:08.1914616Z 2025-08-26T20:22:08.1915106Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1915272Z 2025-08-26T20:22:08.1915454Z warnings.warn(msg) 2025-08-26T20:22:08.1915601Z 2025-08-26T20:22:08.1915972Z --- Parse Warning: 29 / 146 --- 2025-08-26T20:22:08.1917914Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=print_assert_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=286. 2025-08-26T20:22:08.1918438Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1918594Z 2025-08-26T20:22:08.1919017Z Test if two objects are equal, and print an error message if test fails. 2025-08-26T20:22:08.1919184Z 2025-08-26T20:22:08.1919458Z The test is performed with ``actual == desired``. 2025-08-26T20:22:08.1919621Z 2025-08-26T20:22:08.1919788Z Parameters 2025-08-26T20:22:08.1920008Z ---------- 2025-08-26T20:22:08.1920195Z test_string : str 2025-08-26T20:22:08.1920385Z The message supplied to AssertionError. 2025-08-26T20:22:08.1920488Z actual : object 2025-08-26T20:22:08.1920643Z The object to test for equality against `desired`. 2025-08-26T20:22:08.1920733Z desired : object 2025-08-26T20:22:08.1920843Z The expected result. 2025-08-26T20:22:08.1920956Z 2025-08-26T20:22:08.1921041Z Examples 2025-08-26T20:22:08.1921143Z -------- 2025-08-26T20:22:08.1921259Z >>> np.testing.print_assert_equal( 2025-08-26T20:22:08.1921389Z ... "Test XYZ of func xyz", [0, 1], [0, 1] 2025-08-26T20:22:08.1921486Z ... ) # doctest: +SKIP 2025-08-26T20:22:08.1921600Z >>> np.testing.print_assert_equal( 2025-08-26T20:22:08.1921726Z ... "Test XYZ of func xyz", [0, 1], [0, 2] 2025-08-26T20:22:08.1921822Z ... ) # doctest: +SKIP 2025-08-26T20:22:08.1921943Z Traceback (most recent call last): 2025-08-26T20:22:08.1922026Z ... 2025-08-26T20:22:08.1922157Z AssertionError: Test XYZ of func xyz failed 2025-08-26T20:22:08.1922255Z ACTUAL: 2025-08-26T20:22:08.1922338Z [0, 1] 2025-08-26T20:22:08.1922437Z DESIRED: 2025-08-26T20:22:08.1922519Z [0, 2] 2025-08-26T20:22:08.1922599Z 2025-08-26T20:22:08.1922690Z 2025-08-26T20:22:08.1922943Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1923025Z 2025-08-26T20:22:08.1923133Z warnings.warn(msg) 2025-08-26T20:22:08.1923216Z 2025-08-26T20:22:08.1923427Z --- Parse Warning: 30 / 146 --- 2025-08-26T20:22:08.1924358Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=331. 2025-08-26T20:22:08.1924618Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1924709Z 2025-08-26T20:22:08.1924915Z Raises an AssertionError if two items are not equal up to desired 2025-08-26T20:22:08.1925016Z precision. 2025-08-26T20:22:08.1925096Z 2025-08-26T20:22:08.1925280Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-26T20:22:08.1925477Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-26T20:22:08.1925668Z instead of this function for more consistent floating point 2025-08-26T20:22:08.1925779Z comparisons. 2025-08-26T20:22:08.1925903Z 2025-08-26T20:22:08.1926117Z The test verifies that the elements of `actual` and `desired` satisfy. 2025-08-26T20:22:08.1926256Z 2025-08-26T20:22:08.1926417Z ``abs(desired-actual) < float64(1.5 * 10**(-decimal))`` 2025-08-26T20:22:08.1926508Z 2025-08-26T20:22:08.1926738Z That is a looser test than originally documented, but agrees with what the 2025-08-26T20:22:08.1926972Z actual implementation in `assert_array_almost_equal` did up to rounding 2025-08-26T20:22:08.1927220Z vagaries. An exception is raised at conflicting values. For ndarrays this 2025-08-26T20:22:08.1927339Z delegates to assert_array_almost_equal 2025-08-26T20:22:08.1927434Z 2025-08-26T20:22:08.1927521Z Parameters 2025-08-26T20:22:08.1927606Z ---------- 2025-08-26T20:22:08.1927713Z actual : array_like 2025-08-26T20:22:08.1927812Z The object to check. 2025-08-26T20:22:08.1927907Z desired : array_like 2025-08-26T20:22:08.1928068Z The expected object. 2025-08-26T20:22:08.1928166Z decimal : int, optional 2025-08-26T20:22:08.1928295Z Desired precision, default is 7. 2025-08-26T20:22:08.1928391Z err_msg : str, optional 2025-08-26T20:22:08.1928547Z The error message to be printed in case of failure. 2025-08-26T20:22:08.1928657Z verbose : bool, optional 2025-08-26T20:22:08.1928860Z If True, the conflicting values are appended to the error message. 2025-08-26T20:22:08.1928952Z 2025-08-26T20:22:08.1929036Z Raises 2025-08-26T20:22:08.1929119Z ------ 2025-08-26T20:22:08.1929220Z AssertionError 2025-08-26T20:22:08.1929414Z If actual and desired are not equal up to specified precision. 2025-08-26T20:22:08.1929506Z 2025-08-26T20:22:08.1929590Z See Also 2025-08-26T20:22:08.1929672Z -------- 2025-08-26T20:22:08.1929919Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-26T20:22:08.1930078Z relative and/or absolute precision. 2025-08-26T20:22:08.1930301Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-26T20:22:08.1930384Z 2025-08-26T20:22:08.1930468Z Examples 2025-08-26T20:22:08.1930570Z -------- 2025-08-26T20:22:08.1930735Z >>> from torch._numpy.testing import assert_almost_equal 2025-08-26T20:22:08.1930868Z >>> assert_almost_equal(2.3333333333333, 2.33333334) 2025-08-26T20:22:08.1931052Z >>> assert_almost_equal(2.3333333333333, 2.33333334, decimal=10) 2025-08-26T20:22:08.1931164Z Traceback (most recent call last): 2025-08-26T20:22:08.1931262Z ... 2025-08-26T20:22:08.1931355Z AssertionError: 2025-08-26T20:22:08.1931478Z Arrays are not almost equal to 10 decimals 2025-08-26T20:22:08.1931584Z ACTUAL: 2.3333333333333 2025-08-26T20:22:08.1931686Z DESIRED: 2.33333334 2025-08-26T20:22:08.1931765Z 2025-08-26T20:22:08.1931865Z >>> assert_almost_equal( 2025-08-26T20:22:08.1932082Z ... np.array([1.0, 2.3333333333333]), np.array([1.0, 2.33333334]), decimal=9 2025-08-26T20:22:08.1932166Z ... ) 2025-08-26T20:22:08.1932294Z Traceback (most recent call last): 2025-08-26T20:22:08.1932376Z ... 2025-08-26T20:22:08.1932470Z AssertionError: 2025-08-26T20:22:08.1932603Z Arrays are not almost equal to 9 decimals 2025-08-26T20:22:08.1932690Z 2025-08-26T20:22:08.1932799Z Mismatched elements: 1 / 2 (50%) 2025-08-26T20:22:08.1932942Z Max absolute difference: 6.666699636781459e-09 2025-08-26T20:22:08.1933072Z Max relative difference: 2.8571569790287484e-09 2025-08-26T20:22:08.1933222Z x: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-08-26T20:22:08.1933356Z y: torch.ndarray([1.0000, 2.3333], dtype=float64) 2025-08-26T20:22:08.1933436Z 2025-08-26T20:22:08.1933531Z 2025-08-26T20:22:08.1933785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1933878Z 2025-08-26T20:22:08.1933978Z warnings.warn(msg) 2025-08-26T20:22:08.1934059Z 2025-08-26T20:22:08.1934264Z --- Parse Warning: 31 / 146 --- 2025-08-26T20:22:08.1935219Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_approx_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=457. 2025-08-26T20:22:08.1935491Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1935570Z 2025-08-26T20:22:08.1935789Z Raises an AssertionError if two items are not equal up to significant 2025-08-26T20:22:08.1935887Z digits. 2025-08-26T20:22:08.1935967Z 2025-08-26T20:22:08.1936157Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-26T20:22:08.1936336Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-26T20:22:08.1936526Z instead of this function for more consistent floating point 2025-08-26T20:22:08.1936681Z comparisons. 2025-08-26T20:22:08.1936761Z 2025-08-26T20:22:08.1936961Z Given two numbers, check that they are approximately equal. 2025-08-26T20:22:08.1937181Z Approximately equal is defined as the number of significant digits 2025-08-26T20:22:08.1937267Z that agree. 2025-08-26T20:22:08.1937358Z 2025-08-26T20:22:08.1937446Z Parameters 2025-08-26T20:22:08.1937543Z ---------- 2025-08-26T20:22:08.1937632Z actual : scalar 2025-08-26T20:22:08.1937730Z The object to check. 2025-08-26T20:22:08.1937832Z desired : scalar 2025-08-26T20:22:08.1937930Z The expected object. 2025-08-26T20:22:08.1938036Z significant : int, optional 2025-08-26T20:22:08.1938162Z Desired precision, default is 7. 2025-08-26T20:22:08.1938259Z err_msg : str, optional 2025-08-26T20:22:08.1938424Z The error message to be printed in case of failure. 2025-08-26T20:22:08.1938550Z verbose : bool, optional 2025-08-26T20:22:08.1938755Z If True, the conflicting values are appended to the error message. 2025-08-26T20:22:08.1938847Z 2025-08-26T20:22:08.1938930Z Raises 2025-08-26T20:22:08.1939024Z ------ 2025-08-26T20:22:08.1939115Z AssertionError 2025-08-26T20:22:08.1939307Z If actual and desired are not equal up to specified precision. 2025-08-26T20:22:08.1939401Z 2025-08-26T20:22:08.1939485Z See Also 2025-08-26T20:22:08.1939566Z -------- 2025-08-26T20:22:08.1939813Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-26T20:22:08.1939940Z relative and/or absolute precision. 2025-08-26T20:22:08.1940159Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-26T20:22:08.1940238Z 2025-08-26T20:22:08.1940321Z Examples 2025-08-26T20:22:08.1940515Z -------- 2025-08-26T20:22:08.1940634Z >>> np.testing.assert_approx_equal( 2025-08-26T20:22:08.1940760Z ... 0.12345677777777e-20, 0.1234567e-20 2025-08-26T20:22:08.1940857Z ... ) # doctest: +SKIP 2025-08-26T20:22:08.1940970Z >>> np.testing.assert_approx_equal( 2025-08-26T20:22:08.1941074Z ... 0.12345670e-20, 2025-08-26T20:22:08.1941184Z ... 0.12345671e-20, # doctest: +SKIP 2025-08-26T20:22:08.1941290Z ... significant=8, 2025-08-26T20:22:08.1941373Z ... ) 2025-08-26T20:22:08.1941489Z >>> np.testing.assert_approx_equal( 2025-08-26T20:22:08.1941592Z ... 0.12345670e-20, 2025-08-26T20:22:08.1941700Z ... 0.12345672e-20, # doctest: +SKIP 2025-08-26T20:22:08.1941794Z ... significant=8, 2025-08-26T20:22:08.1941887Z ... ) 2025-08-26T20:22:08.1941998Z Traceback (most recent call last): 2025-08-26T20:22:08.1942094Z ... 2025-08-26T20:22:08.1942186Z AssertionError: 2025-08-26T20:22:08.1942317Z Items are not equal to 8 significant digits: 2025-08-26T20:22:08.1942421Z ACTUAL: 1.234567e-21 2025-08-26T20:22:08.1942516Z DESIRED: 1.2345672e-21 2025-08-26T20:22:08.1942606Z 2025-08-26T20:22:08.1942770Z the evaluated condition that raises the exception is 2025-08-26T20:22:08.1942848Z 2025-08-26T20:22:08.1943084Z >>> abs(0.12345670e-20 / 1e-21 - 0.12345672e-20 / 1e-21) >= 10 ** -(8 - 1) 2025-08-26T20:22:08.1943166Z True 2025-08-26T20:22:08.1943257Z 2025-08-26T20:22:08.1943336Z 2025-08-26T20:22:08.1943587Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1943680Z 2025-08-26T20:22:08.1943777Z warnings.warn(msg) 2025-08-26T20:22:08.1943859Z 2025-08-26T20:22:08.1944072Z --- Parse Warning: 32 / 146 --- 2025-08-26T20:22:08.1945001Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=744. 2025-08-26T20:22:08.1945276Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1945410Z 2025-08-26T20:22:08.1945619Z Raises an AssertionError if two array_like objects are not equal. 2025-08-26T20:22:08.1945714Z 2025-08-26T20:22:08.1945920Z Given two array_like objects, check that the shape is equal and all 2025-08-26T20:22:08.1946152Z elements of these objects are equal (but see the Notes for the special 2025-08-26T20:22:08.1946353Z handling of a scalar). An exception is raised at shape mismatch or 2025-08-26T20:22:08.1946578Z conflicting values. In contrast to the standard usage in numpy, NaNs 2025-08-26T20:22:08.1946808Z are compared like numbers, no assertion is raised if both objects have 2025-08-26T20:22:08.1946915Z NaNs in the same positions. 2025-08-26T20:22:08.1947012Z 2025-08-26T20:22:08.1947239Z The usual caution for verifying equality with floating point numbers is 2025-08-26T20:22:08.1947325Z advised. 2025-08-26T20:22:08.1947444Z 2025-08-26T20:22:08.1947534Z Parameters 2025-08-26T20:22:08.1947631Z ---------- 2025-08-26T20:22:08.1947721Z x : array_like 2025-08-26T20:22:08.1947829Z The actual object to check. 2025-08-26T20:22:08.1947929Z y : array_like 2025-08-26T20:22:08.1948040Z The desired, expected object. 2025-08-26T20:22:08.1948149Z err_msg : str, optional 2025-08-26T20:22:08.1948305Z The error message to be printed in case of failure. 2025-08-26T20:22:08.1948408Z verbose : bool, optional 2025-08-26T20:22:08.1948620Z If True, the conflicting values are appended to the error message. 2025-08-26T20:22:08.1948718Z strict : bool, optional 2025-08-26T20:22:08.1948926Z If True, raise an AssertionError when either the shape or the data 2025-08-26T20:22:08.1949101Z type of the array_like objects does not match. The special 2025-08-26T20:22:08.1949304Z handling for scalars mentioned in the Notes section is disabled. 2025-08-26T20:22:08.1949398Z 2025-08-26T20:22:08.1949481Z Raises 2025-08-26T20:22:08.1949564Z ------ 2025-08-26T20:22:08.1949671Z AssertionError 2025-08-26T20:22:08.1949806Z If actual and desired objects are not equal. 2025-08-26T20:22:08.1949900Z 2025-08-26T20:22:08.1949984Z See Also 2025-08-26T20:22:08.1950067Z -------- 2025-08-26T20:22:08.1950314Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-26T20:22:08.1950441Z relative and/or absolute precision. 2025-08-26T20:22:08.1950661Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-26T20:22:08.1950742Z 2025-08-26T20:22:08.1950823Z Notes 2025-08-26T20:22:08.1950919Z ----- 2025-08-26T20:22:08.1951102Z When one of `x` and `y` is a scalar and the other is array_like, the 2025-08-26T20:22:08.1951336Z function checks that each element of the array_like object is equal to 2025-08-26T20:22:08.1951558Z the scalar. This behaviour can be disabled with the `strict` parameter. 2025-08-26T20:22:08.1951640Z 2025-08-26T20:22:08.1951737Z Examples 2025-08-26T20:22:08.1951822Z -------- 2025-08-26T20:22:08.1951958Z The first assert does not raise an exception: 2025-08-26T20:22:08.1952109Z 2025-08-26T20:22:08.1952222Z >>> np.testing.assert_array_equal( 2025-08-26T20:22:08.1952372Z ... [1.0, 2.33333, np.nan], [np.exp(0), 2.33333, np.nan] 2025-08-26T20:22:08.1952509Z ... ) 2025-08-26T20:22:08.1952587Z 2025-08-26T20:22:08.1952825Z Use `assert_allclose` or one of the nulp (number of floating point values) 2025-08-26T20:22:08.1952936Z functions for these cases instead: 2025-08-26T20:22:08.1953028Z 2025-08-26T20:22:08.1953135Z >>> np.testing.assert_allclose( 2025-08-26T20:22:08.1953330Z ... [1.0, np.pi, np.nan], [1, np.sqrt(np.pi) ** 2, np.nan], rtol=1e-10, atol=0 2025-08-26T20:22:08.1953423Z ... ) 2025-08-26T20:22:08.1953502Z 2025-08-26T20:22:08.1953716Z As mentioned in the Notes section, `assert_array_equal` has special 2025-08-26T20:22:08.1953991Z handling for scalars. Here the test checks that each value in `x` is 3: 2025-08-26T20:22:08.1954072Z 2025-08-26T20:22:08.1954192Z >>> x = np.full((2, 5), fill_value=3) 2025-08-26T20:22:08.1954311Z >>> np.testing.assert_array_equal(x, 3) 2025-08-26T20:22:08.1954401Z 2025-08-26T20:22:08.1954618Z Use `strict` to raise an AssertionError when comparing a scalar with an 2025-08-26T20:22:08.1954701Z array: 2025-08-26T20:22:08.1954796Z 2025-08-26T20:22:08.1954964Z >>> np.testing.assert_array_equal(x, 3, strict=True) 2025-08-26T20:22:08.1955076Z Traceback (most recent call last): 2025-08-26T20:22:08.1955173Z ... 2025-08-26T20:22:08.1955265Z AssertionError: 2025-08-26T20:22:08.1955375Z Arrays are not equal 2025-08-26T20:22:08.1955462Z 2025-08-26T20:22:08.1955560Z (shapes (2, 5), () mismatch) 2025-08-26T20:22:08.1955680Z x: torch.ndarray([[3, 3, 3, 3, 3], 2025-08-26T20:22:08.1955812Z [3, 3, 3, 3, 3]]) 2025-08-26T20:22:08.1955921Z y: torch.ndarray(3) 2025-08-26T20:22:08.1956004Z 2025-08-26T20:22:08.1956219Z The `strict` parameter also ensures that the array data types match: 2025-08-26T20:22:08.1956313Z 2025-08-26T20:22:08.1956409Z >>> x = np.array([2, 2, 2]) 2025-08-26T20:22:08.1956540Z >>> y = np.array([2.0, 2.0, 2.0], dtype=np.float32) 2025-08-26T20:22:08.1956702Z >>> np.testing.assert_array_equal(x, y, strict=True) 2025-08-26T20:22:08.1956813Z Traceback (most recent call last): 2025-08-26T20:22:08.1956908Z ... 2025-08-26T20:22:08.1957001Z AssertionError: 2025-08-26T20:22:08.1957095Z Arrays are not equal 2025-08-26T20:22:08.1957194Z 2025-08-26T20:22:08.1957341Z (dtypes dtype("int64"), dtype("float32") mismatch) 2025-08-26T20:22:08.1957457Z x: torch.ndarray([2, 2, 2]) 2025-08-26T20:22:08.1957559Z y: torch.ndarray([2., 2., 2.]) 2025-08-26T20:22:08.1957640Z 2025-08-26T20:22:08.1957908Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1957993Z 2025-08-26T20:22:08.1958103Z warnings.warn(msg) 2025-08-26T20:22:08.1958182Z 2025-08-26T20:22:08.1958379Z --- Parse Warning: 33 / 146 --- 2025-08-26T20:22:08.1959357Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_array_almost_equal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=851. 2025-08-26T20:22:08.1959619Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1959715Z 2025-08-26T20:22:08.1959931Z Raises an AssertionError if two objects are not equal up to desired 2025-08-26T20:22:08.1960020Z precision. 2025-08-26T20:22:08.1960117Z 2025-08-26T20:22:08.1960294Z .. note:: It is recommended to use one of `assert_allclose`, 2025-08-26T20:22:08.1960485Z `assert_array_almost_equal_nulp` or `assert_array_max_ulp` 2025-08-26T20:22:08.1960676Z instead of this function for more consistent floating point 2025-08-26T20:22:08.1960771Z comparisons. 2025-08-26T20:22:08.1960892Z 2025-08-26T20:22:08.1961131Z The test verifies identical shapes and that the elements of ``actual`` and 2025-08-26T20:22:08.1961240Z ``desired`` satisfy. 2025-08-26T20:22:08.1961319Z 2025-08-26T20:22:08.1961453Z ``abs(desired-actual) < 1.5 * 10**(-decimal)`` 2025-08-26T20:22:08.1961544Z 2025-08-26T20:22:08.1961770Z That is a looser test than originally documented, but agrees with what the 2025-08-26T20:22:08.1962027Z actual implementation did up to rounding vagaries. An exception is raised 2025-08-26T20:22:08.1962260Z at shape mismatch or conflicting values. In contrast to the standard usage 2025-08-26T20:22:08.1962476Z in numpy, NaNs are compared like numbers, no assertion is raised if both 2025-08-26T20:22:08.1962614Z objects have NaNs in the same positions. 2025-08-26T20:22:08.1962695Z 2025-08-26T20:22:08.1962831Z Parameters 2025-08-26T20:22:08.1962931Z ---------- 2025-08-26T20:22:08.1963019Z x : array_like 2025-08-26T20:22:08.1963138Z The actual object to check. 2025-08-26T20:22:08.1963224Z y : array_like 2025-08-26T20:22:08.1963334Z The desired, expected object. 2025-08-26T20:22:08.1963443Z decimal : int, optional 2025-08-26T20:22:08.1963556Z Desired precision, default is 6. 2025-08-26T20:22:08.1963666Z err_msg : str, optional 2025-08-26T20:22:08.1963818Z The error message to be printed in case of failure. 2025-08-26T20:22:08.1963918Z verbose : bool, optional 2025-08-26T20:22:08.1964165Z If True, the conflicting values are appended to the error message. 2025-08-26T20:22:08.1964246Z 2025-08-26T20:22:08.1964342Z Raises 2025-08-26T20:22:08.1964425Z ------ 2025-08-26T20:22:08.1964515Z AssertionError 2025-08-26T20:22:08.1964748Z If actual and desired are not equal up to specified precision. 2025-08-26T20:22:08.1964829Z 2025-08-26T20:22:08.1964916Z See Also 2025-08-26T20:22:08.1965013Z -------- 2025-08-26T20:22:08.1965248Z assert_allclose: Compare two array_like objects for equality with desired 2025-08-26T20:22:08.1965388Z relative and/or absolute precision. 2025-08-26T20:22:08.1965594Z assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal 2025-08-26T20:22:08.1965673Z 2025-08-26T20:22:08.1965769Z Examples 2025-08-26T20:22:08.1965851Z -------- 2025-08-26T20:22:08.1965994Z the first assert does not raise an exception 2025-08-26T20:22:08.1966073Z 2025-08-26T20:22:08.1966325Z >>> np.testing.assert_array_almost_equal([1.0, 2.333, np.nan], [1.0, 2.333, np.nan]) 2025-08-26T20:22:08.1966415Z 2025-08-26T20:22:08.1966537Z >>> np.testing.assert_array_almost_equal( 2025-08-26T20:22:08.1966698Z ... [1.0, 2.33333, np.nan], [1.0, 2.33339, np.nan], decimal=5 2025-08-26T20:22:08.1966783Z ... ) 2025-08-26T20:22:08.1966898Z Traceback (most recent call last): 2025-08-26T20:22:08.1966992Z ... 2025-08-26T20:22:08.1967086Z AssertionError: 2025-08-26T20:22:08.1967209Z Arrays are not almost equal to 5 decimals 2025-08-26T20:22:08.1967306Z 2025-08-26T20:22:08.1967414Z Mismatched elements: 1 / 3 (33.3%) 2025-08-26T20:22:08.1967560Z Max absolute difference: 5.999999999994898e-05 2025-08-26T20:22:08.1967688Z Max relative difference: 2.5713661239633743e-05 2025-08-26T20:22:08.1967851Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-08-26T20:22:08.1968024Z y: torch.ndarray([1.0000, 2.3334, nan], dtype=float64) 2025-08-26T20:22:08.1968103Z 2025-08-26T20:22:08.1968238Z >>> np.testing.assert_array_almost_equal( 2025-08-26T20:22:08.1968375Z ... [1.0, 2.33333, np.nan], [1.0, 2.33333, 5], decimal=5 2025-08-26T20:22:08.1968457Z ... ) 2025-08-26T20:22:08.1968579Z Traceback (most recent call last): 2025-08-26T20:22:08.1968662Z ... 2025-08-26T20:22:08.1968766Z AssertionError: 2025-08-26T20:22:08.1968888Z Arrays are not almost equal to 5 decimals 2025-08-26T20:22:08.1968976Z 2025-08-26T20:22:08.1969120Z x and y nan location mismatch: 2025-08-26T20:22:08.1969278Z x: torch.ndarray([1.0000, 2.3333, nan], dtype=float64) 2025-08-26T20:22:08.1969445Z y: torch.ndarray([1.0000, 2.3333, 5.0000], dtype=float64) 2025-08-26T20:22:08.1969525Z 2025-08-26T20:22:08.1969605Z 2025-08-26T20:22:08.1969869Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1969950Z 2025-08-26T20:22:08.1970047Z warnings.warn(msg) 2025-08-26T20:22:08.1970139Z 2025-08-26T20:22:08.1970336Z --- Parse Warning: 34 / 146 --- 2025-08-26T20:22:08.1971304Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=clear_and_catch_warnings in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_numpy/testing/utils.py line=1848. 2025-08-26T20:22:08.1971627Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1971860Z Context manager that resets warning registry for catching warnings 2025-08-26T20:22:08.1971942Z 2025-08-26T20:22:08.1972186Z Warnings can be slippery, because, whenever a warning is triggered, Python 2025-08-26T20:22:08.1972424Z adds a ``__warningregistry__`` member to the *calling* module. This makes 2025-08-26T20:22:08.1972661Z it impossible to retrigger the warning in this module, whatever you put in 2025-08-26T20:22:08.1972917Z the warnings filters. This context manager accepts a sequence of `modules` 2025-08-26T20:22:08.1973056Z as a keyword argument to its constructor and: 2025-08-26T20:22:08.1973135Z 2025-08-26T20:22:08.1973374Z * stores and removes any ``__warningregistry__`` entries in given `modules` 2025-08-26T20:22:08.1973488Z on entry; 2025-08-26T20:22:08.1973691Z * resets ``__warningregistry__`` to its previous state on exit. 2025-08-26T20:22:08.1973772Z 2025-08-26T20:22:08.1973995Z This makes it possible to trigger any warning afresh inside the context 2025-08-26T20:22:08.1974188Z manager without disturbing the state of warnings outside. 2025-08-26T20:22:08.1974267Z 2025-08-26T20:22:08.1974512Z For compatibility with Python 3.0, please consider all arguments to be 2025-08-26T20:22:08.1974604Z keyword-only. 2025-08-26T20:22:08.1974683Z 2025-08-26T20:22:08.1974785Z Parameters 2025-08-26T20:22:08.1974869Z ---------- 2025-08-26T20:22:08.1974969Z record : bool, optional 2025-08-26T20:22:08.1975168Z Specifies whether warnings should be captured by a custom 2025-08-26T20:22:08.1975404Z implementation of ``warnings.showwarning()`` and be appended to a list 2025-08-26T20:22:08.1975623Z returned by the context manager. Otherwise None is returned by the 2025-08-26T20:22:08.1975852Z context manager. The objects appended to the list are arguments whose 2025-08-26T20:22:08.1976020Z attributes mirror the arguments to ``showwarning()``. 2025-08-26T20:22:08.1976140Z modules : sequence, optional 2025-08-26T20:22:08.1976359Z Sequence of modules for which to reset warnings registry on entry and 2025-08-26T20:22:08.1976562Z restore on exit. To work correctly, all 'ignore' filters should 2025-08-26T20:22:08.1976674Z filter by one of these modules. 2025-08-26T20:22:08.1976754Z 2025-08-26T20:22:08.1976851Z Examples 2025-08-26T20:22:08.1976938Z -------- 2025-08-26T20:22:08.1977075Z >>> import warnings 2025-08-26T20:22:08.1977259Z >>> with np.testing.clear_and_catch_warnings( # doctest: +SKIP 2025-08-26T20:22:08.1977377Z ... modules=[np.core.fromnumeric] 2025-08-26T20:22:08.1977474Z ... ): 2025-08-26T20:22:08.1977600Z ... warnings.simplefilter("always") 2025-08-26T20:22:08.1977841Z ... warnings.filterwarnings("ignore", module="np.core.fromnumeric") 2025-08-26T20:22:08.1978039Z ... # do something that raises a warning but ignore those in 2025-08-26T20:22:08.1978144Z ... # np.core.fromnumeric 2025-08-26T20:22:08.1978236Z 2025-08-26T20:22:08.1978489Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1978580Z 2025-08-26T20:22:08.1978676Z warnings.warn(msg) 2025-08-26T20:22:08.1978769Z 2025-08-26T20:22:08.1978957Z --- Parse Warning: 35 / 146 --- 2025-08-26T20:22:08.1979890Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Conv1d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/conv.py line=354. 2025-08-26T20:22:08.1980153Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1980529Z Applies a 1D convolution over a quantized input signal composed of 2025-08-26T20:22:08.1980645Z several quantized input planes. 2025-08-26T20:22:08.1980740Z 2025-08-26T20:22:08.1980952Z For details on input arguments, parameters, and implementation see 2025-08-26T20:22:08.1981059Z :class:`~torch.nn.Conv1d`. 2025-08-26T20:22:08.1981154Z 2025-08-26T20:22:08.1981246Z .. note:: 2025-08-26T20:22:08.1981458Z Only `zeros` is supported for the :attr:`padding_mode` argument. 2025-08-26T20:22:08.1981540Z 2025-08-26T20:22:08.1981626Z .. note:: 2025-08-26T20:22:08.1981819Z Only `torch.quint8` is supported for the input data type. 2025-08-26T20:22:08.1981900Z 2025-08-26T20:22:08.1981979Z 2025-08-26T20:22:08.1982082Z Attributes: 2025-08-26T20:22:08.1982293Z weight (Tensor): packed tensor derived from the learnable weight 2025-08-26T20:22:08.1982433Z parameter. 2025-08-26T20:22:08.1982581Z scale (Tensor): scalar for the output scale 2025-08-26T20:22:08.1982745Z zero_point (Tensor): scalar for the output zero point 2025-08-26T20:22:08.1982842Z 2025-08-26T20:22:08.1982992Z See :class:`~torch.nn.Conv1d` for other attributes. 2025-08-26T20:22:08.1983085Z 2025-08-26T20:22:08.1983175Z Examples:: 2025-08-26T20:22:08.1983256Z 2025-08-26T20:22:08.1983418Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_QENGINE) 2025-08-26T20:22:08.1983552Z >>> m = nn.quantized.Conv1d(16, 33, 3, stride=2) 2025-08-26T20:22:08.1983677Z >>> input = torch.randn(20, 16, 100) 2025-08-26T20:22:08.1983786Z >>> # quantize input to quint8 2025-08-26T20:22:08.1983885Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1984105Z >>> q_input = torch.quantize_per_tensor(input, scale=1.0, zero_point=0, 2025-08-26T20:22:08.1984233Z ... dtype=torch.quint8) 2025-08-26T20:22:08.1984347Z >>> output = m(q_input) 2025-08-26T20:22:08.1984423Z 2025-08-26T20:22:08.1984505Z 2025-08-26T20:22:08.1984775Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1984857Z 2025-08-26T20:22:08.1984954Z warnings.warn(msg) 2025-08-26T20:22:08.1985045Z 2025-08-26T20:22:08.1985236Z --- Parse Warning: 36 / 146 --- 2025-08-26T20:22:08.1986140Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LSTM in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/nn/quantized/modules/rnn.py line=12. 2025-08-26T20:22:08.1986399Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1986535Z A quantized long short-term memory (LSTM). 2025-08-26T20:22:08.1986615Z 2025-08-26T20:22:08.1986894Z For the description and the argument types, please, refer to :class:`~torch.nn.LSTM` 2025-08-26T20:22:08.1986985Z 2025-08-26T20:22:08.1987074Z Attributes: 2025-08-26T20:22:08.1987226Z layers : instances of the `_LSTMLayer` 2025-08-26T20:22:08.1987316Z 2025-08-26T20:22:08.1987398Z .. note:: 2025-08-26T20:22:08.1987622Z To access the weights and biases, you need to access them per layer. 2025-08-26T20:22:08.1987795Z See examples in :class:`~torch.ao.nn.quantizable.LSTM` 2025-08-26T20:22:08.1987875Z 2025-08-26T20:22:08.1987976Z Examples:: 2025-08-26T20:22:08.1988077Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.1988196Z >>> custom_module_config = { 2025-08-26T20:22:08.1988332Z ... 'float_to_observed_custom_module_class': { 2025-08-26T20:22:08.1988457Z ... nn.LSTM: nn.quantizable.LSTM, 2025-08-26T20:22:08.1988553Z ... }, 2025-08-26T20:22:08.1988704Z ... 'observed_to_quantized_custom_module_class': { 2025-08-26T20:22:08.1988925Z ... nn.quantizable.LSTM: nn.quantized.LSTM, 2025-08-26T20:22:08.1989008Z ... } 2025-08-26T20:22:08.1989091Z ... } 2025-08-26T20:22:08.1989323Z >>> tq.prepare(model, prepare_custom_module_class=custom_module_config) 2025-08-26T20:22:08.1989535Z >>> tq.convert(model, convert_custom_module_class=custom_module_config) 2025-08-26T20:22:08.1989633Z 2025-08-26T20:22:08.1989885Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.1989963Z 2025-08-26T20:22:08.1990070Z warnings.warn(msg) 2025-08-26T20:22:08.1990146Z 2025-08-26T20:22:08.1990342Z --- Parse Warning: 37 / 146 --- 2025-08-26T20:22:08.1991532Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ActivationSparsifier in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/activation_sparsifier/activation_sparsifier.py line=16. 2025-08-26T20:22:08.1992042Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.1992139Z 2025-08-26T20:22:08.1992404Z The Activation sparsifier class aims to sparsify/prune activations in a neural 2025-08-26T20:22:08.1992640Z network. The idea is to attach the sparsifier to a layer (or layers) and it 2025-08-26T20:22:08.1992885Z zeroes out the activations based on the mask_fn (or sparsification function) 2025-08-26T20:22:08.1992978Z input by the user. 2025-08-26T20:22:08.1993213Z The mask_fn is applied once all the inputs are aggregated and reduced i.e. 2025-08-26T20:22:08.1993369Z mask = mask_fn(reduce_fn(aggregate_fn(activations))) 2025-08-26T20:22:08.1993458Z 2025-08-26T20:22:08.1993543Z Note:: 2025-08-26T20:22:08.1993859Z The sparsification mask is computed on the input **before it goes through the attached layer**. 2025-08-26T20:22:08.1993953Z 2025-08-26T20:22:08.1994036Z Args: 2025-08-26T20:22:08.1994145Z model (nn.Module): 2025-08-26T20:22:08.1994368Z The model whose layers will be sparsified. The layers that needs to be 2025-08-26T20:22:08.1994609Z sparsified should be added separately using the register_layer() function 2025-08-26T20:22:08.1994738Z aggregate_fn (Optional, Callable): 2025-08-26T20:22:08.1994992Z default aggregate_fn that is used if not specified while registering the layer. 2025-08-26T20:22:08.1995167Z specifies how inputs should be aggregated over time. 2025-08-26T20:22:08.1995450Z The aggregate_fn should usually take 2 torch tensors and return the aggregated tensor. 2025-08-26T20:22:08.1995535Z Example 2025-08-26T20:22:08.1995720Z def add_agg_fn(tensor1, tensor2): return tensor1 + tensor2 2025-08-26T20:22:08.1995837Z reduce_fn (Optional, Callable): 2025-08-26T20:22:08.1996099Z default reduce_fn that is used if not specified while registering the layer. 2025-08-26T20:22:08.1996352Z reduce_fn will be called on the aggregated tensor i.e. the tensor obtained after 2025-08-26T20:22:08.1996554Z calling agg_fn() on all inputs. 2025-08-26T20:22:08.1996659Z Example 2025-08-26T20:22:08.1996854Z def mean_reduce_fn(agg_tensor): return agg_tensor.mean(dim=0) 2025-08-26T20:22:08.1996982Z mask_fn (Optional, Callable): 2025-08-26T20:22:08.1997293Z default mask_fn that is used to create the sparsification mask using the tensor obtained after 2025-08-26T20:22:08.1997559Z calling the reduce_fn(). This is used by default if a custom one is passed in the 2025-08-26T20:22:08.1997663Z register_layer(). 2025-08-26T20:22:08.1998006Z Note that the mask_fn() definition should contain the sparse arguments that is passed in sparse_config 2025-08-26T20:22:08.1998197Z arguments. 2025-08-26T20:22:08.1998309Z features (Optional, list): 2025-08-26T20:22:08.1998448Z default selected features to sparsify. 2025-08-26T20:22:08.1998710Z If this is non-empty, then the mask_fn will be applied for each feature of the input. 2025-08-26T20:22:08.1998807Z For example, 2025-08-26T20:22:08.1999075Z mask = [mask_fn(reduce_fn(aggregated_fn(input[feature])) for feature in features] 2025-08-26T20:22:08.1999188Z feature_dim (Optional, int): 2025-08-26T20:22:08.1999477Z default dimension of input features. Again, features along this dim will be chosen 2025-08-26T20:22:08.1999583Z for sparsification. 2025-08-26T20:22:08.1999687Z sparse_config (Dict): 2025-08-26T20:22:08.1999919Z Default configuration for the mask_fn. This config will be passed 2025-08-26T20:22:08.2000063Z with the mask_fn() 2025-08-26T20:22:08.2000150Z 2025-08-26T20:22:08.2000236Z Example: 2025-08-26T20:22:08.2000332Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2000441Z >>> model = SomeModel() 2025-08-26T20:22:08.2000683Z >>> act_sparsifier = ActivationSparsifier(...) # init activation sparsifier 2025-08-26T20:22:08.2000800Z >>> # Initialize aggregate_fn 2025-08-26T20:22:08.2000892Z >>> def agg_fn(x, y): 2025-08-26T20:22:08.2000983Z >>> return x + y 2025-08-26T20:22:08.2001073Z >>> 2025-08-26T20:22:08.2001174Z >>> # Initialize reduce_fn 2025-08-26T20:22:08.2001266Z >>> def reduce_fn(x): 2025-08-26T20:22:08.2001387Z >>> return torch.mean(x, dim=0) 2025-08-26T20:22:08.2001467Z >>> 2025-08-26T20:22:08.2001577Z >>> # Initialize mask_fn 2025-08-26T20:22:08.2001672Z >>> def mask_fn(data): 2025-08-26T20:22:08.2001816Z >>> return torch.eye(data.shape).to(data.device) 2025-08-26T20:22:08.2001909Z >>> 2025-08-26T20:22:08.2001986Z >>> 2025-08-26T20:22:08.2002113Z >>> act_sparsifier.register_layer( 2025-08-26T20:22:08.2002212Z ... model.some_layer, 2025-08-26T20:22:08.2002309Z ... aggregate_fn=agg_fn, 2025-08-26T20:22:08.2002416Z ... reduce_fn=reduce_fn, 2025-08-26T20:22:08.2002508Z ... mask_fn=mask_fn, 2025-08-26T20:22:08.2002585Z ... ) 2025-08-26T20:22:08.2002677Z >>> 2025-08-26T20:22:08.2002776Z >>> # start training process 2025-08-26T20:22:08.2002878Z >>> for _ in [...]: 2025-08-26T20:22:08.2002971Z >>> # epoch starts 2025-08-26T20:22:08.2003141Z >>> # model.forward(), compute_loss() and model.backwards() 2025-08-26T20:22:08.2003243Z >>> # epoch ends 2025-08-26T20:22:08.2003349Z >>> act_sparsifier.step() 2025-08-26T20:22:08.2003460Z >>> # end training process 2025-08-26T20:22:08.2003568Z >>> sparsifier.squash_mask() 2025-08-26T20:22:08.2003647Z 2025-08-26T20:22:08.2003916Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2004020Z 2025-08-26T20:22:08.2004129Z warnings.warn(msg) 2025-08-26T20:22:08.2004208Z 2025-08-26T20:22:08.2004411Z --- Parse Warning: 38 / 146 --- 2025-08-26T20:22:08.2005624Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseDataScheduler.get_schedule_param in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/_experimental/data_scheduler/base_data_scheduler.py line=91. 2025-08-26T20:22:08.2005885Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2005974Z 2025-08-26T20:22:08.2006179Z Abstract method that needs to be implemented by the child class. 2025-08-26T20:22:08.2006435Z The expected return type should is a dictionary of name to schedule_param value 2025-08-26T20:22:08.2006769Z The returned values will be updated in sparsifier when the scheduler step() function 2025-08-26T20:22:08.2006860Z is called. 2025-08-26T20:22:08.2006951Z 2025-08-26T20:22:08.2007037Z Example: 2025-08-26T20:22:08.2007148Z >>> def get_schedule_param(self): 2025-08-26T20:22:08.2007255Z ... new_param = {} 2025-08-26T20:22:08.2007412Z ... for name in self.sparsifier.data_groups.keys(): 2025-08-26T20:22:08.2007526Z ... new_param[name] = ( 2025-08-26T20:22:08.2007733Z ... self.sparsifier.data_groups[name][self.schedule_param] * 0.5 2025-08-26T20:22:08.2007817Z ... ) 2025-08-26T20:22:08.2007926Z ... return new_param 2025-08-26T20:22:08.2008006Z 2025-08-26T20:22:08.2008357Z When the step() function is called, the value in self.sparsifier.data_groups[name][self.schedule_param] 2025-08-26T20:22:08.2008474Z would be halved 2025-08-26T20:22:08.2008554Z 2025-08-26T20:22:08.2008820Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2008900Z 2025-08-26T20:22:08.2008998Z warnings.warn(msg) 2025-08-26T20:22:08.2009089Z 2025-08-26T20:22:08.2009273Z --- Parse Warning: 39 / 146 --- 2025-08-26T20:22:08.2010353Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BaseSparsifier.squash_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/pruning/sparsifier/base_sparsifier.py line=229. 2025-08-26T20:22:08.2010614Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2010800Z Squashes the sparse masks into the appropriate tensors. 2025-08-26T20:22:08.2010880Z 2025-08-26T20:22:08.2011086Z If either the `params_to_keep` or `params_to_keep_per_layer` is set, 2025-08-26T20:22:08.2011288Z the module will have a `sparse_params` dict attached to it. 2025-08-26T20:22:08.2011374Z 2025-08-26T20:22:08.2011473Z Args: 2025-08-26T20:22:08.2011661Z params_to_keep: List of keys to save in the module or a dict 2025-08-26T20:22:08.2011825Z representing the modules and keys that will have 2025-08-26T20:22:08.2011964Z sparsity parameters saved 2025-08-26T20:22:08.2012179Z params_to_keep_per_layer: Dict to specify the params that should be 2025-08-26T20:22:08.2012345Z saved for specific layers. The keys in the dict 2025-08-26T20:22:08.2012504Z should be the module fqn, while the values should 2025-08-26T20:22:08.2012666Z be a list of strings with the names of the variables 2025-08-26T20:22:08.2012810Z to save in the `sparse_params` 2025-08-26T20:22:08.2012895Z 2025-08-26T20:22:08.2013000Z Examples: 2025-08-26T20:22:08.2013139Z >>> # xdoctest: +SKIP("locals are undefined") 2025-08-26T20:22:08.2013258Z >>> # Don't save any sparse params 2025-08-26T20:22:08.2013421Z >>> sparsifier.squash_mask() 2025-08-26T20:22:08.2013563Z >>> hasattr(model.submodule1, "sparse_params") 2025-08-26T20:22:08.2013662Z False 2025-08-26T20:22:08.2013743Z 2025-08-26T20:22:08.2013864Z >>> # Keep sparse params per layer 2025-08-26T20:22:08.2013989Z >>> sparsifier.squash_mask( 2025-08-26T20:22:08.2014103Z ... params_to_keep_per_layer={ 2025-08-26T20:22:08.2014253Z ... "submodule1.linear1": ("foo", "bar"), 2025-08-26T20:22:08.2014382Z ... "submodule2.linear42": ("baz",), 2025-08-26T20:22:08.2014468Z ... } 2025-08-26T20:22:08.2014567Z ... ) 2025-08-26T20:22:08.2014724Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-26T20:22:08.2014873Z {'foo': 42, 'bar': 24} 2025-08-26T20:22:08.2015047Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-26T20:22:08.2015141Z {'baz': 0.1} 2025-08-26T20:22:08.2015236Z 2025-08-26T20:22:08.2015361Z >>> # Keep sparse params for all layers 2025-08-26T20:22:08.2015534Z >>> sparsifier.squash_mask(params_to_keep=("foo", "bar")) 2025-08-26T20:22:08.2015699Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-26T20:22:08.2015797Z {'foo': 42, 'bar': 24} 2025-08-26T20:22:08.2015965Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-26T20:22:08.2016064Z {'foo': 42, 'bar': 24} 2025-08-26T20:22:08.2016142Z 2025-08-26T20:22:08.2016352Z >>> # Keep some sparse params for all layers, and specific ones for 2025-08-26T20:22:08.2016481Z >>> # some other layers 2025-08-26T20:22:08.2016609Z >>> sparsifier.squash_mask( 2025-08-26T20:22:08.2016729Z ... params_to_keep=("foo", "bar"), 2025-08-26T20:22:08.2016922Z ... params_to_keep_per_layer={"submodule2.linear42": ("baz",)}, 2025-08-26T20:22:08.2017016Z ... ) 2025-08-26T20:22:08.2017170Z >>> print(model.submodule1.linear1.sparse_params) 2025-08-26T20:22:08.2017279Z {'foo': 42, 'bar': 24} 2025-08-26T20:22:08.2017434Z >>> print(model.submodule2.linear42.sparse_params) 2025-08-26T20:22:08.2017542Z {'foo': 42, 'bar': 24, 'baz': 0.1} 2025-08-26T20:22:08.2017636Z 2025-08-26T20:22:08.2017889Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2017980Z 2025-08-26T20:22:08.2018076Z warnings.warn(msg) 2025-08-26T20:22:08.2018154Z 2025-08-26T20:22:08.2018352Z --- Parse Warning: 40 / 146 --- 2025-08-26T20:22:08.2019392Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DTypeConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/backend_config/backend_config.py line=181. 2025-08-26T20:22:08.2019667Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2019749Z 2025-08-26T20:22:08.2020000Z Config object that specifies the supported data types passed as arguments to 2025-08-26T20:22:08.2020254Z quantize ops in the reference model spec, for input and output activations, 2025-08-26T20:22:08.2020427Z weights, and biases. 2025-08-26T20:22:08.2020532Z 2025-08-26T20:22:08.2020692Z For example, consider the following reference model: 2025-08-26T20:22:08.2020773Z 2025-08-26T20:22:08.2020949Z quant1 - [dequant1 - fp32_linear - quant2] - dequant2 2025-08-26T20:22:08.2021033Z 2025-08-26T20:22:08.2021262Z The pattern in the square brackets refers to the reference pattern of 2025-08-26T20:22:08.2021503Z statically quantized linear. Setting the input dtype as `torch.quint8` 2025-08-26T20:22:08.2021765Z in the DTypeConfig means we pass in `torch.quint8` as the dtype argument 2025-08-26T20:22:08.2022002Z to the first quantize op (quant1). Similarly, setting the output dtype as 2025-08-26T20:22:08.2022223Z `torch.quint8` means we pass in `torch.quint8` as the dtype argument to 2025-08-26T20:22:08.2022347Z the second quantize op (quant2). 2025-08-26T20:22:08.2022427Z 2025-08-26T20:22:08.2022643Z Note that the dtype here does not refer to the interface dtypes of the 2025-08-26T20:22:08.2022860Z op. For example, the "input dtype" here is not the dtype of the input 2025-08-26T20:22:08.2023073Z tensor passed to the quantized linear op. Though it can still be the 2025-08-26T20:22:08.2023285Z same as the interface dtype, this is not always the case, e.g. the 2025-08-26T20:22:08.2023504Z interface dtype is fp32 in dynamic quantization but the "input dtype" 2025-08-26T20:22:08.2023780Z specified in the DTypeConfig would still be quint8. The semantics of 2025-08-26T20:22:08.2024006Z dtypes here are the same as the semantics of the dtypes specified in 2025-08-26T20:22:08.2024095Z the observers. 2025-08-26T20:22:08.2024188Z 2025-08-26T20:22:08.2024393Z These dtypes are matched against the ones specified in the user's 2025-08-26T20:22:08.2024607Z QConfig. If there is a match, and the QConfig satisfies the constraints 2025-08-26T20:22:08.2024837Z specified in the DTypeConfig (if any), then we will quantize the given 2025-08-26T20:22:08.2025060Z pattern using this DTypeConfig. Otherwise, the QConfig is ignored and 2025-08-26T20:22:08.2025187Z the pattern will not be quantized. 2025-08-26T20:22:08.2025267Z 2025-08-26T20:22:08.2025366Z Example usage:: 2025-08-26T20:22:08.2025464Z 2025-08-26T20:22:08.2025598Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:22:08.2025721Z >>> dtype_config1 = DTypeConfig( 2025-08-26T20:22:08.2025833Z ... input_dtype=torch.quint8, 2025-08-26T20:22:08.2025942Z ... output_dtype=torch.quint8, 2025-08-26T20:22:08.2026065Z ... weight_dtype=torch.qint8, 2025-08-26T20:22:08.2026170Z ... bias_dtype=torch.float) 2025-08-26T20:22:08.2026262Z 2025-08-26T20:22:08.2026369Z >>> dtype_config2 = DTypeConfig( 2025-08-26T20:22:08.2026497Z ... input_dtype=DTypeWithConstraints( 2025-08-26T20:22:08.2026611Z ... dtype=torch.quint8, 2025-08-26T20:22:08.2026719Z ... quant_min_lower_bound=0, 2025-08-26T20:22:08.2026830Z ... quant_max_upper_bound=255, 2025-08-26T20:22:08.2026924Z ... ), 2025-08-26T20:22:08.2027052Z ... output_dtype=DTypeWithConstraints( 2025-08-26T20:22:08.2027166Z ... dtype=torch.quint8, 2025-08-26T20:22:08.2027274Z ... quant_min_lower_bound=0, 2025-08-26T20:22:08.2027384Z ... quant_max_upper_bound=255, 2025-08-26T20:22:08.2027483Z ... ), 2025-08-26T20:22:08.2027610Z ... weight_dtype=DTypeWithConstraints( 2025-08-26T20:22:08.2027724Z ... dtype=torch.qint8, 2025-08-26T20:22:08.2027837Z ... quant_min_lower_bound=-128, 2025-08-26T20:22:08.2027950Z ... quant_max_upper_bound=127, 2025-08-26T20:22:08.2028045Z ... ), 2025-08-26T20:22:08.2028147Z ... bias_dtype=torch.float) 2025-08-26T20:22:08.2028240Z 2025-08-26T20:22:08.2028349Z >>> dtype_config1.input_dtype 2025-08-26T20:22:08.2028468Z torch.quint8 2025-08-26T20:22:08.2028559Z 2025-08-26T20:22:08.2028665Z >>> dtype_config2.input_dtype 2025-08-26T20:22:08.2028754Z torch.quint8 2025-08-26T20:22:08.2028845Z 2025-08-26T20:22:08.2028982Z >>> dtype_config2.input_dtype_with_constraints 2025-08-26T20:22:08.2029535Z DTypeWithConstraints(dtype=torch.quint8, quant_min_lower_bound=0, quant_max_upper_bound=255, scale_min_lower_bound=None, scale_max_upper_bound=None) 2025-08-26T20:22:08.2029615Z 2025-08-26T20:22:08.2029892Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2029983Z 2025-08-26T20:22:08.2030080Z warnings.warn(msg) 2025-08-26T20:22:08.2030172Z 2025-08-26T20:22:08.2030368Z --- Parse Warning: 41 / 146 --- 2025-08-26T20:22:08.2031391Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReport in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report.py line=24. 2025-08-26T20:22:08.2031664Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2031742Z 2025-08-26T20:22:08.2032060Z The ModelReport class aims to provide users an easy way to diagnose issues that they run into 2025-08-26T20:22:08.2032413Z with their models. The class works with all traceable GraphModules to help diagnose issues, 2025-08-26T20:22:08.2032728Z though the requirements on the type of model more-so depends on the specific report the user 2025-08-26T20:22:08.2033028Z is trying to generate. With respect to the reports, the ModelReport class is initialized with 2025-08-26T20:22:08.2033299Z a set of Detector classes, each of which generate reports on quantization configuration 2025-08-26T20:22:08.2033412Z issues a use might have. 2025-08-26T20:22:08.2033490Z 2025-08-26T20:22:08.2033631Z Currently supports generating reports on: 2025-08-26T20:22:08.2033860Z - Suggestions for per-channel vs. per-tensor quantization (nn.Module) 2025-08-26T20:22:08.2034127Z - Suggestions for dynamic vs static quantization for linear layers (Graph Modules) 2025-08-26T20:22:08.2034429Z - Suggestions for input-weight equalization for linear and conv layers (Graph Modules) 2025-08-26T20:22:08.2034672Z - Suggestions for outlier detection for all layers (Graph Modules) 2025-08-26T20:22:08.2034764Z 2025-08-26T20:22:08.2035160Z The ModelReport class has the primary functionality of inserting observers (primarily the ModelReportObserver) 2025-08-26T20:22:08.2035534Z where needed for each detector to gather the information it needs, and then after calibration, the ModelReport 2025-08-26T20:22:08.2035921Z class compiles the report generated by each Detector class into a single report to return to the user. It also 2025-08-26T20:22:08.2036134Z has the capability to remove all the observers it inserted as well. 2025-08-26T20:22:08.2036229Z 2025-08-26T20:22:08.2036509Z * :attr:`_model` The model we wish to generate the report for. Must be a traceable GraphModule 2025-08-26T20:22:08.2036589Z 2025-08-26T20:22:08.2036977Z * :attr:`_desired_report_detectors` The set of Detectors representing desired reports from the ModelReport class 2025-08-26T20:22:08.2037297Z Make sure that these are all unique types of detectors [do not have more than 1 of the same class] 2025-08-26T20:22:08.2037391Z 2025-08-26T20:22:08.2037676Z * :attr:`_desired_detector_names` The set of detector names of the _desired_report_detectors. 2025-08-26T20:22:08.2037919Z This set is generated by calling the get_detector_name() of each detector 2025-08-26T20:22:08.2038002Z 2025-08-26T20:22:08.2038335Z * :attr:`_detector_name_to_observer_fqns` The mapping from each detector to fqns of observers of interest 2025-08-26T20:22:08.2038665Z The purpose of this is to keep track of what observers were inserted for each detector, so that they 2025-08-26T20:22:08.2038783Z can be removed at the end if desired 2025-08-26T20:22:08.2038875Z 2025-08-26T20:22:08.2039191Z * :attr:`_prepared_flag` A boolean flag that keeps track of whether we have prepared the model or not 2025-08-26T20:22:08.2039424Z This is to ensure we only insert observers once with the ModelReport instance 2025-08-26T20:22:08.2039518Z 2025-08-26T20:22:08.2039775Z * :attr:`_removed_observers` A boolean to track if we have removed observers already 2025-08-26T20:22:08.2040104Z The purpose is to ensure we don't attempt to remove observers twice with the same ModelReport 2025-08-26T20:22:08.2040423Z instance. This also allows the functionality where we can generate the report multiple times 2025-08-26T20:22:08.2040569Z as long as we haven't removed the observers yet. 2025-08-26T20:22:08.2040661Z 2025-08-26T20:22:08.2040743Z Note: 2025-08-26T20:22:08.2041050Z This class was initially designed to work with the Fx Graph Mode workflow in mind. However, 2025-08-26T20:22:08.2041364Z full functionality is available as long as there is a traceable GraphModule that is being used. 2025-08-26T20:22:08.2041659Z One method to get a traceable GraphModule without going through the Fx workflow is to use 2025-08-26T20:22:08.2041788Z the QuantizationTracer class. 2025-08-26T20:22:08.2041868Z 2025-08-26T20:22:08.2042032Z General Flow for Fx workflow: 2025-08-26T20:22:08.2042427Z 1.) Initialize ModelReport object with reports of interest by passing in initialized detector objects and model 2025-08-26T20:22:08.2042560Z 2.) Prepare your model with prepare_fx 2025-08-26T20:22:08.2042817Z 3.) Call model_report.prepare_detailed_calibration to add relevant observers 2025-08-26T20:22:08.2042928Z 4.) Calibrate your model with data 2025-08-26T20:22:08.2043297Z 5.) Call model_report.generate_report on your model to generate report and optionally remove added observers 2025-08-26T20:22:08.2043384Z Optional 2025-08-26T20:22:08.2043655Z 6.) Call model_report.generate_visualizer to get a ModelReportVisualizer instance 2025-08-26T20:22:08.2043915Z 7.) To help in parsing report information and debugging, view report info as a: 2025-08-26T20:22:08.2044005Z - Table 2025-08-26T20:22:08.2044136Z - Histogram 2025-08-26T20:22:08.2044228Z - Line plot 2025-08-26T20:22:08.2044551Z 8.) Call model_report.generate_qconfigs to generate the qconfigs based on the report suggestions 2025-08-26T20:22:08.2044647Z 2025-08-26T20:22:08.2044767Z Example (with QuantizationTracer): 2025-08-26T20:22:08.2044880Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2044992Z >>> # get the necessary qconfig 2025-08-26T20:22:08.2045110Z >>> config = PrepareCustomConfig() 2025-08-26T20:22:08.2045280Z >>> skipped_module_names, skipped_module_classes = ( 2025-08-26T20:22:08.2045443Z ... get_skipped_module_name_and_classes(config, False) 2025-08-26T20:22:08.2045545Z ... ) 2025-08-26T20:22:08.2045627Z 2025-08-26T20:22:08.2045761Z >>> # initialize our model and get GraphModule 2025-08-26T20:22:08.2045874Z >>> model = SomeModel() 2025-08-26T20:22:08.2046128Z >>> tracer = QuantizationTracer(skipped_module_names, skipped_module_classes) 2025-08-26T20:22:08.2046319Z >>> graph_module = GraphModule(model, tracer.trace(model)) 2025-08-26T20:22:08.2046401Z 2025-08-26T20:22:08.2046556Z >>> # get our set of detectors and ModelReport instance 2025-08-26T20:22:08.2046671Z >>> detector_set = set( 2025-08-26T20:22:08.2046754Z ... [ 2025-08-26T20:22:08.2046893Z ... DynamicStaticDetector(tolerance=0.5), 2025-08-26T20:22:08.2047099Z ... InputWeightEqualizationDetector(ratio_threshold=0.7), 2025-08-26T20:22:08.2047181Z ... ] 2025-08-26T20:22:08.2047274Z ... ) 2025-08-26T20:22:08.2047482Z >>> tracer_reporter = ModelReport(graph_module, tracer_detector_set) 2025-08-26T20:22:08.2047560Z 2025-08-26T20:22:08.2047731Z >>> # now we insert the observers and calibrate the model 2025-08-26T20:22:08.2047988Z >>> tracer_model_with_observers = tracer_reporter.prepare_detailed_calibration() 2025-08-26T20:22:08.2048134Z >>> for i in range(num_callibration_batches): 2025-08-26T20:22:08.2048271Z >>> example_input = get_callibration_input() 2025-08-26T20:22:08.2048408Z >>> tracer_model_with_observers(example_input) 2025-08-26T20:22:08.2048524Z 2025-08-26T20:22:08.2048784Z >>> # finally we generate the reports and optionally remove the observers we inserted 2025-08-26T20:22:08.2048948Z >>> reports = tracer_reporter.generate_model_report( 2025-08-26T20:22:08.2049066Z ... remove_inserted_observers=True 2025-08-26T20:22:08.2049147Z ... ) 2025-08-26T20:22:08.2049238Z 2025-08-26T20:22:08.2049463Z >>> # Optional: we can generate the qconfig mapping based on the suggestions 2025-08-26T20:22:08.2049639Z >>> qconfigs = model_report.generate_qconfig_mapping() 2025-08-26T20:22:08.2049719Z 2025-08-26T20:22:08.2049959Z >>> # Optional: we can generate the equalization mapping based on the suggestions 2025-08-26T20:22:08.2050150Z >>> qconfigs = model_report.generate_equalization_mapping() 2025-08-26T20:22:08.2050231Z 2025-08-26T20:22:08.2050570Z >>> # Optional: we get a ModelReportVisualizer instance to do any visualizations desired 2025-08-26T20:22:08.2050782Z >>> model_report_visualizer = tracer_reporter.generate_visualizer() 2025-08-26T20:22:08.2050861Z 2025-08-26T20:22:08.2050951Z 2025-08-26T20:22:08.2051201Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2051291Z 2025-08-26T20:22:08.2051387Z warnings.warn(msg) 2025-08-26T20:22:08.2051465Z 2025-08-26T20:22:08.2051674Z --- Parse Warning: 42 / 146 --- 2025-08-26T20:22:08.2052925Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_filtered_tables in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=301. 2025-08-26T20:22:08.2053233Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2053312Z 2025-08-26T20:22:08.2053577Z Takes in optional filter values and generates two tables with desired information. 2025-08-26T20:22:08.2053673Z 2025-08-26T20:22:08.2053882Z The generated tables are presented in both a list-of-lists format 2025-08-26T20:22:08.2053975Z 2025-08-26T20:22:08.2054182Z The reason for the two tables are that they handle different things: 2025-08-26T20:22:08.2054342Z 1.) the first table handles all tensor level information 2025-08-26T20:22:08.2054570Z 2.) the second table handles and displays all channel based information 2025-08-26T20:22:08.2054650Z 2025-08-26T20:22:08.2054981Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-08-26T20:22:08.2055310Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-08-26T20:22:08.2055668Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-08-26T20:22:08.2055758Z 2025-08-26T20:22:08.2055856Z Tensor table columns: 2025-08-26T20:22:08.2056060Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-08-26T20:22:08.2056215Z ---- --------- --------- --------- --------- --------- 2025-08-26T20:22:08.2056294Z 2025-08-26T20:22:08.2056413Z Per-Channel table columns: 2025-08-26T20:22:08.2056633Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-08-26T20:22:08.2056811Z ---- --------- ------- --------- --------- --------- --------- 2025-08-26T20:22:08.2056889Z 2025-08-26T20:22:08.2056975Z Args: 2025-08-26T20:22:08.2057249Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-26T20:22:08.2057361Z contain this filter substring 2025-08-26T20:22:08.2057539Z Default = "", results in all the features being printed 2025-08-26T20:22:08.2057802Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-26T20:22:08.2058090Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-26T20:22:08.2058182Z 2025-08-26T20:22:08.2058297Z Returns a dictionary with two keys: 2025-08-26T20:22:08.2058482Z (Dict[str, Tuple[List, List]]) A dict containing two keys: 2025-08-26T20:22:08.2058607Z "tensor_level_info", "channel_level_info" 2025-08-26T20:22:08.2058719Z Each key maps to a tuple with: 2025-08-26T20:22:08.2058851Z A list of the headers of each table 2025-08-26T20:22:08.2059033Z A list of lists containing the table information row by row 2025-08-26T20:22:08.2059221Z The 0th index row will contain the headers of the columns 2025-08-26T20:22:08.2059352Z The rest of the rows will contain data 2025-08-26T20:22:08.2059433Z 2025-08-26T20:22:08.2059588Z Example Use: 2025-08-26T20:22:08.2059720Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2059884Z >>> mod_report_visualizer.generate_filtered_tables( 2025-08-26T20:22:08.2060079Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-26T20:22:08.2060428Z ... ) # generates table with per_channel_min info for all modules in block 1 of the model 2025-08-26T20:22:08.2060531Z 2025-08-26T20:22:08.2060783Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2060876Z 2025-08-26T20:22:08.2060972Z warnings.warn(msg) 2025-08-26T20:22:08.2061053Z 2025-08-26T20:22:08.2061263Z --- Parse Warning: 43 / 146 --- 2025-08-26T20:22:08.2062540Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_table_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=399. 2025-08-26T20:22:08.2062853Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2062935Z 2025-08-26T20:22:08.2063207Z Takes in optional filter values and prints out formatted tables of the information. 2025-08-26T20:22:08.2063298Z 2025-08-26T20:22:08.2063637Z The reason for the two tables printed out instead of one large one are that they handle different things: 2025-08-26T20:22:08.2063813Z 1.) the first table handles all tensor level information 2025-08-26T20:22:08.2064029Z 2.) the second table handles and displays all channel based information 2025-08-26T20:22:08.2064107Z 2025-08-26T20:22:08.2064439Z The reasoning for this is that having all the info in one table can make it ambiguous which collected 2025-08-26T20:22:08.2064768Z statistics are global, and which are actually per-channel, so it's better to split it up into two 2025-08-26T20:22:08.2065136Z tables. This also makes the information much easier to digest given the plethora of statistics collected 2025-08-26T20:22:08.2065219Z 2025-08-26T20:22:08.2065316Z Tensor table columns: 2025-08-26T20:22:08.2065519Z idx layer_fqn feature_1 feature_2 feature_3 .... feature_n 2025-08-26T20:22:08.2065668Z ---- --------- --------- --------- --------- --------- 2025-08-26T20:22:08.2065761Z 2025-08-26T20:22:08.2065868Z Per-Channel table columns: 2025-08-26T20:22:08.2065946Z 2025-08-26T20:22:08.2066175Z idx layer_fqn channel feature_1 feature_2 feature_3 .... feature_n 2025-08-26T20:22:08.2066336Z ---- --------- ------- --------- --------- --------- --------- 2025-08-26T20:22:08.2066429Z 2025-08-26T20:22:08.2066512Z Args: 2025-08-26T20:22:08.2066775Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-26T20:22:08.2066902Z contain this filter substring 2025-08-26T20:22:08.2067063Z Default = "", results in all the features being printed 2025-08-26T20:22:08.2067361Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-26T20:22:08.2067607Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-26T20:22:08.2067685Z 2025-08-26T20:22:08.2067786Z Example Use: 2025-08-26T20:22:08.2067916Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2068092Z >>> mod_report_visualizer.generate_table_visualization( 2025-08-26T20:22:08.2068286Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-26T20:22:08.2068367Z ... ) 2025-08-26T20:22:08.2068566Z >>> # prints out neatly formatted table with per_channel_min info 2025-08-26T20:22:08.2068691Z >>> # for all modules in block 1 of the model 2025-08-26T20:22:08.2068784Z 2025-08-26T20:22:08.2069084Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2069163Z 2025-08-26T20:22:08.2069274Z warnings.warn(msg) 2025-08-26T20:22:08.2069352Z 2025-08-26T20:22:08.2069551Z --- Parse Warning: 44 / 146 --- 2025-08-26T20:22:08.2070824Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_plot_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=564. 2025-08-26T20:22:08.2071086Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2071177Z 2025-08-26T20:22:08.2071413Z Takes in a feature and optional module_filter and plots of the desired data. 2025-08-26T20:22:08.2071505Z 2025-08-26T20:22:08.2071804Z For per channel features, it averages the value across the channels and plots a point 2025-08-26T20:22:08.2072063Z per module. The reason for this is that for models with hundreds of channels, it can 2025-08-26T20:22:08.2072357Z be hard to differentiate one channel line from another, and so the point of generating 2025-08-26T20:22:08.2072625Z a single average point per module is to give a sense of general trends that encourage 2025-08-26T20:22:08.2072736Z further deep dives. 2025-08-26T20:22:08.2072817Z 2025-08-26T20:22:08.2072899Z Note: 2025-08-26T20:22:08.2073177Z Only features in the report that have tensor value data are plottable by this class 2025-08-26T20:22:08.2073344Z When the tensor information is plotted, it will plot: 2025-08-26T20:22:08.2073495Z idx as the x val, feature value as the y_val 2025-08-26T20:22:08.2073663Z When the channel information is plotted, it will plot: 2025-08-26T20:22:08.2073927Z the first idx of each module as the x val, feature value as the y_val [for each channel] 2025-08-26T20:22:08.2074169Z The reason for this is that we want to be able to compare values across the 2025-08-26T20:22:08.2074401Z channels for same layer, and it will be hard if values are staggered by idx 2025-08-26T20:22:08.2074586Z This means each module is represented by only 1 x value 2025-08-26T20:22:08.2074669Z Args: 2025-08-26T20:22:08.2074904Z feature_filter (str): Filters the features presented to only those that 2025-08-26T20:22:08.2075014Z contain this filter substring 2025-08-26T20:22:08.2075271Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-26T20:22:08.2075530Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-26T20:22:08.2075609Z 2025-08-26T20:22:08.2075710Z Example Use: 2025-08-26T20:22:08.2075840Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2076004Z >>> mod_report_visualizer.generate_plot_visualization( 2025-08-26T20:22:08.2076214Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-26T20:22:08.2076323Z ... ) 2025-08-26T20:22:08.2076503Z >>> # outputs line plot of per_channel_min information for all 2025-08-26T20:22:08.2076701Z >>> # modules in block1 of model each channel gets it's own line, 2025-08-26T20:22:08.2076878Z >>> # and it's plotted across the in-order modules on the x-axis 2025-08-26T20:22:08.2076973Z 2025-08-26T20:22:08.2077226Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2077306Z 2025-08-26T20:22:08.2077418Z warnings.warn(msg) 2025-08-26T20:22:08.2077498Z 2025-08-26T20:22:08.2077697Z --- Parse Warning: 45 / 146 --- 2025-08-26T20:22:08.2079041Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ModelReportVisualizer.generate_histogram_visualization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/ao/quantization/fx/_model_report/model_report_visualizer.py line=643. 2025-08-26T20:22:08.2079322Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2079403Z 2025-08-26T20:22:08.2079682Z Takes in a feature and optional module_filter and plots the histogram of desired data. 2025-08-26T20:22:08.2079776Z 2025-08-26T20:22:08.2079862Z Note: 2025-08-26T20:22:08.2080143Z Only features in the report that have tensor value data can be viewed as a histogram 2025-08-26T20:22:08.2080409Z If you want to plot a histogram from all the channel values of a specific feature for 2025-08-26T20:22:08.2080656Z a specific model, make sure to specify both the model and the feature properly 2025-08-26T20:22:08.2080915Z in the filters and you should be able to see a distribution of the channel data 2025-08-26T20:22:08.2081022Z 2025-08-26T20:22:08.2081120Z Args: 2025-08-26T20:22:08.2081388Z feature_filter (str, optional): Filters the features presented to only those that 2025-08-26T20:22:08.2081502Z contain this filter substring 2025-08-26T20:22:08.2081683Z Default = "", results in all the features being printed 2025-08-26T20:22:08.2081940Z module_fqn_filter (str, optional): Only includes modules that contains this string 2025-08-26T20:22:08.2082199Z Default = "", results in all the modules in the reports to be visible in the table 2025-08-26T20:22:08.2082419Z num_bins (int, optional): The number of bins to create the histogram with 2025-08-26T20:22:08.2082607Z Default = 10, the values will be split into 10 equal sized bins 2025-08-26T20:22:08.2082702Z 2025-08-26T20:22:08.2082794Z Example Use: 2025-08-26T20:22:08.2082905Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2083204Z >>> mod_report_visualizer.generategenerate_histogram_visualization_plot_visualization( 2025-08-26T20:22:08.2083405Z ... feature_filter="per_channel_min", module_fqn_filter="block1" 2025-08-26T20:22:08.2083504Z ... ) 2025-08-26T20:22:08.2083783Z # outputs histogram of per_channel_min information for all modules in block1 of model 2025-08-26T20:22:08.2084051Z information is gathered across all channels for all modules in block 1 for the 2025-08-26T20:22:08.2084268Z per_channel_min and is displayed in a histogram of equally sized bins 2025-08-26T20:22:08.2084348Z 2025-08-26T20:22:08.2084608Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2084689Z 2025-08-26T20:22:08.2084798Z warnings.warn(msg) 2025-08-26T20:22:08.2084876Z 2025-08-26T20:22:08.2085060Z --- Parse Warning: 46 / 146 --- 2025-08-26T20:22:08.2085982Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=record_function in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/autograd/profiler.py line=734. 2025-08-26T20:22:08.2086246Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2086667Z Context manager/function decorator that adds a label to a code block/function when running autograd profiler. 2025-08-26T20:22:08.2086850Z Label will only appear if CPU activity tracing is enabled. 2025-08-26T20:22:08.2086929Z 2025-08-26T20:22:08.2087072Z It is useful when tracing the code profile. 2025-08-26T20:22:08.2087150Z 2025-08-26T20:22:08.2087245Z Args: 2025-08-26T20:22:08.2087388Z name (str): Label assigned to the block of code. 2025-08-26T20:22:08.2087570Z node_id (int): ID of node, for distributed profiling. Unset in 2025-08-26T20:22:08.2087686Z non-distributed cases. 2025-08-26T20:22:08.2087765Z 2025-08-26T20:22:08.2087865Z Example: 2025-08-26T20:22:08.2088046Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_AUTOGRAD_PROFILER) 2025-08-26T20:22:08.2088227Z >>> x = torch.randn((1, 1), requires_grad=True) 2025-08-26T20:22:08.2088404Z >>> with torch.autograd.profiler.profile() as prof: 2025-08-26T20:22:08.2088500Z ... y = x**2 2025-08-26T20:22:08.2088673Z ... with torch.autograd.profiler.record_function( 2025-08-26T20:22:08.2088766Z ... "label-z" 2025-08-26T20:22:08.2088866Z ... ): # label the block 2025-08-26T20:22:08.2088971Z ... z = y**3 2025-08-26T20:22:08.2089064Z ... y.backward() 2025-08-26T20:22:08.2089183Z >>> # xdoctest: +IGNORE_WANT 2025-08-26T20:22:08.2089320Z >>> # NOTE: some columns were removed for brevity 2025-08-26T20:22:08.2089524Z >>> print(prof.key_averages().table(sort_by="self_cpu_time_total")) 2025-08-26T20:22:08.2089734Z ----------------------------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2089996Z Name Self CPU total % CPU time avg Number of Calls 2025-08-26T20:22:08.2090201Z ----------------------------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2090335Z pow 60.77% 47.470us 3 2025-08-26T20:22:08.2090465Z mul 21.73% 25.465us 2 2025-08-26T20:22:08.2090653Z PowBackward0 12.03% 121.891us 1 2025-08-26T20:22:08.2090876Z torch::autograd::AccumulateGrad 2.70% 6.324us 1 2025-08-26T20:22:08.2091033Z label-z 2.13% 12.421us 1 2025-08-26T20:22:08.2091234Z torch::autograd::GraphRoot 0.64% 1.503us 1 2025-08-26T20:22:08.2091439Z ----------------------------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2091550Z Self CPU time total: 234.344us 2025-08-26T20:22:08.2091655Z CUDA time total: 0.000us 2025-08-26T20:22:08.2091943Z 2025-08-26T20:22:08.2092024Z 2025-08-26T20:22:08.2092280Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2092375Z 2025-08-26T20:22:08.2092472Z warnings.warn(msg) 2025-08-26T20:22:08.2092564Z 2025-08-26T20:22:08.2093991Z --- Parse Warning: 47 / 146 --- 2025-08-26T20:22:08.2094961Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DeviceMesh.__getitem__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/device_mesh.py line=721. 2025-08-26T20:22:08.2095210Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.2095294Z 2025-08-26T20:22:08.2095585Z Slice the current DeviceMesh based on the mesh_dim_names given to create a submesh. 2025-08-26T20:22:08.2095850Z The submesh created consists of the dimensions and the communicators indicated by 2025-08-26T20:22:08.2096056Z ``mesh_dim_names`` 2025-08-26T20:22:08.2096137Z 2025-08-26T20:22:08.2096222Z Args: 2025-08-26T20:22:08.2096470Z mesh_dim_names (Union[str, Tuple[str]]): the name or the tuple of names of the 2025-08-26T20:22:08.2096661Z mesh dimension of the DeviceMesh to create the submesh for. 2025-08-26T20:22:08.2096761Z Returns: 2025-08-26T20:22:08.2096868Z A :class:`DeviceMesh` object 2025-08-26T20:22:08.2096949Z 2025-08-26T20:22:08.2097248Z The following program runs on each process/rank in an SPMD manner in a world size of 8. 2025-08-26T20:22:08.2097348Z In the first example: 2025-08-26T20:22:08.2097599Z Calling mesh_2d["tp"] on rank 0, 1, 2, 3 returns a 1D submesh of DeviceMesh:([0, 1, 2, 3]). 2025-08-26T20:22:08.2097936Z Calling mesh_2d["tp"] on rank 4, 5, 6, 7 returns a 1D submesh of DeviceMesh:([4, 5, 6, 7]). 2025-08-26T20:22:08.2098165Z Calling mesh_2d["dp"] on rank 0, 4 returns a 1D submesh of DeviceMesh:([0, 4]). 2025-08-26T20:22:08.2098399Z Calling mesh_2d["dp"] on rank 1, 5 returns a 1D submesh of DeviceMesh:([1, 5]). 2025-08-26T20:22:08.2098618Z Calling mesh_2d["dp"] on rank 2, 6 returns a 1D submesh of DeviceMesh:([2, 6]). 2025-08-26T20:22:08.2098848Z Calling mesh_2d["dp"] on rank 3, 7 returns a 1D submesh of DeviceMesh:([3, 7]). 2025-08-26T20:22:08.2098927Z 2025-08-26T20:22:08.2099026Z In the second example: 2025-08-26T20:22:08.2099301Z Calling mesh_3d["dp", "cp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 1], [4, 5]]). 2025-08-26T20:22:08.2099564Z Calling mesh_3d["dp", "cp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 3], [6, 7]]). 2025-08-26T20:22:08.2099879Z Calling mesh_3d["cp", "dp"] on rank 0, 1, 4, 5 returns a 2D submesh of DeviceMesh:([[0, 4], [1, 5]]). 2025-08-26T20:22:08.2100137Z Calling mesh_3d["cp", "dp"] on rank 2, 3, 6, 7 returns a 2D submesh of DeviceMesh:([[2, 6], [3, 7]]). 2025-08-26T20:22:08.2100218Z 2025-08-26T20:22:08.2100321Z Example:: 2025-08-26T20:22:08.2100501Z 2025-08-26T20:22:08.2100629Z >>> # xdoctest: +SKIP("no rank") 2025-08-26T20:22:08.2100802Z >>> from torch.distributed.device_mesh import DeviceMesh 2025-08-26T20:22:08.2100884Z >>> 2025-08-26T20:22:08.2101100Z >>> # Initialize a 2D device mesh as (2, 4) to represent the topology 2025-08-26T20:22:08.2101243Z >>> # of cross-host(dim 0), and within-host (dim 1). 2025-08-26T20:22:08.2101506Z >>> mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-08-26T20:22:08.2101607Z >>> tp_mesh = mesh_2d["tp"] 2025-08-26T20:22:08.2101705Z >>> dp_mesh = mesh_2d["dp"] 2025-08-26T20:22:08.2101802Z >>> 2025-08-26T20:22:08.2101901Z >>> # Initialize a 3D mesh. 2025-08-26T20:22:08.2102192Z >>> mesh_3d = init_device_mesh(device_type="cuda", (2,2,2), mesh_dim_names=("dp", "pp", "cp")) 2025-08-26T20:22:08.2102499Z >>> # The order of the mesh_dim_names provided deteremines the order of dimensions in the submesh. 2025-08-26T20:22:08.2102615Z >>> dp_cp_mesh = mesh_3d["dp", "cp"] 2025-08-26T20:22:08.2102739Z >>> cp_dp_mesh = mesh_3d["cp", "dp"] 2025-08-26T20:22:08.2102819Z 2025-08-26T20:22:08.2103507Z Original Error: SyntaxError('positional argument follows keyword argument', ('', 6, 82, 'mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp"))\n', 6, 83)) 2025-08-26T20:22:08.2103589Z 2025-08-26T20:22:08.2103831Z mesh_2d = init_device_mesh(device_type="cuda", (2,4), mesh_dim_names=("dp", "tp")) 2025-08-26T20:22:08.2103961Z ^ 2025-08-26T20:22:08.2104061Z warnings.warn(msg) 2025-08-26T20:22:08.2104151Z 2025-08-26T20:22:08.2104352Z --- Parse Warning: 48 / 146 --- 2025-08-26T20:22:08.2105316Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=batch_isend_irecv in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2710. 2025-08-26T20:22:08.2105626Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2105708Z 2025-08-26T20:22:08.2105970Z Send or Receive a batch of tensors asynchronously and return a list of requests. 2025-08-26T20:22:08.2106050Z 2025-08-26T20:22:08.2106297Z Process each of the operations in ``p2p_op_list`` and return the corresponding 2025-08-26T20:22:08.2106505Z requests. NCCL, Gloo, and UCC backend are currently supported. 2025-08-26T20:22:08.2106584Z 2025-08-26T20:22:08.2106681Z Args: 2025-08-26T20:22:08.2106956Z p2p_op_list: A list of point-to-point operations(type of each operator is 2025-08-26T20:22:08.2107185Z ``torch.distributed.P2POp``). The order of the isend/irecv in the list 2025-08-26T20:22:08.2107408Z matters and it needs to match with corresponding isend/irecv on the 2025-08-26T20:22:08.2107499Z remote end. 2025-08-26T20:22:08.2107591Z 2025-08-26T20:22:08.2107675Z Returns: 2025-08-26T20:22:08.2107921Z A list of distributed request objects returned by calling the corresponding 2025-08-26T20:22:08.2108036Z op in the op_list. 2025-08-26T20:22:08.2108114Z 2025-08-26T20:22:08.2108213Z Examples: 2025-08-26T20:22:08.2108325Z >>> # xdoctest: +SKIP("no rank") 2025-08-26T20:22:08.2108515Z >>> send_tensor = torch.arange(2, dtype=torch.float32) + 2 * rank 2025-08-26T20:22:08.2108681Z >>> recv_tensor = torch.randn(2, dtype=torch.float32) 2025-08-26T20:22:08.2108920Z >>> send_op = dist.P2POp(dist.isend, send_tensor, (rank + 1) % world_size) 2025-08-26T20:22:08.2109039Z >>> recv_op = dist.P2POp( 2025-08-26T20:22:08.2109228Z ... dist.irecv, recv_tensor, (rank - 1 + world_size) % world_size 2025-08-26T20:22:08.2109311Z ... ) 2025-08-26T20:22:08.2109455Z >>> reqs = batch_isend_irecv([send_op, recv_op]) 2025-08-26T20:22:08.2109548Z >>> for req in reqs: 2025-08-26T20:22:08.2109651Z >>> req.wait() 2025-08-26T20:22:08.2109738Z >>> recv_tensor 2025-08-26T20:22:08.2109839Z tensor([2, 3]) # Rank 0 2025-08-26T20:22:08.2109964Z tensor([0, 1]) # Rank 1 2025-08-26T20:22:08.2110045Z 2025-08-26T20:22:08.2110285Z .. note:: Note that when this API is used with the NCCL PG backend, users must set 2025-08-26T20:22:08.2110521Z the current GPU device with `torch.cuda.set_device`, otherwise it will 2025-08-26T20:22:08.2110634Z lead to unexpected hang issues. 2025-08-26T20:22:08.2110730Z 2025-08-26T20:22:08.2110941Z In addition, if this API is the first collective call in the ``group`` 2025-08-26T20:22:08.2111160Z passed to ``dist.P2POp``, all ranks of the ``group`` must participate in 2025-08-26T20:22:08.2111403Z this API call; otherwise, the behavior is undefined. If this API call is 2025-08-26T20:22:08.2111622Z not the first collective call in the ``group``, batched P2P operations 2025-08-26T20:22:08.2111830Z involving only a subset of ranks of the ``group`` are allowed. 2025-08-26T20:22:08.2111911Z 2025-08-26T20:22:08.2112172Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2112254Z 2025-08-26T20:22:08.2112352Z warnings.warn(msg) 2025-08-26T20:22:08.2112447Z 2025-08-26T20:22:08.2112639Z --- Parse Warning: 49 / 146 --- 2025-08-26T20:22:08.2113575Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_reduce in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=2842. 2025-08-26T20:22:08.2113849Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2113962Z 2025-08-26T20:22:08.2114247Z Reduces the tensor data across all machines in a way that all get the final result. 2025-08-26T20:22:08.2114330Z 2025-08-26T20:22:08.2114571Z After the call ``tensor`` is going to be bitwise identical in all processes. 2025-08-26T20:22:08.2114654Z 2025-08-26T20:22:08.2114765Z Complex tensors are supported. 2025-08-26T20:22:08.2114860Z 2025-08-26T20:22:08.2114945Z Args: 2025-08-26T20:22:08.2115150Z tensor (Tensor): Input and output of the collective. The function 2025-08-26T20:22:08.2115267Z operates in-place. 2025-08-26T20:22:08.2115386Z op (optional): One of the values from 2025-08-26T20:22:08.2115521Z ``torch.distributed.ReduceOp`` 2025-08-26T20:22:08.2115724Z enum. Specifies an operation used for element-wise reductions. 2025-08-26T20:22:08.2116009Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:22:08.2116156Z the default process group will be used. 2025-08-26T20:22:08.2116351Z async_op (bool, optional): Whether this op should be an async op 2025-08-26T20:22:08.2116443Z 2025-08-26T20:22:08.2116532Z Returns: 2025-08-26T20:22:08.2116674Z Async work handle, if async_op is set to True. 2025-08-26T20:22:08.2116837Z None, if not async_op or if not part of the group 2025-08-26T20:22:08.2116921Z 2025-08-26T20:22:08.2117019Z Examples: 2025-08-26T20:22:08.2117126Z >>> # xdoctest: +SKIP("no rank") 2025-08-26T20:22:08.2117259Z >>> # All tensors below are of torch.int64 type. 2025-08-26T20:22:08.2117391Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:22:08.2117511Z >>> device = torch.device(f"cuda:{rank}") 2025-08-26T20:22:08.2117784Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-08-26T20:22:08.2117869Z >>> tensor 2025-08-26T20:22:08.2117983Z tensor([1, 2], device='cuda:0') # Rank 0 2025-08-26T20:22:08.2118110Z tensor([3, 4], device='cuda:1') # Rank 1 2025-08-26T20:22:08.2118242Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-08-26T20:22:08.2118336Z >>> tensor 2025-08-26T20:22:08.2118448Z tensor([4, 6], device='cuda:0') # Rank 0 2025-08-26T20:22:08.2118559Z tensor([4, 6], device='cuda:1') # Rank 1 2025-08-26T20:22:08.2118649Z 2025-08-26T20:22:08.2118786Z >>> # All tensors below are of torch.cfloat type. 2025-08-26T20:22:08.2118902Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:22:08.2119016Z >>> tensor = torch.tensor( 2025-08-26T20:22:08.2119162Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-08-26T20:22:08.2119269Z ... ) + 2 * rank * (1 + 1j) 2025-08-26T20:22:08.2119353Z >>> tensor 2025-08-26T20:22:08.2119496Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-08-26T20:22:08.2119646Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-08-26T20:22:08.2119782Z >>> dist.all_reduce(tensor, op=ReduceOp.SUM) 2025-08-26T20:22:08.2119877Z >>> tensor 2025-08-26T20:22:08.2120010Z tensor([4.+4.j, 6.+6.j], device='cuda:0') # Rank 0 2025-08-26T20:22:08.2120146Z tensor([4.+4.j, 6.+6.j], device='cuda:1') # Rank 1 2025-08-26T20:22:08.2120243Z 2025-08-26T20:22:08.2120328Z 2025-08-26T20:22:08.2120595Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2120682Z 2025-08-26T20:22:08.2120777Z warnings.warn(msg) 2025-08-26T20:22:08.2120870Z 2025-08-26T20:22:08.2121058Z --- Parse Warning: 50 / 146 --- 2025-08-26T20:22:08.2122027Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=gather_object in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3202. 2025-08-26T20:22:08.2122292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2122406Z 2025-08-26T20:22:08.2122642Z Gathers picklable objects from the whole group in a single process. 2025-08-26T20:22:08.2122721Z 2025-08-26T20:22:08.2122966Z Similar to :func:`gather`, but Python objects can be passed in. Note that the 2025-08-26T20:22:08.2123115Z object must be picklable in order to be gathered. 2025-08-26T20:22:08.2123194Z 2025-08-26T20:22:08.2123289Z Args: 2025-08-26T20:22:08.2123421Z obj (Any): Input object. Must be picklable. 2025-08-26T20:22:08.2123642Z object_gather_list (list[Any]): Output list. On the ``dst`` rank, it 2025-08-26T20:22:08.2123825Z should be correctly sized as the size of the group for this 2025-08-26T20:22:08.2124044Z collective and will contain the output. Must be ``None`` on non-dst 2025-08-26T20:22:08.2124227Z ranks. (default is ``None``) 2025-08-26T20:22:08.2124549Z dst (int, optional): Destination rank on global process group (regardless of ``group`` argument). 2025-08-26T20:22:08.2124757Z (If both ``dst`` and ``group_dst`` are None, default is global rank 0) 2025-08-26T20:22:08.2124987Z group: (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:22:08.2125176Z the default process group will be used. Default is ``None``. 2025-08-26T20:22:08.2125534Z group_dst (int, optional): Destination rank on ``group``. Invalid to specify both ``dst`` and ``group_dst`` 2025-08-26T20:22:08.2125616Z 2025-08-26T20:22:08.2125715Z Returns: 2025-08-26T20:22:08.2125897Z None. On the ``dst`` rank, ``object_gather_list`` will contain the 2025-08-26T20:22:08.2126002Z output of the collective. 2025-08-26T20:22:08.2126123Z 2025-08-26T20:22:08.2126342Z .. note:: Note that this API differs slightly from the gather collective 2025-08-26T20:22:08.2126572Z since it does not provide an async_op handle and thus will be a blocking 2025-08-26T20:22:08.2126657Z call. 2025-08-26T20:22:08.2126738Z 2025-08-26T20:22:08.2126981Z .. note:: For NCCL-based processed groups, internal tensor representations 2025-08-26T20:22:08.2127191Z of objects must be moved to the GPU device before communication takes 2025-08-26T20:22:08.2127350Z place. In this case, the device used is given by 2025-08-26T20:22:08.2127570Z ``torch.cuda.current_device()`` and it is the user's responsibility to 2025-08-26T20:22:08.2127774Z ensure that this is set so that each rank has an individual GPU, via 2025-08-26T20:22:08.2127892Z ``torch.cuda.set_device()``. 2025-08-26T20:22:08.2127971Z 2025-08-26T20:22:08.2128070Z .. warning:: 2025-08-26T20:22:08.2128306Z Object collectives have a number of serious performance and scalability 2025-08-26T20:22:08.2128486Z limitations. See :ref:`object_collectives` for details. 2025-08-26T20:22:08.2128577Z 2025-08-26T20:22:08.2128664Z .. warning:: 2025-08-26T20:22:08.2128874Z :func:`gather_object` uses ``pickle`` module implicitly, which is 2025-08-26T20:22:08.2129091Z known to be insecure. It is possible to construct malicious pickle data 2025-08-26T20:22:08.2129305Z which will execute arbitrary code during unpickling. Only call this 2025-08-26T20:22:08.2129427Z function with data you trust. 2025-08-26T20:22:08.2129507Z 2025-08-26T20:22:08.2129606Z .. warning:: 2025-08-26T20:22:08.2129817Z Calling :func:`gather_object` with GPU tensors is not well supported 2025-08-26T20:22:08.2130042Z and inefficient as it incurs GPU -> CPU transfer since tensors would be 2025-08-26T20:22:08.2130222Z pickled. Please consider using :func:`gather` instead. 2025-08-26T20:22:08.2130305Z 2025-08-26T20:22:08.2130404Z Example:: 2025-08-26T20:22:08.2130542Z >>> # xdoctest: +SKIP("need process group init") 2025-08-26T20:22:08.2130720Z >>> # Note: Process group initialization omitted on each rank. 2025-08-26T20:22:08.2130879Z >>> import torch.distributed as dist 2025-08-26T20:22:08.2130985Z >>> # Assumes world_size of 3. 2025-08-26T20:22:08.2131171Z >>> gather_objects = ["foo", 12, {1: 2}] # any picklable object 2025-08-26T20:22:08.2131292Z >>> output = [None for _ in gather_objects] 2025-08-26T20:22:08.2131390Z >>> dist.gather_object( 2025-08-26T20:22:08.2131523Z ... gather_objects[dist.get_rank()], 2025-08-26T20:22:08.2131654Z ... output if dist.get_rank() == 0 else None, 2025-08-26T20:22:08.2131752Z ... dst=0 2025-08-26T20:22:08.2131834Z ... ) 2025-08-26T20:22:08.2131929Z >>> # On rank 0 2025-08-26T20:22:08.2132025Z >>> output 2025-08-26T20:22:08.2132115Z ['foo', 12, {1: 2}] 2025-08-26T20:22:08.2132196Z 2025-08-26T20:22:08.2132514Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2132596Z 2025-08-26T20:22:08.2132704Z warnings.warn(msg) 2025-08-26T20:22:08.2132783Z 2025-08-26T20:22:08.2132977Z --- Parse Warning: 51 / 146 --- 2025-08-26T20:22:08.2133930Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_gather in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=3798. 2025-08-26T20:22:08.2134193Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2134285Z 2025-08-26T20:22:08.2134427Z Gathers tensors from the whole group in a list. 2025-08-26T20:22:08.2134509Z 2025-08-26T20:22:08.2134662Z Complex and uneven sized tensors are supported. 2025-08-26T20:22:08.2134741Z 2025-08-26T20:22:08.2134905Z Args: 2025-08-26T20:22:08.2135085Z tensor_list (list[Tensor]): Output list. It should contain 2025-08-26T20:22:08.2135305Z correctly-sized tensors to be used for output of the collective. 2025-08-26T20:22:08.2135440Z Uneven sized tensors are supported. 2025-08-26T20:22:08.2135629Z tensor (Tensor): Tensor to be broadcast from current process. 2025-08-26T20:22:08.2135872Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:22:08.2135997Z the default process group will be used. 2025-08-26T20:22:08.2136190Z async_op (bool, optional): Whether this op should be an async op 2025-08-26T20:22:08.2136288Z 2025-08-26T20:22:08.2136371Z Returns: 2025-08-26T20:22:08.2136528Z Async work handle, if async_op is set to True. 2025-08-26T20:22:08.2136676Z None, if not async_op or if not part of the group 2025-08-26T20:22:08.2136755Z 2025-08-26T20:22:08.2136856Z Examples: 2025-08-26T20:22:08.2136996Z >>> # xdoctest: +SKIP("need process group init") 2025-08-26T20:22:08.2137146Z >>> # All tensors below are of torch.int64 dtype. 2025-08-26T20:22:08.2137265Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:22:08.2137397Z >>> device = torch.device(f"cuda:{rank}") 2025-08-26T20:22:08.2137502Z >>> tensor_list = [ 2025-08-26T20:22:08.2137712Z ... torch.zeros(2, dtype=torch.int64, device=device) for _ in range(2) 2025-08-26T20:22:08.2137806Z ... ] 2025-08-26T20:22:08.2137897Z >>> tensor_list 2025-08-26T20:22:08.2138096Z [tensor([0, 0], device='cuda:0'), tensor([0, 0], device='cuda:0')] # Rank 0 2025-08-26T20:22:08.2144990Z [tensor([0, 0], device='cuda:1'), tensor([0, 0], device='cuda:1')] # Rank 1 2025-08-26T20:22:08.2145297Z >>> tensor = torch.arange(2, dtype=torch.int64, device=device) + 1 + 2 * rank 2025-08-26T20:22:08.2145384Z >>> tensor 2025-08-26T20:22:08.2145505Z tensor([1, 2], device='cuda:0') # Rank 0 2025-08-26T20:22:08.2145632Z tensor([3, 4], device='cuda:1') # Rank 1 2025-08-26T20:22:08.2145772Z >>> dist.all_gather(tensor_list, tensor) 2025-08-26T20:22:08.2145863Z >>> tensor_list 2025-08-26T20:22:08.2146161Z [tensor([1, 2], device='cuda:0'), tensor([3, 4], device='cuda:0')] # Rank 0 2025-08-26T20:22:08.2146372Z [tensor([1, 2], device='cuda:1'), tensor([3, 4], device='cuda:1')] # Rank 1 2025-08-26T20:22:08.2146454Z 2025-08-26T20:22:08.2146616Z >>> # All tensors below are of torch.cfloat dtype. 2025-08-26T20:22:08.2146738Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:22:08.2146835Z >>> tensor_list = [ 2025-08-26T20:22:08.2147067Z ... torch.zeros(2, dtype=torch.cfloat, device=device) for _ in range(2) 2025-08-26T20:22:08.2147151Z ... ] 2025-08-26T20:22:08.2147261Z >>> tensor_list 2025-08-26T20:22:08.2147517Z [tensor([0.+0.j, 0.+0.j], device='cuda:0'), tensor([0.+0.j, 0.+0.j], device='cuda:0')] # Rank 0 2025-08-26T20:22:08.2147818Z [tensor([0.+0.j, 0.+0.j], device='cuda:1'), tensor([0.+0.j, 0.+0.j], device='cuda:1')] # Rank 1 2025-08-26T20:22:08.2147939Z >>> tensor = torch.tensor( 2025-08-26T20:22:08.2148088Z ... [1 + 1j, 2 + 2j], dtype=torch.cfloat, device=device 2025-08-26T20:22:08.2148200Z ... ) + 2 * rank * (1 + 1j) 2025-08-26T20:22:08.2148286Z >>> tensor 2025-08-26T20:22:08.2148425Z tensor([1.+1.j, 2.+2.j], device='cuda:0') # Rank 0 2025-08-26T20:22:08.2148575Z tensor([3.+3.j, 4.+4.j], device='cuda:1') # Rank 1 2025-08-26T20:22:08.2148699Z >>> dist.all_gather(tensor_list, tensor) 2025-08-26T20:22:08.2148803Z >>> tensor_list 2025-08-26T20:22:08.2149045Z [tensor([1.+1.j, 2.+2.j], device='cuda:0'), tensor([3.+3.j, 4.+4.j], device='cuda:0')] # Rank 0 2025-08-26T20:22:08.2149284Z [tensor([1.+1.j, 2.+2.j], device='cuda:1'), tensor([3.+3.j, 4.+4.j], device='cuda:1')] # Rank 1 2025-08-26T20:22:08.2149411Z 2025-08-26T20:22:08.2149489Z 2025-08-26T20:22:08.2149765Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2149845Z 2025-08-26T20:22:08.2149945Z warnings.warn(msg) 2025-08-26T20:22:08.2150036Z 2025-08-26T20:22:08.2150277Z --- Parse Warning: 52 / 146 --- 2025-08-26T20:22:08.2151254Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all_single in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4504. 2025-08-26T20:22:08.2151517Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2151597Z 2025-08-26T20:22:08.2151864Z Split input tensor and then scatter the split list to all processes in a group. 2025-08-26T20:22:08.2151945Z 2025-08-26T20:22:08.2152223Z Later the received tensors are concatenated from all the processes in the group 2025-08-26T20:22:08.2152347Z and returned as a single output tensor. 2025-08-26T20:22:08.2152430Z 2025-08-26T20:22:08.2152557Z Complex tensors are supported. 2025-08-26T20:22:08.2152634Z 2025-08-26T20:22:08.2152724Z Args: 2025-08-26T20:22:08.2152903Z output (Tensor): Gathered concatenated output tensor. 2025-08-26T20:22:08.2153027Z input (Tensor): Input tensor to scatter. 2025-08-26T20:22:08.2153262Z output_split_sizes: (list[Int], optional): Output split sizes for dim 0 2025-08-26T20:22:08.2153467Z if specified None or empty, dim 0 of ``output`` tensor must divide 2025-08-26T20:22:08.2153573Z equally by ``world_size``. 2025-08-26T20:22:08.2153797Z input_split_sizes: (list[Int], optional): Input split sizes for dim 0 2025-08-26T20:22:08.2153994Z if specified None or empty, dim 0 of ``input`` tensor must divide 2025-08-26T20:22:08.2154114Z equally by ``world_size``. 2025-08-26T20:22:08.2154343Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:22:08.2154473Z the default process group will be used. 2025-08-26T20:22:08.2154686Z async_op (bool, optional): Whether this op should be an async op. 2025-08-26T20:22:08.2154798Z 2025-08-26T20:22:08.2154895Z Returns: 2025-08-26T20:22:08.2155036Z Async work handle, if async_op is set to True. 2025-08-26T20:22:08.2155183Z None, if not async_op or if not part of the group. 2025-08-26T20:22:08.2155275Z 2025-08-26T20:22:08.2155372Z .. warning:: 2025-08-26T20:22:08.2155559Z `all_to_all_single` is experimental and subject to change. 2025-08-26T20:22:08.2155641Z 2025-08-26T20:22:08.2155727Z Examples: 2025-08-26T20:22:08.2155859Z >>> # xdoctest: +SKIP("Undefined rank") 2025-08-26T20:22:08.2155973Z >>> input = torch.arange(4) + rank * 4 2025-08-26T20:22:08.2156057Z >>> input 2025-08-26T20:22:08.2156169Z tensor([0, 1, 2, 3]) # Rank 0 2025-08-26T20:22:08.2156269Z tensor([4, 5, 6, 7]) # Rank 1 2025-08-26T20:22:08.2156424Z tensor([8, 9, 10, 11]) # Rank 2 2025-08-26T20:22:08.2156522Z tensor([12, 13, 14, 15]) # Rank 3 2025-08-26T20:22:08.2156666Z >>> output = torch.empty([4], dtype=torch.int64) 2025-08-26T20:22:08.2156800Z >>> dist.all_to_all_single(output, input) 2025-08-26T20:22:08.2156885Z >>> output 2025-08-26T20:22:08.2156995Z tensor([0, 4, 8, 12]) # Rank 0 2025-08-26T20:22:08.2157093Z tensor([1, 5, 9, 13]) # Rank 1 2025-08-26T20:22:08.2157193Z tensor([2, 6, 10, 14]) # Rank 2 2025-08-26T20:22:08.2157301Z tensor([3, 7, 11, 15]) # Rank 3 2025-08-26T20:22:08.2157381Z 2025-08-26T20:22:08.2157553Z >>> # Essentially, it is similar to following operation: 2025-08-26T20:22:08.2157691Z >>> scatter_list = list(input.chunk(world_size)) 2025-08-26T20:22:08.2157828Z >>> gather_list = list(output.chunk(world_size)) 2025-08-26T20:22:08.2157972Z >>> for i in range(world_size): 2025-08-26T20:22:08.2158202Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src = i) 2025-08-26T20:22:08.2158295Z 2025-08-26T20:22:08.2158416Z >>> # Another example with uneven split 2025-08-26T20:22:08.2158500Z >>> input 2025-08-26T20:22:08.2158673Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-08-26T20:22:08.2158831Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-08-26T20:22:08.2158996Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-08-26T20:22:08.2159149Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-08-26T20:22:08.2159241Z >>> input_splits 2025-08-26T20:22:08.2159374Z [2, 2, 1, 1] # Rank 0 2025-08-26T20:22:08.2159492Z [3, 2, 2, 2] # Rank 1 2025-08-26T20:22:08.2159627Z [2, 1, 1, 1] # Rank 2 2025-08-26T20:22:08.2159744Z [2, 2, 2, 1] # Rank 3 2025-08-26T20:22:08.2159840Z >>> output_splits 2025-08-26T20:22:08.2159969Z [2, 3, 2, 2] # Rank 0 2025-08-26T20:22:08.2160086Z [2, 2, 1, 2] # Rank 1 2025-08-26T20:22:08.2160215Z [1, 2, 1, 2] # Rank 2 2025-08-26T20:22:08.2160332Z [1, 2, 1, 1] # Rank 3 2025-08-26T20:22:08.2160424Z >>> output = ... 2025-08-26T20:22:08.2160638Z >>> dist.all_to_all_single(output, input, output_splits, input_splits) 2025-08-26T20:22:08.2160720Z >>> output 2025-08-26T20:22:08.2160885Z tensor([ 0, 1, 10, 11, 12, 20, 21, 30, 31]) # Rank 0 2025-08-26T20:22:08.2161042Z tensor([ 2, 3, 13, 14, 22, 32, 33]) # Rank 1 2025-08-26T20:22:08.2161193Z tensor([ 4, 15, 16, 23, 34, 35]) # Rank 2 2025-08-26T20:22:08.2161388Z tensor([ 5, 17, 18, 24, 36]) # Rank 3 2025-08-26T20:22:08.2161466Z 2025-08-26T20:22:08.2161557Z 2025-08-26T20:22:08.2161717Z >>> # Another example with tensors of torch.cfloat type. 2025-08-26T20:22:08.2161820Z >>> input = torch.tensor( 2025-08-26T20:22:08.2161971Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-08-26T20:22:08.2162070Z ... ) + 4 * rank * (1 + 1j) 2025-08-26T20:22:08.2162167Z >>> input 2025-08-26T20:22:08.2162341Z tensor([1+1j, 2+2j, 3+3j, 4+4j]) # Rank 0 2025-08-26T20:22:08.2162509Z tensor([5+5j, 6+6j, 7+7j, 8+8j]) # Rank 1 2025-08-26T20:22:08.2162760Z tensor([9+9j, 10+10j, 11+11j, 12+12j]) # Rank 2 2025-08-26T20:22:08.2162943Z tensor([13+13j, 14+14j, 15+15j, 16+16j]) # Rank 3 2025-08-26T20:22:08.2163100Z >>> output = torch.empty([4], dtype=torch.int64) 2025-08-26T20:22:08.2163225Z >>> dist.all_to_all_single(output, input) 2025-08-26T20:22:08.2163309Z >>> output 2025-08-26T20:22:08.2163492Z tensor([1+1j, 5+5j, 9+9j, 13+13j]) # Rank 0 2025-08-26T20:22:08.2163665Z tensor([2+2j, 6+6j, 10+10j, 14+14j]) # Rank 1 2025-08-26T20:22:08.2163851Z tensor([3+3j, 7+7j, 11+11j, 15+15j]) # Rank 2 2025-08-26T20:22:08.2164021Z tensor([4+4j, 8+8j, 12+12j, 16+16j]) # Rank 3 2025-08-26T20:22:08.2164099Z 2025-08-26T20:22:08.2164361Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2164465Z 2025-08-26T20:22:08.2164577Z warnings.warn(msg) 2025-08-26T20:22:08.2164660Z 2025-08-26T20:22:08.2164863Z --- Parse Warning: 53 / 146 --- 2025-08-26T20:22:08.2165809Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=all_to_all in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/distributed_c10d.py line=4646. 2025-08-26T20:22:08.2166071Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2166165Z 2025-08-26T20:22:08.2166532Z Scatters list of input tensors to all processes in a group and return gathered list of tensors in output list. 2025-08-26T20:22:08.2166611Z 2025-08-26T20:22:08.2166736Z Complex tensors are supported. 2025-08-26T20:22:08.2166818Z 2025-08-26T20:22:08.2166912Z Args: 2025-08-26T20:22:08.2167130Z output_tensor_list (list[Tensor]): List of tensors to be gathered one 2025-08-26T20:22:08.2167221Z per rank. 2025-08-26T20:22:08.2167464Z input_tensor_list (list[Tensor]): List of tensors to scatter one per rank. 2025-08-26T20:22:08.2167695Z group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:22:08.2167835Z the default process group will be used. 2025-08-26T20:22:08.2168030Z async_op (bool, optional): Whether this op should be an async op. 2025-08-26T20:22:08.2168111Z 2025-08-26T20:22:08.2168207Z Returns: 2025-08-26T20:22:08.2168345Z Async work handle, if async_op is set to True. 2025-08-26T20:22:08.2168503Z None, if not async_op or if not part of the group. 2025-08-26T20:22:08.2168582Z 2025-08-26T20:22:08.2168672Z .. warning:: 2025-08-26T20:22:08.2168832Z `all_to_all` is experimental and subject to change. 2025-08-26T20:22:08.2168910Z 2025-08-26T20:22:08.2169008Z Examples: 2025-08-26T20:22:08.2169128Z >>> # xdoctest: +SKIP("Undefined rank") 2025-08-26T20:22:08.2169244Z >>> input = torch.arange(4) + rank * 4 2025-08-26T20:22:08.2169368Z >>> input = list(input.chunk(4)) 2025-08-26T20:22:08.2169451Z >>> input 2025-08-26T20:22:08.2169649Z [tensor([0]), tensor([1]), tensor([2]), tensor([3])] # Rank 0 2025-08-26T20:22:08.2169824Z [tensor([4]), tensor([5]), tensor([6]), tensor([7])] # Rank 1 2025-08-26T20:22:08.2169987Z [tensor([8]), tensor([9]), tensor([10]), tensor([11])] # Rank 2 2025-08-26T20:22:08.2170163Z [tensor([12]), tensor([13]), tensor([14]), tensor([15])] # Rank 3 2025-08-26T20:22:08.2170342Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-08-26T20:22:08.2170466Z >>> dist.all_to_all(output, input) 2025-08-26T20:22:08.2170549Z >>> output 2025-08-26T20:22:08.2170712Z [tensor([0]), tensor([4]), tensor([8]), tensor([12])] # Rank 0 2025-08-26T20:22:08.2170888Z [tensor([1]), tensor([5]), tensor([9]), tensor([13])] # Rank 1 2025-08-26T20:22:08.2171098Z [tensor([2]), tensor([6]), tensor([10]), tensor([14])] # Rank 2 2025-08-26T20:22:08.2171271Z [tensor([3]), tensor([7]), tensor([11]), tensor([15])] # Rank 3 2025-08-26T20:22:08.2171350Z 2025-08-26T20:22:08.2171512Z >>> # Essentially, it is similar to following operation: 2025-08-26T20:22:08.2171626Z >>> scatter_list = input 2025-08-26T20:22:08.2171723Z >>> gather_list = output 2025-08-26T20:22:08.2171842Z >>> for i in range(world_size): 2025-08-26T20:22:08.2172062Z >>> dist.scatter(gather_list[i], scatter_list if i == rank else [], src=i) 2025-08-26T20:22:08.2172141Z 2025-08-26T20:22:08.2172238Z >>> input 2025-08-26T20:22:08.2172391Z tensor([0, 1, 2, 3, 4, 5]) # Rank 0 2025-08-26T20:22:08.2172548Z tensor([10, 11, 12, 13, 14, 15, 16, 17, 18]) # Rank 1 2025-08-26T20:22:08.2172740Z tensor([20, 21, 22, 23, 24]) # Rank 2 2025-08-26T20:22:08.2172900Z tensor([30, 31, 32, 33, 34, 35, 36]) # Rank 3 2025-08-26T20:22:08.2173005Z >>> input_splits 2025-08-26T20:22:08.2173128Z [2, 2, 1, 1] # Rank 0 2025-08-26T20:22:08.2173258Z [3, 2, 2, 2] # Rank 1 2025-08-26T20:22:08.2173374Z [2, 1, 1, 1] # Rank 2 2025-08-26T20:22:08.2173504Z [2, 2, 2, 1] # Rank 3 2025-08-26T20:22:08.2173610Z >>> output_splits 2025-08-26T20:22:08.2173727Z [2, 3, 2, 2] # Rank 0 2025-08-26T20:22:08.2173856Z [2, 2, 1, 2] # Rank 1 2025-08-26T20:22:08.2173973Z [1, 2, 1, 2] # Rank 2 2025-08-26T20:22:08.2174096Z [1, 2, 1, 1] # Rank 3 2025-08-26T20:22:08.2174238Z >>> input = list(input.split(input_splits)) 2025-08-26T20:22:08.2174324Z >>> input 2025-08-26T20:22:08.2174538Z [tensor([0, 1]), tensor([2, 3]), tensor([4]), tensor([5])] # Rank 0 2025-08-26T20:22:08.2174737Z [tensor([10, 11, 12]), tensor([13, 14]), tensor([15, 16]), tensor([17, 18])] # Rank 1 2025-08-26T20:22:08.2174940Z [tensor([20, 21]), tensor([22]), tensor([23]), tensor([24])] # Rank 2 2025-08-26T20:22:08.2175151Z [tensor([30, 31]), tensor([32, 33]), tensor([34, 35]), tensor([36])] # Rank 3 2025-08-26T20:22:08.2175242Z >>> output = ... 2025-08-26T20:22:08.2175370Z >>> dist.all_to_all(output, input) 2025-08-26T20:22:08.2175455Z >>> output 2025-08-26T20:22:08.2175654Z [tensor([0, 1]), tensor([10, 11, 12]), tensor([20, 21]), tensor([30, 31])] # Rank 0 2025-08-26T20:22:08.2175868Z [tensor([2, 3]), tensor([13, 14]), tensor([22]), tensor([32, 33])] # Rank 1 2025-08-26T20:22:08.2176069Z [tensor([4]), tensor([15, 16]), tensor([23]), tensor([34, 35])] # Rank 2 2025-08-26T20:22:08.2176308Z [tensor([5]), tensor([17, 18]), tensor([24]), tensor([36])] # Rank 3 2025-08-26T20:22:08.2176388Z 2025-08-26T20:22:08.2176548Z >>> # Another example with tensors of torch.cfloat type. 2025-08-26T20:22:08.2176665Z >>> input = torch.tensor( 2025-08-26T20:22:08.2176803Z ... [1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=torch.cfloat 2025-08-26T20:22:08.2176913Z ... ) + 4 * rank * (1 + 1j) 2025-08-26T20:22:08.2177024Z >>> input = list(input.chunk(4)) 2025-08-26T20:22:08.2177106Z >>> input 2025-08-26T20:22:08.2177334Z [tensor([1+1j]), tensor([2+2j]), tensor([3+3j]), tensor([4+4j])] # Rank 0 2025-08-26T20:22:08.2177544Z [tensor([5+5j]), tensor([6+6j]), tensor([7+7j]), tensor([8+8j])] # Rank 1 2025-08-26T20:22:08.2177820Z [tensor([9+9j]), tensor([10+10j]), tensor([11+11j]), tensor([12+12j])] # Rank 2 2025-08-26T20:22:08.2178045Z [tensor([13+13j]), tensor([14+14j]), tensor([15+15j]), tensor([16+16j])] # Rank 3 2025-08-26T20:22:08.2178240Z >>> output = list(torch.empty([4], dtype=torch.int64).chunk(4)) 2025-08-26T20:22:08.2178353Z >>> dist.all_to_all(output, input) 2025-08-26T20:22:08.2178436Z >>> output 2025-08-26T20:22:08.2178657Z [tensor([1+1j]), tensor([5+5j]), tensor([9+9j]), tensor([13+13j])] # Rank 0 2025-08-26T20:22:08.2178863Z [tensor([2+2j]), tensor([6+6j]), tensor([10+10j]), tensor([14+14j])] # Rank 1 2025-08-26T20:22:08.2179082Z [tensor([3+3j]), tensor([7+7j]), tensor([11+11j]), tensor([15+15j])] # Rank 2 2025-08-26T20:22:08.2179290Z [tensor([4+4j]), tensor([8+8j]), tensor([12+12j]), tensor([16+16j])] # Rank 3 2025-08-26T20:22:08.2179393Z 2025-08-26T20:22:08.2179485Z 2025-08-26T20:22:08.2179745Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2179839Z 2025-08-26T20:22:08.2179936Z warnings.warn(msg) 2025-08-26T20:22:08.2180014Z 2025-08-26T20:22:08.2180231Z --- Parse Warning: 54 / 146 --- 2025-08-26T20:22:08.2181184Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=__doc__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/launch.py line=2. 2025-08-26T20:22:08.2181463Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2181545Z 2025-08-26T20:22:08.2181664Z Module ``torch.distributed.launch``. 2025-08-26T20:22:08.2181760Z 2025-08-26T20:22:08.2182010Z ``torch.distributed.launch`` is a module that spawns up multiple distributed 2025-08-26T20:22:08.2182180Z training processes on each of the training nodes. 2025-08-26T20:22:08.2182262Z 2025-08-26T20:22:08.2182361Z .. warning:: 2025-08-26T20:22:08.2182452Z 2025-08-26T20:22:08.2182706Z This module is going to be deprecated in favor of :ref:`torchrun `. 2025-08-26T20:22:08.2182785Z 2025-08-26T20:22:08.2183038Z The utility can be used for single-node distributed training, in which one or 2025-08-26T20:22:08.2183275Z more processes per node will be spawned. The utility can be used for either 2025-08-26T20:22:08.2183503Z CPU training or GPU training. If the utility is used for GPU training, 2025-08-26T20:22:08.2183749Z each distributed process will be operating on a single GPU. This can achieve 2025-08-26T20:22:08.2183979Z well-improved single-node training performance. It can also be used in 2025-08-26T20:22:08.2184257Z multi-node distributed training, by spawning up multiple processes on each node 2025-08-26T20:22:08.2184491Z for well-improved multi-node distributed training performance as well. 2025-08-26T20:22:08.2184734Z This will especially be beneficial for systems with multiple Infiniband 2025-08-26T20:22:08.2184986Z interfaces that have direct-GPU support, since all of them can be utilized for 2025-08-26T20:22:08.2185148Z aggregated communication bandwidth. 2025-08-26T20:22:08.2185228Z 2025-08-26T20:22:08.2185459Z In both cases of single-node distributed training or multi-node distributed 2025-08-26T20:22:08.2185707Z training, this utility will launch the given number of processes per node 2025-08-26T20:22:08.2185931Z (``--nproc-per-node``). If used for GPU training, this number needs to be less 2025-08-26T20:22:08.2186160Z or equal to the number of GPUs on the current system (``nproc_per_node``), 2025-08-26T20:22:08.2186360Z and each process will be operating on a single GPU from *GPU 0 to 2025-08-26T20:22:08.2186466Z GPU (nproc_per_node - 1)*. 2025-08-26T20:22:08.2186563Z 2025-08-26T20:22:08.2186666Z **How to use this module:** 2025-08-26T20:22:08.2186759Z 2025-08-26T20:22:08.2186962Z 1. Single-Node multi-process distributed training 2025-08-26T20:22:08.2187042Z 2025-08-26T20:22:08.2187138Z :: 2025-08-26T20:22:08.2187217Z 2025-08-26T20:22:08.2187452Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-26T20:22:08.2187650Z YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 and all other 2025-08-26T20:22:08.2187775Z arguments of your training script) 2025-08-26T20:22:08.2187866Z 2025-08-26T20:22:08.2188075Z 2. Multi-Node multi-process distributed training: (e.g. two nodes) 2025-08-26T20:22:08.2188156Z 2025-08-26T20:22:08.2188246Z 2025-08-26T20:22:08.2188391Z Node 1: *(IP: 192.168.1.1, and has a free port: 1234)* 2025-08-26T20:22:08.2188478Z 2025-08-26T20:22:08.2188558Z :: 2025-08-26T20:22:08.2188636Z 2025-08-26T20:22:08.2188912Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-26T20:22:08.2189077Z --nnodes=2 --node-rank=0 --master-addr="192.168.1.1" 2025-08-26T20:22:08.2189295Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-08-26T20:22:08.2189447Z and all other arguments of your training script) 2025-08-26T20:22:08.2189525Z 2025-08-26T20:22:08.2189618Z Node 2: 2025-08-26T20:22:08.2189697Z 2025-08-26T20:22:08.2189779Z :: 2025-08-26T20:22:08.2189869Z 2025-08-26T20:22:08.2190097Z python -m torch.distributed.launch --nproc-per-node=NUM_GPUS_YOU_HAVE 2025-08-26T20:22:08.2190263Z --nnodes=2 --node-rank=1 --master-addr="192.168.1.1" 2025-08-26T20:22:08.2190469Z --master-port=1234 YOUR_TRAINING_SCRIPT.py (--arg1 --arg2 --arg3 2025-08-26T20:22:08.2190620Z and all other arguments of your training script) 2025-08-26T20:22:08.2190715Z 2025-08-26T20:22:08.2190878Z 3. To look up what optional arguments this module offers: 2025-08-26T20:22:08.2190968Z 2025-08-26T20:22:08.2191052Z :: 2025-08-26T20:22:08.2191130Z 2025-08-26T20:22:08.2191280Z python -m torch.distributed.launch --help 2025-08-26T20:22:08.2191361Z 2025-08-26T20:22:08.2191456Z 2025-08-26T20:22:08.2191557Z **Important Notices:** 2025-08-26T20:22:08.2191634Z 2025-08-26T20:22:08.2192001Z 1. This utility and multi-process distributed (single-node or 2025-08-26T20:22:08.2192250Z multi-node) GPU training currently only achieves the best performance using 2025-08-26T20:22:08.2192514Z the NCCL distributed backend. Thus NCCL backend is the recommended backend to 2025-08-26T20:22:08.2192614Z use for GPU training. 2025-08-26T20:22:08.2192692Z 2025-08-26T20:22:08.2192920Z 2. In your training program, you must parse the command-line argument: 2025-08-26T20:22:08.2193150Z ``--local-rank=LOCAL_PROCESS_RANK``, which will be provided by this module. 2025-08-26T20:22:08.2193398Z If your training program uses GPUs, you should ensure that your code only 2025-08-26T20:22:08.2193592Z runs on the GPU device of LOCAL_PROCESS_RANK. This can be done by: 2025-08-26T20:22:08.2193768Z 2025-08-26T20:22:08.2193888Z Parsing the local_rank argument 2025-08-26T20:22:08.2193966Z 2025-08-26T20:22:08.2194048Z :: 2025-08-26T20:22:08.2194139Z 2025-08-26T20:22:08.2194236Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2194344Z >>> import argparse 2025-08-26T20:22:08.2194471Z >>> parser = argparse.ArgumentParser() 2025-08-26T20:22:08.2194666Z >>> parser.add_argument("--local-rank", "--local_rank", type=int) 2025-08-26T20:22:08.2194792Z >>> args = parser.parse_args() 2025-08-26T20:22:08.2194872Z 2025-08-26T20:22:08.2195009Z Set your device to local rank using either 2025-08-26T20:22:08.2195092Z 2025-08-26T20:22:08.2195173Z :: 2025-08-26T20:22:08.2195267Z 2025-08-26T20:22:08.2195469Z >>> torch.cuda.set_device(args.local_rank) # before your code runs 2025-08-26T20:22:08.2195549Z 2025-08-26T20:22:08.2195710Z or 2025-08-26T20:22:08.2195789Z 2025-08-26T20:22:08.2195888Z :: 2025-08-26T20:22:08.2195969Z 2025-08-26T20:22:08.2196105Z >>> with torch.cuda.device(args.local_rank): 2025-08-26T20:22:08.2196216Z >>> # your code to run 2025-08-26T20:22:08.2196298Z >>> ... 2025-08-26T20:22:08.2196390Z 2025-08-26T20:22:08.2196496Z .. versionchanged:: 2.0.0 2025-08-26T20:22:08.2196577Z 2025-08-26T20:22:08.2196837Z The launcher will passes the ``--local-rank=`` argument to your script. 2025-08-26T20:22:08.2197076Z From PyTorch 2.0.0 onwards, the dashed ``--local-rank`` is preferred over the 2025-08-26T20:22:08.2197233Z previously used underscored ``--local_rank``. 2025-08-26T20:22:08.2197315Z 2025-08-26T20:22:08.2197550Z For backward compatibility, it may be necessary for users to handle both 2025-08-26T20:22:08.2197871Z cases in their argument parsing code. This means including both ``"--local-rank"`` 2025-08-26T20:22:08.2198083Z and ``"--local_rank"`` in the argument parser. If only ``"--local_rank"`` is 2025-08-26T20:22:08.2198346Z provided, the launcher will trigger an error: "error: unrecognized arguments: 2025-08-26T20:22:08.2198575Z --local-rank=". For training code that only supports PyTorch 2.0.0+, 2025-08-26T20:22:08.2198724Z including ``"--local-rank"`` should be sufficient. 2025-08-26T20:22:08.2198815Z 2025-08-26T20:22:08.2199047Z 3. In your training program, you are supposed to call the following function 2025-08-26T20:22:08.2199297Z at the beginning to start the distributed backend. It is strongly recommended 2025-08-26T20:22:08.2199516Z that ``init_method=env://``. Other init methods (e.g. ``tcp://``) may work, 2025-08-26T20:22:08.2199708Z but ``env://`` is the one that is officially supported by this module. 2025-08-26T20:22:08.2199804Z 2025-08-26T20:22:08.2199884Z :: 2025-08-26T20:22:08.2199975Z 2025-08-26T20:22:08.2200182Z >>> torch.distributed.init_process_group(backend='YOUR BACKEND', 2025-08-26T20:22:08.2200319Z >>> init_method='env://') 2025-08-26T20:22:08.2200413Z 2025-08-26T20:22:08.2200647Z 4. In your training program, you can either use regular distributed functions 2025-08-26T20:22:08.2200893Z or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your 2025-08-26T20:22:08.2201099Z training program uses GPUs for training and you would like to use 2025-08-26T20:22:08.2201288Z :func:`torch.nn.parallel.DistributedDataParallel` module, 2025-08-26T20:22:08.2201406Z here is how to configure it. 2025-08-26T20:22:08.2201486Z 2025-08-26T20:22:08.2201582Z :: 2025-08-26T20:22:08.2201662Z 2025-08-26T20:22:08.2201852Z >>> model = torch.nn.parallel.DistributedDataParallel(model, 2025-08-26T20:22:08.2202002Z >>> device_ids=[args.local_rank], 2025-08-26T20:22:08.2202146Z >>> output_device=args.local_rank) 2025-08-26T20:22:08.2202249Z 2025-08-26T20:22:08.2202500Z Please ensure that ``device_ids`` argument is set to be the only GPU device id 2025-08-26T20:22:08.2202730Z that your code will be operating on. This is generally the local rank of the 2025-08-26T20:22:08.2202976Z process. In other words, the ``device_ids`` needs to be ``[args.local_rank]``, 2025-08-26T20:22:08.2203191Z and ``output_device`` needs to be ``args.local_rank`` in order to use this 2025-08-26T20:22:08.2203284Z utility 2025-08-26T20:22:08.2203366Z 2025-08-26T20:22:08.2203608Z 5. Another way to pass ``local_rank`` to the subprocesses via environment variable 2025-08-26T20:22:08.2203831Z ``LOCAL_RANK``. This behavior is enabled when you launch the script with 2025-08-26T20:22:08.2204053Z ``--use-env=True``. You must adjust the subprocess example above to replace 2025-08-26T20:22:08.2204315Z ``args.local_rank`` with ``os.environ['LOCAL_RANK']``; the launcher 2025-08-26T20:22:08.2204487Z will not pass ``--local-rank`` when you specify this flag. 2025-08-26T20:22:08.2204570Z 2025-08-26T20:22:08.2204668Z .. warning:: 2025-08-26T20:22:08.2204749Z 2025-08-26T20:22:08.2204945Z ``local_rank`` is NOT globally unique: it is only unique per process 2025-08-26T20:22:08.2205145Z on a machine. Thus, don't use it to decide if you should, e.g., 2025-08-26T20:22:08.2205266Z write to a networked filesystem. See 2025-08-26T20:22:08.2205494Z https://github.com/pytorch/pytorch/issues/12042 for an example of 2025-08-26T20:22:08.2205658Z how things can go wrong if you don't do this correctly. 2025-08-26T20:22:08.2205737Z 2025-08-26T20:22:08.2205830Z 2025-08-26T20:22:08.2205909Z 2025-08-26T20:22:08.2206001Z 2025-08-26T20:22:08.2206282Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2206366Z 2025-08-26T20:22:08.2206474Z warnings.warn(msg) 2025-08-26T20:22:08.2206570Z 2025-08-26T20:22:08.2206797Z --- Parse Warning: 55 / 146 --- 2025-08-26T20:22:08.2207850Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=init_from_local_shards in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/__init__.py line=361. 2025-08-26T20:22:08.2208127Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2208210Z 2025-08-26T20:22:08.2208464Z Creates an :class:`ShardedTensor` from local shards and the global metadata. 2025-08-26T20:22:08.2208614Z Needs to be called on all ranks in an SPMD fashion. 2025-08-26T20:22:08.2208695Z 2025-08-26T20:22:08.2208792Z Args: 2025-08-26T20:22:08.2209072Z local_shards (List[:class `torch.distributed._shard.sharded_tensor.Shard`]): A list 2025-08-26T20:22:08.2209258Z of shards that represent the local shards on this rank. 2025-08-26T20:22:08.2209486Z global_size (int...): a list, tuple, or `torch.Size` of integers defining the 2025-08-26T20:22:08.2209610Z shape of the overall sharded tensor. 2025-08-26T20:22:08.2209700Z 2025-08-26T20:22:08.2209789Z Keyword args: 2025-08-26T20:22:08.2210065Z process_group (ProcessGroup, optional): The process group to work on. If None, 2025-08-26T20:22:08.2210190Z the default process group will be used. 2025-08-26T20:22:08.2210364Z init_rrefs (bool, optional): Whether or not to initialize 2025-08-26T20:22:08.2210583Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-08-26T20:22:08.2210777Z Need to initialize the RPC Framework if specified as ``True``. 2025-08-26T20:22:08.2210886Z Default: ``False``. 2025-08-26T20:22:08.2210967Z 2025-08-26T20:22:08.2211051Z Returns: 2025-08-26T20:22:08.2211216Z A :class:`ShardedTensor` object handle on this rank 2025-08-26T20:22:08.2211294Z 2025-08-26T20:22:08.2211415Z 2025-08-26T20:22:08.2211502Z Examples: 2025-08-26T20:22:08.2211753Z Suppose we want construct a sharded tensor on two ranks, global size = (10, 5), 2025-08-26T20:22:08.2211947Z each shard have a (5, 5) local tensor, we can do it like below: 2025-08-26T20:22:08.2212027Z 2025-08-26T20:22:08.2212111Z on rank 0: 2025-08-26T20:22:08.2212242Z >>> # xdoctest: +SKIP("not distributed") 2025-08-26T20:22:08.2212365Z >>> local_shard_metadata = ShardMetadata( 2025-08-26T20:22:08.2212478Z >>> shard_offsets=[0, 0], 2025-08-26T20:22:08.2212580Z >>> shard_lengths=[5, 5], 2025-08-26T20:22:08.2212686Z >>> placement="rank:0/cuda:0" 2025-08-26T20:22:08.2212782Z >>> ) 2025-08-26T20:22:08.2212977Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-08-26T20:22:08.2213228Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-08-26T20:22:08.2213308Z 2025-08-26T20:22:08.2213398Z on rank 1: 2025-08-26T20:22:08.2213528Z >>> # xdoctest: +SKIP("not distributed") 2025-08-26T20:22:08.2213650Z >>> local_shard_metadata = ShardMetadata( 2025-08-26T20:22:08.2213764Z >>> shard_offsets=[5, 0], 2025-08-26T20:22:08.2213864Z >>> shard_lengths=[5, 5], 2025-08-26T20:22:08.2213969Z >>> placement="rank:1/cuda:1" 2025-08-26T20:22:08.2214062Z >>> ) 2025-08-26T20:22:08.2214254Z >>> local_shards = [Shard(torch.randn(5, 5), local_shard_metadata)] 2025-08-26T20:22:08.2214458Z >>> sharded_tensor = init_from_local_shards(local_shards, [10, 5]) 2025-08-26T20:22:08.2214535Z 2025-08-26T20:22:08.2214785Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2214903Z 2025-08-26T20:22:08.2215002Z warnings.warn(msg) 2025-08-26T20:22:08.2215096Z 2025-08-26T20:22:08.2215292Z --- Parse Warning: 56 / 146 --- 2025-08-26T20:22:08.2216383Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor._init_from_local_tensor in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=835. 2025-08-26T20:22:08.2216660Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2216741Z 2025-08-26T20:22:08.2217008Z Initialize a ShardedTensor given only one local tensor, global sharded tensor 2025-08-26T20:22:08.2217120Z size and sharding spec on each rank. 2025-08-26T20:22:08.2217202Z 2025-08-26T20:22:08.2217298Z Args: 2025-08-26T20:22:08.2217522Z local_tensor (Tensor): Single tensor of local shard stored in each rank. 2025-08-26T20:22:08.2217795Z sharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): 2025-08-26T20:22:08.2217968Z The specification describing how to shard the Tensor. 2025-08-26T20:22:08.2218137Z global_size (Sequence[int]): Size of the sharded tensor. 2025-08-26T20:22:08.2218400Z process_group (ProcessGroup, optional): The process group to aggregate on. 2025-08-26T20:22:08.2218495Z Default: None 2025-08-26T20:22:08.2218679Z init_rrefs (bool, optional): Whether or not to initialize 2025-08-26T20:22:08.2218885Z :class:`torch.distributed.rpc.RRef`s pointing to remote shards. 2025-08-26T20:22:08.2219079Z Need to initialize the RPC Framework if specified as ``True``. 2025-08-26T20:22:08.2219188Z Default: ``False``. 2025-08-26T20:22:08.2219268Z 2025-08-26T20:22:08.2219365Z Returns: 2025-08-26T20:22:08.2219608Z A :class:`ShardedTensor` sharded based on the given sharding_spec with local 2025-08-26T20:22:08.2219728Z tensor stored in the current rank. 2025-08-26T20:22:08.2219820Z 2025-08-26T20:22:08.2219909Z Examples: 2025-08-26T20:22:08.2220019Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2220183Z >>> # All tensors below are of torch.int64 type. 2025-08-26T20:22:08.2220301Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:22:08.2220576Z >>> tensor = torch.arange(2, dtype=torch.int64) + 1 + 2 * rank 2025-08-26T20:22:08.2220777Z >>> local_tensor = torch.unsqueeze(torch.cat([tensor, tensor + 2])) 2025-08-26T20:22:08.2220880Z >>> local_tensor 2025-08-26T20:22:08.2220978Z tensor([[1, 2, 3, 4]]) # Rank 0 2025-08-26T20:22:08.2221075Z tensor([[3, 4, 5, 6]]) # Rank 1 2025-08-26T20:22:08.2221183Z >>> sharding_dim = 0 2025-08-26T20:22:08.2221305Z >>> sharding_spec = ChunkShardingSpec( 2025-08-26T20:22:08.2221404Z dim=sharding_dim, 2025-08-26T20:22:08.2221511Z placements=[ 2025-08-26T20:22:08.2221610Z "rank:0/cuda:0", 2025-08-26T20:22:08.2221769Z "rank:1/cuda:1", 2025-08-26T20:22:08.2221853Z ], 2025-08-26T20:22:08.2221935Z ) 2025-08-26T20:22:08.2222086Z >>> st = ShardedTensor._init_from_local_tensor( 2025-08-26T20:22:08.2222211Z ... local_tensor, sharding_spec, [2, 4] 2025-08-26T20:22:08.2222303Z ... ) 2025-08-26T20:22:08.2222387Z >>> st 2025-08-26T20:22:08.2222481Z ShardedTensor( 2025-08-26T20:22:08.2222598Z ShardedTensorMetadata( 2025-08-26T20:22:08.2222697Z shards_metadata=[ 2025-08-26T20:22:08.2222981Z ShardMetadata(shard_offsets=[0, 0], shard_sizes=[1, 4], placement=rank:0/cuda:0), 2025-08-26T20:22:08.2223245Z ShardMetadata(shard_offsets=[1, 0], shard_sizes=[1, 4], placement=rank:1/cuda:1), 2025-08-26T20:22:08.2223332Z ], 2025-08-26T20:22:08.2223448Z size=torch.Size([2, 4]) 2025-08-26T20:22:08.2223572Z ) 2025-08-26T20:22:08.2223684Z >>> st.local_tensor() 2025-08-26T20:22:08.2223788Z tensor([1, 2, 3, 4]) # Rank 0 2025-08-26T20:22:08.2223884Z tensor([3, 4, 5, 6]) # Rank 1 2025-08-26T20:22:08.2223982Z 2025-08-26T20:22:08.2224250Z Warning: This API is experimental and subject to change. It lacks of a fully across 2025-08-26T20:22:08.2224490Z rank validations, and we only validate the local shard on the current rank. 2025-08-26T20:22:08.2224717Z We fully rely on the user to ensure local tensor is sharded based on the 2025-08-26T20:22:08.2224817Z sharding spec. 2025-08-26T20:22:08.2224910Z 2025-08-26T20:22:08.2225162Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2225241Z 2025-08-26T20:22:08.2225348Z warnings.warn(msg) 2025-08-26T20:22:08.2225427Z 2025-08-26T20:22:08.2225637Z --- Parse Warning: 57 / 146 --- 2025-08-26T20:22:08.2226694Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardedTensor.reshard in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharded_tensor/api.py line=1076. 2025-08-26T20:22:08.2226969Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2227048Z 2025-08-26T20:22:08.2227299Z Reshard a sharded tensor given the ``resharding_spec``. For now, we only support 2025-08-26T20:22:08.2227403Z single local shard. 2025-08-26T20:22:08.2227484Z 2025-08-26T20:22:08.2227704Z If ``resharding_spec`` is same as the original one, this becomes a no-op. 2025-08-26T20:22:08.2227955Z If only ``resharding_spec`` shares the same sharding dim with the original one, 2025-08-26T20:22:08.2228062Z we swap local shards directly. 2025-08-26T20:22:08.2228331Z For more generic cases, we merge different shards across different ranks and split 2025-08-26T20:22:08.2228582Z the local shards based on the ``resharding_spec`` via `all_to_all` collective API. 2025-08-26T20:22:08.2228677Z 2025-08-26T20:22:08.2228758Z Args: 2025-08-26T20:22:08.2229044Z resharding_spec (:class:`torch.distributed._shard.sharding_spec.ShardingSpec`): The 2025-08-26T20:22:08.2229254Z specification describing how the tensor is sharded. 2025-08-26T20:22:08.2229335Z 2025-08-26T20:22:08.2229419Z Returns: 2025-08-26T20:22:08.2229633Z A :class:`ShardedTensor` object whose local shards are resharded. 2025-08-26T20:22:08.2229712Z 2025-08-26T20:22:08.2229810Z Examples: 2025-08-26T20:22:08.2229906Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2230024Z >>> # We have 2 process groups, 2 ranks. 2025-08-26T20:22:08.2230206Z >>> tensor = torch.arange(4, dtype=torch.int64) + 1 + 2 * rank 2025-08-26T20:22:08.2230332Z >>> tensor = torch.stack([tensor, tensor]) 2025-08-26T20:22:08.2230427Z >>> tensor 2025-08-26T20:22:08.2230549Z tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) # Rank 0 2025-08-26T20:22:08.2230750Z tensor([[3, 4, 5, 6], [3, 4, 5, 6]]) # Rank 1 2025-08-26T20:22:08.2230879Z tensor([[5, 6, 7, 8], [5, 6, 7, 8]]) # Rank 2 2025-08-26T20:22:08.2231002Z tensor([[7, 8, 9, 10], [7, 8, 9, 10]]) # Rank 3 2025-08-26T20:22:08.2231109Z >>> sharding_dim = 0 2025-08-26T20:22:08.2231219Z >>> spec = ChunkShardingSpec( 2025-08-26T20:22:08.2231316Z dim=sharding_dim, 2025-08-26T20:22:08.2231424Z placements=[ 2025-08-26T20:22:08.2231518Z "rank:0/cuda:0", 2025-08-26T20:22:08.2231625Z "rank:1/cuda:1", 2025-08-26T20:22:08.2231715Z "rank:2/cuda:2", 2025-08-26T20:22:08.2231805Z "rank:3/cuda:3", 2025-08-26T20:22:08.2231901Z ], 2025-08-26T20:22:08.2231981Z ) 2025-08-26T20:22:08.2232085Z >>> current_offsets = [0] * 2 2025-08-26T20:22:08.2232231Z >>> current_offsets[0] = rank * 2 2025-08-26T20:22:08.2232349Z >>> shard_metadata = ShardMetadata( 2025-08-26T20:22:08.2232511Z shard_offsets=copy.deepcopy(current_offsets), 2025-08-26T20:22:08.2232623Z shard_sizes=tensor.size(), 2025-08-26T20:22:08.2232746Z placement=spec.placements[rank], 2025-08-26T20:22:08.2232840Z ) 2025-08-26T20:22:08.2232937Z >>> local_shards = [ 2025-08-26T20:22:08.2233037Z Shard( 2025-08-26T20:22:08.2233150Z tensor=tensor, 2025-08-26T20:22:08.2233261Z metadata=shard_metadata, 2025-08-26T20:22:08.2233358Z ) 2025-08-26T20:22:08.2233438Z ] 2025-08-26T20:22:08.2233680Z >>> st = ShardedTensor._init_from_local_shards(local_shards, tensor.size()) 2025-08-26T20:22:08.2233778Z >>> sharding_dim = 1 2025-08-26T20:22:08.2233902Z >>> resharding_spec = ChunkShardingSpec( 2025-08-26T20:22:08.2234015Z dim=sharding_dim, 2025-08-26T20:22:08.2234110Z placements=[ 2025-08-26T20:22:08.2234218Z "rank:0/cuda:0", 2025-08-26T20:22:08.2234311Z "rank:1/cuda:1", 2025-08-26T20:22:08.2234404Z "rank:2/cuda:2", 2025-08-26T20:22:08.2234508Z "rank:3/cuda:3", 2025-08-26T20:22:08.2234590Z ], 2025-08-26T20:22:08.2234672Z ) 2025-08-26T20:22:08.2234794Z >>> st.reshard(resharding_spec) 2025-08-26T20:22:08.2234910Z >>> tensor = st.local_shards()[0].tensor 2025-08-26T20:22:08.2235007Z >>> tensor 2025-08-26T20:22:08.2235149Z tensor([[1], [1], [3], [3], [5], [5], [7], [7]]) # Rank 0 2025-08-26T20:22:08.2235287Z tensor([[2], [2], [4], [4], [6], [6], [8], [8]]) # Rank 1 2025-08-26T20:22:08.2235437Z tensor([[3], [3], [5], [5], [7], [7], [9], [9]]) # Rank 2 2025-08-26T20:22:08.2235576Z tensor([[4], [4], [6], [6], [8], [8], [10], [10]]) # Rank 3 2025-08-26T20:22:08.2235671Z 2025-08-26T20:22:08.2235927Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2236005Z 2025-08-26T20:22:08.2236114Z warnings.warn(msg) 2025-08-26T20:22:08.2236221Z 2025-08-26T20:22:08.2236425Z --- Parse Warning: 58 / 146 --- 2025-08-26T20:22:08.2237410Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ShardingPlan in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/_shard/sharding_plan/api.py line=12. 2025-08-26T20:22:08.2237673Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2237768Z 2025-08-26T20:22:08.2237985Z Representation of a sharding plan, describes how to shard a module 2025-08-26T20:22:08.2238271Z across hosts. `plan` is used to shard module parameters according to the spec provided, 2025-08-26T20:22:08.2238558Z `output_plan` and `return_local_tensor` are optional, they are used to specify the output 2025-08-26T20:22:08.2238856Z layout of a module with a spec, and when to convert back to data parallel fashion. 2025-08-26T20:22:08.2238951Z 2025-08-26T20:22:08.2239032Z Args: 2025-08-26T20:22:08.2239312Z plan (Dict[str, Union[:class:`torch.distributed._shard.sharding_spec.ShardingSpec`, 2025-08-26T20:22:08.2239479Z :class:`torch.distributed._shard.sharder.Sharder`]): 2025-08-26T20:22:08.2239751Z a dict describes how to shard a module, there're currently two ways to shard a module: 2025-08-26T20:22:08.2240013Z 1. directly shard a module parameter by a `ShardingSpec`, keyed by the name of 2025-08-26T20:22:08.2240134Z a parameter to a `ShardingSpec`. 2025-08-26T20:22:08.2240401Z 2. shard a submodule by applying a `Sharder` on it, keyed by the name of a module 2025-08-26T20:22:08.2240505Z to a `Sharder` object. 2025-08-26T20:22:08.2240874Z output_plan (Dict[str, :class:`torch.distributed._shard.sharding_spec.ShardingSpec`), optional): 2025-08-26T20:22:08.2241134Z a dict specifies the layout of a module's output which produces a ShardedTensor, 2025-08-26T20:22:08.2241370Z keyed by the name of module to ShardingSpec("" in key means the root module). 2025-08-26T20:22:08.2241477Z Default: `None` 2025-08-26T20:22:08.2241728Z return_local_tensor (List[str], optional): a list of string, each element enables 2025-08-26T20:22:08.2241977Z a module's sharded output to be returned as a Tensor from its local shards to 2025-08-26T20:22:08.2242226Z ensure further processing in a data parallel fashion. ("" in list means the 2025-08-26T20:22:08.2242319Z root module). 2025-08-26T20:22:08.2242422Z Default: None 2025-08-26T20:22:08.2242506Z Example: 2025-08-26T20:22:08.2242800Z Suppose we want to shard a module with two linear layers and then run it with DDP, we also 2025-08-26T20:22:08.2243086Z want to convert the output of the second linear layer back to DDP, we can do it as follows: 2025-08-26T20:22:08.2243165Z 2025-08-26T20:22:08.2243348Z >>> # xdoctest: +REQUIRES(module:torch._C._distributed_c10d) 2025-08-26T20:22:08.2243457Z >>> class MyModule(nn.Module): 2025-08-26T20:22:08.2243581Z >>> def __init__(self) -> None: 2025-08-26T20:22:08.2243681Z >>> super().__init__() 2025-08-26T20:22:08.2243784Z >>> self.fc1 = nn.Linear() 2025-08-26T20:22:08.2243897Z >>> self.gelu = nn.GELU() 2025-08-26T20:22:08.2244000Z >>> self.fc2 = nn.Linear() 2025-08-26T20:22:08.2244116Z >>> self.relu = nn.Linear() 2025-08-26T20:22:08.2244197Z >>> 2025-08-26T20:22:08.2244301Z >>> def forward(self, input): 2025-08-26T20:22:08.2244485Z >>> return self.relu(self.fc2(self.gelu(self.fc1(input)))) 2025-08-26T20:22:08.2244567Z 2025-08-26T20:22:08.2244644Z 2025-08-26T20:22:08.2244791Z >>> # xdoctest: +SKIP("Undefined spec1, spec2) 2025-08-26T20:22:08.2244901Z >>> sharding_plan = ShardingPlan( 2025-08-26T20:22:08.2245030Z >>> plan={ 2025-08-26T20:22:08.2245130Z >>> "fc1.weight": spec1, 2025-08-26T20:22:08.2245228Z >>> "fc2.weight": spec2 2025-08-26T20:22:08.2245322Z >>> }, 2025-08-26T20:22:08.2245418Z >>> output_plan={ 2025-08-26T20:22:08.2245531Z >>> "fc2": output_spec 2025-08-26T20:22:08.2245612Z >>> }, 2025-08-26T20:22:08.2245723Z >>> return_local_tensor=["fc2"] 2025-08-26T20:22:08.2245819Z >>> ) 2025-08-26T20:22:08.2245900Z 2025-08-26T20:22:08.2246166Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2246246Z 2025-08-26T20:22:08.2246339Z warnings.warn(msg) 2025-08-26T20:22:08.2246430Z 2025-08-26T20:22:08.2246620Z --- Parse Warning: 59 / 146 --- 2025-08-26T20:22:08.2247796Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=post_localSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/post_localSGD_hook.py line=72. 2025-08-26T20:22:08.2248065Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2248144Z 2025-08-26T20:22:08.2248264Z Run post-localSGD algorithm. 2025-08-26T20:22:08.2248345Z 2025-08-26T20:22:08.2248594Z This DDP communication hook is used for running post-localSGD algorithm, 2025-08-26T20:22:08.2248755Z by combining with a model averaging component (e.g., 2025-08-26T20:22:08.2249084Z :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager`) 2025-08-26T20:22:08.2249211Z that runs after the optimizer step. 2025-08-26T20:22:08.2249290Z 2025-08-26T20:22:08.2249415Z Args: 2025-08-26T20:22:08.2249642Z state (PostLocalSGDState): State information to run post-localSGD. 2025-08-26T20:22:08.2249919Z Users mainly need to tune ``start_localSGD_iter`` to determine when to start local SGD. 2025-08-26T20:22:08.2250351Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-08-26T20:22:08.2250600Z Note that since DDP comm hook only supports single process single device mode, 2025-08-26T20:22:08.2250762Z only exactly one tensor is stored in this bucket. 2025-08-26T20:22:08.2250842Z 2025-08-26T20:22:08.2250926Z Returns: 2025-08-26T20:22:08.2251181Z Future handler of the communication, which updates the gradients in place. 2025-08-26T20:22:08.2251262Z 2025-08-26T20:22:08.2251366Z Example:: 2025-08-26T20:22:08.2251465Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2251719Z >>> state = PostLocalSGDState(process_group=process_group, subgroup=subgroup, 2025-08-26T20:22:08.2251856Z start_localSGD_iter=10) 2025-08-26T20:22:08.2252030Z >>> ddp_model.register_comm_hook(state, post_localSGD_hook) 2025-08-26T20:22:08.2252379Z >>> # Also need to establish a model averaging module and run model averaging after ``optimizer.step()``. 2025-08-26T20:22:08.2252724Z >>> # Please refer to the examples in ``torch.distributed.algorithms.model_averaging.averagers`` module. 2025-08-26T20:22:08.2252804Z 2025-08-26T20:22:08.2253066Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2253147Z 2025-08-26T20:22:08.2253257Z warnings.warn(msg) 2025-08-26T20:22:08.2253336Z 2025-08-26T20:22:08.2253524Z --- Parse Warning: 60 / 146 --- 2025-08-26T20:22:08.2254599Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=powerSGD_hook in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/ddp_comm_hooks/powerSGD_hook.py line=342. 2025-08-26T20:22:08.2254867Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2254989Z 2025-08-26T20:22:08.2255098Z Implement PowerSGD algorithm. 2025-08-26T20:22:08.2255176Z 2025-08-26T20:22:08.2255417Z This DDP communication hook implements PowerSGD gradient compression 2025-08-26T20:22:08.2255653Z algorithm described in the `paper `_. 2025-08-26T20:22:08.2255904Z Once gradient tensors are aggregated across all workers, this hook applies 2025-08-26T20:22:08.2256005Z compression as follows: 2025-08-26T20:22:08.2256084Z 2025-08-26T20:22:08.2256535Z 1. Views the input flattened 1D gradient tensor as a list of per-parameter tensors, and divides all the tensors into two groups: 2025-08-26T20:22:08.2256618Z 2025-08-26T20:22:08.2257052Z 1.1 The tensors that should be compressed before allreduce, because the compression can give enough saving in bandwidth. 2025-08-26T20:22:08.2257200Z 2025-08-26T20:22:08.2257609Z 1.2 Rest of the tensors will be directly allreduced without compression, including all the vector tensors (for biases). 2025-08-26T20:22:08.2257701Z 2025-08-26T20:22:08.2257810Z 2. Handles uncompressed tensors: 2025-08-26T20:22:08.2257901Z 2025-08-26T20:22:08.2258414Z 2.1. Allocate contiguous memory for those uncompressed tensors, and allreduces all the uncompressed tensors as a batch, without compression; 2025-08-26T20:22:08.2258492Z 2025-08-26T20:22:08.2258845Z 2.2. Copies the individual uncompressed tensors from the contiguous memory back to the input tensor. 2025-08-26T20:22:08.2258924Z 2025-08-26T20:22:08.2259170Z 3. Handles the tensors that should be compressed by PowerSGD compression: 2025-08-26T20:22:08.2259250Z 2025-08-26T20:22:08.2259488Z 3.1. For each tensor M, creates two low-rank tensors P and Q for decomposing M, 2025-08-26T20:22:08.2259841Z such that M = PQ^T, where Q is initialized from a standard normal distribution and orthogonalized; 2025-08-26T20:22:08.2259921Z 2025-08-26T20:22:08.2260083Z 3.2. Computes each P in Ps, which is equal to MQ; 2025-08-26T20:22:08.2260162Z 2025-08-26T20:22:08.2260269Z 3.3. Allreduces Ps as a batch; 2025-08-26T20:22:08.2260442Z 2025-08-26T20:22:08.2260562Z 3.4. Orthogonalizes each P in Ps; 2025-08-26T20:22:08.2260640Z 2025-08-26T20:22:08.2260849Z 3.5. Computes each Q in Qs, which is approximately equal to M^TP; 2025-08-26T20:22:08.2260928Z 2025-08-26T20:22:08.2261047Z 3.6. Allreduces Qs as a batch; 2025-08-26T20:22:08.2261143Z 2025-08-26T20:22:08.2261437Z 3.7. Computes each M among all the compressed tensors, which is approximately equal to PQ^T. 2025-08-26T20:22:08.2261530Z 2025-08-26T20:22:08.2261943Z Note that this communication hook enforces vanilla allreduce for the first ``state.start_powerSGD_iter`` iterations. 2025-08-26T20:22:08.2262231Z This not only gives the user more control over the tradeoff between speedup and accuracy, 2025-08-26T20:22:08.2262675Z but also helps abstract away some complexity of the internal optimization of DDP for future communication hook developers. 2025-08-26T20:22:08.2262755Z 2025-08-26T20:22:08.2262850Z Args: 2025-08-26T20:22:08.2263282Z state (PowerSGDState): State information to configure the compression rate and support error feedback, warm start, etc. 2025-08-26T20:22:08.2263636Z To tune the compression configs, mainly need to tune ``matrix_approximation_rank``, ``start_powerSGD_iter`` 2025-08-26T20:22:08.2263758Z and ``min_compression_rate``. 2025-08-26T20:22:08.2264175Z bucket (dist.GradBucket): Bucket that stores a 1D flattened gradient tensor that batches multiple per-variable tensors. 2025-08-26T20:22:08.2264432Z Note that since DDP comm hook only supports single process single device mode, 2025-08-26T20:22:08.2264589Z only exactly one tensor is stored in this bucket. 2025-08-26T20:22:08.2264681Z 2025-08-26T20:22:08.2264767Z Returns: 2025-08-26T20:22:08.2265046Z Future handler of the communication, which updates the gradients in place. 2025-08-26T20:22:08.2265136Z 2025-08-26T20:22:08.2265225Z Example:: 2025-08-26T20:22:08.2265321Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2265606Z >>> state = PowerSGDState(process_group=process_group, matrix_approximation_rank=1, 2025-08-26T20:22:08.2265766Z start_powerSGD_iter=10, min_compression_rate=0.5) 2025-08-26T20:22:08.2265939Z >>> ddp_model.register_comm_hook(state, powerSGD_hook) 2025-08-26T20:22:08.2266016Z 2025-08-26T20:22:08.2266268Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2266361Z 2025-08-26T20:22:08.2266457Z warnings.warn(msg) 2025-08-26T20:22:08.2266548Z 2025-08-26T20:22:08.2266746Z --- Parse Warning: 61 / 146 --- 2025-08-26T20:22:08.2267909Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PeriodicModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/averagers.py line=38. 2025-08-26T20:22:08.2268190Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2268269Z 2025-08-26T20:22:08.2268471Z Averages parameters periodically after the warm-up stage. 2025-08-26T20:22:08.2268550Z 2025-08-26T20:22:08.2268803Z This can be used for running `post-local SGD `_, 2025-08-26T20:22:08.2269009Z by running :class:`~torch.nn.DistributedDataParallel` (DDP) 2025-08-26T20:22:08.2269242Z using the subgroups created by :meth:`~torch.distributed.new_subgroups`. 2025-08-26T20:22:08.2269366Z 2025-08-26T20:22:08.2269449Z Args: 2025-08-26T20:22:08.2269614Z period (int): The number of steps per model averaging. 2025-08-26T20:22:08.2269892Z Usually the period should be greater than ``1`` to reduce the communication cost. 2025-08-26T20:22:08.2270017Z Otherwise, only DDP needs to be used. 2025-08-26T20:22:08.2270236Z warmup_steps (int): The number of warm-up steps. During this stage, 2025-08-26T20:22:08.2270354Z model averaging is skipped. 2025-08-26T20:22:08.2270549Z process_group: The process group to be used for all-reduce. 2025-08-26T20:22:08.2270690Z If ``None``, the default process group, which 2025-08-26T20:22:08.2270875Z is created by :func:`torch.distributed.init_process_group`, 2025-08-26T20:22:08.2271006Z will be used. (default: ``None``) 2025-08-26T20:22:08.2271085Z 2025-08-26T20:22:08.2271171Z Example:: 2025-08-26T20:22:08.2271266Z 2025-08-26T20:22:08.2271392Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2271497Z >>> import torch 2025-08-26T20:22:08.2271615Z >>> import torch.distributed as dist 2025-08-26T20:22:08.2271929Z >>> import torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook as post_localSGD 2025-08-26T20:22:08.2272206Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-08-26T20:22:08.2272307Z >>> import torch.nn as nn 2025-08-26T20:22:08.2272404Z >>> 2025-08-26T20:22:08.2272580Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-08-26T20:22:08.2272686Z >>> torch.cuda.set_device(rank) 2025-08-26T20:22:08.2272825Z >>> module = nn.Linear(1, 1, bias=False).cuda() 2025-08-26T20:22:08.2272977Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-26T20:22:08.2273125Z >>> module, device_ids=[rank], output_device=rank 2025-08-26T20:22:08.2273208Z >>> ) 2025-08-26T20:22:08.2273356Z >>> # Register a post-localSGD communication hook. 2025-08-26T20:22:08.2273661Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-08-26T20:22:08.2273851Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-26T20:22:08.2273944Z >>> 2025-08-26T20:22:08.2274207Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-08-26T20:22:08.2274360Z >>> # After 100 steps, run model averaging every 4 steps. 2025-08-26T20:22:08.2274686Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-26T20:22:08.2274934Z >>> averager = averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-08-26T20:22:08.2275055Z >>> for step in range(0, 200): 2025-08-26T20:22:08.2275159Z >>> optimizer.zero_grad() 2025-08-26T20:22:08.2275270Z >>> loss = loss_fn(output, labels) 2025-08-26T20:22:08.2275378Z >>> loss.backward() 2025-08-26T20:22:08.2275524Z >>> optimizer.step() 2025-08-26T20:22:08.2275727Z >>> # Will average model parameters globally every 4 steps. Thus, 2025-08-26T20:22:08.2275929Z >>> # inter-node communication only occurs every 4 iterations after 2025-08-26T20:22:08.2276052Z >>> # the initial ``warmup_steps`` period. 2025-08-26T20:22:08.2276221Z >>> averager.average_parameters(model.parameters()) 2025-08-26T20:22:08.2276299Z 2025-08-26T20:22:08.2276561Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2276642Z 2025-08-26T20:22:08.2276735Z warnings.warn(msg) 2025-08-26T20:22:08.2276824Z 2025-08-26T20:22:08.2277010Z --- Parse Warning: 62 / 146 --- 2025-08-26T20:22:08.2278243Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=HierarchicalModelAverager in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/algorithms/model_averaging/hierarchical_model_averager.py line=19. 2025-08-26T20:22:08.2278533Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2278622Z 2025-08-26T20:22:08.2278953Z Runs hierarchical model averaging (`hierarchical SGD `_). 2025-08-26T20:22:08.2279033Z 2025-08-26T20:22:08.2279349Z Process groups of different sizes are organized in a hierarchy, and they average parameters 2025-08-26T20:22:08.2279556Z by using different periods concurrently after the warm-up stage. 2025-08-26T20:22:08.2279968Z This is an extension of :class:`~torch.distributed.algorithms.model_averaging.averagers.PeriodicModelAverager` 2025-08-26T20:22:08.2280301Z that supports `post-local SGD `_, which essentially only supports 2025-08-26T20:22:08.2280608Z a two-level hierarchy: the intra-machine level and the global level, where the intra-machine 2025-08-26T20:22:08.2280973Z level is usually embedded in :meth:`~torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook`. 2025-08-26T20:22:08.2281271Z Similarly, the process groups within this class do not have such an intra-machine process 2025-08-26T20:22:08.2281554Z subgroup, which should be embedded by the post-local SGD communication hook instead. 2025-08-26T20:22:08.2281634Z 2025-08-26T20:22:08.2281713Z Args: 2025-08-26T20:22:08.2281982Z period_group_size_dict: An ordered dict mapping keys of model averaging period to 2025-08-26T20:22:08.2282181Z process group size, used for initializing process groups of 2025-08-26T20:22:08.2282414Z different sizes in a hierarchy to average parameters concurrently. 2025-08-26T20:22:08.2282627Z Particularly, at each iteration, there will be at most a single 2025-08-26T20:22:08.2282860Z process group that runs averaging -- the period of such group should 2025-08-26T20:22:08.2283082Z have the largest period which the current step can be divided by. 2025-08-26T20:22:08.2283277Z For example, if the dict has three keys: 2, 4, and 8, 2025-08-26T20:22:08.2283494Z then this means totally three process groups will be created to 2025-08-26T20:22:08.2283702Z average parameters every 2, 4, and 8 iterations, respectively. 2025-08-26T20:22:08.2283901Z At the 4th iteration, only the second process group will run 2025-08-26T20:22:08.2284075Z averaging, because the first process group should be a 2025-08-26T20:22:08.2284293Z subset of the second process group, and no need to execute the first 2025-08-26T20:22:08.2284426Z process group redundantly. 2025-08-26T20:22:08.2284671Z On the other hand, the third process group can only be triggered 2025-08-26T20:22:08.2284908Z every 8 iterations, so it will not be triggered at the 4th iteration. 2025-08-26T20:22:08.2285208Z warmup_steps (int): The number of warm-up steps. During this stage, model averaging is skipped. 2025-08-26T20:22:08.2285657Z process_group (ProcessGroup, optional): The overall process group containing all the processes that runs model averaging. 2025-08-26T20:22:08.2285829Z If ``None``, the default process group, which is created 2025-08-26T20:22:08.2286035Z by :func:`torch.distributed.init_process_group`, will be used. 2025-08-26T20:22:08.2286165Z (default: ``None``) 2025-08-26T20:22:08.2286269Z 2025-08-26T20:22:08.2286369Z Example:: 2025-08-26T20:22:08.2286491Z >>> # xdoctest: +SKIP('undefined rank') 2025-08-26T20:22:08.2286611Z >>> from collections import OrderedDict 2025-08-26T20:22:08.2286712Z >>> import torch 2025-08-26T20:22:08.2286829Z >>> import torch.distributed as dist 2025-08-26T20:22:08.2287108Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-08-26T20:22:08.2287214Z >>> PostLocalSGDState, 2025-08-26T20:22:08.2287312Z >>> post_localSGD_hook, 2025-08-26T20:22:08.2287405Z >>> ) 2025-08-26T20:22:08.2287781Z >>> import torch.distributed.algorithms.model_averaging.hierarchical_model_averager as hierarchicalSGD 2025-08-26T20:22:08.2287891Z >>> import torch.nn as nn 2025-08-26T20:22:08.2287973Z >>> 2025-08-26T20:22:08.2288150Z >>> dist.init_process_group("nccl", rank=rank, world_size=16) 2025-08-26T20:22:08.2288267Z >>> torch.cuda.set_device(rank) 2025-08-26T20:22:08.2288404Z >>> module = nn.Linear(1, 1, bias=False).to(rank) 2025-08-26T20:22:08.2288571Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-26T20:22:08.2288708Z >>> module, device_ids=[rank], output_device=rank 2025-08-26T20:22:08.2288788Z >>> ) 2025-08-26T20:22:08.2288941Z >>> # Register a post-localSGD communication hook. 2025-08-26T20:22:08.2289214Z >>> # Assume that each machine has 4 GPUs, then each intra-machine subgroup has a size of 4. 2025-08-26T20:22:08.2289340Z >>> subgroup, _ = dist.new_subgroups() 2025-08-26T20:22:08.2289650Z >>> state = PostLocalSGDState(process_group=None, subgroup=subgroup, start_localSGD_iter=100) 2025-08-26T20:22:08.2289810Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-26T20:22:08.2289901Z >>> 2025-08-26T20:22:08.2290183Z >>> # Average parameters among each group of 8 processes every 4 iterations, and among all 2025-08-26T20:22:08.2290313Z >>> # the 16 processes every 16 iterations. 2025-08-26T20:22:08.2290497Z >>> averager = hierarchicalSGD.HierarchicalModelAverager( 2025-08-26T20:22:08.2290728Z >>> period_group_size_dict=OrderedDict([(4, 8), (16, 16)]), warmup_steps=100) 2025-08-26T20:22:08.2291082Z >>> # Note that ``warmup_steps`` must be the same as ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-26T20:22:08.2291349Z >>> # In the first 100 steps, run global gradient averaging like normal DDP at every step. 2025-08-26T20:22:08.2291513Z >>> # After 100 steps, run model averaging at two levels. 2025-08-26T20:22:08.2291618Z >>> for step in range(0, 200): 2025-08-26T20:22:08.2291949Z >>> optimizer.zero_grad() 2025-08-26T20:22:08.2292078Z >>> loss = loss_fn(output, labels) 2025-08-26T20:22:08.2292176Z >>> loss.backward() 2025-08-26T20:22:08.2292288Z >>> optimizer.step() 2025-08-26T20:22:08.2292439Z >>> # Average parameters after ``optimizer.step()``. 2025-08-26T20:22:08.2292844Z >>> # Thus, the inter-node communication only occurs periodically after ``warmup_steps``. 2025-08-26T20:22:08.2293014Z >>> averager.average_parameters(model.parameters()) 2025-08-26T20:22:08.2293094Z 2025-08-26T20:22:08.2293201Z .. warning :: 2025-08-26T20:22:08.2293448Z The last group size in the dict must be the size of the provided ``process_group``, 2025-08-26T20:22:08.2293681Z which indicates model averaging at the highest level of the hierarchy. 2025-08-26T20:22:08.2293995Z If ``process_group`` is not provided, then the last group size should be equal to the world size. 2025-08-26T20:22:08.2294073Z 2025-08-26T20:22:08.2294173Z .. warning :: 2025-08-26T20:22:08.2294405Z `HierarchicalModelAverager` is experimental and subject to change. 2025-08-26T20:22:08.2294485Z 2025-08-26T20:22:08.2294746Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2294863Z 2025-08-26T20:22:08.2294976Z warnings.warn(msg) 2025-08-26T20:22:08.2295059Z 2025-08-26T20:22:08.2295269Z --- Parse Warning: 63 / 146 --- 2025-08-26T20:22:08.2296369Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BroadcastingTorchSaveReader in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=40. 2025-08-26T20:22:08.2296631Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2296725Z 2025-08-26T20:22:08.2297017Z StorageReader for reading a Torch Save file. This reader will read the entire checkpoint 2025-08-26T20:22:08.2297265Z on the coordinator rank, and then broadcast and shard each tensor to all ranks. 2025-08-26T20:22:08.2297362Z 2025-08-26T20:22:08.2297525Z . N.B. Intended to be used with DynamicMetaLoadPlanner 2025-08-26T20:22:08.2297624Z 2025-08-26T20:22:08.2297714Z .. warning:: 2025-08-26T20:22:08.2297891Z Current implementation only supports loading Tensors. 2025-08-26T20:22:08.2297986Z 2025-08-26T20:22:08.2298102Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2298214Z >>> sd = {"mode": model} 2025-08-26T20:22:08.2298303Z >>> dcp.load( 2025-08-26T20:22:08.2298387Z >>> sd, 2025-08-26T20:22:08.2298554Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-08-26T20:22:08.2298680Z >>> planner=DynamicMetaLoadPlanner(), 2025-08-26T20:22:08.2298798Z >>> checkpoint_id="path_to_model.pt" 2025-08-26T20:22:08.2298893Z >>> ) 2025-08-26T20:22:08.2298974Z 2025-08-26T20:22:08.2299241Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2299322Z 2025-08-26T20:22:08.2299420Z warnings.warn(msg) 2025-08-26T20:22:08.2299515Z 2025-08-26T20:22:08.2299698Z --- Parse Warning: 64 / 146 --- 2025-08-26T20:22:08.2300842Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DynamicMetaLoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/format_utils.py line=151. 2025-08-26T20:22:08.2301155Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2301236Z 2025-08-26T20:22:08.2301618Z Extension of DefaultLoadPlanner, which creates a new Metadata object based on the passed in state dict, 2025-08-26T20:22:08.2301944Z avoiding the need to read metadata from disk. This is useful when reading formats which don't have a 2025-08-26T20:22:08.2302074Z metadata file, like Torch Save files. 2025-08-26T20:22:08.2302155Z 2025-08-26T20:22:08.2302338Z . N.B. Intended to be used with BroadcastingTorchSaveReader 2025-08-26T20:22:08.2302436Z 2025-08-26T20:22:08.2302528Z .. warning:: 2025-08-26T20:22:08.2302715Z Current implementation only supports loading Tensors. 2025-08-26T20:22:08.2302798Z 2025-08-26T20:22:08.2302966Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2303076Z >>> sd = {"mode": model} 2025-08-26T20:22:08.2303167Z >>> dcp.load( 2025-08-26T20:22:08.2303264Z >>> sd, 2025-08-26T20:22:08.2303418Z >>> storage_reader=BroadcastingTorchSaveReader(), 2025-08-26T20:22:08.2303540Z >>> planner=DynamicMetaLoadPlanner(), 2025-08-26T20:22:08.2303666Z >>> checkpoint_id="path_to_model.pt" 2025-08-26T20:22:08.2303748Z >>> ) 2025-08-26T20:22:08.2303841Z 2025-08-26T20:22:08.2304096Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2304175Z 2025-08-26T20:22:08.2304284Z warnings.warn(msg) 2025-08-26T20:22:08.2304363Z 2025-08-26T20:22:08.2304550Z --- Parse Warning: 65 / 146 --- 2025-08-26T20:22:08.2305620Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_sharded_optimizer_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/optimizer.py line=221. 2025-08-26T20:22:08.2305910Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2306003Z 2025-08-26T20:22:08.2306212Z Load a state_dict in conjunction with FSDP sharded optimizer state. 2025-08-26T20:22:08.2306305Z 2025-08-26T20:22:08.2306471Z This is the current recommended way to checkpoint FSDP. 2025-08-26T20:22:08.2306569Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2306737Z >>> import torch.distributed.checkpoint as dist_cp 2025-08-26T20:22:08.2306820Z >>> # Save 2025-08-26T20:22:08.2306919Z >>> model: torch.nn.Model 2025-08-26T20:22:08.2307051Z >>> optim_params = model.parameters() 2025-08-26T20:22:08.2307194Z >>> optim = torch.optim.SGD(optim_params, lr=0.01) 2025-08-26T20:22:08.2307291Z >>> # Save 2025-08-26T20:22:08.2307555Z >>> with FSDP.state_dict_type(model, StateDictType.SHARDED_STATE_DICT): 2025-08-26T20:22:08.2307651Z >>> state_dict = { 2025-08-26T20:22:08.2307819Z >>> "optimizer": FSDP.optim_state_dict(model, optim), 2025-08-26T20:22:08.2307931Z >>> "model": model.state_dict() 2025-08-26T20:22:08.2308032Z >>> } 2025-08-26T20:22:08.2308139Z >>> dist_cp.save_state_dict( 2025-08-26T20:22:08.2308242Z >>> state_dict=optim_state, 2025-08-26T20:22:08.2308435Z >>> storage_writer=dist_cp.FileSystemWriter("checkpoint"), 2025-08-26T20:22:08.2308576Z >>> planner=dist_cp.DefaultSavePlanner(), 2025-08-26T20:22:08.2308675Z >>> ) 2025-08-26T20:22:08.2308757Z >>> 2025-08-26T20:22:08.2308843Z >>> # Load 2025-08-26T20:22:08.2309081Z >>> with FSDP.state_dict_type(model_tp, StateDictType.SHARDED_STATE_DICT): 2025-08-26T20:22:08.2309211Z >>> model_state_dict = model_tp.state_dict() 2025-08-26T20:22:08.2309318Z >>> checkpoint = { 2025-08-26T20:22:08.2309425Z >>> "model": model_state_dict 2025-08-26T20:22:08.2309509Z >>> } 2025-08-26T20:22:08.2309622Z >>> dist_cp.load_state_dict( 2025-08-26T20:22:08.2309726Z >>> state_dict=checkpoint, 2025-08-26T20:22:08.2309960Z >>> storage_reader=dist_cp.FileSystemReader(checkpoint_file), 2025-08-26T20:22:08.2310095Z >>> planner=dist_cp.DefaultLoadPlanner(), 2025-08-26T20:22:08.2310175Z >>> ) 2025-08-26T20:22:08.2310340Z >>> model.load_state_dict(checkpoint["model_state"]) 2025-08-26T20:22:08.2310422Z >>> 2025-08-26T20:22:08.2310594Z >>> optim_state = dist_cp.load_sharded_optimizer_state_dict( 2025-08-26T20:22:08.2310705Z >>> model_state_dict, 2025-08-26T20:22:08.2310815Z >>> optimizer_key="optimizer", 2025-08-26T20:22:08.2311003Z >>> storage_reader=dist_cp.FileSystemReader("checkpoint"), 2025-08-26T20:22:08.2311086Z >>> ) 2025-08-26T20:22:08.2311170Z >>> 2025-08-26T20:22:08.2311328Z >>> flattened_osd = FSDP.optim_state_dict_to_load( 2025-08-26T20:22:08.2311504Z >>> model, optim, optim_state["optimizer"] 2025-08-26T20:22:08.2311597Z >>> ) 2025-08-26T20:22:08.2311680Z >>> 2025-08-26T20:22:08.2311803Z >>> optim.load_state_dict(flattened_osd) 2025-08-26T20:22:08.2311896Z 2025-08-26T20:22:08.2312149Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2312242Z 2025-08-26T20:22:08.2312338Z warnings.warn(msg) 2025-08-26T20:22:08.2312419Z 2025-08-26T20:22:08.2312615Z --- Parse Warning: 66 / 146 --- 2025-08-26T20:22:08.2313569Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SavePlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=122. 2025-08-26T20:22:08.2313845Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2313952Z 2025-08-26T20:22:08.2314244Z Abstract class defining the protocol used by save_state_dict to plan the save process. 2025-08-26T20:22:08.2314335Z 2025-08-26T20:22:08.2314632Z SavePlanners are stateful objects that can be used to customize the whole save process. 2025-08-26T20:22:08.2314726Z 2025-08-26T20:22:08.2315002Z SavePlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-08-26T20:22:08.2315117Z will be visible to the whole process. 2025-08-26T20:22:08.2315208Z 2025-08-26T20:22:08.2315485Z A planner subclass can expect the following sequence of calls during save_state_dict: 2025-08-26T20:22:08.2315576Z 2025-08-26T20:22:08.2315698Z 1) set_up_planner - called on all ranks. 2025-08-26T20:22:08.2315822Z Signals the start of a checkpoint save. 2025-08-26T20:22:08.2315913Z 2025-08-26T20:22:08.2316036Z 2) create_local_plan - called on all ranks. 2025-08-26T20:22:08.2316338Z Process the state_dict and produces a `SavePlan` that will be sent for global planning. 2025-08-26T20:22:08.2316422Z 2025-08-26T20:22:08.2316604Z 3) create_global_plan - called on the coordinator rank only. 2025-08-26T20:22:08.2316816Z Takes the SavePlan from all ranks and make any global decision. 2025-08-26T20:22:08.2316896Z 2025-08-26T20:22:08.2317007Z 4) finish_plan - called on all ranks. 2025-08-26T20:22:08.2317237Z This gives each rank a chance to adjust to global planning decisions. 2025-08-26T20:22:08.2317317Z 2025-08-26T20:22:08.2317482Z 5) resolve_data - called multiple times on each rank 2025-08-26T20:22:08.2317685Z Lookups a value on the `state_dict` for the storage layer to write. 2025-08-26T20:22:08.2317765Z 2025-08-26T20:22:08.2318084Z Users are recommended to extend DefaultSavePlanner instead of this interface directly as 2025-08-26T20:22:08.2318265Z most changes can be expressed by changes in a single method. 2025-08-26T20:22:08.2318360Z 2025-08-26T20:22:08.2318484Z There are 3 usual patterns of extension: 2025-08-26T20:22:08.2318567Z 2025-08-26T20:22:08.2318830Z Rewriting state_dict. This is the simplest way to extend the save process as it 2025-08-26T20:22:08.2319089Z doesn't requite understanding the intrincacies of how SavePlan works: 2025-08-26T20:22:08.2319181Z 2025-08-26T20:22:08.2319296Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2319430Z >>> class RenamePlanner(DefaultSavePlanner): 2025-08-26T20:22:08.2319539Z >>> def set_up_planner( 2025-08-26T20:22:08.2319623Z >>> self, 2025-08-26T20:22:08.2319746Z >>> state_dict: STATE_DICT_TYPE, 2025-08-26T20:22:08.2319872Z >>> storage_meta: Optional[StorageMeta], 2025-08-26T20:22:08.2319977Z >>> is_coordinator: bool, 2025-08-26T20:22:08.2320075Z >>> ) -> None: 2025-08-26T20:22:08.2320182Z >>> # prefix all keys with `foo_`` 2025-08-26T20:22:08.2320539Z >>> super().set_up_planner({"foo_" + k: v for k, v in state_dict.items()}, storage_meta, is_coordinator) 2025-08-26T20:22:08.2320619Z 2025-08-26T20:22:08.2320951Z Modifying local plan and lookup in tandem. This is useful when fine control of how data is persisted 2025-08-26T20:22:08.2321044Z 2025-08-26T20:22:08.2321158Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2321296Z >>> class FP16Planner(DefaultSavePlanner): 2025-08-26T20:22:08.2321403Z >>> def create_local_plan(self): 2025-08-26T20:22:08.2321523Z >>> plan = super().create_local_plan() 2025-08-26T20:22:08.2321632Z >>> for p in plan: 2025-08-26T20:22:08.2321747Z >>> if p.tensor_data is not None: 2025-08-26T20:22:08.2321905Z >>> p.tensor_data.properties.dtype = torch.float16 2025-08-26T20:22:08.2322010Z >>> return plan 2025-08-26T20:22:08.2322091Z >>> 2025-08-26T20:22:08.2322220Z >>> def resolve_data(self, write_item): 2025-08-26T20:22:08.2322372Z >>> item = super().resolve_data(write_item) 2025-08-26T20:22:08.2322650Z >>> return item if write_item.type == WriteItemType.BYTE_IO else item.to(torch.float16) 2025-08-26T20:22:08.2322747Z 2025-08-26T20:22:08.2323090Z Using the global planning step to make central decisions that can't be made individually by each rank 2025-08-26T20:22:08.2323189Z 2025-08-26T20:22:08.2323305Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2323418Z >>> from itertools import zip_longest 2025-08-26T20:22:08.2323545Z >>> from dataclasses import replace 2025-08-26T20:22:08.2323715Z >>> class DDPLoadBalancingPlanner(DefaultSavePlanner): 2025-08-26T20:22:08.2324003Z >>> # This uses the default local plan behavior of having all non-sharded writes in rank 0 2025-08-26T20:22:08.2324133Z >>> # This sample doesn't handle ShardedTensors 2025-08-26T20:22:08.2324264Z >>> def create_global_plan(self, all_plans): 2025-08-26T20:22:08.2324431Z >>> iters = [iter(all_plans[0].items)] * len(all_plans) 2025-08-26T20:22:08.2324533Z >>> items_per_rank = [ 2025-08-26T20:22:08.2324684Z >>> [item for item in items if item is not None] 2025-08-26T20:22:08.2324842Z >>> for items in zip(*zip_longest(*iters), strict=True) 2025-08-26T20:22:08.2324924Z >>> ] 2025-08-26T20:22:08.2325032Z >>> all_plans = [ 2025-08-26T20:22:08.2325146Z >>> replace(plan, items=items) 2025-08-26T20:22:08.2325348Z >>> for plan, items in zip(all_plans, items_per_rank, strict=True) 2025-08-26T20:22:08.2325430Z >>> ] 2025-08-26T20:22:08.2325570Z >>> return super().create_global_plan(all_plans) 2025-08-26T20:22:08.2325662Z 2025-08-26T20:22:08.2325926Z Finally, some planners need to save additional metadata in the checkpoint, this is 2025-08-26T20:22:08.2326204Z accomplished by having each rank contribute their data items in the local plan and 2025-08-26T20:22:08.2326320Z the global planner aggregate them: 2025-08-26T20:22:08.2326410Z 2025-08-26T20:22:08.2326536Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2326737Z >>> class SaveExtraDataPlanner(DefaultSavePlanner): 2025-08-26T20:22:08.2326881Z >>> def create_local_plan(self) -> SavePlan: 2025-08-26T20:22:08.2326999Z >>> plan = super().create_local_plan() 2025-08-26T20:22:08.2327162Z >>> return replace(plan, planner_data="per-rank-data") 2025-08-26T20:22:08.2327257Z >>> 2025-08-26T20:22:08.2327555Z >>> def create_global_plan(self, all_plans: List[SavePlan]) -> Tuple[List[SavePlan], Metadata]: 2025-08-26T20:22:08.2327761Z >>> global_plan, metadata = super().create_global_plan(all_plans) 2025-08-26T20:22:08.2327917Z >>> merged_data = [p.planner_data for p in global_plan] 2025-08-26T20:22:08.2328086Z >>> metadata = replace(metadata, planner_data=merged_data) 2025-08-26T20:22:08.2328216Z >>> return global_plan, metadata 2025-08-26T20:22:08.2328296Z 2025-08-26T20:22:08.2328611Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2328695Z 2025-08-26T20:22:08.2328794Z warnings.warn(msg) 2025-08-26T20:22:08.2328890Z 2025-08-26T20:22:08.2329086Z --- Parse Warning: 67 / 146 --- 2025-08-26T20:22:08.2330068Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LoadPlanner in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/planner.py line=305. 2025-08-26T20:22:08.2330332Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2330412Z 2025-08-26T20:22:08.2330715Z Abstract class defining the protocol used by load_state_dict to plan the load process. 2025-08-26T20:22:08.2330796Z 2025-08-26T20:22:08.2331094Z LoadPlanner are stateful objects that can be used to customize the whole load process. 2025-08-26T20:22:08.2331207Z 2025-08-26T20:22:08.2331491Z LoadPlanner acts as an access proxy to the state_dict, so any transformation done to it 2025-08-26T20:22:08.2331626Z will be visible to the whole process. 2025-08-26T20:22:08.2331707Z 2025-08-26T20:22:08.2331995Z A planner subclass can expect the following sequence of calls during load_state_dict: 2025-08-26T20:22:08.2332077Z 2025-08-26T20:22:08.2332195Z 1) set_up_planner - called on all ranks. 2025-08-26T20:22:08.2332338Z Signals the start of loading a checkpoint. 2025-08-26T20:22:08.2332420Z 2025-08-26T20:22:08.2332555Z 2) create_local_plan - called on all ranks. 2025-08-26T20:22:08.2332841Z Process the state_dict and produces a `LoadPlan` that will be sent for global planning. 2025-08-26T20:22:08.2332920Z 2025-08-26T20:22:08.2333112Z 3) create_global_plan - called on the coordinator rank only. 2025-08-26T20:22:08.2333314Z Takes the LoadPlan from all ranks and make any global decision. 2025-08-26T20:22:08.2333412Z 2025-08-26T20:22:08.2333557Z 4) load_bytes - called multiple times on each rank 2025-08-26T20:22:08.2333728Z This is called once per non-tensor value in state_dict. 2025-08-26T20:22:08.2333819Z 2025-08-26T20:22:08.2334040Z 5) resolve_tensor and commit_tensor - called multiple times on each rank 2025-08-26T20:22:08.2334232Z They are called in pair for each Tensor value in state_dict. 2025-08-26T20:22:08.2334309Z 2025-08-26T20:22:08.2334610Z Users are recommended to extend DefaultLoadPlanner instead of this interface directly as 2025-08-26T20:22:08.2334809Z most changes can be expressed by changes in a single method. 2025-08-26T20:22:08.2334887Z 2025-08-26T20:22:08.2335013Z There are two usual patterns of extension: 2025-08-26T20:22:08.2335104Z 2025-08-26T20:22:08.2335355Z Rewriting state_dict. This is the simplest way to extend the load process as it 2025-08-26T20:22:08.2335627Z doesn't requite understanding the intrincacies of how LoadPlan works. We need 2025-08-26T20:22:08.2335852Z to keep a reference to the original state_dict as load happens in place so 2025-08-26T20:22:08.2336012Z we need to be able to perform it in place 2025-08-26T20:22:08.2336104Z 2025-08-26T20:22:08.2336217Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2336362Z >>> class RenamePlanner(DefaultLoadPlanner): 2025-08-26T20:22:08.2336459Z >>> def set_up_planner( 2025-08-26T20:22:08.2336543Z >>> self, 2025-08-26T20:22:08.2336667Z >>> state_dict: STATE_DICT_TYPE, 2025-08-26T20:22:08.2336766Z >>> metadata: Metadata, 2025-08-26T20:22:08.2336884Z >>> is_coordinator: bool, 2025-08-26T20:22:08.2336972Z >>> ) -> None: 2025-08-26T20:22:08.2337099Z >>> self.original_state_dict = state_dict 2025-08-26T20:22:08.2337286Z >>> state_dict = {"foo_" + k: v for k, v in state_dict.items()} 2025-08-26T20:22:08.2337372Z >>> 2025-08-26T20:22:08.2337555Z >>> if self.flatten_sharded_tensors: 2025-08-26T20:22:08.2337708Z >>> state_dict = _flatten_sharded_tensors(state_dict) 2025-08-26T20:22:08.2337793Z >>> 2025-08-26T20:22:08.2337918Z >>> if self.flatten_state_dict: 2025-08-26T20:22:08.2338102Z >>> state_dict, self.mappings = flatten_state_dict(state_dict) 2025-08-26T20:22:08.2338198Z >>> 2025-08-26T20:22:08.2338308Z >>> self.state_dict = state_dict 2025-08-26T20:22:08.2338414Z >>> self.metadata = metadata 2025-08-26T20:22:08.2338549Z >>> self.is_coordinator = is_coordinator 2025-08-26T20:22:08.2338633Z >>> 2025-08-26T20:22:08.2338754Z >>> def load_bytes(self, read_item, value): 2025-08-26T20:22:08.2338868Z >>> # Remove the "foo_" prefix 2025-08-26T20:22:08.2339187Z >>> self.original_state_dict[read_item.dest_index.fqn[4:]] = torch.load(value, weights_only=False) 2025-08-26T20:22:08.2339306Z 2025-08-26T20:22:08.2339386Z 2025-08-26T20:22:08.2339652Z Modifying resolve_tensor and commit_tensor to handle load time transformation. 2025-08-26T20:22:08.2339746Z 2025-08-26T20:22:08.2339860Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2340027Z >>> class MetaModelMaterialize(DefaultSavePlanner): 2025-08-26T20:22:08.2340146Z >>> def resolve_tensor(self, read_item): 2025-08-26T20:22:08.2340281Z >>> tensor = super().resolve_tensor(read_item) 2025-08-26T20:22:08.2340519Z >>> return torch.empty_like(tensor, device="cpu") 2025-08-26T20:22:08.2340602Z >>> 2025-08-26T20:22:08.2340753Z >>> def commit_tensor(self, read_item, tensor): 2025-08-26T20:22:08.2340911Z >>> self.state_dict[read_item.dest_index.fqn] = tensor 2025-08-26T20:22:08.2340992Z 2025-08-26T20:22:08.2341262Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2341346Z 2025-08-26T20:22:08.2341458Z warnings.warn(msg) 2025-08-26T20:22:08.2341539Z 2025-08-26T20:22:08.2341745Z --- Parse Warning: 68 / 146 --- 2025-08-26T20:22:08.2342742Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict.py line=1118. 2025-08-26T20:22:08.2343011Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2343106Z 2025-08-26T20:22:08.2343271Z Return the model state_dict and optimizers state_dict. 2025-08-26T20:22:08.2343351Z 2025-08-26T20:22:08.2343587Z ``get_state_dict`` can process any module that is parallelized by PyTorch 2025-08-26T20:22:08.2343843Z FSDP/fully_shard, DDP/replicate, tensor_parallel/parallelize_module, and any 2025-08-26T20:22:08.2344107Z combination of these parallelisms. The main functions of ``get_state_dict`` 2025-08-26T20:22:08.2344330Z are: 1.) returning a model and optimizer state_dict that can be resharded 2025-08-26T20:22:08.2344539Z with a different number of trainers and/or different parallelisms. 2025-08-26T20:22:08.2344836Z 2.) hiding the parallelism-specific state_dict APIs. Users don't have to call 2025-08-26T20:22:08.2344923Z these APIs. 2025-08-26T20:22:08.2345058Z 3.) sanity checking the result state_dict. 2025-08-26T20:22:08.2345138Z 2025-08-26T20:22:08.2345345Z The keys of the result state dictionary are the canonical FQNs (Fully 2025-08-26T20:22:08.2345591Z Qualified Names). A canonical FQN refers to the FQN based on a parameter's 2025-08-26T20:22:08.2345825Z position in an nn.Module hierarchy. More specifically, a canonical FQN to a 2025-08-26T20:22:08.2346042Z parameter is the FQN returned by ``module.named_parameters()`` or 2025-08-26T20:22:08.2346249Z ``module.named_buffers()`` when the module is not distributed by any 2025-08-26T20:22:08.2346507Z parallelisms. Since the optimizer internally uses parameter IDs to represent 2025-08-26T20:22:08.2346802Z a parameter, there will be a conversion from the parameter IDs to the 2025-08-26T20:22:08.2346922Z canonical FQNs when calling this API. 2025-08-26T20:22:08.2347014Z 2025-08-26T20:22:08.2347230Z ``get_state_dict`` can also process a module that is not parallelized. In 2025-08-26T20:22:08.2347450Z such a case, ``get_state_dict`` only performs one function -- converting the 2025-08-26T20:22:08.2347603Z optimizer parameter IDs to the canonical FQNs. 2025-08-26T20:22:08.2347682Z 2025-08-26T20:22:08.2347781Z Example: 2025-08-26T20:22:08.2347880Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2347971Z >>> import torch 2025-08-26T20:22:08.2348224Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-26T20:22:08.2348425Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-08-26T20:22:08.2348687Z >>> from torch.distributed.checkpoint.state_dict import get_state_dict 2025-08-26T20:22:08.2348771Z 2025-08-26T20:22:08.2348900Z >>> fsdp_model = FSDP(copy.deepcopy(model)) 2025-08-26T20:22:08.2349096Z >>> fsdp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-08-26T20:22:08.2349221Z >>> ddp_model = DDP(copy.deepcopy(model)) 2025-08-26T20:22:08.2349410Z >>> ddp_optim = torch.optim.Adam(model.parameters(), lr=1e-3) 2025-08-26T20:22:08.2349489Z 2025-08-26T20:22:08.2349567Z 2025-08-26T20:22:08.2349811Z >>> ddp_state_dict, ddp_optim_state_dict = get_state_dict(ddp_model, ddp_optim) 2025-08-26T20:22:08.2349980Z >>> fsdp_state_dict, fsdp_optim_state_dict = get_state_dict( 2025-08-26T20:22:08.2350095Z ... fsdp_model, fsdp_optim 2025-08-26T20:22:08.2350178Z ... ) 2025-08-26T20:22:08.2350256Z 2025-08-26T20:22:08.2350480Z >>> # if we simply call ddp_model.state_dict() and fsdp_model.state_dict(), 2025-08-26T20:22:08.2350584Z >>> # the asserts will fail. 2025-08-26T20:22:08.2350728Z >>> assert ddp_state_dict == fsdp_state_dict 2025-08-26T20:22:08.2350874Z >>> assert ddp_optim_state == fsdp_optim_state_dict 2025-08-26T20:22:08.2350956Z 2025-08-26T20:22:08.2351044Z 2025-08-26T20:22:08.2351126Z Args: 2025-08-26T20:22:08.2351269Z model (nn.Module): the nn.Module to the model. 2025-08-26T20:22:08.2351464Z optimizers (Union[None, Optimizer, Iterable[Optimizer]]): 2025-08-26T20:22:08.2351620Z The optimizers that are used to optimize ``model``. 2025-08-26T20:22:08.2351909Z submodules (deprecated): Optional[set[nn.Module]]: only return the model parameters 2025-08-26T20:22:08.2352021Z that belong to the submodules. 2025-08-26T20:22:08.2352203Z options (StateDictOptions): the options to control how 2025-08-26T20:22:08.2352408Z model state_dict and optimizer state_dict should be returned. See 2025-08-26T20:22:08.2352531Z `StateDictOptions` for the details. 2025-08-26T20:22:08.2352625Z 2025-08-26T20:22:08.2352713Z Returns: 2025-08-26T20:22:08.2352906Z ``Tuple`` that contain model state_dict and optimizer state_dict. 2025-08-26T20:22:08.2353033Z 2025-08-26T20:22:08.2353264Z :rtype: typing.Tuple[typing.Dict[str, ValueType], OptimizerStateType] 2025-08-26T20:22:08.2353358Z 2025-08-26T20:22:08.2353609Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2353691Z 2025-08-26T20:22:08.2353802Z warnings.warn(msg) 2025-08-26T20:22:08.2353882Z 2025-08-26T20:22:08.2354086Z --- Parse Warning: 69 / 146 --- 2025-08-26T20:22:08.2355057Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_loader.py line=69. 2025-08-26T20:22:08.2355321Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2355463Z 2025-08-26T20:22:08.2355663Z Load a checkpoint into a distributed state dict in SPMD style. 2025-08-26T20:22:08.2355755Z 2025-08-26T20:22:08.2355976Z Each rank must have the same keys in their ``state_dict`` provided to this 2025-08-26T20:22:08.2356205Z API. Mismatched keys may result in hangs or errors. If unsure, you can use 2025-08-26T20:22:08.2356434Z the ``utils._assert_same_keys`` API to check (but may incur communication 2025-08-26T20:22:08.2356517Z costs). 2025-08-26T20:22:08.2356607Z 2025-08-26T20:22:08.2356787Z Each rank will try to read the least amount of data necessary 2025-08-26T20:22:08.2357017Z to fulfill the requested `state_dict`. When loading :class:`ShardedTensor` 2025-08-26T20:22:08.2357274Z or :class:`DTensor` instances, each rank only reads data for their local shards. 2025-08-26T20:22:08.2357354Z 2025-08-26T20:22:08.2357663Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-08-26T20:22:08.2357921Z load will first call ``state_dict`` before attempting deserialization, followed by 2025-08-26T20:22:08.2358091Z ``load_state_dict`` once the deserialization is complete. 2025-08-26T20:22:08.2358359Z For each non-``Stateful`` object, load will deserialize the object, and then replace 2025-08-26T20:22:08.2358509Z it in the ``state_dict`` with the deserialized object. 2025-08-26T20:22:08.2358601Z 2025-08-26T20:22:08.2358698Z .. warning:: 2025-08-26T20:22:08.2358866Z All tensors in ``state_dict`` must be allocated on their 2025-08-26T20:22:08.2359049Z destination device *prior to* calling this function. 2025-08-26T20:22:08.2359130Z 2025-08-26T20:22:08.2359368Z All non-tensor data is loaded using `torch.load()` and modified in place 2025-08-26T20:22:08.2359460Z on state_dict. 2025-08-26T20:22:08.2359541Z 2025-08-26T20:22:08.2359646Z .. warning:: 2025-08-26T20:22:08.2359853Z Users must call `load_state_dict` on the root module to ensure load 2025-08-26T20:22:08.2360046Z pos-processing and non-tensor data properly propagates. 2025-08-26T20:22:08.2360136Z 2025-08-26T20:22:08.2360219Z .. note: 2025-08-26T20:22:08.2360458Z If no process group is initialized, this function will assume the intent 2025-08-26T20:22:08.2360685Z is to load a checkpoint into the local process. This can be useful in the 2025-08-26T20:22:08.2360943Z case of local inference, and when using regular Tensors (as opposed to DTensor 2025-08-26T20:22:08.2361044Z or ShardedTensor) 2025-08-26T20:22:08.2361124Z 2025-08-26T20:22:08.2361221Z .. note: 2025-08-26T20:22:08.2361364Z Rank 0 is assumed to be the coordinator rank. 2025-08-26T20:22:08.2361457Z 2025-08-26T20:22:08.2361542Z Args: 2025-08-26T20:22:08.2361756Z state_dict (Dict[str, Any]): The state_dict to load the checkpoint into. 2025-08-26T20:22:08.2361917Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-26T20:22:08.2362126Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-26T20:22:08.2362373Z depends on the storage. It can be a path to a folder or to a file. 2025-08-26T20:22:08.2362541Z It can also be a key if the storage is a key-value store. 2025-08-26T20:22:08.2362635Z (Default: ``None``) 2025-08-26T20:22:08.2362779Z storage_reader (Optional[StorageReader]): 2025-08-26T20:22:08.2362983Z Instance of StorageWriter used to perform reads. If this is not 2025-08-26T20:22:08.2363199Z specified, DCP will automatically infer the reader based on the 2025-08-26T20:22:08.2363398Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-26T20:22:08.2363505Z be raised. (Default: ``None``) 2025-08-26T20:22:08.2363634Z planner (Optional[LoadPlanner]): 2025-08-26T20:22:08.2363837Z Instance of LoadPlanner. If this is not specified, the default 2025-08-26T20:22:08.2364031Z planner will be used. (Default: ``None``) 2025-08-26T20:22:08.2364161Z process_group (Optional[ProcessGroup]): 2025-08-26T20:22:08.2364343Z ProcessGroup to be used for cross-rank synchronization. 2025-08-26T20:22:08.2364452Z (Default: ``None``) 2025-08-26T20:22:08.2364662Z no_dist (bool): If ``True``, this function will assume the intent is to load 2025-08-26T20:22:08.2364931Z a checkpoint without using cross-rank synchronization. (Default: ``False``) 2025-08-26T20:22:08.2365013Z Returns: 2025-08-26T20:22:08.2365095Z None. 2025-08-26T20:22:08.2365188Z 2025-08-26T20:22:08.2365273Z Examples 2025-08-26T20:22:08.2365370Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2365480Z >>> my_model = MyModule() 2025-08-26T20:22:08.2365617Z >>> optimizer = Adagrad(my_model.parameters()) 2025-08-26T20:22:08.2365782Z >>> model_state_dict = my_model.state_dict() 2025-08-26T20:22:08.2366018Z >>> fs_storage_reader = torch.distributed.checkpoint.FileSystemReader( 2025-08-26T20:22:08.2366115Z ... "/checkpoint/1" 2025-08-26T20:22:08.2366212Z ... ) 2025-08-26T20:22:08.2366291Z 2025-08-26T20:22:08.2366456Z >>> torch.distributed.checkpoint.load_state_dict( 2025-08-26T20:22:08.2366567Z >>> state_dict=model_state_dict, 2025-08-26T20:22:08.2366684Z >>> storage_reader=fs_storage_reader, 2025-08-26T20:22:08.2366777Z >>> ) 2025-08-26T20:22:08.2366860Z 2025-08-26T20:22:08.2367067Z >>> # module.load_state_dict() function might have customized steps 2025-08-26T20:22:08.2367196Z >>> # to flush the state_dict, must call it to 2025-08-26T20:22:08.2367302Z >>> # ensure correct behavior. 2025-08-26T20:22:08.2367443Z >>> my_model.load_state_dict(model_state_dict) 2025-08-26T20:22:08.2367522Z 2025-08-26T20:22:08.2367622Z .. note:: 2025-08-26T20:22:08.2367834Z load_state_dict uses collectives to coordinate reads across ranks. 2025-08-26T20:22:08.2368045Z For NCCL-based process groups, internal tensor representations of 2025-08-26T20:22:08.2368286Z objects must be moved to the GPU device before communication takes place. 2025-08-26T20:22:08.2368503Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-08-26T20:22:08.2368741Z and it is the user's responsibility to ensure that this is set so that each 2025-08-26T20:22:08.2368925Z rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-08-26T20:22:08.2369008Z 2025-08-26T20:22:08.2369270Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2369350Z 2025-08-26T20:22:08.2369459Z warnings.warn(msg) 2025-08-26T20:22:08.2369538Z 2025-08-26T20:22:08.2369733Z --- Parse Warning: 70 / 146 --- 2025-08-26T20:22:08.2370710Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=97. 2025-08-26T20:22:08.2371003Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2371095Z 2025-08-26T20:22:08.2371216Z Save a distributed model in SPMD style. 2025-08-26T20:22:08.2371296Z 2025-08-26T20:22:08.2371498Z This function is different from ``torch.save()`` as it handles 2025-08-26T20:22:08.2371756Z ``ShardedTensor`` , and ``DTensor`` by having each rank only save their local shards. 2025-08-26T20:22:08.2371845Z 2025-08-26T20:22:08.2372100Z For each ``Stateful`` object (having both a ``state_dict`` and a ``load_state_dict``), 2025-08-26T20:22:08.2372252Z save will call ``state_dict`` before serialization. 2025-08-26T20:22:08.2372341Z 2025-08-26T20:22:08.2372432Z .. warning:: 2025-08-26T20:22:08.2372763Z There is no guarantees of Backwards Compatibility across PyTorch versions 2025-08-26T20:22:08.2372864Z for saved state_dicts. 2025-08-26T20:22:08.2372944Z 2025-08-26T20:22:08.2373045Z .. warning:: 2025-08-26T20:22:08.2373254Z If using the `process_group` argument, make sure that only its ranks 2025-08-26T20:22:08.2373457Z call `save_state_dict` and that all data in state_dict belong to it. 2025-08-26T20:22:08.2373549Z 2025-08-26T20:22:08.2373633Z .. note:: 2025-08-26T20:22:08.2373903Z When saving checkpoint for FSDP's `ShardingStrategy.HYBRID_SHARD`, only one of 2025-08-26T20:22:08.2374164Z the shard_group should be calling `save_state_dict` and the corresponding process 2025-08-26T20:22:08.2374272Z group needs to be passed in. 2025-08-26T20:22:08.2374363Z 2025-08-26T20:22:08.2374449Z .. note:: 2025-08-26T20:22:08.2374725Z If no process group is available, this function assumes the intention is to save the 2025-08-26T20:22:08.2374865Z state_dict in the local process. 2025-08-26T20:22:08.2374950Z 2025-08-26T20:22:08.2375044Z .. note: 2025-08-26T20:22:08.2375184Z Rank 0 is assumed to be the coordinator rank. 2025-08-26T20:22:08.2375276Z 2025-08-26T20:22:08.2375355Z 2025-08-26T20:22:08.2375440Z Args: 2025-08-26T20:22:08.2375606Z state_dict (Dict[str, Any]): The state_dict to save. 2025-08-26T20:22:08.2375751Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-26T20:22:08.2375969Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-26T20:22:08.2376170Z depends on the storage. It can be a path to a folder or to a file. 2025-08-26T20:22:08.2376335Z It can also be a key if the storage is a key-value store. 2025-08-26T20:22:08.2376442Z (Default: ``None``) 2025-08-26T20:22:08.2376575Z storage_writer (Optional[StorageWriter]): 2025-08-26T20:22:08.2376797Z Instance of StorageWriter used to perform writes. If this is not 2025-08-26T20:22:08.2377002Z specified, DCP will automatically infer the writer based on the 2025-08-26T20:22:08.2377201Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-26T20:22:08.2377323Z be raised. (Default: ``None``) 2025-08-26T20:22:08.2377438Z planner (Optional[SavePlanner]): 2025-08-26T20:22:08.2377648Z Instance of SavePlanner. If this is not specified, the default 2025-08-26T20:22:08.2377777Z planner will be used. (Default: ``None``) 2025-08-26T20:22:08.2377907Z process_group (Optional[ProcessGroup]): 2025-08-26T20:22:08.2378117Z ProcessGroup to be used for cross-rank synchronization. 2025-08-26T20:22:08.2378260Z (Default: ``None``) 2025-08-26T20:22:08.2378363Z no_dist (bool): 2025-08-26T20:22:08.2378529Z If ``True``, this function will assume the intent is to load 2025-08-26T20:22:08.2378657Z a checkpoint on a single rank/process. 2025-08-26T20:22:08.2378771Z (Default: ``False``) 2025-08-26T20:22:08.2379058Z use_collectives (bool): If ``False``, this function will assume the intent is to save 2025-08-26T20:22:08.2379269Z a checkpoint without using cross-rank synchronization. 2025-08-26T20:22:08.2379364Z (Default: ``True``) 2025-08-26T20:22:08.2379585Z This configuration is experimental and should be used with caution. 2025-08-26T20:22:08.2379863Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-08-26T20:22:08.2379942Z 2025-08-26T20:22:08.2380036Z Returns: 2025-08-26T20:22:08.2380215Z Metadata: Metadata object for the saved checkpoint. 2025-08-26T20:22:08.2380294Z 2025-08-26T20:22:08.2380470Z Example: 2025-08-26T20:22:08.2380569Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2380667Z >>> my_model = MyModule() 2025-08-26T20:22:08.2380762Z 2025-08-26T20:22:08.2380873Z >>> state_dict = {"model": my_model} 2025-08-26T20:22:08.2381021Z 2025-08-26T20:22:08.2381253Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-08-26T20:22:08.2381350Z ... "/checkpoint/1" 2025-08-26T20:22:08.2381442Z ... ) 2025-08-26T20:22:08.2381570Z >>> torch.distributed.checkpoint.save( 2025-08-26T20:22:08.2381685Z >>> state_dict=state_dict, 2025-08-26T20:22:08.2381803Z >>> storage_writer=fs_storage_writer, 2025-08-26T20:22:08.2381886Z >>> ) 2025-08-26T20:22:08.2381977Z 2025-08-26T20:22:08.2382062Z .. note:: 2025-08-26T20:22:08.2382288Z save_state_dict uses collectives to coordinate writes across ranks. 2025-08-26T20:22:08.2382500Z For NCCL-based process groups, internal tensor representations of 2025-08-26T20:22:08.2382727Z objects must be moved to the GPU device before communication takes place. 2025-08-26T20:22:08.2382986Z In this case, the device used is given by ``torch.cuda.current_device()`` 2025-08-26T20:22:08.2383194Z and it is the user's responsibility to ensure that this is set so that 2025-08-26T20:22:08.2383409Z each rank has an individual GPU, via ``torch.cuda.set_device()``. 2025-08-26T20:22:08.2383491Z 2025-08-26T20:22:08.2383742Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2383835Z 2025-08-26T20:22:08.2383931Z warnings.warn(msg) 2025-08-26T20:22:08.2384026Z 2025-08-26T20:22:08.2384228Z --- Parse Warning: 71 / 146 --- 2025-08-26T20:22:08.2385215Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_save in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/checkpoint/state_dict_saver.py line=230. 2025-08-26T20:22:08.2385490Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2385761Z Asynchronous version of ``save``. This code first de-stages the state_dict on to the 2025-08-26T20:22:08.2386059Z staging storage (defaults to CPU memory), and then calls the `save` in a separate thread. 2025-08-26T20:22:08.2386141Z 2025-08-26T20:22:08.2386231Z .. warning:: 2025-08-26T20:22:08.2386404Z This feature is experimental and subject to change. 2025-08-26T20:22:08.2386548Z MUST CALL CLOSE AFTER LAST CHECKPOINT IS SAVED 2025-08-26T20:22:08.2386640Z 2025-08-26T20:22:08.2386724Z Args: 2025-08-26T20:22:08.2386879Z state_dict (Dict[str, Any]): The state_dict to save. 2025-08-26T20:22:08.2387035Z checkpoint_id (Union[str, os.PathLike, None]): 2025-08-26T20:22:08.2387255Z The ID of this checkpoint instance. The meaning of the checkpoint_id 2025-08-26T20:22:08.2387474Z depends on the storage. It can be a path to a folder or to a file. 2025-08-26T20:22:08.2387644Z It can also be a key if the storage is a key-value store. 2025-08-26T20:22:08.2387745Z (Default: ``None``) 2025-08-26T20:22:08.2387895Z storage_writer (Optional[StorageWriter]): 2025-08-26T20:22:08.2388142Z Instance of StorageWriter used to perform 'stage' and 'save'. If 2025-08-26T20:22:08.2388392Z this is not specified, DCP will automatically infer the writer based on the 2025-08-26T20:22:08.2388598Z checkpoint_id. If checkpoint_id is also None, an exception will 2025-08-26T20:22:08.2388714Z be raised. (Default: ``None``) 2025-08-26T20:22:08.2388850Z planner (Optional[SavePlanner]): 2025-08-26T20:22:08.2389051Z Instance of SavePlanner. If this is not specified, the default 2025-08-26T20:22:08.2389197Z planner will be used. (Default: ``None``) 2025-08-26T20:22:08.2389330Z process_group (Optional[ProcessGroup]): 2025-08-26T20:22:08.2389516Z ProcessGroup to be used for cross-rank synchronization. 2025-08-26T20:22:08.2389691Z (Default: ``None``) 2025-08-26T20:22:08.2389855Z async_checkpointer_type (AsyncCheckpointerType): 2025-08-26T20:22:08.2390042Z whether to do checkpoint in separate thread or process 2025-08-26T20:22:08.2390190Z (Default: ``AsyncCheckpointerType.THREAD``) 2025-08-26T20:22:08.2390301Z async_stager (AsyncStager): 2025-08-26T20:22:08.2390593Z provides staging implementation. If storage_writer implements AsyncStager 2025-08-26T20:22:08.2390805Z and async_stager is provided, async_stager will be used for staging 2025-08-26T20:22:08.2390917Z no_dist (bool): 2025-08-26T20:22:08.2391087Z If ``True``, this function will assume the intent is to save 2025-08-26T20:22:08.2391230Z a checkpoint on a single rank/process. 2025-08-26T20:22:08.2391334Z (Default: ``False``) 2025-08-26T20:22:08.2391868Z use_collectives: If False, Save the checkpoint without rank coordination. (Default: ``True``) 2025-08-26T20:22:08.2392108Z This configuration is experimental and should be used with caution. 2025-08-26T20:22:08.2392383Z It will change the format of the saved checkpoint and may not be backward compatible. 2025-08-26T20:22:08.2392479Z 2025-08-26T20:22:08.2392565Z Returns: 2025-08-26T20:22:08.2392780Z Future: A future holding the resultant Metadata object from `save`. 2025-08-26T20:22:08.2392875Z 2025-08-26T20:22:08.2392963Z Example: 2025-08-26T20:22:08.2393064Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2393183Z >>> my_model = MyModule() 2025-08-26T20:22:08.2393264Z 2025-08-26T20:22:08.2393391Z >>> state_dict = {"model": my_model} 2025-08-26T20:22:08.2393474Z 2025-08-26T20:22:08.2393708Z >>> fs_storage_writer = torch.distributed.checkpoint.FileSystemWriter( 2025-08-26T20:22:08.2393819Z ... "/checkpoint/1" 2025-08-26T20:22:08.2393905Z ... ) 2025-08-26T20:22:08.2394129Z >>> checkpoint_future = torch.distributed.checkpoint.async_save( 2025-08-26T20:22:08.2394240Z >>> state_dict=state_dict, 2025-08-26T20:22:08.2394366Z >>> storage_writer=fs_storage_writer, 2025-08-26T20:22:08.2394456Z >>> ) 2025-08-26T20:22:08.2394538Z >>> 2025-08-26T20:22:08.2394650Z >>> # ... do some work ... 2025-08-26T20:22:08.2394730Z >>> 2025-08-26T20:22:08.2394843Z >>> checkpoint_future.result() 2025-08-26T20:22:08.2394934Z 2025-08-26T20:22:08.2395014Z 2025-08-26T20:22:08.2395267Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2395358Z 2025-08-26T20:22:08.2395453Z warnings.warn(msg) 2025-08-26T20:22:08.2395544Z 2025-08-26T20:22:08.2395741Z --- Parse Warning: 72 / 146 --- 2025-08-26T20:22:08.2396794Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=construct_and_record_rdzv_event in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/elastic/events/__init__.py line=94. 2025-08-26T20:22:08.2397146Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2397227Z 2025-08-26T20:22:08.2397442Z Initialize rendezvous event object and record its operations. 2025-08-26T20:22:08.2397522Z 2025-08-26T20:22:08.2397607Z Args: 2025-08-26T20:22:08.2397749Z run_id (str): The run id of the rendezvous. 2025-08-26T20:22:08.2397896Z message (str): The message describing the event. 2025-08-26T20:22:08.2398162Z node_state (NodeState): The state of the node (INIT, RUNNING, SUCCEEDED, FAILED). 2025-08-26T20:22:08.2398350Z name (str): Event name. (E.g. Current action being performed). 2025-08-26T20:22:08.2398471Z hostname (str): Hostname of the node. 2025-08-26T20:22:08.2398715Z pid (Optional[int]): The process id of the node. 2025-08-26T20:22:08.2398962Z master_endpoint (str): The master endpoint for the rendezvous store, if known. 2025-08-26T20:22:08.2399242Z local_id (Optional[int]): The local_id of the node, if defined in dynamic_rendezvous.py 2025-08-26T20:22:08.2399400Z rank (Optional[int]): The rank of the node, if known. 2025-08-26T20:22:08.2399499Z Returns: 2025-08-26T20:22:08.2399581Z None 2025-08-26T20:22:08.2399666Z Example: 2025-08-26T20:22:08.2399805Z >>> # See DynamicRendezvousHandler class 2025-08-26T20:22:08.2399895Z >>> def _record( 2025-08-26T20:22:08.2399980Z ... self, 2025-08-26T20:22:08.2400087Z ... message: str, 2025-08-26T20:22:08.2400227Z ... node_state: NodeState = NodeState.RUNNING, 2025-08-26T20:22:08.2400348Z ... rank: Optional[int] = None, 2025-08-26T20:22:08.2400471Z ... ) -> None: 2025-08-26T20:22:08.2400596Z ... construct_and_record_rdzv_event( 2025-08-26T20:22:08.2400771Z ... name=f"{self.__class__.__name__}.{get_method_name()}", 2025-08-26T20:22:08.2400892Z ... run_id=self._settings.run_id, 2025-08-26T20:22:08.2401002Z ... message=message, 2025-08-26T20:22:08.2401107Z ... node_state=node_state, 2025-08-26T20:22:08.2401226Z ... hostname=self._this_node.addr, 2025-08-26T20:22:08.2401345Z ... pid=self._this_node.pid, 2025-08-26T20:22:08.2401467Z ... local_id=self._this_node.local_id, 2025-08-26T20:22:08.2401572Z ... rank=rank, 2025-08-26T20:22:08.2401654Z ... ) 2025-08-26T20:22:08.2401733Z 2025-08-26T20:22:08.2401999Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2402081Z 2025-08-26T20:22:08.2402189Z warnings.warn(msg) 2025-08-26T20:22:08.2402270Z 2025-08-26T20:22:08.2402459Z --- Parse Warning: 73 / 146 --- 2025-08-26T20:22:08.2403397Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixedPrecision in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=114. 2025-08-26T20:22:08.2403662Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2403754Z 2025-08-26T20:22:08.2403920Z This configures FSDP-native mixed precision training. 2025-08-26T20:22:08.2403999Z 2025-08-26T20:22:08.2404099Z Attributes: 2025-08-26T20:22:08.2404333Z param_dtype (Optional[torch.dtype]): This specifies the dtype for model 2025-08-26T20:22:08.2404545Z parameters during forward and backward and thus the dtype for 2025-08-26T20:22:08.2404766Z forward and backward computation. Outside forward and backward, the 2025-08-26T20:22:08.2404959Z *sharded* parameters are kept in full precision (e.g. for the 2025-08-26T20:22:08.2405182Z optimizer step), and for model checkpointing, the parameters are 2025-08-26T20:22:08.2405339Z always saved in full precision. (Default: ``None``) 2025-08-26T20:22:08.2405597Z reduce_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-08-26T20:22:08.2405811Z gradient reduction (i.e. reduce-scatter or all-reduce). If this is 2025-08-26T20:22:08.2405987Z ``None`` but ``param_dtype`` is not ``None``, then this takes on 2025-08-26T20:22:08.2406198Z the ``param_dtype`` value, still running gradient reduction in low 2025-08-26T20:22:08.2406410Z precision. This is permitted to differ from ``param_dtype``, e.g. 2025-08-26T20:22:08.2406618Z to force gradient reduction to run in full precision. (Default: 2025-08-26T20:22:08.2406705Z ``None``) 2025-08-26T20:22:08.2406917Z buffer_dtype (Optional[torch.dtype]): This specifies the dtype for 2025-08-26T20:22:08.2407177Z buffers. FSDP does not shard buffers. Rather, FSDP casts them to 2025-08-26T20:22:08.2407375Z ``buffer_dtype`` in the first forward pass and keeps them in that 2025-08-26T20:22:08.2407598Z dtype thereafter. For model checkpointing, the buffers are saved 2025-08-26T20:22:08.2407780Z in full precision except for ``LOCAL_STATE_DICT``. (Default: 2025-08-26T20:22:08.2407872Z ``None``) 2025-08-26T20:22:08.2408080Z keep_low_precision_grads (bool): If ``False``, then FSDP upcasts 2025-08-26T20:22:08.2408295Z gradients to full precision after the backward pass in preparation 2025-08-26T20:22:08.2408510Z for the optimizer step. If ``True``, then FSDP keeps the gradients 2025-08-26T20:22:08.2408708Z in the dtype used for gradient reduction, which can save memory if 2025-08-26T20:22:08.2408914Z using a custom optimizer that supports running in low precision. 2025-08-26T20:22:08.2409054Z (Default: ``False``) 2025-08-26T20:22:08.2409265Z cast_forward_inputs (bool): If ``True``, then this FSDP module casts 2025-08-26T20:22:08.2409480Z its forward args and kwargs to ``param_dtype``. This is to ensure 2025-08-26T20:22:08.2409692Z that parameter and input dtypes match for forward computation, as 2025-08-26T20:22:08.2409908Z required by many ops. This may need to be set to ``True`` when only 2025-08-26T20:22:08.2410126Z applying mixed precision to some but not all FSDP modules, in which 2025-08-26T20:22:08.2410333Z case a mixed-precision FSDP submodule needs to recast its inputs. 2025-08-26T20:22:08.2410445Z (Default: ``False``) 2025-08-26T20:22:08.2410663Z cast_root_forward_inputs (bool): If ``True``, then the root FSDP module 2025-08-26T20:22:08.2410873Z casts its forward args and kwargs to ``param_dtype``, overriding 2025-08-26T20:22:08.2411062Z the value of ``cast_forward_inputs``. For non-root FSDP modules, 2025-08-26T20:22:08.2411206Z this does not do anything. (Default: ``True``) 2025-08-26T20:22:08.2411432Z _module_classes_to_ignore: (Sequence[Type[nn.Module]]): This specifies 2025-08-26T20:22:08.2411616Z module classes to ignore for mixed precision when using an 2025-08-26T20:22:08.2411808Z ``auto_wrap_policy``: Modules of these classes will have FSDP 2025-08-26T20:22:08.2412015Z applied to them separately with mixed precision disabled (meaning 2025-08-26T20:22:08.2412214Z that the final FSDP construction would deviate from the specified 2025-08-26T20:22:08.2412413Z policy). If ``auto_wrap_policy`` is not specified, then this does 2025-08-26T20:22:08.2412608Z not do anything. This API is experimental and subject to change. 2025-08-26T20:22:08.2412729Z (Default: ``(_BatchNorm,)``) 2025-08-26T20:22:08.2412810Z 2025-08-26T20:22:08.2412982Z .. note:: This API is experimental and subject to change. 2025-08-26T20:22:08.2413077Z 2025-08-26T20:22:08.2413296Z .. note:: Only floating point tensors are cast to their specified dtypes. 2025-08-26T20:22:08.2413418Z 2025-08-26T20:22:08.2413601Z .. note:: In ``summon_full_params``, parameters are forced to full 2025-08-26T20:22:08.2413712Z precision, but buffers are not. 2025-08-26T20:22:08.2413803Z 2025-08-26T20:22:08.2414006Z .. note:: Layer norm and batch norm accumulate in ``float32`` even when 2025-08-26T20:22:08.2414225Z their inputs are in a low precision like ``float16`` or ``bfloat16``. 2025-08-26T20:22:08.2414454Z Disabling FSDP's mixed precision for those norm modules only means that 2025-08-26T20:22:08.2414669Z the affine parameters are kept in ``float32``. However, this incurs 2025-08-26T20:22:08.2414918Z separate all-gathers and reduce-scatters for those norm modules, which 2025-08-26T20:22:08.2415137Z may be inefficient, so if the workload permits, the user should prefer 2025-08-26T20:22:08.2415351Z to still apply mixed precision to those modules. 2025-08-26T20:22:08.2415435Z 2025-08-26T20:22:08.2415643Z .. note:: By default, if the user passes a model with any ``_BatchNorm`` 2025-08-26T20:22:08.2415861Z modules and specifies an ``auto_wrap_policy``, then the batch norm 2025-08-26T20:22:08.2416085Z modules will have FSDP applied to them separately with mixed precision 2025-08-26T20:22:08.2416272Z disabled. See the ``_module_classes_to_ignore`` argument. 2025-08-26T20:22:08.2416353Z 2025-08-26T20:22:08.2416571Z .. note:: ``MixedPrecision`` has ``cast_root_forward_inputs=True`` and 2025-08-26T20:22:08.2416783Z ``cast_forward_inputs=False`` by default. For the root FSDP instance, 2025-08-26T20:22:08.2416954Z its ``cast_root_forward_inputs`` takes precedence over its 2025-08-26T20:22:08.2417141Z ``cast_forward_inputs``. For non-root FSDP instances, their 2025-08-26T20:22:08.2417390Z ``cast_root_forward_inputs`` values are ignored. The default setting is 2025-08-26T20:22:08.2417623Z sufficient for the typical case where each FSDP instance has the same 2025-08-26T20:22:08.2417853Z ``MixedPrecision`` configuration and only needs to cast inputs to the 2025-08-26T20:22:08.2418031Z ``param_dtype`` at the beginning of the model's forward pass. 2025-08-26T20:22:08.2418121Z 2025-08-26T20:22:08.2418327Z .. note:: For nested FSDP instances with different ``MixedPrecision`` 2025-08-26T20:22:08.2418576Z configurations, we recommend setting individual ``cast_forward_inputs`` 2025-08-26T20:22:08.2425548Z values to configure casting inputs or not before each instance's 2025-08-26T20:22:08.2425821Z forward. In such a case, since the casts happen before each FSDP 2025-08-26T20:22:08.2426044Z instance's forward, a parent FSDP instance should have its non-FSDP 2025-08-26T20:22:08.2426301Z submodules run before its FSDP submodules to avoid the activation dtype 2025-08-26T20:22:08.2426518Z being changed due to a different ``MixedPrecision`` configuration. 2025-08-26T20:22:08.2426609Z 2025-08-26T20:22:08.2426707Z Example:: 2025-08-26T20:22:08.2426802Z 2025-08-26T20:22:08.2426937Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2427120Z >>> model = nn.Sequential(nn.Linear(3, 3), nn.Linear(3, 3)) 2025-08-26T20:22:08.2427218Z >>> model[1] = FSDP( 2025-08-26T20:22:08.2427309Z >>> model[1], 2025-08-26T20:22:08.2427629Z >>> mixed_precision=MixedPrecision(param_dtype=torch.float16, cast_forward_inputs=True), 2025-08-26T20:22:08.2427714Z >>> ) 2025-08-26T20:22:08.2427822Z >>> model = FSDP( 2025-08-26T20:22:08.2427910Z >>> model, 2025-08-26T20:22:08.2428218Z >>> mixed_precision=MixedPrecision(param_dtype=torch.bfloat16, cast_forward_inputs=True), 2025-08-26T20:22:08.2428316Z >>> ) 2025-08-26T20:22:08.2428398Z 2025-08-26T20:22:08.2428625Z The above shows a working example. On the other hand, if ``model[1]`` 2025-08-26T20:22:08.2428910Z were replaced with ``model[0]``, meaning that the submodule using 2025-08-26T20:22:08.2429135Z different ``MixedPrecision`` ran its forward first, then ``model[1]`` 2025-08-26T20:22:08.2429370Z would incorrectly see ``float16`` activations instead of ``bfloat16`` 2025-08-26T20:22:08.2429456Z ones. 2025-08-26T20:22:08.2429549Z 2025-08-26T20:22:08.2429631Z 2025-08-26T20:22:08.2429883Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2429979Z 2025-08-26T20:22:08.2430078Z warnings.warn(msg) 2025-08-26T20:22:08.2430157Z 2025-08-26T20:22:08.2430416Z --- Parse Warning: 74 / 146 --- 2025-08-26T20:22:08.2431429Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullStateDictConfig in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/api.py line=295. 2025-08-26T20:22:08.2431709Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2431793Z 2025-08-26T20:22:08.2432013Z ``FullStateDictConfig`` is a config class meant to be used with 2025-08-26T20:22:08.2432209Z ``StateDictType.FULL_STATE_DICT``. We recommend enabling both 2025-08-26T20:22:08.2432417Z ``offload_to_cpu=True`` and ``rank0_only=True`` when saving full state 2025-08-26T20:22:08.2432651Z dicts to save GPU memory and CPU memory, respectively. This config class 2025-08-26T20:22:08.2432848Z is meant to be used via the :func:`state_dict_type` context manager as 2025-08-26T20:22:08.2432950Z follows: 2025-08-26T20:22:08.2433032Z 2025-08-26T20:22:08.2433163Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2433445Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-26T20:22:08.2433580Z >>> fsdp = FSDP(model, auto_wrap_policy=...) 2025-08-26T20:22:08.2433792Z >>> cfg = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) 2025-08-26T20:22:08.2434017Z >>> with FSDP.state_dict_type(fsdp, StateDictType.FULL_STATE_DICT, cfg): 2025-08-26T20:22:08.2434129Z >>> state = fsdp.state_dict() 2025-08-26T20:22:08.2434352Z >>> # `state` will be empty on non rank 0 and contain CPU tensors on rank 0. 2025-08-26T20:22:08.2434589Z >>> # To reload checkpoint for inference, finetuning, transfer learning, etc: 2025-08-26T20:22:08.2434822Z >>> model = model_fn() # Initialize model in preparation for wrapping with FSDP 2025-08-26T20:22:08.2434940Z >>> if dist.get_rank() == 0: 2025-08-26T20:22:08.2435122Z >>> # Load checkpoint only on rank 0 to avoid memory redundancy 2025-08-26T20:22:08.2435279Z >>> state_dict = torch.load("my_checkpoint.pt") 2025-08-26T20:22:08.2435406Z >>> model.load_state_dict(state_dict) 2025-08-26T20:22:08.2435640Z >>> # All ranks initialize FSDP module as usual. `sync_module_states` argument 2025-08-26T20:22:08.2435894Z >>> # communicates loaded checkpoint states from rank 0 to rest of the world. 2025-08-26T20:22:08.2435989Z >>> fsdp = FSDP( 2025-08-26T20:22:08.2436088Z ... model, 2025-08-26T20:22:08.2436221Z ... device_id=torch.cuda.current_device(), 2025-08-26T20:22:08.2436323Z ... auto_wrap_policy=..., 2025-08-26T20:22:08.2436442Z ... sync_module_states=True, 2025-08-26T20:22:08.2436522Z ... ) 2025-08-26T20:22:08.2436748Z >>> # After this point, all ranks have FSDP model with loaded checkpoint. 2025-08-26T20:22:08.2436829Z 2025-08-26T20:22:08.2436917Z Attributes: 2025-08-26T20:22:08.2437126Z rank0_only (bool): If ``True``, then only rank 0 saves the full state 2025-08-26T20:22:08.2437329Z dict, and nonzero ranks save an empty dict. If ``False``, then all 2025-08-26T20:22:08.2437500Z ranks save the full state dict. (Default: ``False``) 2025-08-26T20:22:08.2437608Z 2025-08-26T20:22:08.2437861Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2437953Z 2025-08-26T20:22:08.2438048Z warnings.warn(msg) 2025-08-26T20:22:08.2438124Z 2025-08-26T20:22:08.2438330Z --- Parse Warning: 75 / 146 --- 2025-08-26T20:22:08.2439508Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.set_state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=634. 2025-08-26T20:22:08.2439780Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2440027Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-08-26T20:22:08.2440120Z 2025-08-26T20:22:08.2440433Z Also takes (optional) configuration for the model's and optimizer's state dict. 2025-08-26T20:22:08.2440643Z The target module does not have to be a FSDP module. If the target 2025-08-26T20:22:08.2440864Z module is a FSDP module, its ``state_dict_type`` will also be changed. 2025-08-26T20:22:08.2440943Z 2025-08-26T20:22:08.2441150Z .. note:: This API should be called for only the top-level (root) 2025-08-26T20:22:08.2441236Z module. 2025-08-26T20:22:08.2441317Z 2025-08-26T20:22:08.2441536Z .. note:: This API enables users to transparently use the conventional 2025-08-26T20:22:08.2441727Z ``state_dict`` API to take model checkpoints in cases where the 2025-08-26T20:22:08.2441943Z root FSDP module is wrapped by another ``nn.Module``. For example, 2025-08-26T20:22:08.2442177Z the following will ensure ``state_dict`` is called on all non-FSDP 2025-08-26T20:22:08.2442416Z instances, while dispatching into `sharded_state_dict` implementation 2025-08-26T20:22:08.2442521Z for FSDP: 2025-08-26T20:22:08.2442601Z 2025-08-26T20:22:08.2442704Z Example:: 2025-08-26T20:22:08.2442783Z 2025-08-26T20:22:08.2442916Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2443038Z >>> model = DDP(FSDP(...)) 2025-08-26T20:22:08.2443175Z >>> FSDP.set_state_dict_type( 2025-08-26T20:22:08.2443276Z >>> model, 2025-08-26T20:22:08.2443405Z >>> StateDictType.SHARDED_STATE_DICT, 2025-08-26T20:22:08.2443616Z >>> state_dict_config = ShardedStateDictConfig(offload_to_cpu=True), 2025-08-26T20:22:08.2443850Z >>> optim_state_dict_config = OptimStateDictConfig(offload_to_cpu=True), 2025-08-26T20:22:08.2443936Z >>> ) 2025-08-26T20:22:08.2444079Z >>> param_state_dict = model.state_dict() 2025-08-26T20:22:08.2444251Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-08-26T20:22:08.2444332Z 2025-08-26T20:22:08.2444428Z Args: 2025-08-26T20:22:08.2444553Z module (torch.nn.Module): Root module. 2025-08-26T20:22:08.2444793Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-08-26T20:22:08.2445030Z state_dict_config (Optional[StateDictConfig]): the configuration for the 2025-08-26T20:22:08.2445141Z target ``state_dict_type``. 2025-08-26T20:22:08.2445405Z optim_state_dict_config (Optional[OptimStateDictConfig]): the configuration 2025-08-26T20:22:08.2445523Z for the optimizer state dict. 2025-08-26T20:22:08.2445614Z 2025-08-26T20:22:08.2445701Z Returns: 2025-08-26T20:22:08.2445926Z A StateDictSettings that include the previous state_dict type and 2025-08-26T20:22:08.2446059Z configuration for the module. 2025-08-26T20:22:08.2446145Z 2025-08-26T20:22:08.2446407Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2446515Z 2025-08-26T20:22:08.2446659Z warnings.warn(msg) 2025-08-26T20:22:08.2446752Z 2025-08-26T20:22:08.2446942Z --- Parse Warning: 76 / 146 --- 2025-08-26T20:22:08.2448121Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.state_dict_type in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=792. 2025-08-26T20:22:08.2448383Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2448631Z Set the ``state_dict_type`` of all the descendant FSDP modules of the target module. 2025-08-26T20:22:08.2448725Z 2025-08-26T20:22:08.2449089Z This context manager has the same functions as :meth:`set_state_dict_type`. Read the document of 2025-08-26T20:22:08.2449235Z :meth:`set_state_dict_type` for the detail. 2025-08-26T20:22:08.2449316Z 2025-08-26T20:22:08.2449409Z Example:: 2025-08-26T20:22:08.2449498Z 2025-08-26T20:22:08.2449629Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2449749Z >>> model = DDP(FSDP(...)) 2025-08-26T20:22:08.2449861Z >>> with FSDP.state_dict_type( 2025-08-26T20:22:08.2449951Z >>> model, 2025-08-26T20:22:08.2450092Z >>> StateDictType.SHARDED_STATE_DICT, 2025-08-26T20:22:08.2450176Z >>> ): 2025-08-26T20:22:08.2450311Z >>> checkpoint = model.state_dict() 2025-08-26T20:22:08.2450391Z 2025-08-26T20:22:08.2450475Z Args: 2025-08-26T20:22:08.2450640Z module (torch.nn.Module): Root module. 2025-08-26T20:22:08.2450878Z state_dict_type (StateDictType): the desired ``state_dict_type`` to set. 2025-08-26T20:22:08.2451105Z state_dict_config (Optional[StateDictConfig]): the model ``state_dict`` 2025-08-26T20:22:08.2451278Z configuration for the target ``state_dict_type``. 2025-08-26T20:22:08.2451511Z optim_state_dict_config (Optional[OptimStateDictConfig]): the optimizer 2025-08-26T20:22:08.2451720Z ``state_dict`` configuration for the target ``state_dict_type``. 2025-08-26T20:22:08.2451802Z 2025-08-26T20:22:08.2452064Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2452145Z 2025-08-26T20:22:08.2452241Z warnings.warn(msg) 2025-08-26T20:22:08.2452331Z 2025-08-26T20:22:08.2452516Z --- Parse Warning: 77 / 146 --- 2025-08-26T20:22:08.2453704Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1805. 2025-08-26T20:22:08.2453967Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2454047Z 2025-08-26T20:22:08.2454298Z Transform the state-dict of an optimizer corresponding to a sharded model. 2025-08-26T20:22:08.2454377Z 2025-08-26T20:22:08.2454581Z The given state-dict can be transformed to one of three types: 2025-08-26T20:22:08.2454877Z 1) full optimizer state_dict, 2) sharded optimizer state_dict, 3) local optimizer state_dict. 2025-08-26T20:22:08.2454956Z 2025-08-26T20:22:08.2455197Z For full optimizer state_dict, all states are unflattened and not sharded. 2025-08-26T20:22:08.2455412Z Rank0 only and CPU only can be specified via :meth:`state_dict_type` to 2025-08-26T20:22:08.2455511Z avoid OOM. 2025-08-26T20:22:08.2455590Z 2025-08-26T20:22:08.2455826Z For sharded optimizer state_dict, all states are unflattened but sharded. 2025-08-26T20:22:08.2456038Z CPU only can be specified via :meth:`state_dict_type` to further save 2025-08-26T20:22:08.2456153Z memory. 2025-08-26T20:22:08.2456246Z 2025-08-26T20:22:08.2456460Z For local state_dict, no transformation will be performed. But a state 2025-08-26T20:22:08.2456697Z will be converted from nn.Tensor to ShardedTensor to represent its sharding 2025-08-26T20:22:08.2456822Z nature (this is not supported yet). 2025-08-26T20:22:08.2456904Z 2025-08-26T20:22:08.2456993Z Example:: 2025-08-26T20:22:08.2457085Z 2025-08-26T20:22:08.2457215Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2457463Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-26T20:22:08.2457619Z >>> from torch.distributed.fsdp import StateDictType 2025-08-26T20:22:08.2457798Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-08-26T20:22:08.2458071Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-08-26T20:22:08.2458173Z >>> # Save a checkpoint 2025-08-26T20:22:08.2458283Z >>> model, optim = ... 2025-08-26T20:22:08.2458390Z >>> FSDP.set_state_dict_type( 2025-08-26T20:22:08.2458473Z >>> model, 2025-08-26T20:22:08.2458603Z >>> StateDictType.FULL_STATE_DICT, 2025-08-26T20:22:08.2458734Z >>> FullStateDictConfig(rank0_only=False), 2025-08-26T20:22:08.2458887Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-26T20:22:08.2458967Z >>> ) 2025-08-26T20:22:08.2459078Z >>> state_dict = model.state_dict() 2025-08-26T20:22:08.2459257Z >>> optim_state_dict = FSDP.optim_state_dict(model, optim) 2025-08-26T20:22:08.2459400Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-08-26T20:22:08.2459537Z >>> # Load a checkpoint 2025-08-26T20:22:08.2459635Z >>> model, optim = ... 2025-08-26T20:22:08.2459788Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-08-26T20:22:08.2459907Z >>> FSDP.set_state_dict_type( 2025-08-26T20:22:08.2459994Z >>> model, 2025-08-26T20:22:08.2460122Z >>> StateDictType.FULL_STATE_DICT, 2025-08-26T20:22:08.2460251Z >>> FullStateDictConfig(rank0_only=False), 2025-08-26T20:22:08.2460484Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-26T20:22:08.2460583Z >>> ) 2025-08-26T20:22:08.2460702Z >>> model.load_state_dict(state_dict) 2025-08-26T20:22:08.2460869Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-08-26T20:22:08.2460982Z >>> model, optim, optim_state_dict 2025-08-26T20:22:08.2461063Z >>> ) 2025-08-26T20:22:08.2461201Z >>> optim.load_state_dict(optim_state_dict) 2025-08-26T20:22:08.2461280Z 2025-08-26T20:22:08.2461378Z Args: 2025-08-26T20:22:08.2461584Z model (torch.nn.Module): Root module (which may or may not be a 2025-08-26T20:22:08.2461788Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-08-26T20:22:08.2461936Z were passed into the optimizer ``optim``. 2025-08-26T20:22:08.2462116Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-08-26T20:22:08.2462208Z parameters. 2025-08-26T20:22:08.2462436Z optim_state_dict (Dict[str, Any]): the target optimizer state_dict to 2025-08-26T20:22:08.2462646Z transform. If the value is None, optim.state_dict() will be used. ( 2025-08-26T20:22:08.2462760Z Default: ``None``) 2025-08-26T20:22:08.2463000Z group (dist.ProcessGroup): Model's process group across which parameters 2025-08-26T20:22:08.2463198Z are sharded or ``None`` if using the default process group. ( 2025-08-26T20:22:08.2463294Z Default: ``None``) 2025-08-26T20:22:08.2463376Z 2025-08-26T20:22:08.2463473Z Returns: 2025-08-26T20:22:08.2463666Z Dict[str, Any]: A :class:`dict` containing the optimizer state for 2025-08-26T20:22:08.2463832Z ``model``. The sharding of the optimizer state is based on 2025-08-26T20:22:08.2463979Z ``state_dict_type``. 2025-08-26T20:22:08.2464059Z 2025-08-26T20:22:08.2464325Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2464402Z 2025-08-26T20:22:08.2464500Z warnings.warn(msg) 2025-08-26T20:22:08.2464591Z 2025-08-26T20:22:08.2464793Z --- Parse Warning: 78 / 146 --- 2025-08-26T20:22:08.2466008Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FullyShardedDataParallel.optim_state_dict_to_load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/fsdp/fully_sharded_data_parallel.py line=1903. 2025-08-26T20:22:08.2466272Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2466354Z 2025-08-26T20:22:08.2466774Z Convert an optimizer state-dict so that it can be loaded into the optimizer associated with the FSDP model. 2025-08-26T20:22:08.2466857Z 2025-08-26T20:22:08.2467037Z Given a ``optim_state_dict`` that is transformed through 2025-08-26T20:22:08.2467248Z :meth:`optim_state_dict`, it gets converted to the flattened optimizer 2025-08-26T20:22:08.2467457Z state_dict that can be loaded to ``optim`` which is the optimizer for 2025-08-26T20:22:08.2467656Z ``model``. ``model`` must be sharded by FullyShardedDataParallel. 2025-08-26T20:22:08.2467734Z 2025-08-26T20:22:08.2467877Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2468113Z >>> from torch.distributed.fsdp import FullyShardedDataParallel as FSDP 2025-08-26T20:22:08.2468270Z >>> from torch.distributed.fsdp import StateDictType 2025-08-26T20:22:08.2468460Z >>> from torch.distributed.fsdp import FullStateDictConfig 2025-08-26T20:22:08.2468693Z >>> from torch.distributed.fsdp import FullOptimStateDictConfig 2025-08-26T20:22:08.2468804Z >>> # Save a checkpoint 2025-08-26T20:22:08.2468902Z >>> model, optim = ... 2025-08-26T20:22:08.2469010Z >>> FSDP.set_state_dict_type( 2025-08-26T20:22:08.2469109Z >>> model, 2025-08-26T20:22:08.2469228Z >>> StateDictType.FULL_STATE_DICT, 2025-08-26T20:22:08.2469371Z >>> FullStateDictConfig(rank0_only=False), 2025-08-26T20:22:08.2469513Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-26T20:22:08.2469595Z >>> ) 2025-08-26T20:22:08.2469719Z >>> state_dict = model.state_dict() 2025-08-26T20:22:08.2469837Z >>> original_osd = optim.state_dict() 2025-08-26T20:22:08.2469979Z >>> optim_state_dict = FSDP.optim_state_dict( 2025-08-26T20:22:08.2470063Z >>> model, 2025-08-26T20:22:08.2470146Z >>> optim, 2025-08-26T20:22:08.2470274Z >>> optim_state_dict=original_osd 2025-08-26T20:22:08.2470355Z >>> ) 2025-08-26T20:22:08.2470514Z >>> save_a_checkpoint(state_dict, optim_state_dict) 2025-08-26T20:22:08.2470610Z >>> # Load a checkpoint 2025-08-26T20:22:08.2470769Z >>> model, optim = ... 2025-08-26T20:22:08.2470931Z >>> state_dict, optim_state_dict = load_a_checkpoint() 2025-08-26T20:22:08.2471038Z >>> FSDP.set_state_dict_type( 2025-08-26T20:22:08.2471134Z >>> model, 2025-08-26T20:22:08.2471251Z >>> StateDictType.FULL_STATE_DICT, 2025-08-26T20:22:08.2471379Z >>> FullStateDictConfig(rank0_only=False), 2025-08-26T20:22:08.2471533Z >>> FullOptimStateDictConfig(rank0_only=False), 2025-08-26T20:22:08.2471617Z >>> ) 2025-08-26T20:22:08.2471743Z >>> model.load_state_dict(state_dict) 2025-08-26T20:22:08.2471893Z >>> optim_state_dict = FSDP.optim_state_dict_to_load( 2025-08-26T20:22:08.2472009Z >>> model, optim, optim_state_dict 2025-08-26T20:22:08.2472100Z >>> ) 2025-08-26T20:22:08.2472225Z >>> optim.load_state_dict(optim_state_dict) 2025-08-26T20:22:08.2472304Z 2025-08-26T20:22:08.2472429Z Args: 2025-08-26T20:22:08.2472622Z model (torch.nn.Module): Root module (which may or may not be a 2025-08-26T20:22:08.2472835Z :class:`FullyShardedDataParallel` instance) whose parameters 2025-08-26T20:22:08.2472968Z were passed into the optimizer ``optim``. 2025-08-26T20:22:08.2473146Z optim (torch.optim.Optimizer): Optimizer for ``model`` 's 2025-08-26T20:22:08.2473250Z parameters. 2025-08-26T20:22:08.2473459Z optim_state_dict (Dict[str, Any]): The optimizer states to be loaded. 2025-08-26T20:22:08.2473670Z is_named_optimizer (bool): Is this optimizer a NamedOptimizer or 2025-08-26T20:22:08.2473861Z KeyedOptimizer. Only set to True if ``optim`` is TorchRec's 2025-08-26T20:22:08.2474042Z KeyedOptimizer or torch.distributed's NamedOptimizer. 2025-08-26T20:22:08.2474299Z load_directly (bool): If this is set to True, this API will also 2025-08-26T20:22:08.2474499Z call optim.load_state_dict(result) before returning the result. 2025-08-26T20:22:08.2474733Z Otherwise, users are responsible to call ``optim.load_state_dict()`` 2025-08-26T20:22:08.2474833Z (Default: ``False``) 2025-08-26T20:22:08.2475069Z group (dist.ProcessGroup): Model's process group across which parameters 2025-08-26T20:22:08.2475266Z are sharded or ``None`` if using the default process group. ( 2025-08-26T20:22:08.2475362Z Default: ``None``) 2025-08-26T20:22:08.2475455Z 2025-08-26T20:22:08.2475708Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2475787Z 2025-08-26T20:22:08.2475895Z warnings.warn(msg) 2025-08-26T20:22:08.2475975Z 2025-08-26T20:22:08.2476180Z --- Parse Warning: 79 / 146 --- 2025-08-26T20:22:08.2477223Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.__init__ in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=129. 2025-08-26T20:22:08.2477501Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2477581Z 2025-08-26T20:22:08.2477806Z RemoteModule instance can only be created after RPC initialization. 2025-08-26T20:22:08.2477905Z 2025-08-26T20:22:08.2478099Z It creates a user-specified module on a specified remote node. 2025-08-26T20:22:08.2478344Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-08-26T20:22:08.2478449Z executed on the remote node. 2025-08-26T20:22:08.2478681Z It takes care of autograd recording to ensure the backward pass propagates 2025-08-26T20:22:08.2478849Z gradients back to the corresponding remote module. 2025-08-26T20:22:08.2479216Z It can be shared across processors using `RPC framework `__, 2025-08-26T20:22:08.2479424Z without incurring any overheads of copying the actual module, 2025-08-26T20:22:08.2479624Z which is equivalent to an :class:`~torch.distributed.rpc.RRef` 2025-08-26T20:22:08.2479732Z pointing to the remote module. 2025-08-26T20:22:08.2479824Z 2025-08-26T20:22:08.2480021Z The arguments of ``forward_async`` and ``forward`` are the same as 2025-08-26T20:22:08.2480231Z the ``forward`` method of the module returned by the ``module_cls``. 2025-08-26T20:22:08.2480310Z 2025-08-26T20:22:08.2480617Z Apart from ``forward_async`` and ``forward``, no other methods are supported from nn.Module for now. 2025-08-26T20:22:08.2480707Z 2025-08-26T20:22:08.2480958Z Particularly, to create a hybrid model, typically the local modules should be 2025-08-26T20:22:08.2481342Z created outside of remote modules, rather than as submodules of any remote module (by calling ``add_module``). 2025-08-26T20:22:08.2481440Z Hybrid Example: 2025-08-26T20:22:08.2481558Z >>> class HybridModel(nn.Module): 2025-08-26T20:22:08.2481711Z >>> def __init__(self) -> None: 2025-08-26T20:22:08.2481823Z >>> nn.Module.__init__(self) 2025-08-26T20:22:08.2481978Z >>> self.remote_embedding = RemoteModule(...) 2025-08-26T20:22:08.2482103Z >>> self.local_linear = nn.Linear(...) 2025-08-26T20:22:08.2482184Z 2025-08-26T20:22:08.2482396Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-08-26T20:22:08.2482644Z that has ``forward`` method signature, ``def forward(input: Tensor) -> Tensor:``, 2025-08-26T20:22:08.2482863Z the generated ``RemoteModule`` will have 2 methods in signature of 2025-08-26T20:22:08.2482995Z ``def forward(input: Tensor) -> Tensor:`` and 2025-08-26T20:22:08.2483160Z ``def forward_async(input: Tensor) -> Future[Tensor]:``. 2025-08-26T20:22:08.2483257Z 2025-08-26T20:22:08.2483398Z .. note:: 2025-08-26T20:22:08.2483545Z If the remote module is placed on a cuda device, 2025-08-26T20:22:08.2483795Z any input CPU tensors will be automatically moved to the same cuda device, 2025-08-26T20:22:08.2484191Z and GPU tensors are returned over the wire according to the device map of the remote worker on TensorPipe RPC backend. 2025-08-26T20:22:08.2484282Z 2025-08-26T20:22:08.2484364Z Args: 2025-08-26T20:22:08.2484656Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-26T20:22:08.2484955Z The device can be a local device or a remote device specified by one of the following remote 2025-08-26T20:22:08.2485042Z formats: 2025-08-26T20:22:08.2485134Z 2025-08-26T20:22:08.2485275Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-08-26T20:22:08.2485452Z 2. "/" (ex: "trainer0/cuda:0"). 2025-08-26T20:22:08.2485544Z 2025-08-26T20:22:08.2485792Z In addition, the device field can be optional and the default value is "cpu". 2025-08-26T20:22:08.2485923Z module_cls (nn.Module): For example, 2025-08-26T20:22:08.2486032Z >>> class MyModule(nn.Module): 2025-08-26T20:22:08.2486133Z >>> def forward(input): 2025-08-26T20:22:08.2486245Z >>> return input + 1 2025-08-26T20:22:08.2486330Z >>> 2025-08-26T20:22:08.2486445Z >>> module_cls = MyModule 2025-08-26T20:22:08.2486643Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-08-26T20:22:08.2486834Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-08-26T20:22:08.2487122Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-08-26T20:22:08.2487353Z to be created. The type object should be decorated by @torch.jit.interface. 2025-08-26T20:22:08.2487588Z If not provided, the generated RemoteModule is not torchscript-able. 2025-08-26T20:22:08.2487821Z Warning, this is an experimental API and susceptible to frequent changes. 2025-08-26T20:22:08.2487915Z 2025-08-26T20:22:08.2487999Z Returns: 2025-08-26T20:22:08.2488239Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-26T20:22:08.2488477Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-08-26T20:22:08.2488749Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-26T20:22:08.2488905Z on the user-provided module on the remote side. 2025-08-26T20:22:08.2488986Z 2025-08-26T20:22:08.2489075Z Example:: 2025-08-26T20:22:08.2489238Z Run the following code in two different processes: 2025-08-26T20:22:08.2489317Z 2025-08-26T20:22:08.2489431Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:22:08.2489534Z >>> # On worker 0: 2025-08-26T20:22:08.2489627Z >>> import torch 2025-08-26T20:22:08.2489765Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2489899Z >>> from torch import nn, Tensor 2025-08-26T20:22:08.2490115Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-26T20:22:08.2490206Z >>> 2025-08-26T20:22:08.2490344Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-26T20:22:08.2490475Z >>> remote_linear_module = RemoteModule( 2025-08-26T20:22:08.2490602Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-26T20:22:08.2490683Z >>> ) 2025-08-26T20:22:08.2490800Z >>> input = torch.randn(128, 20) 2025-08-26T20:22:08.2490953Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-08-26T20:22:08.2491065Z >>> ret = ret_fut.wait() 2025-08-26T20:22:08.2491160Z >>> rpc.shutdown() 2025-08-26T20:22:08.2491241Z 2025-08-26T20:22:08.2491343Z >>> # On worker 1: 2025-08-26T20:22:08.2491433Z >>> import torch 2025-08-26T20:22:08.2491617Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2491869Z >>> 2025-08-26T20:22:08.2492011Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-26T20:22:08.2492118Z >>> rpc.shutdown() 2025-08-26T20:22:08.2492198Z 2025-08-26T20:22:08.2492451Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2492543Z 2025-08-26T20:22:08.2492642Z warnings.warn(msg) 2025-08-26T20:22:08.2492734Z 2025-08-26T20:22:08.2492938Z --- Parse Warning: 80 / 146 --- 2025-08-26T20:22:08.2494001Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_RemoteModule.init_from_module_rref in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=506. 2025-08-26T20:22:08.2494353Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2494450Z 2025-08-26T20:22:08.2494779Z Besides the constructor, a RemoteModule instance can also be initialized given a module RRef. 2025-08-26T20:22:08.2494861Z 2025-08-26T20:22:08.2495201Z This alternate initialization method can be particularly useful if we want to create multiple 2025-08-26T20:22:08.2495517Z RemoteModule instances that share the same underlying module and reduce memory consumption. 2025-08-26T20:22:08.2495611Z 2025-08-26T20:22:08.2495887Z Moreover, this also provides a workaround for passing script RemoteModule over RPC, 2025-08-26T20:22:08.2496067Z which is not supported. The recommended way is as follows: 2025-08-26T20:22:08.2496164Z 2025-08-26T20:22:08.2496284Z 1. the sender creates a RemoteModule; 2025-08-26T20:22:08.2496443Z 2. the sender sends its ``module_rref`` over RPC; 2025-08-26T20:22:08.2496783Z 3. the receiver calls this method to initialize another RemoteModule using the same ``module_rref``. 2025-08-26T20:22:08.2496866Z 2025-08-26T20:22:08.2496970Z Example:: 2025-08-26T20:22:08.2497124Z Run the following code in two different processes: 2025-08-26T20:22:08.2497221Z 2025-08-26T20:22:08.2497338Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:22:08.2497430Z >>> # On worker 0: 2025-08-26T20:22:08.2497533Z >>> import torch 2025-08-26T20:22:08.2497657Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2497778Z >>> from torch import nn, Tensor 2025-08-26T20:22:08.2497995Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-26T20:22:08.2498076Z >>> 2025-08-26T20:22:08.2498224Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-26T20:22:08.2498335Z >>> remote_module = RemoteModule( 2025-08-26T20:22:08.2498473Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-26T20:22:08.2498555Z >>> ) 2025-08-26T20:22:08.2498635Z >>> 2025-08-26T20:22:08.2498763Z >>> remote_module1 = rpc.rpc_sync( 2025-08-26T20:22:08.2498858Z >>> "worker1/cpu", 2025-08-26T20:22:08.2499038Z >>> RemoteModule.init_from_module_rref, 2025-08-26T20:22:08.2499193Z >>> ("worker1/cpu", remote_module1.get_module_rref()), 2025-08-26T20:22:08.2499274Z >>> ) 2025-08-26T20:22:08.2499380Z >>> rpc.shutdown() 2025-08-26T20:22:08.2499461Z 2025-08-26T20:22:08.2499550Z >>> # On worker 1: 2025-08-26T20:22:08.2499651Z >>> import torch 2025-08-26T20:22:08.2499775Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2499868Z >>> 2025-08-26T20:22:08.2500004Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-26T20:22:08.2500098Z >>> rpc.shutdown() 2025-08-26T20:22:08.2500191Z 2025-08-26T20:22:08.2500273Z Args: 2025-08-26T20:22:08.2500661Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-26T20:22:08.2501037Z The device can be a local device or a remote device specified by one of the following remote 2025-08-26T20:22:08.2501127Z formats: 2025-08-26T20:22:08.2501217Z 2025-08-26T20:22:08.2501356Z 1. "rank:/" (ex: "rank:0/cuda:0"). 2025-08-26T20:22:08.2501519Z 2. "/" (ex: "trainer0/cuda:0"). 2025-08-26T20:22:08.2501598Z 2025-08-26T20:22:08.2501841Z In addition, the device field can be optional and the default value is "cpu". 2025-08-26T20:22:08.2502102Z module_rref (RRef[nn.Module]): The module reference shared by both the caller and 2025-08-26T20:22:08.2502209Z the created remote module. 2025-08-26T20:22:08.2502495Z _module_interface_cls (type, optional): The TorchScript interface type for the module 2025-08-26T20:22:08.2502728Z to be created. The type object should be decorated by @torch.jit.interface. 2025-08-26T20:22:08.2502979Z If not provided, the generated RemoteModule is not torchscript-able. 2025-08-26T20:22:08.2503221Z Warning, this is an experimental API and susceptible to frequent changes. 2025-08-26T20:22:08.2503303Z 2025-08-26T20:22:08.2503397Z Returns: 2025-08-26T20:22:08.2503639Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-26T20:22:08.2503871Z user-provided ``module_rref``, it has a blocking ``forward`` method and an 2025-08-26T20:22:08.2504152Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-26T20:22:08.2504295Z on the user-provided module on the remote side. 2025-08-26T20:22:08.2504389Z 2025-08-26T20:22:08.2504641Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2504719Z 2025-08-26T20:22:08.2504827Z warnings.warn(msg) 2025-08-26T20:22:08.2504908Z 2025-08-26T20:22:08.2505127Z --- Parse Warning: 81 / 146 --- 2025-08-26T20:22:08.2506102Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RemoteModule in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/nn/api/remote_module.py line=598. 2025-08-26T20:22:08.2506369Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2506463Z 2025-08-26T20:22:08.2506695Z A RemoteModule instance can only be created after RPC initialization. 2025-08-26T20:22:08.2506789Z 2025-08-26T20:22:08.2506984Z It creates a user-specified module on a specified remote node. 2025-08-26T20:22:08.2507216Z It behaves like a regular ``nn.Module`` except that the ``forward`` method is 2025-08-26T20:22:08.2507334Z executed on the remote node. 2025-08-26T20:22:08.2507570Z It takes care of autograd recording to ensure the backward pass propagates 2025-08-26T20:22:08.2507742Z gradients back to the corresponding remote module. 2025-08-26T20:22:08.2507824Z 2025-08-26T20:22:08.2508042Z It generates two methods ``forward_async`` and ``forward`` based on the 2025-08-26T20:22:08.2508296Z signature of the ``forward`` method of ``module_cls``. ``forward_async`` 2025-08-26T20:22:08.2508545Z runs asynchronously and returns a Future. The arguments of ``forward_async`` 2025-08-26T20:22:08.2508751Z and ``forward`` are the same as the ``forward`` method of the module 2025-08-26T20:22:08.2508861Z returned by the ``module_cls``. 2025-08-26T20:22:08.2508940Z 2025-08-26T20:22:08.2509147Z For example, if ``module_cls`` returns an instance of ``nn.Linear``, 2025-08-26T20:22:08.2509395Z that has ``forward`` method signature: ``def forward(input: Tensor) -> Tensor:``, 2025-08-26T20:22:08.2509628Z the generated ``RemoteModule`` will have 2 methods with the signatures: 2025-08-26T20:22:08.2509710Z 2025-08-26T20:22:08.2509835Z | ``def forward(input: Tensor) -> Tensor:`` 2025-08-26T20:22:08.2510057Z | ``def forward_async(input: Tensor) -> Future[Tensor]:`` 2025-08-26T20:22:08.2510138Z 2025-08-26T20:22:08.2510233Z Args: 2025-08-26T20:22:08.2510531Z remote_device (str): Device on the destination worker where we'd like to place this module. 2025-08-26T20:22:08.2510874Z The format should be "/", where the device field can be parsed as torch.device type. 2025-08-26T20:22:08.2511026Z E.g., "trainer0/cpu", "trainer0", "ps0/cuda:0". 2025-08-26T20:22:08.2511271Z In addition, the device field can be optional and the default value is "cpu". 2025-08-26T20:22:08.2511529Z module_cls (nn.Module): Class for the module to be created remotely. For example, 2025-08-26T20:22:08.2511609Z 2025-08-26T20:22:08.2511720Z >>> class MyModule(nn.Module): 2025-08-26T20:22:08.2511835Z >>> def forward(input): 2025-08-26T20:22:08.2511964Z >>> return input + 1 2025-08-26T20:22:08.2512063Z >>> 2025-08-26T20:22:08.2512165Z >>> module_cls = MyModule 2025-08-26T20:22:08.2512247Z 2025-08-26T20:22:08.2512457Z args (Sequence, optional): args to be passed to ``module_cls``. 2025-08-26T20:22:08.2512648Z kwargs (Dict, optional): kwargs to be passed to ``module_cls``. 2025-08-26T20:22:08.2512741Z 2025-08-26T20:22:08.2512824Z Returns: 2025-08-26T20:22:08.2513064Z A remote module instance which wraps the :class:`~nn.Module` created by the 2025-08-26T20:22:08.2513309Z user-provided ``module_cls``, it has a blocking ``forward`` method and an 2025-08-26T20:22:08.2513582Z asynchronous ``forward_async`` method that returns a future of the ``forward`` call 2025-08-26T20:22:08.2513735Z on the user-provided module on the remote side. 2025-08-26T20:22:08.2513814Z 2025-08-26T20:22:08.2513907Z Example:: 2025-08-26T20:22:08.2514068Z Run the following code in two different processes: 2025-08-26T20:22:08.2514152Z 2025-08-26T20:22:08.2514278Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:22:08.2514371Z >>> # On worker 0: 2025-08-26T20:22:08.2514495Z >>> import torch 2025-08-26T20:22:08.2514630Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2514737Z >>> from torch import nn, Tensor 2025-08-26T20:22:08.2514955Z >>> from torch.distributed.nn.api.remote_module import RemoteModule 2025-08-26T20:22:08.2515047Z >>> 2025-08-26T20:22:08.2515187Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-26T20:22:08.2515323Z >>> remote_linear_module = RemoteModule( 2025-08-26T20:22:08.2515449Z >>> "worker1/cpu", nn.Linear, args=(20, 30), 2025-08-26T20:22:08.2515531Z >>> ) 2025-08-26T20:22:08.2515654Z >>> input = torch.randn(128, 20) 2025-08-26T20:22:08.2515808Z >>> ret_fut = remote_linear_module.forward_async(input) 2025-08-26T20:22:08.2515923Z >>> ret = ret_fut.wait() 2025-08-26T20:22:08.2516022Z >>> rpc.shutdown() 2025-08-26T20:22:08.2516102Z 2025-08-26T20:22:08.2516202Z >>> # On worker 1: 2025-08-26T20:22:08.2516359Z >>> import torch 2025-08-26T20:22:08.2516493Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2516575Z >>> 2025-08-26T20:22:08.2516710Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-26T20:22:08.2516816Z >>> rpc.shutdown() 2025-08-26T20:22:08.2516894Z 2025-08-26T20:22:08.2517088Z Furthermore, a more practical example that is combined with 2025-08-26T20:22:08.2517579Z `DistributedDataParallel `__ (DDP) 2025-08-26T20:22:08.2517906Z can be found in this `tutorial `__. 2025-08-26T20:22:08.2517994Z 2025-08-26T20:22:08.2518251Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2518387Z 2025-08-26T20:22:08.2518484Z warnings.warn(msg) 2025-08-26T20:22:08.2518562Z 2025-08-26T20:22:08.2518768Z --- Parse Warning: 82 / 146 --- 2025-08-26T20:22:08.2519773Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/optimizer.py line=129. 2025-08-26T20:22:08.2520043Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2520122Z 2025-08-26T20:22:08.2520364Z DistributedOptimizer takes remote references to parameters scattered 2025-08-26T20:22:08.2520613Z across workers and applies the given optimizer locally for each parameter. 2025-08-26T20:22:08.2520692Z 2025-08-26T20:22:08.2520938Z This class uses :meth:`~torch.distributed.autograd.get_gradients` in order 2025-08-26T20:22:08.2521124Z to retrieve the gradients for specific parameters. 2025-08-26T20:22:08.2521204Z 2025-08-26T20:22:08.2521310Z Concurrent calls to 2025-08-26T20:22:08.2521518Z :meth:`~torch.distributed.optim.DistributedOptimizer.step`, 2025-08-26T20:22:08.2521669Z either from the same or different clients, will 2025-08-26T20:22:08.2521892Z be serialized on each worker -- as each worker's optimizer can only work 2025-08-26T20:22:08.2522097Z on one set of gradients at a time. However, there is no guarantee that 2025-08-26T20:22:08.2522351Z the full forward-backward-optimizer sequence will execute for one client 2025-08-26T20:22:08.2522567Z at a time. This means that the gradients being applied may not correspond 2025-08-26T20:22:08.2522801Z to the latest forward pass executed on a given worker. Also, there is no 2025-08-26T20:22:08.2522915Z guaranteed ordering across workers. 2025-08-26T20:22:08.2522996Z 2025-08-26T20:22:08.2523268Z `DistributedOptimizer` creates the local optimizer with TorchScript enabled 2025-08-26T20:22:08.2523496Z by default, so that optimizer updates are not blocked by the Python Global 2025-08-26T20:22:08.2523751Z Interpreter Lock (GIL) in the case of multithreaded training (e.g. Distributed 2025-08-26T20:22:08.2523990Z Model Parallel). This feature is currently enabled for most optimizers. You 2025-08-26T20:22:08.2524241Z can also follow `the recipe`__ in PyTorch tutorials to enable TorchScript support 2025-08-26T20:22:08.2524362Z for your own custom optimizers. 2025-08-26T20:22:08.2524442Z 2025-08-26T20:22:08.2524538Z Args: 2025-08-26T20:22:08.2524734Z optimizer_class (optim.Optimizer): the class of optimizer to 2025-08-26T20:22:08.2524842Z instantiate on each worker. 2025-08-26T20:22:08.2525060Z params_rref (list[RRef]): list of RRefs to local or remote parameters 2025-08-26T20:22:08.2525153Z to optimize. 2025-08-26T20:22:08.2525383Z args: arguments to pass to the optimizer constructor on each worker. 2025-08-26T20:22:08.2525608Z kwargs: arguments to pass to the optimizer constructor on each worker. 2025-08-26T20:22:08.2525734Z 2025-08-26T20:22:08.2525838Z Example:: 2025-08-26T20:22:08.2525954Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:22:08.2526131Z >>> import torch.distributed.autograd as dist_autograd 2025-08-26T20:22:08.2526258Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2526362Z >>> from torch import optim 2025-08-26T20:22:08.2526562Z >>> from torch.distributed.optim import DistributedOptimizer 2025-08-26T20:22:08.2526645Z >>> 2025-08-26T20:22:08.2526780Z >>> with dist_autograd.context() as context_id: 2025-08-26T20:22:08.2526895Z >>> # Forward pass. 2025-08-26T20:22:08.2527094Z >>> rref1 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 3)) 2025-08-26T20:22:08.2527304Z >>> rref2 = rpc.remote("worker1", torch.add, args=(torch.ones(2), 1)) 2025-08-26T20:22:08.2527480Z >>> loss = rref1.to_here() + rref2.to_here() 2025-08-26T20:22:08.2527564Z >>> 2025-08-26T20:22:08.2527676Z >>> # Backward pass. 2025-08-26T20:22:08.2527827Z >>> dist_autograd.backward(context_id, [loss.sum()]) 2025-08-26T20:22:08.2527919Z >>> 2025-08-26T20:22:08.2528010Z >>> # Optimizer. 2025-08-26T20:22:08.2528134Z >>> dist_optim = DistributedOptimizer( 2025-08-26T20:22:08.2528237Z >>> optim.SGD, 2025-08-26T20:22:08.2528331Z >>> [rref1, rref2], 2025-08-26T20:22:08.2528430Z >>> lr=0.05, 2025-08-26T20:22:08.2528511Z >>> ) 2025-08-26T20:22:08.2528624Z >>> dist_optim.step(context_id) 2025-08-26T20:22:08.2528714Z 2025-08-26T20:22:08.2528869Z __ https://github.com/pytorch/tutorials/pull/1465 2025-08-26T20:22:08.2528960Z 2025-08-26T20:22:08.2529246Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2529352Z 2025-08-26T20:22:08.2529465Z warnings.warn(msg) 2025-08-26T20:22:08.2529544Z 2025-08-26T20:22:08.2529735Z --- Parse Warning: 83 / 146 --- 2025-08-26T20:22:08.2530811Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PostLocalSGDOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/post_localSGD_optimizer.py line=9. 2025-08-26T20:22:08.2531078Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2531169Z 2025-08-26T20:22:08.2531558Z Wraps an arbitrary :class:`torch.optim.Optimizer` and runs `post-local SGD `_, 2025-08-26T20:22:08.2531724Z This optimizer runs local optimizer at every step. 2025-08-26T20:22:08.2532059Z After the warm-up stage, it averages parameters periodically after the local optimizer is applied. 2025-08-26T20:22:08.2532141Z 2025-08-26T20:22:08.2532237Z Args: 2025-08-26T20:22:08.2532348Z optim: The local optimizer. 2025-08-26T20:22:08.2532579Z averager: A model averager instance to run post-localSGD algorithm. 2025-08-26T20:22:08.2532665Z 2025-08-26T20:22:08.2532754Z Example:: 2025-08-26T20:22:08.2532847Z 2025-08-26T20:22:08.2532977Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.2533070Z >>> import torch 2025-08-26T20:22:08.2533199Z >>> import torch.distributed as dist 2025-08-26T20:22:08.2533469Z >>> import torch.distributed.algorithms.model_averaging.averagers as averagers 2025-08-26T20:22:08.2533581Z >>> import torch.nn as nn 2025-08-26T20:22:08.2533774Z >>> from torch.distributed.optim import PostLocalSGDOptimizer 2025-08-26T20:22:08.2534045Z >>> from torch.distributed.algorithms.ddp_comm_hooks.post_localSGD_hook import ( 2025-08-26T20:22:08.2534160Z >>> PostLocalSGDState, 2025-08-26T20:22:08.2534258Z >>> post_localSGD_hook, 2025-08-26T20:22:08.2534352Z >>> ) 2025-08-26T20:22:08.2534433Z >>> 2025-08-26T20:22:08.2534586Z >>> model = nn.parallel.DistributedDataParallel( 2025-08-26T20:22:08.2534773Z >>> module, device_ids=[rank], output_device=rank 2025-08-26T20:22:08.2534854Z >>> ) 2025-08-26T20:22:08.2534945Z >>> 2025-08-26T20:22:08.2535091Z >>> # Register a post-localSGD communication hook. 2025-08-26T20:22:08.2535387Z >>> state = PostLocalSGDState(process_group=None, subgroup=None, start_localSGD_iter=100) 2025-08-26T20:22:08.2535560Z >>> model.register_comm_hook(state, post_localSGD_hook) 2025-08-26T20:22:08.2535641Z >>> 2025-08-26T20:22:08.2535856Z >>> # Create a post-localSGD optimizer that wraps a local optimizer. 2025-08-26T20:22:08.2536104Z >>> # Note that ``warmup_steps`` used in ``PostLocalSGDOptimizer`` must be the same as 2025-08-26T20:22:08.2536274Z >>> # ``start_localSGD_iter`` used in ``PostLocalSGDState``. 2025-08-26T20:22:08.2536543Z >>> local_optim = torch.optim.SGD(params=model.parameters(), lr=0.01) 2025-08-26T20:22:08.2536663Z >>> opt = PostLocalSGDOptimizer( 2025-08-26T20:22:08.2536781Z >>> optim=local_optim, 2025-08-26T20:22:08.2537027Z >>> averager=averagers.PeriodicModelAverager(period=4, warmup_steps=100) 2025-08-26T20:22:08.2537109Z >>> ) 2025-08-26T20:22:08.2537207Z >>> 2025-08-26T20:22:08.2537428Z >>> # In the first 100 steps, DDP runs global gradient averaging at every step. 2025-08-26T20:22:08.2537738Z >>> # After 100 steps, DDP runs gradient averaging within each subgroup (intra-node by default), 2025-08-26T20:22:08.2538115Z >>> # and post-localSGD optimizer runs global model averaging every 4 steps after applying the local optimizer. 2025-08-26T20:22:08.2538222Z >>> for step in range(0, 200): 2025-08-26T20:22:08.2538358Z >>> opt.zero_grad() 2025-08-26T20:22:08.2538470Z >>> loss = loss_fn(output, labels) 2025-08-26T20:22:08.2538585Z >>> loss.backward() 2025-08-26T20:22:08.2538677Z >>> opt.step() 2025-08-26T20:22:08.2538758Z 2025-08-26T20:22:08.2539022Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2539102Z 2025-08-26T20:22:08.2539209Z warnings.warn(msg) 2025-08-26T20:22:08.2539323Z 2025-08-26T20:22:08.2539515Z --- Parse Warning: 84 / 146 --- 2025-08-26T20:22:08.2540721Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZeroRedundancyOptimizer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/optim/zero_redundancy_optimizer.py line=284. 2025-08-26T20:22:08.2540986Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2541080Z 2025-08-26T20:22:08.2541495Z Wrap an arbitrary :class:`optim.Optimizer ` and shards its states across ranks in the group. 2025-08-26T20:22:08.2541573Z 2025-08-26T20:22:08.2541832Z The sharing is done as described by `ZeRO `_. 2025-08-26T20:22:08.2541915Z 2025-08-26T20:22:08.2542075Z The local optimizer instance in each rank is only 2025-08-26T20:22:08.2542317Z responsible for updating approximately ``1 / world_size`` parameters and 2025-08-26T20:22:08.2542517Z hence only needs to keep ``1 / world_size`` optimizer states. After 2025-08-26T20:22:08.2542775Z parameters are updated locally, each rank will broadcast its parameters to 2025-08-26T20:22:08.2542960Z all other peers to keep all model replicas in the same state. 2025-08-26T20:22:08.2543165Z ``ZeroRedundancyOptimizer`` can be used in conjunction with 2025-08-26T20:22:08.2543424Z :class:`torch.nn.parallel.DistributedDataParallel` to reduce per-rank peak 2025-08-26T20:22:08.2543523Z memory consumption. 2025-08-26T20:22:08.2543615Z 2025-08-26T20:22:08.2543879Z ``ZeroRedundancyOptimizer`` uses a sorted-greedy algorithm to pack a number 2025-08-26T20:22:08.2544119Z of parameters at each rank. Each parameter belongs to a single rank and is 2025-08-26T20:22:08.2544400Z not divided among ranks. The partition is arbitrary and might not match the 2025-08-26T20:22:08.2544531Z the parameter registration or usage order. 2025-08-26T20:22:08.2544621Z 2025-08-26T20:22:08.2544708Z Arguments: 2025-08-26T20:22:08.2544912Z params (``Iterable``): an ``Iterable`` of :class:`torch.Tensor` s 2025-08-26T20:22:08.2545096Z or :class:`dict` s giving all parameters, which will be sharded 2025-08-26T20:22:08.2545189Z across ranks. 2025-08-26T20:22:08.2545280Z 2025-08-26T20:22:08.2545368Z Keyword Args: 2025-08-26T20:22:08.2545600Z optimizer_class (:class:`torch.nn.Optimizer`): the class of the local 2025-08-26T20:22:08.2545691Z optimizer. 2025-08-26T20:22:08.2545953Z process_group (``ProcessGroup``, optional): ``torch.distributed`` 2025-08-26T20:22:08.2546160Z ``ProcessGroup`` (default: ``dist.group.WORLD`` initialized by 2025-08-26T20:22:08.2546308Z :meth:`torch.distributed.init_process_group`). 2025-08-26T20:22:08.2546545Z parameters_as_bucket_view (bool, optional): if ``True``, parameters are 2025-08-26T20:22:08.2546754Z packed into buckets to speed up communication, and ``param.data`` 2025-08-26T20:22:08.2546953Z fields point to bucket views at different offsets; if ``False``, 2025-08-26T20:22:08.2547168Z each individual parameter is communicated separately, and each 2025-08-26T20:22:08.2547318Z ``params.data`` stays intact (default: ``False``). 2025-08-26T20:22:08.2547526Z overlap_with_ddp (bool, optional): if ``True``, :meth:`step` is 2025-08-26T20:22:08.2547725Z overlapped with :class:`DistributedDataParallel` 's gradient 2025-08-26T20:22:08.2547969Z synchronization; this requires (1) either a functional optimizer 2025-08-26T20:22:08.2548161Z for the ``optimizer_class`` argument or one with a functional 2025-08-26T20:22:08.2548338Z equivalent and (2) registering a DDP communication hook 2025-08-26T20:22:08.2548547Z constructed from one of the functions in ``ddp_zero_hook.py``; 2025-08-26T20:22:08.2548714Z parameters are packed into buckets matching those in 2025-08-26T20:22:08.2548882Z :class:`DistributedDataParallel`, meaning that the 2025-08-26T20:22:08.2549031Z ``parameters_as_bucket_view`` argument is ignored. 2025-08-26T20:22:08.2549218Z If ``False``, :meth:`step` runs disjointly after the backward pass 2025-08-26T20:22:08.2549326Z (per normal). 2025-08-26T20:22:08.2549426Z (default: ``False``) 2025-08-26T20:22:08.2549647Z **defaults: any trailing arguments, which are forwarded to the local 2025-08-26T20:22:08.2549738Z optimizer. 2025-08-26T20:22:08.2549816Z 2025-08-26T20:22:08.2549920Z Example:: 2025-08-26T20:22:08.2550010Z 2025-08-26T20:22:08.2550108Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2550218Z >>> import torch.nn as nn 2025-08-26T20:22:08.2550421Z >>> from torch.distributed.optim import ZeroRedundancyOptimizer 2025-08-26T20:22:08.2550618Z >>> from torch.nn.parallel import DistributedDataParallel as DDP 2025-08-26T20:22:08.2550855Z >>> model = nn.Sequential(*[nn.Linear(2000, 2000).to(rank) for _ in range(20)]) 2025-08-26T20:22:08.2550972Z >>> ddp = DDP(model, device_ids=[rank]) 2025-08-26T20:22:08.2551099Z >>> opt = ZeroRedundancyOptimizer( 2025-08-26T20:22:08.2551203Z >>> ddp.parameters(), 2025-08-26T20:22:08.2551325Z >>> optimizer_class=torch.optim.Adam, 2025-08-26T20:22:08.2551426Z >>> lr=0.01 2025-08-26T20:22:08.2551508Z >>> ) 2025-08-26T20:22:08.2551631Z >>> ddp(inputs).sum().backward() 2025-08-26T20:22:08.2551722Z >>> opt.step() 2025-08-26T20:22:08.2551806Z 2025-08-26T20:22:08.2551906Z .. warning:: 2025-08-26T20:22:08.2552118Z Currently, ``ZeroRedundancyOptimizer`` requires that all of the 2025-08-26T20:22:08.2552291Z passed-in parameters are the same dense type. 2025-08-26T20:22:08.2552384Z 2025-08-26T20:22:08.2552467Z .. warning:: 2025-08-26T20:22:08.2552686Z If you pass ``overlap_with_ddp=True``, be wary of the following: Given 2025-08-26T20:22:08.2552889Z the way that overlapping :class:`DistributedDataParallel` with 2025-08-26T20:22:08.2553123Z :class:`ZeroRedundancyOptimizer` is currently implemented, the first 2025-08-26T20:22:08.2553350Z two or three training iterations do not perform parameter updates in 2025-08-26T20:22:08.2553538Z the optimizer step, depending on if ``static_graph=False`` or 2025-08-26T20:22:08.2553730Z ``static_graph=True``, respectively. This is because it needs 2025-08-26T20:22:08.2553970Z information about the gradient bucketing strategy used by 2025-08-26T20:22:08.2554200Z :class:`DistributedDataParallel`, which is not finalized until the 2025-08-26T20:22:08.2554402Z second forward pass if ``static_graph=False`` or until the third 2025-08-26T20:22:08.2554605Z forward pass if ``static_graph=True``. To adjust for this, one option 2025-08-26T20:22:08.2554725Z is to prepend dummy inputs. 2025-08-26T20:22:08.2554805Z 2025-08-26T20:22:08.2555069Z .. warning:: ZeroRedundancyOptimizer is experimental and subject to change. 2025-08-26T20:22:08.2555148Z 2025-08-26T20:22:08.2555397Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2555485Z 2025-08-26T20:22:08.2555583Z warnings.warn(msg) 2025-08-26T20:22:08.2555661Z 2025-08-26T20:22:08.2555873Z --- Parse Warning: 85 / 146 --- 2025-08-26T20:22:08.2556906Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_CustomReducer in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/pipelining/microbatch.py line=29. 2025-08-26T20:22:08.2557183Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2557261Z 2025-08-26T20:22:08.2557503Z Custom reducer class that can be used to specify a custom operation that 2025-08-26T20:22:08.2557677Z reduces losses of multiple microbatches into one value. 2025-08-26T20:22:08.2557756Z 2025-08-26T20:22:08.2557852Z Example: 2025-08-26T20:22:08.2557947Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2558056Z >>> sum_reducer = _CustomReducer( 2025-08-26T20:22:08.2558170Z >>> torch.tensor(0.0), 2025-08-26T20:22:08.2558266Z >>> lambda a, b: a + b 2025-08-26T20:22:08.2558356Z >>> ) 2025-08-26T20:22:08.2558435Z 2025-08-26T20:22:08.2558683Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2558777Z 2025-08-26T20:22:08.2558898Z warnings.warn(msg) 2025-08-26T20:22:08.2558986Z 2025-08-26T20:22:08.2559166Z --- Parse Warning: 86 / 146 --- 2025-08-26T20:22:08.2560091Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=async_execution in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/functions.py line=6. 2025-08-26T20:22:08.2560365Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2560444Z 2025-08-26T20:22:08.2560692Z A decorator for a function indicating that the return value of the function 2025-08-26T20:22:08.2560900Z is guaranteed to be a :class:`~torch.futures.Future` object and this 2025-08-26T20:22:08.2561135Z function can run asynchronously on the RPC callee. More specifically, the 2025-08-26T20:22:08.2561379Z callee extracts the :class:`~torch.futures.Future` returned by the wrapped 2025-08-26T20:22:08.2561614Z function and installs subsequent processing steps as a callback to that 2025-08-26T20:22:08.2561857Z :class:`~torch.futures.Future`. The installed callback will read the value 2025-08-26T20:22:08.2562092Z from the :class:`~torch.futures.Future` when completed and send the 2025-08-26T20:22:08.2562284Z value back as the RPC response. That also means the returned 2025-08-26T20:22:08.2562512Z :class:`~torch.futures.Future` only exists on the callee side and is never 2025-08-26T20:22:08.2562748Z sent through RPC. This decorator is useful when the wrapped function's 2025-08-26T20:22:08.2562952Z (``fn``) execution needs to pause and resume due to, e.g., containing 2025-08-26T20:22:08.2563178Z :meth:`~torch.distributed.rpc.rpc_async` or waiting for other signals. 2025-08-26T20:22:08.2563269Z 2025-08-26T20:22:08.2563483Z .. note:: To enable asynchronous execution, applications must pass the 2025-08-26T20:22:08.2563768Z function object returned by this decorator to RPC APIs. If RPC detected 2025-08-26T20:22:08.2564000Z attributes installed by this decorator, it knows that this function 2025-08-26T20:22:08.2564185Z returns a ``Future`` object and will handle that accordingly. 2025-08-26T20:22:08.2564406Z However, this does not mean this decorator has to be outmost one when 2025-08-26T20:22:08.2564631Z defining a function. For example, when combined with ``@staticmethod`` 2025-08-26T20:22:08.2564841Z or ``@classmethod``, ``@rpc.functions.async_execution`` needs to be the 2025-08-26T20:22:08.2565067Z inner decorator to allow the target function be recognized as a static 2025-08-26T20:22:08.2565293Z or class function. This target function can still execute asynchronously 2025-08-26T20:22:08.2565523Z because, when accessed, the static or class method preserves attributes 2025-08-26T20:22:08.2565676Z installed by ``@rpc.functions.async_execution``. 2025-08-26T20:22:08.2565784Z 2025-08-26T20:22:08.2565875Z 2025-08-26T20:22:08.2565967Z Example:: 2025-08-26T20:22:08.2566178Z The returned :class:`~torch.futures.Future` object can come from 2025-08-26T20:22:08.2566316Z :meth:`~torch.distributed.rpc.rpc_async`, 2025-08-26T20:22:08.2566538Z :meth:`~torch.futures.Future.then`, or :class:`~torch.futures.Future` 2025-08-26T20:22:08.2566729Z constructor. The example below shows directly using the 2025-08-26T20:22:08.2566861Z :class:`~torch.futures.Future` returned by 2025-08-26T20:22:08.2566992Z :meth:`~torch.futures.Future.then`. 2025-08-26T20:22:08.2567072Z 2025-08-26T20:22:08.2567193Z >>> from torch.distributed import rpc 2025-08-26T20:22:08.2567281Z >>> 2025-08-26T20:22:08.2567397Z >>> # omitting setup and shutdown RPC 2025-08-26T20:22:08.2567487Z >>> 2025-08-26T20:22:08.2567581Z >>> # On all workers 2025-08-26T20:22:08.2567699Z >>> @rpc.functions.async_execution 2025-08-26T20:22:08.2567829Z >>> def async_add_chained(to, x, y, z): 2025-08-26T20:22:08.2568021Z >>> # This function runs on "worker1" and returns immediately when 2025-08-26T20:22:08.2568220Z >>> # the callback is installed through the `then(cb)` API. In the 2025-08-26T20:22:08.2568402Z >>> # mean time, the `rpc_async` to "worker2" can run concurrently. 2025-08-26T20:22:08.2568561Z >>> # When the return value of that `rpc_async` arrives at 2025-08-26T20:22:08.2568756Z >>> # "worker1", "worker1" will run the lambda function accordingly 2025-08-26T20:22:08.2568942Z >>> # and set the value for the previously returned `Future`, which 2025-08-26T20:22:08.2569131Z >>> # will then trigger RPC to send the result back to "worker0". 2025-08-26T20:22:08.2569294Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-26T20:22:08.2569407Z >>> lambda fut: fut.wait() + z 2025-08-26T20:22:08.2569497Z >>> ) 2025-08-26T20:22:08.2569581Z >>> 2025-08-26T20:22:08.2569679Z >>> # On worker0 2025-08-26T20:22:08.2569773Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2569896Z >>> ret = rpc.rpc_sync( 2025-08-26T20:22:08.2569994Z >>> "worker1", 2025-08-26T20:22:08.2570090Z >>> async_add_chained, 2025-08-26T20:22:08.2570210Z >>> args=("worker2", torch.ones(2), 1, 1) 2025-08-26T20:22:08.2570303Z >>> ) 2025-08-26T20:22:08.2570420Z >>> print(ret) # prints tensor([3., 3.]) 2025-08-26T20:22:08.2570510Z 2025-08-26T20:22:08.2570736Z When combined with TorchScript decorators, this decorator must be the 2025-08-26T20:22:08.2570825Z outmost one. 2025-08-26T20:22:08.2570913Z 2025-08-26T20:22:08.2571016Z >>> from torch import Tensor 2025-08-26T20:22:08.2571142Z >>> from torch.futures import Future 2025-08-26T20:22:08.2571263Z >>> from torch.distributed import rpc 2025-08-26T20:22:08.2571339Z >>> 2025-08-26T20:22:08.2571508Z >>> # omitting setup and shutdown RPC 2025-08-26T20:22:08.2571589Z >>> 2025-08-26T20:22:08.2571694Z >>> # On all workers 2025-08-26T20:22:08.2571791Z >>> @torch.jit.script 2025-08-26T20:22:08.2571934Z >>> def script_add(x: Tensor, y: Tensor) -> Tensor: 2025-08-26T20:22:08.2572037Z >>> return x + y 2025-08-26T20:22:08.2572121Z >>> 2025-08-26T20:22:08.2572232Z >>> @rpc.functions.async_execution 2025-08-26T20:22:08.2572341Z >>> @torch.jit.script 2025-08-26T20:22:08.2572525Z >>> def async_add(to: str, x: Tensor, y: Tensor) -> Future[Tensor]: 2025-08-26T20:22:08.2572679Z >>> return rpc.rpc_async(to, script_add, (x, y)) 2025-08-26T20:22:08.2572758Z >>> 2025-08-26T20:22:08.2572843Z >>> # On worker0 2025-08-26T20:22:08.2572950Z >>> ret = rpc.rpc_sync( 2025-08-26T20:22:08.2573068Z >>> "worker1", 2025-08-26T20:22:08.2573169Z >>> async_add, 2025-08-26T20:22:08.2573289Z >>> args=("worker2", torch.ones(2), 1) 2025-08-26T20:22:08.2573368Z >>> ) 2025-08-26T20:22:08.2573501Z >>> print(ret) # prints tensor([2., 2.]) 2025-08-26T20:22:08.2573580Z 2025-08-26T20:22:08.2573813Z When combined with static or class method, this decorator must be the 2025-08-26T20:22:08.2573901Z inner one. 2025-08-26T20:22:08.2573979Z 2025-08-26T20:22:08.2574109Z >>> from torch.distributed import rpc 2025-08-26T20:22:08.2574187Z >>> 2025-08-26T20:22:08.2574299Z >>> # omitting setup and shutdown RPC 2025-08-26T20:22:08.2574395Z >>> 2025-08-26T20:22:08.2574488Z >>> # On all workers 2025-08-26T20:22:08.2574613Z >>> class AsyncExecutionClass: 2025-08-26T20:22:08.2574692Z >>> 2025-08-26T20:22:08.2574785Z >>> @staticmethod 2025-08-26T20:22:08.2574915Z >>> @rpc.functions.async_execution 2025-08-26T20:22:08.2575035Z >>> def static_async_add(to, x, y, z): 2025-08-26T20:22:08.2575220Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-26T20:22:08.2575332Z >>> lambda fut: fut.wait() + z 2025-08-26T20:22:08.2575417Z >>> ) 2025-08-26T20:22:08.2575508Z >>> 2025-08-26T20:22:08.2575629Z >>> @classmethod 2025-08-26T20:22:08.2575759Z >>> @rpc.functions.async_execution 2025-08-26T20:22:08.2575882Z >>> def class_async_add(cls, to, x, y, z): 2025-08-26T20:22:08.2576008Z >>> ret_fut = torch.futures.Future() 2025-08-26T20:22:08.2576165Z >>> rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-26T20:22:08.2576314Z >>> lambda fut: ret_fut.set_result(fut.wait() + z) 2025-08-26T20:22:08.2576408Z >>> ) 2025-08-26T20:22:08.2576501Z >>> return ret_fut 2025-08-26T20:22:08.2576583Z >>> 2025-08-26T20:22:08.2576713Z >>> @rpc.functions.async_execution 2025-08-26T20:22:08.2576840Z >>> def bound_async_add(self, to, x, y, z): 2025-08-26T20:22:08.2577014Z >>> return rpc.rpc_async(to, torch.add, args=(x, y)).then( 2025-08-26T20:22:08.2577155Z >>> lambda fut: fut.wait() + z 2025-08-26T20:22:08.2577240Z >>> ) 2025-08-26T20:22:08.2577331Z >>> 2025-08-26T20:22:08.2577419Z >>> # On worker0 2025-08-26T20:22:08.2577516Z >>> ret = rpc.rpc_sync( 2025-08-26T20:22:08.2577618Z >>> "worker1", 2025-08-26T20:22:08.2577755Z >>> AsyncExecutionClass.static_async_add, 2025-08-26T20:22:08.2577888Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-08-26T20:22:08.2577974Z >>> ) 2025-08-26T20:22:08.2578093Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:22:08.2578189Z >>> 2025-08-26T20:22:08.2578288Z >>> ret = rpc.rpc_sync( 2025-08-26T20:22:08.2578396Z >>> "worker1", 2025-08-26T20:22:08.2578530Z >>> AsyncExecutionClass.class_async_add, 2025-08-26T20:22:08.2578698Z >>> args=("worker2", torch.ones(2), 1, 2) 2025-08-26T20:22:08.2578794Z >>> ) 2025-08-26T20:22:08.2578915Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:22:08.2579009Z 2025-08-26T20:22:08.2579170Z This decorator also works with RRef helpers, i.e., . 2025-08-26T20:22:08.2579315Z :meth:`torch.distributed.rpc.RRef.rpc_sync`, 2025-08-26T20:22:08.2579484Z :meth:`torch.distributed.rpc.RRef.rpc_async`, and 2025-08-26T20:22:08.2579627Z :meth:`torch.distributed.rpc.RRef.remote`. 2025-08-26T20:22:08.2579723Z 2025-08-26T20:22:08.2579843Z >>> from torch.distributed import rpc 2025-08-26T20:22:08.2579926Z >>> 2025-08-26T20:22:08.2580078Z >>> # reuse the AsyncExecutionClass class above 2025-08-26T20:22:08.2580232Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-26T20:22:08.2580547Z >>> ret = rref.rpc_sync().static_async_add("worker2", torch.ones(2), 1, 2) 2025-08-26T20:22:08.2580687Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:22:08.2580771Z >>> 2025-08-26T20:22:08.2580941Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-26T20:22:08.2581173Z >>> ret = rref.rpc_async().static_async_add("worker2", torch.ones(2), 1, 2).wait() 2025-08-26T20:22:08.2581294Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:22:08.2581393Z >>> 2025-08-26T20:22:08.2581544Z >>> rref = rpc.remote("worker1", AsyncExecutionClass) 2025-08-26T20:22:08.2581789Z >>> ret = rref.remote().static_async_add("worker2", torch.ones(2), 1, 2).to_here() 2025-08-26T20:22:08.2581908Z >>> print(ret) # prints tensor([4., 4.]) 2025-08-26T20:22:08.2581989Z 2025-08-26T20:22:08.2582254Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2582337Z 2025-08-26T20:22:08.2582446Z warnings.warn(msg) 2025-08-26T20:22:08.2582526Z 2025-08-26T20:22:08.2582737Z --- Parse Warning: 87 / 146 --- 2025-08-26T20:22:08.2583827Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TensorPipeRpcBackendOptions.set_device_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/options.py line=113. 2025-08-26T20:22:08.2584089Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2584187Z 2025-08-26T20:22:08.2584391Z Set device mapping between each RPC caller and callee pair. This 2025-08-26T20:22:08.2584587Z function can be called multiple times to incrementally add 2025-08-26T20:22:08.2584702Z device placement configurations. 2025-08-26T20:22:08.2584783Z 2025-08-26T20:22:08.2584880Z Args: 2025-08-26T20:22:08.2584978Z to (str): Callee name. 2025-08-26T20:22:08.2585178Z device_map (Dict of int, str, or torch.device): Device placement 2025-08-26T20:22:08.2585373Z mappings from this worker to the callee. This map must be 2025-08-26T20:22:08.2585463Z invertible. 2025-08-26T20:22:08.2585594Z 2025-08-26T20:22:08.2585679Z Example: 2025-08-26T20:22:08.2585792Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:22:08.2585895Z >>> # both workers 2025-08-26T20:22:08.2585985Z >>> def add(x, y): 2025-08-26T20:22:08.2586138Z >>> print(x) # tensor([1., 1.], device='cuda:1') 2025-08-26T20:22:08.2586243Z >>> return x + y, (x + y).to(2) 2025-08-26T20:22:08.2586324Z >>> 2025-08-26T20:22:08.2586425Z >>> # on worker 0 2025-08-26T20:22:08.2586559Z >>> options = TensorPipeRpcBackendOptions( 2025-08-26T20:22:08.2586674Z >>> num_worker_threads=8, 2025-08-26T20:22:08.2586787Z >>> device_maps={"worker1": {0: 1}} 2025-08-26T20:22:08.2586915Z >>> # maps worker0's cuda:0 to worker1's cuda:1 2025-08-26T20:22:08.2587011Z >>> ) 2025-08-26T20:22:08.2587207Z >>> options.set_device_map("worker1", {1: 2}) 2025-08-26T20:22:08.2587337Z >>> # maps worker0's cuda:1 to worker1's cuda:2 2025-08-26T20:22:08.2587437Z >>> 2025-08-26T20:22:08.2587530Z >>> rpc.init_rpc( 2025-08-26T20:22:08.2587632Z >>> "worker0", 2025-08-26T20:22:08.2587719Z >>> rank=0, 2025-08-26T20:22:08.2587815Z >>> world_size=2, 2025-08-26T20:22:08.2587957Z >>> backend=rpc.BackendType.TENSORPIPE, 2025-08-26T20:22:08.2588066Z >>> rpc_backend_options=options 2025-08-26T20:22:08.2588161Z >>> ) 2025-08-26T20:22:08.2588241Z >>> 2025-08-26T20:22:08.2588337Z >>> x = torch.ones(2) 2025-08-26T20:22:08.2588509Z >>> rets = rpc.rpc_sync("worker1", add, args=(x.to(0), 1)) 2025-08-26T20:22:08.2588693Z >>> # The first argument will be moved to cuda:1 on worker1. When 2025-08-26T20:22:08.2588924Z >>> # sending the return value back, it will follow the invert of 2025-08-26T20:22:08.2589100Z >>> # the device map, and hence will be moved back to cuda:0 and 2025-08-26T20:22:08.2589197Z >>> # cuda:1 on worker0 2025-08-26T20:22:08.2589360Z >>> print(rets[0]) # tensor([2., 2.], device='cuda:0') 2025-08-26T20:22:08.2589506Z >>> print(rets[1]) # tensor([2., 2.], device='cuda:1') 2025-08-26T20:22:08.2589600Z 2025-08-26T20:22:08.2589852Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2589931Z 2025-08-26T20:22:08.2590037Z warnings.warn(msg) 2025-08-26T20:22:08.2590117Z 2025-08-26T20:22:08.2590306Z --- Parse Warning: 88 / 146 --- 2025-08-26T20:22:08.2591422Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=_server_process_global_profile in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/rpc/server_process_global_profiler.py line=19. 2025-08-26T20:22:08.2591861Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2591960Z 2025-08-26T20:22:08.2592161Z It has the same API as ``torch.autograd.profiler.profile`` class, 2025-08-26T20:22:08.2592456Z except that it enables profiling on all threads running RPC server request callbacks. 2025-08-26T20:22:08.2592538Z 2025-08-26T20:22:08.2592821Z Context manager that manages autograd profiler state and holds a summary of results. 2025-08-26T20:22:08.2593069Z Under the hood it just records events of functions being executed in C++ and 2025-08-26T20:22:08.2593302Z exposes those events to Python. You can wrap any code into it and it will 2025-08-26T20:22:08.2593446Z only report runtime of PyTorch functions. 2025-08-26T20:22:08.2593715Z Note: profiler is thread local and is automatically propagated into the async tasks 2025-08-26T20:22:08.2593796Z 2025-08-26T20:22:08.2593894Z Args: 2025-08-26T20:22:08.2594165Z enabled (bool, optional): Setting this to False makes this context manager a no-op. 2025-08-26T20:22:08.2594275Z Default: ``True``. 2025-08-26T20:22:08.2594426Z 2025-08-26T20:22:08.2594710Z use_cuda (bool, optional): Enables timing of CUDA events as well using the cudaEvent API. 2025-08-26T20:22:08.2594923Z Adds approximately 4us of overhead to each tensor operation. 2025-08-26T20:22:08.2595021Z Default: ``False`` 2025-08-26T20:22:08.2595115Z 2025-08-26T20:22:08.2595340Z record_shapes (bool, optional): If shapes recording is set, information 2025-08-26T20:22:08.2595569Z about input dimensions will be collected. This allows one to see which 2025-08-26T20:22:08.2595797Z dimensions have been used under the hood and further group by them 2025-08-26T20:22:08.2596014Z using prof.key_averages(group_by_input_shape=True). Please note that 2025-08-26T20:22:08.2596253Z shape recording might skew your profiling data. It is recommended to 2025-08-26T20:22:08.2596560Z use separate runs with and without shape recording to validate the timing. 2025-08-26T20:22:08.2596789Z Most likely the skew will be negligible for bottom most events (in a case 2025-08-26T20:22:08.2597015Z of nested function calls). But for higher level functions the total 2025-08-26T20:22:08.2597217Z self cpu time might be artificially increased because of the shape 2025-08-26T20:22:08.2597320Z collection. 2025-08-26T20:22:08.2597399Z 2025-08-26T20:22:08.2597665Z profile_memory (bool, optional): Whether to report memory usage, default: ``False`` 2025-08-26T20:22:08.2597757Z 2025-08-26T20:22:08.2597851Z .. warning:: 2025-08-26T20:22:08.2598066Z Enabling memory profiling incurs additional profiler overhead 2025-08-26T20:22:08.2598146Z 2025-08-26T20:22:08.2598233Z .. warning:: 2025-08-26T20:22:08.2598570Z Due to some CUDA multiprocessing limitations (see :ref:`multiprocessing-cuda-note`), 2025-08-26T20:22:08.2598771Z one cannot use the profiler with ``use_cuda = True`` to benchmark 2025-08-26T20:22:08.2599023Z DataLoaders with ``num_workers > 0``. If you wish to benchmark data loading, 2025-08-26T20:22:08.2599187Z please use ``use_cuda = False`` or ``num_workers = 0``. 2025-08-26T20:22:08.2599266Z 2025-08-26T20:22:08.2599362Z Example: 2025-08-26T20:22:08.2599460Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2599565Z >>> # On worker 0: 2025-08-26T20:22:08.2599655Z >>> import torch 2025-08-26T20:22:08.2599781Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2599931Z >>> rpc.init_rpc("worker0", rank=0, world_size=2) 2025-08-26T20:22:08.2600052Z >>> x, y = torch.tensor(1), torch.tensor(2) 2025-08-26T20:22:08.2600179Z >>> outer_profile_rref = rpc.remote( 2025-08-26T20:22:08.2600341Z ... dst_worker_name, rpc._server_process_global_profile 2025-08-26T20:22:08.2600424Z ... ) 2025-08-26T20:22:08.2600570Z >>> outer_profile_rref.rpc_sync().__enter__() 2025-08-26T20:22:08.2600716Z >>> rpc.rpc_sync(dst_worker_name, torch.add, (x, y)) 2025-08-26T20:22:08.2600844Z >>> inner_profile_rref = rpc.remote( 2025-08-26T20:22:08.2601005Z ... dst_worker_name, rpc._server_process_global_profile 2025-08-26T20:22:08.2601085Z ... ) 2025-08-26T20:22:08.2601226Z >>> inner_profile_rref.rpc_sync().__enter__() 2025-08-26T20:22:08.2601370Z >>> rpc.rpc_sync(dst_worker_name, torch.sub, (x, y)) 2025-08-26T20:22:08.2601554Z >>> inner_profile_rref.rpc_sync().__exit__(None, None, None) 2025-08-26T20:22:08.2601724Z >>> outer_profile_rref.rpc_sync().__exit__(None, None, None) 2025-08-26T20:22:08.2601882Z >>> print(inner_profile_rref.rpc_sync().key_averages()) 2025-08-26T20:22:08.2602129Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2602445Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-08-26T20:22:08.2602710Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2602901Z sub 85.06% 76.275us 100.00% 89.667us 89.667us 1 2025-08-26T20:22:08.2603090Z empty 14.94% 13.392us 14.94% 13.392us 13.392us 1 2025-08-26T20:22:08.2603326Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2603435Z Self CPU time total: 89.667us 2025-08-26T20:22:08.2603607Z >>> print(outer_profile_rref.rpc_sync().key_averages()) 2025-08-26T20:22:08.2603829Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2604200Z Name Self CPU total % Self CPU total CPU total % CPU total CPU time avg Number of Calls 2025-08-26T20:22:08.2604426Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2604606Z sub 35.65% 76.275us 41.91% 89.667us 89.667us 1 2025-08-26T20:22:08.2604804Z empty 12.67% 27.101us 12.67% 27.101us 13.551us 2 2025-08-26T20:22:08.2604983Z add 51.68% 110.550us 58.09% 124.259us 124.259us 1 2025-08-26T20:22:08.2605216Z --------- --------------- --------------- --------------- --------------- --------------- --------------- 2025-08-26T20:22:08.2605325Z Self CPU time total: 213.926us 2025-08-26T20:22:08.2605453Z >>> rpc.shutdown() 2025-08-26T20:22:08.2605545Z 2025-08-26T20:22:08.2605637Z >>> # On worker 1: 2025-08-26T20:22:08.2605779Z >>> import torch.distributed.rpc as rpc 2025-08-26T20:22:08.2605919Z >>> rpc.init_rpc("worker1", rank=1, world_size=2) 2025-08-26T20:22:08.2606082Z >>> # wait for worker 0 to finish work, and then shutdown. 2025-08-26T20:22:08.2606189Z >>> rpc.shutdown() 2025-08-26T20:22:08.2606270Z 2025-08-26T20:22:08.2606536Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2606617Z 2025-08-26T20:22:08.2606714Z warnings.warn(msg) 2025-08-26T20:22:08.2606809Z 2025-08-26T20:22:08.2607013Z --- Parse Warning: 89 / 146 --- 2025-08-26T20:22:08.2608022Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=local_map in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_func_map.py line=35. 2025-08-26T20:22:08.2608292Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2608374Z 2025-08-26T20:22:08.2608661Z :meth:`local_map` is an experimental API that allows users to pass :class:`DTensor` s 2025-08-26T20:22:08.2608941Z to a function that is written to be applied on ``torch.Tensor`` s. It is done by extracting 2025-08-26T20:22:08.2609219Z the local components of :class:`DTensor`, call the function, and wrap the outputs to 2025-08-26T20:22:08.2609380Z :class:`DTensor` according to the ``out_placements``. 2025-08-26T20:22:08.2609462Z 2025-08-26T20:22:08.2609557Z Args: 2025-08-26T20:22:08.2609763Z func (Callable): the function to be applied on each local shard of 2025-08-26T20:22:08.2609878Z :class:`DTensor` s. 2025-08-26T20:22:08.2610105Z out_placements (Union[`PlacementType`, Tuple[`PlacementType`, ...]]): 2025-08-26T20:22:08.2610359Z the desired placements of the :class:`DTensor` s in ``func``'s flattened output. 2025-08-26T20:22:08.2610622Z If the flattened ``output`` is a single value, the ``out_placements`` should be 2025-08-26T20:22:08.2610863Z of type `PlacementType`. Otherwise if the flattened ``output`` has multiple 2025-08-26T20:22:08.2611157Z values, the ``out_placements`` should be a tuple of `PlacementType` values 1:1 2025-08-26T20:22:08.2611280Z mapping to the flattened ``output``. 2025-08-26T20:22:08.2611497Z Besides, for :class:`Tensor` output, we use `PlacementType` as its 2025-08-26T20:22:08.2611774Z placements (a `Tuple[Placement]` value). For non-Tensor output, the `PlacementType` 2025-08-26T20:22:08.2611872Z should be `None`. 2025-08-26T20:22:08.2612122Z Note that the only exception is when no :class:`DTensor` argument is passed 2025-08-26T20:22:08.2612345Z in. In this case, even if `out_placements` is not `None`, the result function 2025-08-26T20:22:08.2612665Z should ignore the desired placements because the function is not running with 2025-08-26T20:22:08.2612766Z :class:`DTensor` s. 2025-08-26T20:22:08.2612933Z in_placements (Tuple[`PlacementType`, ...], optional): 2025-08-26T20:22:08.2613230Z the required placements of the :class:`DTensor` s in the flattened inputs of ``func``. 2025-08-26T20:22:08.2613464Z If ``in_placements`` is specified, :meth:`local_map` would examine whether the 2025-08-26T20:22:08.2613704Z placements of each :class:`DTensor` argument is the same as the required 2025-08-26T20:22:08.2613883Z placements or not. If the placements are not the same and 2025-08-26T20:22:08.2614129Z ``redistribute_inputs`` is ``False``, an exception will be raised. Otherwise if 2025-08-26T20:22:08.2614387Z ``redistribute_inputs`` is ``True``, the argument will be first redistributed to 2025-08-26T20:22:08.2614645Z the required sharding placements before passing its local tensor to ``func``. 2025-08-26T20:22:08.2614907Z The only exception is when required placements are not ``None`` and the 2025-08-26T20:22:08.2615152Z argument is a :class:`torch.Tensor`. In this case, the placements examination 2025-08-26T20:22:08.2615380Z will be skipped and the argument will be directly passed to ``func``. 2025-08-26T20:22:08.2615604Z If ``in_placements`` is ``None``, no placements examination will be performed. 2025-08-26T20:22:08.2615699Z Default: None 2025-08-26T20:22:08.2615895Z in_grad_placements (Tuple[`PlacementType`, ...], optional): 2025-08-26T20:22:08.2616102Z the placements hint of the :class:`DTensor` s gradient corresponds 2025-08-26T20:22:08.2616324Z to the flattened input DTensor. This argument is the hint that user 2025-08-26T20:22:08.2616505Z can give to :meth:`to_local` in case the gradient layout of the 2025-08-26T20:22:08.2616720Z local tensor input does not match its :class:`DTensor` input layout. 2025-08-26T20:22:08.2616933Z If not specified, we will assume the gradient layout of the local 2025-08-26T20:22:08.2617152Z tensor input remains the same as the original :class:`DTensor` input 2025-08-26T20:22:08.2617329Z and use that for gradient computation. Default: None. 2025-08-26T20:22:08.2617467Z device_mesh (:class:`DeviceMesh`, optional): 2025-08-26T20:22:08.2617687Z the device mesh that the output :class:`DTensor` s are placed on. If not 2025-08-26T20:22:08.2617952Z specified, this will be inferred from the first input :class:`DTensor`'s device 2025-08-26T20:22:08.2618051Z mesh. Default: None. 2025-08-26T20:22:08.2618141Z 2025-08-26T20:22:08.2618230Z Keyword Args: 2025-08-26T20:22:08.2618351Z redistribute_inputs (bool, optional): 2025-08-26T20:22:08.2618613Z the bool value indicating whether to reshard the input :class:`DTensor` s when 2025-08-26T20:22:08.2618864Z their placements are different from the required input placements. If this 2025-08-26T20:22:08.2619098Z value is ``False`` and some :class:`DTensor` input has a different placement, 2025-08-26T20:22:08.2619264Z an exception will be raised. Default: False. 2025-08-26T20:22:08.2619342Z 2025-08-26T20:22:08.2619438Z Returns: 2025-08-26T20:22:08.2619694Z A ``Callable`` that applies ``func`` to each local shard of the input :class:`DTensor` 2025-08-26T20:22:08.2619941Z and returns a :class:`DTensor` constructed from the return value of ``func``. 2025-08-26T20:22:08.2620021Z 2025-08-26T20:22:08.2620102Z Raises: 2025-08-26T20:22:08.2620427Z AssertionError: For any non-DTensor output, we require its corresponding 2025-08-26T20:22:08.2620690Z output placement in ``out_placements`` be None. An AssertionError will be raised 2025-08-26T20:22:08.2620805Z if this is not the case. 2025-08-26T20:22:08.2620887Z 2025-08-26T20:22:08.2621221Z ValueError: If ``redistribute_inputs=False`` but the input :class:`DTensor` needs 2025-08-26T20:22:08.2621388Z a redistribution according to ``in_placements``. 2025-08-26T20:22:08.2621470Z 2025-08-26T20:22:08.2621567Z Example: 2025-08-26T20:22:08.2621684Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:22:08.2621820Z >>> def mm_allreduce_forward(device_mesh, W, X): 2025-08-26T20:22:08.2621958Z >>> partial_sum_tensor = torch.mm(W, X) 2025-08-26T20:22:08.2622204Z >>> reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh) 2025-08-26T20:22:08.2622321Z >>> return reduced_tensor 2025-08-26T20:22:08.2622404Z >>> 2025-08-26T20:22:08.2622533Z >>> W = torch.randn(12, 8, requires_grad=False) 2025-08-26T20:22:08.2622670Z >>> X = torch.randn(8, 16, requires_grad=False) 2025-08-26T20:22:08.2622766Z >>> Y = torch.mm(W, X) 2025-08-26T20:22:08.2623001Z >>> row_wise = [Shard(0)] # row-wise sharding placements on 1-d mesh 2025-08-26T20:22:08.2623183Z >>> col_wise = [Shard(1)] # col-wise sharding placements on 1-d mesh 2025-08-26T20:22:08.2623267Z >>> 2025-08-26T20:22:08.2623546Z >>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor conversion 2025-08-26T20:22:08.2623670Z >>> local_mm_allreduce_forward = local_map( 2025-08-26T20:22:08.2623784Z >>> mm_allreduce_forward, 2025-08-26T20:22:08.2623901Z >>> out_placements=[Replicate()], 2025-08-26T20:22:08.2624019Z >>> in_placements=[col_wise, row_wise], 2025-08-26T20:22:08.2624138Z >>> device_mesh=device_mesh, 2025-08-26T20:22:08.2624222Z >>> ) 2025-08-26T20:22:08.2624300Z >>> 2025-08-26T20:22:08.2624414Z >>> W_dt = distribute_tensor( 2025-08-26T20:22:08.2624519Z ... W, device_mesh, (col_wise) 2025-08-26T20:22:08.2624641Z ... ) # col-wisely sharded W tensor 2025-08-26T20:22:08.2624744Z >>> X_dt = distribute_tensor( 2025-08-26T20:22:08.2624851Z ... X, device_mesh, (row_wise) 2025-08-26T20:22:08.2624974Z ... ) # row-wisely sharded X tensor 2025-08-26T20:22:08.2625089Z >>> Y_dt = local_mm_allreduce_forward( 2025-08-26T20:22:08.2625201Z ... device_mesh, W_dt, X_dt 2025-08-26T20:22:08.2625346Z ... ) # apply local_mm_allreduce_forward to DTensors 2025-08-26T20:22:08.2625427Z 2025-08-26T20:22:08.2625638Z .. note:: This API is currently experimental and subject to change 2025-08-26T20:22:08.2625719Z 2025-08-26T20:22:08.2625982Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2626061Z 2025-08-26T20:22:08.2626158Z warnings.warn(msg) 2025-08-26T20:22:08.2626250Z 2025-08-26T20:22:08.2626455Z --- Parse Warning: 90 / 146 --- 2025-08-26T20:22:08.2627559Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_sharding in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/experimental/_register_sharding.py line=25. 2025-08-26T20:22:08.2627856Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2627936Z 2025-08-26T20:22:08.2628230Z :meth:`register_sharding` is an experimental API that allows users to register sharding 2025-08-26T20:22:08.2628470Z strategies for an operator when the tensor inputs and outputs are DTensor. 2025-08-26T20:22:08.2628735Z It can be useful when: (1) there doesn't exist a default sharding strategy for ``op``, 2025-08-26T20:22:08.2628978Z e.g. when ``op`` is a custom operator that is not supported by :class:`DTensor`; (2) 2025-08-26T20:22:08.2629262Z when users would like to overwrite default sharding strategies of existing operators. 2025-08-26T20:22:08.2629344Z 2025-08-26T20:22:08.2629429Z Args: 2025-08-26T20:22:08.2629572Z op (Union[OpOverload, List[OpOverload]]): 2025-08-26T20:22:08.2629812Z An op or a list of ops to register the customized sharding function. 2025-08-26T20:22:08.2629892Z 2025-08-26T20:22:08.2629987Z Returns: 2025-08-26T20:22:08.2630253Z A function decorator which can be used to wrap a function that defines the sharding 2025-08-26T20:22:08.2630537Z strategy for the operator specified in ``op``. The defined sharding strategy will be 2025-08-26T20:22:08.2630810Z registered to DTensor and will override the default sharding strategy if DTensor has 2025-08-26T20:22:08.2631109Z already implemented the operator. The customized sharding function takes the same inputs 2025-08-26T20:22:08.2631360Z as the original op (except that if an arg is a :class:`torch.Tensor`, it will be 2025-08-26T20:22:08.2631629Z replaced by a tensor-like object that DTensor uses internally). The function should 2025-08-26T20:22:08.2631943Z return a sequence of 2-tuples, each specifying acceptable output placements and its 2025-08-26T20:22:08.2632064Z corresponding input placements. 2025-08-26T20:22:08.2632156Z 2025-08-26T20:22:08.2632243Z Example: 2025-08-26T20:22:08.2632359Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:22:08.2632504Z >>> @register_sharding(aten._softmax.default) 2025-08-26T20:22:08.2632660Z >>> def custom_softmax_sharding(x, dim, half_to_float): 2025-08-26T20:22:08.2632801Z >>> softmax_dim = dim if dim >= 0 else dim + x.ndim 2025-08-26T20:22:08.2632926Z >>> acceptable_shardings = [] 2025-08-26T20:22:08.2633007Z >>> 2025-08-26T20:22:08.2633194Z >>> all_replicate = ([Replicate()], [Replicate(), None, None]) 2025-08-26T20:22:08.2633335Z >>> acceptable_shardings.append(all_replicate) 2025-08-26T20:22:08.2633417Z >>> 2025-08-26T20:22:08.2633546Z >>> for sharding_dim in range(x.ndim): 2025-08-26T20:22:08.2633663Z >>> if sharding_dim != softmax_dim: 2025-08-26T20:22:08.2633775Z >>> all_sharded = ( 2025-08-26T20:22:08.2633887Z >>> [Shard(sharding_dim)], 2025-08-26T20:22:08.2634013Z >>> [Shard(sharding_dim), None, None], 2025-08-26T20:22:08.2634111Z >>> ) 2025-08-26T20:22:08.2634251Z >>> acceptable_shardings.append(all_sharded) 2025-08-26T20:22:08.2634342Z >>> 2025-08-26T20:22:08.2634454Z >>> return acceptable_shardings 2025-08-26T20:22:08.2634532Z 2025-08-26T20:22:08.2634742Z .. note:: This API is currently experimental and subject to change 2025-08-26T20:22:08.2634822Z 2025-08-26T20:22:08.2635081Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2635161Z 2025-08-26T20:22:08.2635257Z warnings.warn(msg) 2025-08-26T20:22:08.2635346Z 2025-08-26T20:22:08.2635540Z --- Parse Warning: 91 / 146 --- 2025-08-26T20:22:08.2636574Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=428. 2025-08-26T20:22:08.2636868Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2636950Z 2025-08-26T20:22:08.2637344Z Configure the nn.Module's inputs to convert the input tensors of the nn.Module to DTensors at runtime according to 2025-08-26T20:22:08.2637666Z ``input_layouts``, and perform layout redistribution according to the ``desired_input_layouts``. 2025-08-26T20:22:08.2637758Z 2025-08-26T20:22:08.2637845Z Keyword Args: 2025-08-26T20:22:08.2638047Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-26T20:22:08.2638388Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-08-26T20:22:08.2638796Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-08-26T20:22:08.2638933Z as a placeholder. default: None. 2025-08-26T20:22:08.2639165Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-26T20:22:08.2639555Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-26T20:22:08.2639946Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-08-26T20:22:08.2640083Z input_kwarg_layouts (Dict[str, Placement]): 2025-08-26T20:22:08.2640467Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-08-26T20:22:08.2640561Z default: None 2025-08-26T20:22:08.2640732Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-08-26T20:22:08.2641133Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-26T20:22:08.2641282Z have the desired DTensor layouts. default: None. 2025-08-26T20:22:08.2641408Z use_local_output (bool, optional): 2025-08-26T20:22:08.2641766Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-08-26T20:22:08.2641862Z Returns: 2025-08-26T20:22:08.2642179Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs. 2025-08-26T20:22:08.2642257Z 2025-08-26T20:22:08.2642360Z Example:: 2025-08-26T20:22:08.2642465Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:22:08.2642789Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInput 2025-08-26T20:22:08.2642980Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-26T20:22:08.2643064Z >>> ... 2025-08-26T20:22:08.2643386Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-26T20:22:08.2643518Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-26T20:22:08.2643613Z >>> 2025-08-26T20:22:08.2643949Z >>> # According to the style specified below, the first input of attn will be annotated to Sharded DTensor 2025-08-26T20:22:08.2644099Z >>> # and then redistributed to Replicated DTensor. 2025-08-26T20:22:08.2644214Z >>> parallelize_module( 2025-08-26T20:22:08.2644349Z >>> block, # this can be a submodule or module 2025-08-26T20:22:08.2644453Z >>> tp_mesh, 2025-08-26T20:22:08.2644558Z >>> parallelize_plan={ 2025-08-26T20:22:08.2644679Z >>> "attn": PrepareModuleInput( 2025-08-26T20:22:08.2644850Z >>> input_layouts=(Shard(0), None, None, ...), 2025-08-26T20:22:08.2645021Z >>> desired_input_layouts=(Replicate(), None, None, ...) 2025-08-26T20:22:08.2645128Z >>> ), 2025-08-26T20:22:08.2645213Z >>> } 2025-08-26T20:22:08.2645295Z >>> ) 2025-08-26T20:22:08.2645419Z 2025-08-26T20:22:08.2645674Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2645767Z 2025-08-26T20:22:08.2645864Z warnings.warn(msg) 2025-08-26T20:22:08.2645948Z 2025-08-26T20:22:08.2646154Z --- Parse Warning: 92 / 146 --- 2025-08-26T20:22:08.2647181Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=597. 2025-08-26T20:22:08.2647458Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2647537Z 2025-08-26T20:22:08.2647987Z Configure the nn.Module's outputs to convert the output tensors of the nn.Module to DTensors at runtime according to 2025-08-26T20:22:08.2648332Z ``output_layouts``, and perform layout redistribution according to the ``desired_output_layouts``. 2025-08-26T20:22:08.2648412Z 2025-08-26T20:22:08.2648513Z Keyword Args: 2025-08-26T20:22:08.2648682Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-26T20:22:08.2649019Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-08-26T20:22:08.2649415Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-08-26T20:22:08.2649560Z ``None`` need to be specified as a placeholder. 2025-08-26T20:22:08.2649775Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-26T20:22:08.2650158Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-08-26T20:22:08.2650341Z have the desired DTensor layouts. 2025-08-26T20:22:08.2650468Z use_local_output (bool, optional): 2025-08-26T20:22:08.2650831Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-08-26T20:22:08.2650927Z Returns: 2025-08-26T20:22:08.2651219Z A ParallelStyle object that prepares the sharding layouts of the nn.Module's outputs. 2025-08-26T20:22:08.2651310Z 2025-08-26T20:22:08.2651397Z Example:: 2025-08-26T20:22:08.2651505Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:22:08.2651835Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleOutput 2025-08-26T20:22:08.2652024Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-26T20:22:08.2652108Z >>> ... 2025-08-26T20:22:08.2652427Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-26T20:22:08.2652560Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-26T20:22:08.2652657Z >>> 2025-08-26T20:22:08.2653058Z >>> # According to the style specified below, the output of the TransformerBlock will be converted to Replicated DTensor 2025-08-26T20:22:08.2653206Z >>> # and then redistributed to Sharded DTensor. 2025-08-26T20:22:08.2653308Z >>> parallelize_module( 2025-08-26T20:22:08.2653440Z >>> block, # this can be a submodule or module 2025-08-26T20:22:08.2653539Z >>> tp_mesh, 2025-08-26T20:22:08.2653678Z >>> parallelize_plan = PrepareModuleOutput( 2025-08-26T20:22:08.2653795Z >>> output_layouts=Replicate(), 2025-08-26T20:22:08.2653924Z >>> desired_output_layouts=Shard(0) 2025-08-26T20:22:08.2654012Z >>> ) 2025-08-26T20:22:08.2654105Z >>> ) 2025-08-26T20:22:08.2654185Z 2025-08-26T20:22:08.2654439Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2654533Z 2025-08-26T20:22:08.2654631Z warnings.warn(msg) 2025-08-26T20:22:08.2654722Z 2025-08-26T20:22:08.2654955Z --- Parse Warning: 93 / 146 --- 2025-08-26T20:22:08.2656015Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=PrepareModuleInputOutput in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributed/tensor/parallel/style.py line=705. 2025-08-26T20:22:08.2656290Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2656369Z 2025-08-26T20:22:08.2656816Z Configure the nn.Module's inputs (and outputs) to convert the input tensors (and output tensors, respectively) of the nn.Module 2025-08-26T20:22:08.2657234Z to DTensors at runtime according to ``input_layouts`` (and output_layouts, respectively), and perform layout redistribution 2025-08-26T20:22:08.2657676Z according to the ``desired_input_layouts`` (and ``desired_output_layouts``, respectively). This is a combination of 2025-08-26T20:22:08.2657886Z :class:`PrepareModuleInput` and :class:`PrepareModuleOutput`. 2025-08-26T20:22:08.2657968Z 2025-08-26T20:22:08.2658072Z Keyword Args: 2025-08-26T20:22:08.2658272Z input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-26T20:22:08.2658612Z The DTensor layouts of input tensors for the nn.Module, this is used to convert the input tensors to 2025-08-26T20:22:08.2658970Z DTensors. If some inputs are not torch.Tensor or no need to convert to DTensors, ``None`` need to be specified 2025-08-26T20:22:08.2659088Z as a placeholder. default: None. 2025-08-26T20:22:08.2659330Z desired_input_layouts (Union[Placement, Tuple[Optional[Placement]]]): 2025-08-26T20:22:08.2659703Z The desired DTensor layout of input tensors for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-26T20:22:08.2660136Z have the desired DTensor layouts. This argument needs to have the same length with ``input_layouts``. default: None. 2025-08-26T20:22:08.2660271Z input_kwarg_layouts (Dict[str, Placement]): 2025-08-26T20:22:08.2660743Z The DTensor layouts of input kwargs for the nn.Module, this is used to convert the input kwarg tensors to DTensors. 2025-08-26T20:22:08.2660853Z default: None 2025-08-26T20:22:08.2661014Z desired_input_kwarg_layouts: (Dict[str, Placement]): 2025-08-26T20:22:08.2661396Z The desired DTensor layout of input kwargs for the nn.Module, this is used to ensure the inputs of the nn.Module 2025-08-26T20:22:08.2661542Z have the desired DTensor layouts. default: None. 2025-08-26T20:22:08.2661667Z use_local_input (bool, optional): 2025-08-26T20:22:08.2662030Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module inputs, default: False. 2025-08-26T20:22:08.2662198Z output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-26T20:22:08.2662554Z The DTensor layouts of output tensors for the nn.Module, this is used to convert the output tensors to 2025-08-26T20:22:08.2662937Z DTensors if they are :class:`torch.Tensor`. If some outputs are not torch.Tensor or no need to convert to DTensors, 2025-08-26T20:22:08.2663096Z ``None`` need to be specified as a placeholder. 2025-08-26T20:22:08.2663292Z desired_output_layouts (Union[Placement, Tuple[Placement]]): 2025-08-26T20:22:08.2663681Z The desired DTensor layouts of output tensors for the nn.Module, this is used to ensure the outputs of the nn.Module 2025-08-26T20:22:08.2663813Z have the desired DTensor layouts. 2025-08-26T20:22:08.2663931Z use_local_output (bool, optional): 2025-08-26T20:22:08.2664300Z Whether to use local :class:`torch.Tensor` instead of :class:`DTensor` for the module outputs, default: True. 2025-08-26T20:22:08.2664387Z Returns: 2025-08-26T20:22:08.2664757Z A :class:`ParallelStyle` object that prepares the sharding layouts of the nn.Module's inputs and outputs. 2025-08-26T20:22:08.2664905Z 2025-08-26T20:22:08.2664996Z Example:: 2025-08-26T20:22:08.2665114Z >>> # xdoctest: +SKIP(failing) 2025-08-26T20:22:08.2665449Z >>> from torch.distributed.tensor.parallel import parallelize_module, PrepareModuleInputOutput 2025-08-26T20:22:08.2665640Z >>> from torch.distributed.device_mesh import init_device_mesh 2025-08-26T20:22:08.2665735Z >>> ... 2025-08-26T20:22:08.2666040Z >>> block = TransformerBlock(...) # block is a nn.Module that contains an "attn" Attention submodule 2025-08-26T20:22:08.2666182Z >>> tp_mesh = init_device_mesh("cuda", (8,)) 2025-08-26T20:22:08.2666263Z >>> 2025-08-26T20:22:08.2666598Z >>> # According to the style specified below, the first input of attn will be annotated as Sharded DTensor 2025-08-26T20:22:08.2667016Z >>> # and then redistributed to Replicated DTensor, and the output of the TransformerBlock will be annotated 2025-08-26T20:22:08.2667226Z >>> # as Replicated DTensor and then redistributed to Sharded DTensor. 2025-08-26T20:22:08.2667342Z >>> parallelize_module( 2025-08-26T20:22:08.2667474Z >>> block, # this can be a submodule or module 2025-08-26T20:22:08.2667574Z >>> tp_mesh, 2025-08-26T20:22:08.2667678Z >>> parallelize_plan={ 2025-08-26T20:22:08.2667811Z >>> "attn": PrepareModuleInputOutput( 2025-08-26T20:22:08.2667957Z >>> input_layouts=(Shard(0), None, None, ...), 2025-08-26T20:22:08.2668128Z >>> desired_input_layouts=(Replicate(), None, None, ...), 2025-08-26T20:22:08.2668255Z >>> output_layouts=Replicate(), 2025-08-26T20:22:08.2668378Z >>> desired_output_layouts=Shard(0), 2025-08-26T20:22:08.2668490Z >>> ), 2025-08-26T20:22:08.2668584Z >>> } 2025-08-26T20:22:08.2668666Z >>> ) 2025-08-26T20:22:08.2668748Z 2025-08-26T20:22:08.2669016Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2669097Z 2025-08-26T20:22:08.2669204Z warnings.warn(msg) 2025-08-26T20:22:08.2669283Z 2025-08-26T20:22:08.2669483Z --- Parse Warning: 94 / 146 --- 2025-08-26T20:22:08.2670589Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=LowRankMultivariateNormal in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/lowrank_multivariate_normal.py line=56. 2025-08-26T20:22:08.2670852Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2670944Z 2025-08-26T20:22:08.2671250Z Creates a multivariate normal distribution with covariance matrix having a low-rank form 2025-08-26T20:22:08.2671438Z parameterized by :attr:`cov_factor` and :attr:`cov_diag`:: 2025-08-26T20:22:08.2671527Z 2025-08-26T20:22:08.2671706Z covariance_matrix = cov_factor @ cov_factor.T + cov_diag 2025-08-26T20:22:08.2671801Z 2025-08-26T20:22:08.2671885Z Example: 2025-08-26T20:22:08.2672030Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-08-26T20:22:08.2672181Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:22:08.2672299Z >>> m = LowRankMultivariateNormal( 2025-08-26T20:22:08.2672489Z ... torch.zeros(2), torch.tensor([[1.0], [0.0]]), torch.ones(2) 2025-08-26T20:22:08.2672570Z ... ) 2025-08-26T20:22:08.2672859Z >>> m.sample() # normally distributed with mean=`[0,0]`, cov_factor=`[[1],[0]]`, cov_diag=`[1,1]` 2025-08-26T20:22:08.2672969Z tensor([-0.2102, -0.5429]) 2025-08-26T20:22:08.2673048Z 2025-08-26T20:22:08.2673141Z Args: 2025-08-26T20:22:08.2673374Z loc (Tensor): mean of the distribution with shape `batch_shape + event_shape` 2025-08-26T20:22:08.2673636Z cov_factor (Tensor): factor part of low-rank form of covariance matrix with shape 2025-08-26T20:22:08.2673765Z `batch_shape + event_shape + (rank,)` 2025-08-26T20:22:08.2674049Z cov_diag (Tensor): diagonal part of low-rank form of covariance matrix with shape 2025-08-26T20:22:08.2674169Z `batch_shape + event_shape` 2025-08-26T20:22:08.2674249Z 2025-08-26T20:22:08.2674331Z Note: 2025-08-26T20:22:08.2674609Z The computation for determinant and inverse of covariance matrix is avoided when 2025-08-26T20:22:08.2674848Z `cov_factor.shape[1] << cov_factor.shape[0]` thanks to `Woodbury matrix identity 2025-08-26T20:22:08.2675068Z `_ and 2025-08-26T20:22:08.2675360Z `matrix determinant lemma `_. 2025-08-26T20:22:08.2675613Z Thanks to these formulas, we just need to compute the determinant and inverse of 2025-08-26T20:22:08.2675801Z the small size "capacitance" matrix:: 2025-08-26T20:22:08.2675884Z 2025-08-26T20:22:08.2676081Z capacitance = I + cov_factor.T @ inv(cov_diag) @ cov_factor 2025-08-26T20:22:08.2676164Z 2025-08-26T20:22:08.2676416Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2676511Z 2025-08-26T20:22:08.2676608Z warnings.warn(msg) 2025-08-26T20:22:08.2676701Z 2025-08-26T20:22:08.2676885Z --- Parse Warning: 95 / 146 --- 2025-08-26T20:22:08.2677889Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MixtureSameFamily in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/mixture_same_family.py line=15. 2025-08-26T20:22:08.2678163Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2678269Z 2025-08-26T20:22:08.2678505Z The `MixtureSameFamily` distribution implements a (batch of) mixture 2025-08-26T20:22:08.2678757Z distribution where all component are from different parameterizations of 2025-08-26T20:22:08.2678968Z the same distribution type. It is parameterized by a `Categorical` 2025-08-26T20:22:08.2679176Z "selecting distribution" (over `k` component) and a component 2025-08-26T20:22:08.2679381Z distribution, i.e., a `Distribution` with a rightmost batch shape 2025-08-26T20:22:08.2679554Z (equal to `[k]`) which indexes each (batch of) component. 2025-08-26T20:22:08.2679634Z 2025-08-26T20:22:08.2679724Z Examples:: 2025-08-26T20:22:08.2679826Z 2025-08-26T20:22:08.2679945Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.2680161Z >>> # Construct Gaussian Mixture Model in 1D consisting of 5 equally 2025-08-26T20:22:08.2680279Z >>> # weighted normal distributions 2025-08-26T20:22:08.2680397Z >>> mix = D.Categorical(torch.ones(5,)) 2025-08-26T20:22:08.2680560Z >>> comp = D.Normal(torch.randn(5,), torch.rand(5,)) 2025-08-26T20:22:08.2680687Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-26T20:22:08.2680780Z 2025-08-26T20:22:08.2680984Z >>> # Construct Gaussian Mixture Model in 2D consisting of 5 equally 2025-08-26T20:22:08.2681117Z >>> # weighted bivariate normal distributions 2025-08-26T20:22:08.2681249Z >>> mix = D.Categorical(torch.ones(5,)) 2025-08-26T20:22:08.2681362Z >>> comp = D.Independent(D.Normal( 2025-08-26T20:22:08.2681505Z ... torch.randn(5,2), torch.rand(5,2)), 1) 2025-08-26T20:22:08.2681627Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-26T20:22:08.2681709Z 2025-08-26T20:22:08.2681905Z >>> # Construct a batch of 3 Gaussian Mixture Models in 2D each 2025-08-26T20:22:08.2682108Z >>> # consisting of 5 random weighted bivariate normal distributions 2025-08-26T20:22:08.2682245Z >>> mix = D.Categorical(torch.rand(3,5)) 2025-08-26T20:22:08.2682359Z >>> comp = D.Independent(D.Normal( 2025-08-26T20:22:08.2682497Z ... torch.randn(3,5,2), torch.rand(3,5,2)), 1) 2025-08-26T20:22:08.2682636Z >>> gmm = MixtureSameFamily(mix, comp) 2025-08-26T20:22:08.2682750Z 2025-08-26T20:22:08.2682846Z Args: 2025-08-26T20:22:08.2683053Z mixture_distribution: `torch.distributions.Categorical`-like 2025-08-26T20:22:08.2683243Z instance. Manages the probability of selecting component. 2025-08-26T20:22:08.2683428Z The number of categories must match the rightmost batch 2025-08-26T20:22:08.2683617Z dimension of the `component_distribution`. Must have either 2025-08-26T20:22:08.2683777Z scalar `batch_shape` or `batch_shape` matching 2025-08-26T20:22:08.2683916Z `component_distribution.batch_shape[:-1]` 2025-08-26T20:22:08.2684134Z component_distribution: `torch.distributions.Distribution`-like 2025-08-26T20:22:08.2684323Z instance. Right-most batch dimension indexes component. 2025-08-26T20:22:08.2684407Z 2025-08-26T20:22:08.2684723Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2684806Z 2025-08-26T20:22:08.2684903Z warnings.warn(msg) 2025-08-26T20:22:08.2684996Z 2025-08-26T20:22:08.2685183Z --- Parse Warning: 96 / 146 --- 2025-08-26T20:22:08.2686192Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedBernoulli in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_bernoulli.py line=120. 2025-08-26T20:22:08.2686457Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2686535Z 2025-08-26T20:22:08.2686732Z Creates a RelaxedBernoulli distribution, parametrized by 2025-08-26T20:22:08.2686926Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits` 2025-08-26T20:22:08.2687195Z (but not both). This is a relaxed version of the `Bernoulli` distribution, 2025-08-26T20:22:08.2687381Z so the values are in (0, 1), and has reparametrizable samples. 2025-08-26T20:22:08.2687462Z 2025-08-26T20:22:08.2687564Z Example:: 2025-08-26T20:22:08.2687643Z 2025-08-26T20:22:08.2687793Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:22:08.2687924Z >>> m = RelaxedBernoulli(torch.tensor([2.2]), 2025-08-26T20:22:08.2688048Z ... torch.tensor([0.1, 0.2, 0.3, 0.99])) 2025-08-26T20:22:08.2688148Z >>> m.sample() 2025-08-26T20:22:08.2688267Z tensor([ 0.2951, 0.3442, 0.8918, 0.9021]) 2025-08-26T20:22:08.2688357Z 2025-08-26T20:22:08.2688440Z Args: 2025-08-26T20:22:08.2688581Z temperature (Tensor): relaxation temperature 2025-08-26T20:22:08.2688762Z probs (Number, Tensor): the probability of sampling `1` 2025-08-26T20:22:08.2688922Z logits (Number, Tensor): the log-odds of sampling `1` 2025-08-26T20:22:08.2689004Z 2025-08-26T20:22:08.2689275Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2689354Z 2025-08-26T20:22:08.2689464Z warnings.warn(msg) 2025-08-26T20:22:08.2689546Z 2025-08-26T20:22:08.2689732Z --- Parse Warning: 97 / 146 --- 2025-08-26T20:22:08.2690797Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=RelaxedOneHotCategorical in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/distributions/relaxed_categorical.py line=109. 2025-08-26T20:22:08.2691059Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2691156Z 2025-08-26T20:22:08.2691371Z Creates a RelaxedOneHotCategorical distribution parametrized by 2025-08-26T20:22:08.2691569Z :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. 2025-08-26T20:22:08.2692026Z This is a relaxed version of the :class:`OneHotCategorical` distribution, so 2025-08-26T20:22:08.2692195Z its samples are on simplex, and are reparametrizable. 2025-08-26T20:22:08.2692290Z 2025-08-26T20:22:08.2692460Z Example:: 2025-08-26T20:22:08.2692541Z 2025-08-26T20:22:08.2692696Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:22:08.2692852Z >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), 2025-08-26T20:22:08.2692994Z ... torch.tensor([0.1, 0.2, 0.3, 0.4])) 2025-08-26T20:22:08.2693085Z >>> m.sample() 2025-08-26T20:22:08.2693203Z tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) 2025-08-26T20:22:08.2693295Z 2025-08-26T20:22:08.2693377Z Args: 2025-08-26T20:22:08.2693532Z temperature (Tensor): relaxation temperature 2025-08-26T20:22:08.2693650Z probs (Tensor): event probabilities 2025-08-26T20:22:08.2693836Z logits (Tensor): unnormalized log probability for each event 2025-08-26T20:22:08.2693935Z 2025-08-26T20:22:08.2694255Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2694351Z 2025-08-26T20:22:08.2694449Z warnings.warn(msg) 2025-08-26T20:22:08.2694532Z 2025-08-26T20:22:08.2694734Z --- Parse Warning: 98 / 146 --- 2025-08-26T20:22:08.2695749Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assoc_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=245. 2025-08-26T20:22:08.2696026Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2696219Z Return a new dict with new, potentially nested, key value pair 2025-08-26T20:22:08.2696301Z 2025-08-26T20:22:08.2696411Z >>> purchase = { 2025-08-26T20:22:08.2696509Z ... "name": "Alice", 2025-08-26T20:22:08.2696737Z ... "order": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-26T20:22:08.2696861Z ... "credit card": "5555-1234-1234-1234", 2025-08-26T20:22:08.2696944Z ... } 2025-08-26T20:22:08.2697167Z >>> assoc_in(purchase, ["order", "costs"], [0.25, 1.00]) # doctest: +SKIP 2025-08-26T20:22:08.2697275Z {'credit card': '5555-1234-1234-1234', 2025-08-26T20:22:08.2697381Z 'name': 'Alice', 2025-08-26T20:22:08.2697543Z 'order': {'costs': [0.25, 1.00], 'items': ['Apple', 'Orange']}} 2025-08-26T20:22:08.2697625Z 2025-08-26T20:22:08.2697888Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2697968Z 2025-08-26T20:22:08.2698077Z warnings.warn(msg) 2025-08-26T20:22:08.2698156Z 2025-08-26T20:22:08.2698337Z --- Parse Warning: 99 / 146 --- 2025-08-26T20:22:08.2699375Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=update_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=261. 2025-08-26T20:22:08.2699637Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2699801Z Update value in a (potentially) nested dictionary 2025-08-26T20:22:08.2699881Z 2025-08-26T20:22:08.2699966Z inputs: 2025-08-26T20:22:08.2700093Z d - dictionary on which to operate 2025-08-26T20:22:08.2700311Z keys - list or tuple giving the location of the value to be changed in d 2025-08-26T20:22:08.2700554Z func - function to operate on that value 2025-08-26T20:22:08.2700636Z 2025-08-26T20:22:08.2700826Z If keys == [k0,..,kX] and d[k0]..[kX] == v, update_in returns a copy of the 2025-08-26T20:22:08.2701073Z original dictionary with v replaced by func(v), but does not mutate the 2025-08-26T20:22:08.2701178Z original dictionary. 2025-08-26T20:22:08.2701273Z 2025-08-26T20:22:08.2701484Z If k0 is not a key in d, update_in creates nested dictionaries to the depth 2025-08-26T20:22:08.2701699Z specified by the keys, with the innermost value set to func(default). 2025-08-26T20:22:08.2701828Z 2025-08-26T20:22:08.2701930Z >>> inc = lambda x: x + 1 2025-08-26T20:22:08.2702047Z >>> update_in({"a": 0}, ["a"], inc) 2025-08-26T20:22:08.2702132Z {'a': 1} 2025-08-26T20:22:08.2702213Z 2025-08-26T20:22:08.2702321Z >>> transaction = { 2025-08-26T20:22:08.2702414Z ... "name": "Alice", 2025-08-26T20:22:08.2702602Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-26T20:22:08.2702732Z ... "credit card": "5555-1234-1234-1234", 2025-08-26T20:22:08.2702812Z ... } 2025-08-26T20:22:08.2703038Z >>> update_in(transaction, ["purchase", "costs"], sum) # doctest: +SKIP 2025-08-26T20:22:08.2703145Z {'credit card': '5555-1234-1234-1234', 2025-08-26T20:22:08.2703238Z 'name': 'Alice', 2025-08-26T20:22:08.2703472Z 'purchase': {'costs': 1.75, 'items': ['Apple', 'Orange']}} 2025-08-26T20:22:08.2703554Z 2025-08-26T20:22:08.2703687Z >>> # updating a value when k0 is not in d 2025-08-26T20:22:08.2703819Z >>> update_in({}, [1, 2, 3], str, default="bar") 2025-08-26T20:22:08.2703908Z {1: {2: {3: 'bar'}}} 2025-08-26T20:22:08.2704035Z >>> update_in({1: "foo"}, [2, 3, 4], inc, 0) 2025-08-26T20:22:08.2704127Z {1: 'foo', 2: {3: {4: 1}}} 2025-08-26T20:22:08.2704221Z 2025-08-26T20:22:08.2704470Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2704549Z 2025-08-26T20:22:08.2704659Z warnings.warn(msg) 2025-08-26T20:22:08.2704737Z 2025-08-26T20:22:08.2704931Z --- Parse Warning: 100 / 146 --- 2025-08-26T20:22:08.2705954Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=get_in in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=320. 2025-08-26T20:22:08.2706265Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2706445Z Returns coll[i0][i1]...[iX] where [i0, i1, ..., iX]==keys. 2025-08-26T20:22:08.2706524Z 2025-08-26T20:22:08.2706713Z If coll[i0][i1]...[iX] cannot be found, returns ``default``, unless 2025-08-26T20:22:08.2706914Z ``no_default`` is specified, then it raises KeyError or IndexError. 2025-08-26T20:22:08.2706993Z 2025-08-26T20:22:08.2707211Z ``get_in`` is a generalization of ``operator.getitem`` for nested data 2025-08-26T20:22:08.2707342Z structures such as dictionaries and lists. 2025-08-26T20:22:08.2707436Z 2025-08-26T20:22:08.2707531Z >>> transaction = { 2025-08-26T20:22:08.2707627Z ... "name": "Alice", 2025-08-26T20:22:08.2707832Z ... "purchase": {"items": ["Apple", "Orange"], "costs": [0.50, 1.25]}, 2025-08-26T20:22:08.2707956Z ... "credit card": "5555-1234-1234-1234", 2025-08-26T20:22:08.2708052Z ... } 2025-08-26T20:22:08.2708191Z >>> get_in(["purchase", "items", 0], transaction) 2025-08-26T20:22:08.2708276Z 'Apple' 2025-08-26T20:22:08.2708398Z >>> get_in(["name"], transaction) 2025-08-26T20:22:08.2708481Z 'Alice' 2025-08-26T20:22:08.2708609Z >>> get_in(["purchase", "total"], transaction) 2025-08-26T20:22:08.2708769Z >>> get_in(["purchase", "items", "apple"], transaction) 2025-08-26T20:22:08.2708903Z >>> get_in(["purchase", "items", 10], transaction) 2025-08-26T20:22:08.2709055Z >>> get_in(["purchase", "total"], transaction, 0) 2025-08-26T20:22:08.2709136Z 0 2025-08-26T20:22:08.2709248Z >>> get_in(["y"], {}, no_default=True) 2025-08-26T20:22:08.2709378Z Traceback (most recent call last): 2025-08-26T20:22:08.2709459Z ... 2025-08-26T20:22:08.2709561Z KeyError: 'y' 2025-08-26T20:22:08.2709642Z 2025-08-26T20:22:08.2709730Z See Also: 2025-08-26T20:22:08.2709835Z itertoolz.get 2025-08-26T20:22:08.2716545Z operator.getitem 2025-08-26T20:22:08.2716762Z 2025-08-26T20:22:08.2717042Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2717137Z 2025-08-26T20:22:08.2717236Z warnings.warn(msg) 2025-08-26T20:22:08.2717320Z 2025-08-26T20:22:08.2717559Z --- Parse Warning: 101 / 146 --- 2025-08-26T20:22:08.2718613Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=groupby in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/unification/unification_tools.py line=373. 2025-08-26T20:22:08.2718881Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2719022Z Group a collection by a key function 2025-08-26T20:22:08.2719102Z 2025-08-26T20:22:08.2719352Z >>> names = ["Alice", "Bob", "Charlie", "Dan", "Edith", "Frank"] 2025-08-26T20:22:08.2719479Z >>> groupby(len, names) # doctest: +SKIP 2025-08-26T20:22:08.2719645Z {3: ['Bob', 'Dan'], 5: ['Alice', 'Edith', 'Frank'], 7: ['Charlie']} 2025-08-26T20:22:08.2719738Z 2025-08-26T20:22:08.2719848Z >>> iseven = lambda x: x % 2 == 0 2025-08-26T20:22:08.2720035Z >>> groupby(iseven, [1, 2, 3, 4, 5, 6, 7, 8]) # doctest: +SKIP 2025-08-26T20:22:08.2720148Z {False: [1, 3, 5, 7], True: [2, 4, 6, 8]} 2025-08-26T20:22:08.2720229Z 2025-08-26T20:22:08.2720389Z Non-callable keys imply grouping on a member. 2025-08-26T20:22:08.2720469Z 2025-08-26T20:22:08.2720569Z >>> groupby( 2025-08-26T20:22:08.2720661Z ... "gender", 2025-08-26T20:22:08.2720744Z ... [ 2025-08-26T20:22:08.2720882Z ... {"name": "Alice", "gender": "F"}, 2025-08-26T20:22:08.2721031Z ... {"name": "Bob", "gender": "M"}, 2025-08-26T20:22:08.2721161Z ... {"name": "Charlie", "gender": "M"}, 2025-08-26T20:22:08.2721260Z ... ], 2025-08-26T20:22:08.2721359Z ... ) # doctest:+SKIP 2025-08-26T20:22:08.2721486Z {'F': [{'gender': 'F', 'name': 'Alice'}], 2025-08-26T20:22:08.2721597Z 'M': [{'gender': 'M', 'name': 'Bob'}, 2025-08-26T20:22:08.2721709Z {'gender': 'M', 'name': 'Charlie'}]} 2025-08-26T20:22:08.2721804Z 2025-08-26T20:22:08.2721947Z Not to be confused with ``itertools.groupby`` 2025-08-26T20:22:08.2722042Z 2025-08-26T20:22:08.2722133Z See Also: 2025-08-26T20:22:08.2722221Z countby 2025-08-26T20:22:08.2722314Z 2025-08-26T20:22:08.2722570Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2722664Z 2025-08-26T20:22:08.2722763Z warnings.warn(msg) 2025-08-26T20:22:08.2722844Z 2025-08-26T20:22:08.2723051Z --- Parse Warning: 102 / 146 --- 2025-08-26T20:22:08.2723898Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=calculate_gain in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/init.py line=142. 2025-08-26T20:22:08.2724172Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2724405Z Return the recommended gain value for the given nonlinearity function. 2025-08-26T20:22:08.2724486Z 2025-08-26T20:22:08.2724590Z The values are as follows: 2025-08-26T20:22:08.2724684Z 2025-08-26T20:22:08.2724800Z ================= ==================================================== 2025-08-26T20:22:08.2724914Z nonlinearity gain 2025-08-26T20:22:08.2725029Z ================= ==================================================== 2025-08-26T20:22:08.2725134Z Linear / Identity :math:`1` 2025-08-26T20:22:08.2725243Z Conv{1,2,3}D :math:`1` 2025-08-26T20:22:08.2725343Z Sigmoid :math:`1` 2025-08-26T20:22:08.2725464Z Tanh :math:`\frac{5}{3}` 2025-08-26T20:22:08.2725597Z ReLU :math:`\sqrt{2}` 2025-08-26T20:22:08.2725790Z Leaky Relu :math:`\sqrt{\frac{2}{1 + \text{negative\_slope}^2}}` 2025-08-26T20:22:08.2725909Z SELU :math:`\frac{3}{4}` 2025-08-26T20:22:08.2726022Z ================= ==================================================== 2025-08-26T20:22:08.2726114Z 2025-08-26T20:22:08.2726215Z .. warning:: 2025-08-26T20:22:08.2726402Z In order to implement `Self-Normalizing Neural Networks`_ , 2025-08-26T20:22:08.2726656Z you should use ``nonlinearity='linear'`` instead of ``nonlinearity='selu'``. 2025-08-26T20:22:08.2726825Z This gives the initial weights a variance of ``1 / N``, 2025-08-26T20:22:08.2727058Z which is necessary to induce a stable fixed point in the forward pass. 2025-08-26T20:22:08.2727348Z In contrast, the default gain for ``SELU`` sacrifices the normalization 2025-08-26T20:22:08.2727534Z effect for more stable gradient flow in rectangular layers. 2025-08-26T20:22:08.2727629Z 2025-08-26T20:22:08.2727712Z Args: 2025-08-26T20:22:08.2727921Z nonlinearity: the non-linear function (`nn.functional` name) 2025-08-26T20:22:08.2728090Z param: optional parameter for the non-linear function 2025-08-26T20:22:08.2728169Z 2025-08-26T20:22:08.2728267Z Examples: 2025-08-26T20:22:08.2728384Z >>> gain = nn.init.calculate_gain( 2025-08-26T20:22:08.2728497Z ... "leaky_relu", 0.2 2025-08-26T20:22:08.2728625Z ... ) # leaky_relu with negative_slope=0.2 2025-08-26T20:22:08.2728704Z 2025-08-26T20:22:08.2729195Z .. _Self-Normalizing Neural Networks: https://papers.nips.cc/paper/2017/hash/5d44ee6f2c3f71b73125876103c8f6c4-Abstract.html 2025-08-26T20:22:08.2729305Z 2025-08-26T20:22:08.2729576Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2729654Z 2025-08-26T20:22:08.2729751Z warnings.warn(msg) 2025-08-26T20:22:08.2729841Z 2025-08-26T20:22:08.2730064Z --- Parse Warning: 103 / 146 --- 2025-08-26T20:22:08.2730989Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=603. 2025-08-26T20:22:08.2731246Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2731423Z Applies Batch Normalization over a N-Dimensional input. 2025-08-26T20:22:08.2731513Z 2025-08-26T20:22:08.2731861Z The N-D input is a mini-batch of [N-2]D inputs with additional channel dimension) as described in the paper 2025-08-26T20:22:08.2732102Z `Batch Normalization: Accelerating Deep Network Training by Reducing 2025-08-26T20:22:08.2732317Z Internal Covariate Shift `__ . 2025-08-26T20:22:08.2732397Z 2025-08-26T20:22:08.2732494Z .. math:: 2025-08-26T20:22:08.2732572Z 2025-08-26T20:22:08.2732799Z y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta 2025-08-26T20:22:08.2732877Z 2025-08-26T20:22:08.2733104Z The mean and standard-deviation are calculated per-dimension over all 2025-08-26T20:22:08.2733347Z mini-batches of the same process groups. :math:`\gamma` and :math:`\beta` 2025-08-26T20:22:08.2733586Z are learnable parameter vectors of size `C` (where `C` is the input size). 2025-08-26T20:22:08.2733773Z By default, the elements of :math:`\gamma` are sampled from 2025-08-26T20:22:08.2733974Z :math:`\mathcal{U}(0, 1)` and the elements of :math:`\beta` are set to 0. 2025-08-26T20:22:08.2734234Z The standard-deviation is calculated via the biased estimator, equivalent to 2025-08-26T20:22:08.2734369Z `torch.var(input, unbiased=False)`. 2025-08-26T20:22:08.2734447Z 2025-08-26T20:22:08.2734722Z Also by default, during training this layer keeps running estimates of its 2025-08-26T20:22:08.2734951Z computed mean and variance, which are then used for normalization during 2025-08-26T20:22:08.2735194Z evaluation. The running estimates are kept with a default :attr:`momentum` 2025-08-26T20:22:08.2735293Z of 0.1. 2025-08-26T20:22:08.2735374Z 2025-08-26T20:22:08.2735609Z If :attr:`track_running_stats` is set to ``False``, this layer then does not 2025-08-26T20:22:08.2735829Z keep running estimates, and batch statistics are instead used during 2025-08-26T20:22:08.2735932Z evaluation time as well. 2025-08-26T20:22:08.2736024Z 2025-08-26T20:22:08.2736113Z .. note:: 2025-08-26T20:22:08.2736344Z This :attr:`momentum` argument is different from one used in optimizer 2025-08-26T20:22:08.2736619Z classes and the conventional notion of momentum. Mathematically, the 2025-08-26T20:22:08.2736759Z update rule for running statistics here is 2025-08-26T20:22:08.2737040Z :math:`\hat{x}_\text{new} = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times x_t`, 2025-08-26T20:22:08.2737243Z where :math:`\hat{x}` is the estimated statistic and :math:`x_t` is the 2025-08-26T20:22:08.2737356Z new observed value. 2025-08-26T20:22:08.2737437Z 2025-08-26T20:22:08.2737737Z Because the Batch Normalization is done for each channel in the ``C`` dimension, computing 2025-08-26T20:22:08.2737997Z statistics on ``(N, +)`` slices, it's common terminology to call this Volumetric Batch 2025-08-26T20:22:08.2738175Z Normalization or Spatio-temporal Batch Normalization. 2025-08-26T20:22:08.2738268Z 2025-08-26T20:22:08.2738441Z Currently :class:`SyncBatchNorm` only supports 2025-08-26T20:22:08.2738727Z :class:`~torch.nn.DistributedDataParallel` (DDP) with single GPU per process. Use 2025-08-26T20:22:08.2738949Z :meth:`torch.nn.SyncBatchNorm.convert_sync_batchnorm()` to convert 2025-08-26T20:22:08.2739156Z :attr:`BatchNorm*D` layer to :class:`SyncBatchNorm` before wrapping 2025-08-26T20:22:08.2739264Z Network with DDP. 2025-08-26T20:22:08.2739344Z 2025-08-26T20:22:08.2739426Z Args: 2025-08-26T20:22:08.2739601Z num_features: :math:`C` from an expected input of size 2025-08-26T20:22:08.2739698Z :math:`(N, C, +)` 2025-08-26T20:22:08.2739897Z eps: a value added to the denominator for numerical stability. 2025-08-26T20:22:08.2739996Z Default: ``1e-5`` 2025-08-26T20:22:08.2740188Z momentum: the value used for the running_mean and running_var 2025-08-26T20:22:08.2740516Z computation. Can be set to ``None`` for cumulative moving average 2025-08-26T20:22:08.2740644Z (i.e. simple average). Default: 0.1 2025-08-26T20:22:08.2740864Z affine: a boolean value that when set to ``True``, this module has 2025-08-26T20:22:08.2741019Z learnable affine parameters. Default: ``True`` 2025-08-26T20:22:08.2741235Z track_running_stats: a boolean value that when set to ``True``, this 2025-08-26T20:22:08.2741479Z module tracks the running mean and variance, and when set to ``False``, 2025-08-26T20:22:08.2741701Z this module does not track such statistics, and initializes statistics 2025-08-26T20:22:08.2741918Z buffers :attr:`running_mean` and :attr:`running_var` as ``None``. 2025-08-26T20:22:08.2742149Z When these buffers are ``None``, this module always uses batch statistics. 2025-08-26T20:22:08.2742312Z in both training and eval modes. Default: ``True`` 2025-08-26T20:22:08.2742562Z process_group: synchronization of stats happen within each process group 2025-08-26T20:22:08.2742791Z individually. Default behavior is synchronization across the whole 2025-08-26T20:22:08.2742890Z world 2025-08-26T20:22:08.2743001Z 2025-08-26T20:22:08.2743085Z Shape: 2025-08-26T20:22:08.2743204Z - Input: :math:`(N, C, +)` 2025-08-26T20:22:08.2743349Z - Output: :math:`(N, C, +)` (same shape as input) 2025-08-26T20:22:08.2743441Z 2025-08-26T20:22:08.2743529Z .. note:: 2025-08-26T20:22:08.2743774Z Synchronization of batchnorm statistics occurs only while training, i.e. 2025-08-26T20:22:08.2743986Z synchronization is disabled when ``model.eval()`` is set or if 2025-08-26T20:22:08.2744114Z ``self.training`` is otherwise ``False``. 2025-08-26T20:22:08.2744204Z 2025-08-26T20:22:08.2744295Z Examples:: 2025-08-26T20:22:08.2744375Z 2025-08-26T20:22:08.2744486Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2744605Z >>> # With Learnable Parameters 2025-08-26T20:22:08.2744773Z >>> m = nn.SyncBatchNorm(100) 2025-08-26T20:22:08.2744898Z >>> # creating process group (optional) 2025-08-26T20:22:08.2745041Z >>> # ranks is a list of int identifying rank ids. 2025-08-26T20:22:08.2745153Z >>> ranks = list(range(8)) 2025-08-26T20:22:08.2745261Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-08-26T20:22:08.2745421Z >>> # Note: every rank calls into new_group for every 2025-08-26T20:22:08.2745569Z >>> # process group created, even if that rank is not 2025-08-26T20:22:08.2745670Z >>> # part of the group. 2025-08-26T20:22:08.2745927Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-08-26T20:22:08.2746130Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-08-26T20:22:08.2746261Z >>> # Without Learnable Parameters 2025-08-26T20:22:08.2746494Z >>> m = nn.BatchNorm3d(100, affine=False, process_group=process_group) 2025-08-26T20:22:08.2746623Z >>> input = torch.randn(20, 100, 35, 45, 10) 2025-08-26T20:22:08.2746731Z >>> output = m(input) 2025-08-26T20:22:08.2746813Z 2025-08-26T20:22:08.2746940Z >>> # network is nn.BatchNorm layer 2025-08-26T20:22:08.2747214Z >>> sync_bn_network = nn.SyncBatchNorm.convert_sync_batchnorm(network, process_group) 2025-08-26T20:22:08.2747374Z >>> # only single gpu per process is currently supported 2025-08-26T20:22:08.2747598Z >>> ddp_sync_bn_network = torch.nn.parallel.DistributedDataParallel( 2025-08-26T20:22:08.2747716Z >>> sync_bn_network, 2025-08-26T20:22:08.2747855Z >>> device_ids=[args.local_rank], 2025-08-26T20:22:08.2747985Z >>> output_device=args.local_rank) 2025-08-26T20:22:08.2748068Z 2025-08-26T20:22:08.2748329Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2748412Z 2025-08-26T20:22:08.2748521Z warnings.warn(msg) 2025-08-26T20:22:08.2748602Z 2025-08-26T20:22:08.2748812Z --- Parse Warning: 104 / 146 --- 2025-08-26T20:22:08.2749844Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyncBatchNorm.convert_sync_batchnorm in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/batchnorm.py line=830. 2025-08-26T20:22:08.2750106Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2750425Z Converts all :attr:`BatchNorm*D` layers in the model to :class:`torch.nn.SyncBatchNorm` layers. 2025-08-26T20:22:08.2750503Z 2025-08-26T20:22:08.2750587Z Args: 2025-08-26T20:22:08.2750841Z module (nn.Module): module containing one or more :attr:`BatchNorm*D` layers 2025-08-26T20:22:08.2751062Z process_group (optional): process group to scope synchronization, 2025-08-26T20:22:08.2751191Z default is the whole world 2025-08-26T20:22:08.2751302Z 2025-08-26T20:22:08.2751416Z Returns: 2025-08-26T20:22:08.2751680Z The original :attr:`module` with the converted :class:`torch.nn.SyncBatchNorm` 2025-08-26T20:22:08.2751894Z layers. If the original :attr:`module` is a :attr:`BatchNorm*D` layer, 2025-08-26T20:22:08.2752114Z a new :class:`torch.nn.SyncBatchNorm` layer object will be returned 2025-08-26T20:22:08.2752203Z instead. 2025-08-26T20:22:08.2752283Z 2025-08-26T20:22:08.2752385Z Example:: 2025-08-26T20:22:08.2752465Z 2025-08-26T20:22:08.2752605Z >>> # Network with nn.BatchNorm layer 2025-08-26T20:22:08.2752743Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:22:08.2752864Z >>> module = torch.nn.Sequential( 2025-08-26T20:22:08.2753047Z >>> torch.nn.Linear(20, 100), 2025-08-26T20:22:08.2753173Z >>> torch.nn.BatchNorm1d(100), 2025-08-26T20:22:08.2753283Z >>> ).cuda() 2025-08-26T20:22:08.2753410Z >>> # creating process group (optional) 2025-08-26T20:22:08.2753553Z >>> # ranks is a list of int identifying rank ids. 2025-08-26T20:22:08.2753670Z >>> ranks = list(range(8)) 2025-08-26T20:22:08.2753778Z >>> r1, r2 = ranks[:4], ranks[4:] 2025-08-26T20:22:08.2753937Z >>> # Note: every rank calls into new_group for every 2025-08-26T20:22:08.2754087Z >>> # process group created, even if that rank is not 2025-08-26T20:22:08.2754193Z >>> # part of the group. 2025-08-26T20:22:08.2754331Z >>> # xdoctest: +SKIP("distributed") 2025-08-26T20:22:08.2754582Z >>> process_groups = [torch.distributed.new_group(pids) for pids in [r1, r2]] 2025-08-26T20:22:08.2754831Z >>> process_group = process_groups[0 if dist.get_rank() <= 3 else 1] 2025-08-26T20:22:08.2755126Z >>> sync_bn_module = torch.nn.SyncBatchNorm.convert_sync_batchnorm(module, process_group) 2025-08-26T20:22:08.2755209Z 2025-08-26T20:22:08.2755305Z 2025-08-26T20:22:08.2755558Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2755650Z 2025-08-26T20:22:08.2755746Z warnings.warn(msg) 2025-08-26T20:22:08.2755826Z 2025-08-26T20:22:08.2756030Z --- Parse Warning: 105 / 146 --- 2025-08-26T20:22:08.2756906Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Unflatten in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/flatten.py line=66. 2025-08-26T20:22:08.2757176Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2757260Z 2025-08-26T20:22:08.2757575Z Unflattens a tensor dim expanding it to a desired shape. For use with :class:`~nn.Sequential`. 2025-08-26T20:22:08.2757670Z 2025-08-26T20:22:08.2757940Z * :attr:`dim` specifies the dimension of the input tensor to be unflattened, and it can 2025-08-26T20:22:08.2758175Z be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively. 2025-08-26T20:22:08.2758254Z 2025-08-26T20:22:08.2758570Z * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be 2025-08-26T20:22:08.2758831Z a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input; a `NamedShape` 2025-08-26T20:22:08.2758998Z (tuple of `(name, size)` tuples) for `NamedTensor` input. 2025-08-26T20:22:08.2759093Z 2025-08-26T20:22:08.2759178Z Shape: 2025-08-26T20:22:08.2759389Z - Input: :math:`(*, S_{\text{dim}}, *)`, where :math:`S_{\text{dim}}` is the size at 2025-08-26T20:22:08.2759656Z dimension :attr:`dim` and :math:`*` means any number of dimensions including none. 2025-08-26T20:22:08.2759875Z - Output: :math:`(*, U_1, ..., U_n, *)`, where :math:`U` = :attr:`unflattened_size` and 2025-08-26T20:22:08.2760044Z :math:`\prod_{i=1}^n U_i = S_{\text{dim}}`. 2025-08-26T20:22:08.2760123Z 2025-08-26T20:22:08.2760205Z Args: 2025-08-26T20:22:08.2760363Z dim (Union[int, str]): Dimension to be unflattened 2025-08-26T20:22:08.2760706Z unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension 2025-08-26T20:22:08.2760797Z 2025-08-26T20:22:08.2760882Z Examples: 2025-08-26T20:22:08.2760990Z >>> input = torch.randn(2, 50) 2025-08-26T20:22:08.2761100Z >>> # With tuple of ints 2025-08-26T20:22:08.2761197Z >>> m = nn.Sequential( 2025-08-26T20:22:08.2761307Z >>> nn.Linear(50, 50), 2025-08-26T20:22:08.2761415Z >>> nn.Unflatten(1, (2, 5, 5)) 2025-08-26T20:22:08.2761498Z >>> ) 2025-08-26T20:22:08.2761668Z >>> output = m(input) 2025-08-26T20:22:08.2761763Z >>> output.size() 2025-08-26T20:22:08.2761874Z torch.Size([2, 2, 5, 5]) 2025-08-26T20:22:08.2761966Z >>> # With torch.Size 2025-08-26T20:22:08.2762079Z >>> m = nn.Sequential( 2025-08-26T20:22:08.2762190Z >>> nn.Linear(50, 50), 2025-08-26T20:22:08.2762317Z >>> nn.Unflatten(1, torch.Size([2, 5, 5])) 2025-08-26T20:22:08.2762401Z >>> ) 2025-08-26T20:22:08.2762506Z >>> output = m(input) 2025-08-26T20:22:08.2762603Z >>> output.size() 2025-08-26T20:22:08.2762713Z torch.Size([2, 2, 5, 5]) 2025-08-26T20:22:08.2762833Z >>> # With namedshape (tuple of tuples) 2025-08-26T20:22:08.2762984Z >>> input = torch.randn(2, 50, names=("N", "features")) 2025-08-26T20:22:08.2763198Z >>> unflatten = nn.Unflatten("features", (("C", 2), ("H", 5), ("W", 5))) 2025-08-26T20:22:08.2763333Z >>> output = unflatten(input) 2025-08-26T20:22:08.2763441Z >>> output.size() 2025-08-26T20:22:08.2763540Z torch.Size([2, 2, 5, 5]) 2025-08-26T20:22:08.2763620Z 2025-08-26T20:22:08.2763883Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2763963Z 2025-08-26T20:22:08.2764070Z warnings.warn(msg) 2025-08-26T20:22:08.2764146Z 2025-08-26T20:22:08.2764338Z --- Parse Warning: 106 / 146 --- 2025-08-26T20:22:08.2765344Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=TripletMarginWithDistanceLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1798. 2025-08-26T20:22:08.2765604Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2765811Z Creates a criterion that measures the triplet loss given input 2025-08-26T20:22:08.2766005Z tensors :math:`a`, :math:`p`, and :math:`n` (representing anchor, 2025-08-26T20:22:08.2766223Z positive, and negative examples, respectively), and a nonnegative, 2025-08-26T20:22:08.2766487Z real-valued function ("distance function") used to compute the relationship 2025-08-26T20:22:08.2766705Z between the anchor and positive example ("positive distance") and the 2025-08-26T20:22:08.2766867Z anchor and negative example ("negative distance"). 2025-08-26T20:22:08.2766952Z 2025-08-26T20:22:08.2767161Z The unreduced loss (i.e., with :attr:`reduction` set to ``'none'``) 2025-08-26T20:22:08.2767274Z can be described as: 2025-08-26T20:22:08.2767355Z 2025-08-26T20:22:08.2767456Z .. math:: 2025-08-26T20:22:08.2767595Z \ell(a, p, n) = L = \{l_1,\dots,l_N\}^\top, \quad 2025-08-26T20:22:08.2767746Z l_i = \max \{d(a_i, p_i) - d(a_i, n_i) + {\rm margin}, 0\} 2025-08-26T20:22:08.2767841Z 2025-08-26T20:22:08.2768086Z where :math:`N` is the batch size; :math:`d` is a nonnegative, real-valued function 2025-08-26T20:22:08.2768393Z quantifying the closeness of two tensors, referred to as the :attr:`distance_function`; 2025-08-26T20:22:08.2768663Z and :math:`margin` is a nonnegative margin representing the minimum difference 2025-08-26T20:22:08.2768907Z between the positive and negative distances that is required for the loss to 2025-08-26T20:22:08.2769144Z be 0. The input tensors have :math:`N` elements each and can be of any shape 2025-08-26T20:22:08.2769266Z that the distance function can handle. 2025-08-26T20:22:08.2769356Z 2025-08-26T20:22:08.2769470Z If :attr:`reduction` is not ``'none'`` 2025-08-26T20:22:08.2769569Z (default ``'mean'``), then: 2025-08-26T20:22:08.2769660Z 2025-08-26T20:22:08.2769744Z .. math:: 2025-08-26T20:22:08.2769846Z \ell(x, y) = 2025-08-26T20:22:08.2769934Z \begin{cases} 2025-08-26T20:22:08.2770137Z \operatorname{mean}(L), & \text{if reduction} = \text{`mean';}\\ 2025-08-26T20:22:08.2770385Z \operatorname{sum}(L), & \text{if reduction} = \text{`sum'.} 2025-08-26T20:22:08.2770478Z \end{cases} 2025-08-26T20:22:08.2770570Z 2025-08-26T20:22:08.2770803Z See also :class:`~torch.nn.TripletMarginLoss`, which computes the triplet 2025-08-26T20:22:08.2771053Z loss for input tensors using the :math:`l_p` distance as the distance function. 2025-08-26T20:22:08.2771144Z 2025-08-26T20:22:08.2771228Z Args: 2025-08-26T20:22:08.2771512Z distance_function (Callable, optional): A nonnegative, real-valued function that 2025-08-26T20:22:08.2771700Z quantifies the closeness of two tensors. If not specified, 2025-08-26T20:22:08.2771870Z `nn.PairwiseDistance` will be used. Default: ``None`` 2025-08-26T20:22:08.2772149Z margin (float, optional): A nonnegative margin representing the minimum difference 2025-08-26T20:22:08.2772447Z between the positive and negative distances required for the loss to be 0. Larger 2025-08-26T20:22:08.2772736Z margins penalize cases where the negative examples are not distant enough from the 2025-08-26T20:22:08.2772913Z anchors, relative to the positives. Default: :math:`1`. 2025-08-26T20:22:08.2773162Z swap (bool, optional): Whether to use the distance swap described in the paper 2025-08-26T20:22:08.2773433Z `Learning shallow convolutional feature descriptors with triplet losses` by 2025-08-26T20:22:08.2773667Z V. Balntas, E. Riba et al. If True, and if the positive example is closer to the 2025-08-26T20:22:08.2773946Z negative example than the anchor is, swaps the positive example and the anchor in 2025-08-26T20:22:08.2774081Z the loss computation. Default: ``False``. 2025-08-26T20:22:08.2774372Z reduction (str, optional): Specifies the (optional) reduction to apply to the output: 2025-08-26T20:22:08.2774560Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-08-26T20:22:08.2774739Z ``'mean'``: the sum of the output will be divided by the number of 2025-08-26T20:22:08.2774990Z elements in the output, ``'sum'``: the output will be summed. Default: ``'mean'`` 2025-08-26T20:22:08.2775071Z 2025-08-26T20:22:08.2775163Z 2025-08-26T20:22:08.2775246Z Shape: 2025-08-26T20:22:08.2775487Z - Input: :math:`(N, *)` where :math:`*` represents any number of additional dimensions 2025-08-26T20:22:08.2775629Z as supported by the distance function. 2025-08-26T20:22:08.2775881Z - Output: A Tensor of shape :math:`(N)` if :attr:`reduction` is ``'none'``, or a scalar 2025-08-26T20:22:08.2775984Z otherwise. 2025-08-26T20:22:08.2776060Z 2025-08-26T20:22:08.2776145Z Examples: 2025-08-26T20:22:08.2776237Z 2025-08-26T20:22:08.2776340Z >>> # Initialize embeddings 2025-08-26T20:22:08.2776461Z >>> embedding = nn.Embedding(1000, 128) 2025-08-26T20:22:08.2776599Z >>> anchor_ids = torch.randint(0, 1000, (1,)) 2025-08-26T20:22:08.2776758Z >>> positive_ids = torch.randint(0, 1000, (1,)) 2025-08-26T20:22:08.2776897Z >>> negative_ids = torch.randint(0, 1000, (1,)) 2025-08-26T20:22:08.2777009Z >>> anchor = embedding(anchor_ids) 2025-08-26T20:22:08.2777128Z >>> positive = embedding(positive_ids) 2025-08-26T20:22:08.2777255Z >>> negative = embedding(negative_ids) 2025-08-26T20:22:08.2777336Z >>> 2025-08-26T20:22:08.2777458Z >>> # Built-in Distance Function 2025-08-26T20:22:08.2777556Z >>> triplet_loss = \ 2025-08-26T20:22:08.2777832Z >>> nn.TripletMarginWithDistanceLoss(distance_function=nn.PairwiseDistance()) 2025-08-26T20:22:08.2777996Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-26T20:22:08.2778099Z >>> output.backward() 2025-08-26T20:22:08.2778193Z >>> 2025-08-26T20:22:08.2778348Z >>> # Custom Distance Function 2025-08-26T20:22:08.2778451Z >>> def l_infinity(x1, x2): 2025-08-26T20:22:08.2778619Z >>> return torch.max(torch.abs(x1 - x2), dim=1).values 2025-08-26T20:22:08.2778698Z >>> 2025-08-26T20:22:08.2778894Z >>> # xdoctest: +SKIP("FIXME: Would call backwards a second time") 2025-08-26T20:22:08.2778989Z >>> triplet_loss = ( 2025-08-26T20:22:08.2779269Z >>> nn.TripletMarginWithDistanceLoss(distance_function=l_infinity, margin=1.5)) 2025-08-26T20:22:08.2779433Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-26T20:22:08.2779535Z >>> output.backward() 2025-08-26T20:22:08.2779624Z >>> 2025-08-26T20:22:08.2779739Z >>> # Custom Distance Function (Lambda) 2025-08-26T20:22:08.2779833Z >>> triplet_loss = ( 2025-08-26T20:22:08.2779974Z >>> nn.TripletMarginWithDistanceLoss( 2025-08-26T20:22:08.2780222Z >>> distance_function=lambda x, y: 1.0 - F.cosine_similarity(x, y))) 2025-08-26T20:22:08.2780464Z >>> output = triplet_loss(anchor, positive, negative) 2025-08-26T20:22:08.2780567Z >>> output.backward() 2025-08-26T20:22:08.2780646Z 2025-08-26T20:22:08.2780747Z Reference: 2025-08-26T20:22:08.2781054Z V. Balntas, et al.: Learning shallow convolutional feature descriptors with triplet losses: 2025-08-26T20:22:08.2781289Z https://bmva-archive.org.uk/bmvc/2016/papers/paper119/index.html 2025-08-26T20:22:08.2781370Z 2025-08-26T20:22:08.2781626Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 17)) 2025-08-26T20:22:08.2781715Z 2025-08-26T20:22:08.2781812Z warnings.warn(msg) 2025-08-26T20:22:08.2781889Z 2025-08-26T20:22:08.2782108Z --- Parse Warning: 107 / 146 --- 2025-08-26T20:22:08.2782967Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CTCLoss in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/loss.py line=1933. 2025-08-26T20:22:08.2783247Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2783391Z The Connectionist Temporal Classification loss. 2025-08-26T20:22:08.2783488Z 2025-08-26T20:22:08.2783869Z Calculates loss between a continuous (unsegmented) time series and a target sequence. CTCLoss sums over the 2025-08-26T20:22:08.2784221Z probability of possible alignments of input to target, producing a loss value which is differentiable 2025-08-26T20:22:08.2784566Z with respect to each input node. The alignment of input to target is assumed to be "many-to-one", which 2025-08-26T20:22:08.2784880Z limits the length of the target sequence such that it must be :math:`\leq` the input length. 2025-08-26T20:22:08.2784961Z 2025-08-26T20:22:08.2785047Z Args: 2025-08-26T20:22:08.2785226Z blank (int, optional): blank label. Default :math:`0`. 2025-08-26T20:22:08.2785467Z reduction (str, optional): Specifies the reduction to apply to the output: 2025-08-26T20:22:08.2785696Z ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, 2025-08-26T20:22:08.2785901Z ``'mean'``: the output losses will be divided by the target lengths and 2025-08-26T20:22:08.2786148Z then the mean over the batch is taken, ``'sum'``: the output losses will be summed. 2025-08-26T20:22:08.2786257Z Default: ``'mean'`` 2025-08-26T20:22:08.2786372Z zero_infinity (bool, optional): 2025-08-26T20:22:08.2786579Z Whether to zero infinite losses and the associated gradients. 2025-08-26T20:22:08.2786675Z Default: ``False`` 2025-08-26T20:22:08.2786863Z Infinite losses mainly occur when the inputs are too short 2025-08-26T20:22:08.2786990Z to be aligned to the targets. 2025-08-26T20:22:08.2787070Z 2025-08-26T20:22:08.2787225Z Shape: 2025-08-26T20:22:08.2787414Z - Log_probs: Tensor of size :math:`(T, N, C)` or :math:`(T, C)`, 2025-08-26T20:22:08.2787542Z where :math:`T = \text{input length}`, 2025-08-26T20:22:08.2787668Z :math:`N = \text{batch size}`, and 2025-08-26T20:22:08.2787826Z :math:`C = \text{number of classes (including blank)}`. 2025-08-26T20:22:08.2788059Z The logarithmized probabilities of the outputs (e.g. obtained with 2025-08-26T20:22:08.2788195Z :func:`torch.nn.functional.log_softmax`). 2025-08-26T20:22:08.2788325Z - Targets: Tensor of size :math:`(N, S)` or 2025-08-26T20:22:08.2788494Z :math:`(\operatorname{sum}(\text{target\_lengths}))`, 2025-08-26T20:22:08.2788618Z where :math:`N = \text{batch size}` and 2025-08-26T20:22:08.2788792Z :math:`S = \text{max target length, if shape is } (N, S)`. 2025-08-26T20:22:08.2789020Z It represents the target sequences. Each element in the target 2025-08-26T20:22:08.2789259Z sequence is a class index. And the target index cannot be blank (default=0). 2025-08-26T20:22:08.2789429Z In the :math:`(N, S)` form, targets are padded to the 2025-08-26T20:22:08.2789567Z length of the longest sequence, and stacked. 2025-08-26T20:22:08.2789768Z In the :math:`(\operatorname{sum}(\text{target\_lengths}))` form, 2025-08-26T20:22:08.2789903Z the targets are assumed to be un-padded and 2025-08-26T20:22:08.2790021Z concatenated within 1 dimension. 2025-08-26T20:22:08.2790238Z - Input_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-08-26T20:22:08.2790437Z where :math:`N = \text{batch size}`. It represents the lengths of the 2025-08-26T20:22:08.2790648Z inputs (must each be :math:`\leq T`). And the lengths are specified 2025-08-26T20:22:08.2790883Z for each sequence to achieve masking under the assumption that sequences 2025-08-26T20:22:08.2791008Z are padded to equal lengths. 2025-08-26T20:22:08.2791219Z - Target_lengths: Tuple or tensor of size :math:`(N)` or :math:`()`, 2025-08-26T20:22:08.2791430Z where :math:`N = \text{batch size}`. It represents lengths of the targets. 2025-08-26T20:22:08.2791869Z Lengths are specified for each sequence to achieve masking under the 2025-08-26T20:22:08.2792117Z assumption that sequences are padded to equal lengths. If target shape is 2025-08-26T20:22:08.2792306Z :math:`(N,S)`, target_lengths are effectively the stop index 2025-08-26T20:22:08.2792560Z :math:`s_n` for each target sequence, such that ``target_n = targets[n,0:s_n]`` for 2025-08-26T20:22:08.2792738Z each target in a batch. Lengths must each be :math:`\leq S` 2025-08-26T20:22:08.2793001Z If the targets are given as a 1d tensor that is the concatenation of individual 2025-08-26T20:22:08.2793236Z targets, the target_lengths must add up to the total length of the tensor. 2025-08-26T20:22:08.2793524Z - Output: scalar if :attr:`reduction` is ``'mean'`` (default) or 2025-08-26T20:22:08.2793753Z ``'sum'``. If :attr:`reduction` is ``'none'``, then :math:`(N)` if input is batched or 2025-08-26T20:22:08.2793960Z :math:`()` if input is unbatched, where :math:`N = \text{batch size}`. 2025-08-26T20:22:08.2794040Z 2025-08-26T20:22:08.2794126Z Examples: 2025-08-26T20:22:08.2794219Z 2025-08-26T20:22:08.2794327Z >>> # Target are to be padded 2025-08-26T20:22:08.2794437Z >>> T = 50 # Input sequence length 2025-08-26T20:22:08.2794584Z >>> C = 20 # Number of classes (including blank) 2025-08-26T20:22:08.2794676Z >>> N = 16 # Batch size 2025-08-26T20:22:08.2795023Z >>> S = 30 # Target sequence length of longest target in batch (padding length) 2025-08-26T20:22:08.2795205Z >>> S_min = 10 # Minimum target length, for demonstration purposes 2025-08-26T20:22:08.2795291Z >>> 2025-08-26T20:22:08.2795503Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-08-26T20:22:08.2795710Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-08-26T20:22:08.2795804Z >>> 2025-08-26T20:22:08.2796038Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-26T20:22:08.2796253Z >>> target = torch.randint(low=1, high=C, size=(N, S), dtype=torch.long) 2025-08-26T20:22:08.2796350Z >>> 2025-08-26T20:22:08.2796566Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-08-26T20:22:08.2796693Z >>> target_lengths = torch.randint( 2025-08-26T20:22:08.2796829Z ... low=S_min, 2025-08-26T20:22:08.2796917Z ... high=S, 2025-08-26T20:22:08.2797025Z ... size=(N,), 2025-08-26T20:22:08.2797129Z ... dtype=torch.long, 2025-08-26T20:22:08.2797225Z ... ) 2025-08-26T20:22:08.2797333Z >>> ctc_loss = nn.CTCLoss() 2025-08-26T20:22:08.2797516Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-26T20:22:08.2797628Z >>> loss.backward() 2025-08-26T20:22:08.2797709Z >>> 2025-08-26T20:22:08.2797804Z >>> 2025-08-26T20:22:08.2797917Z >>> # Target are to be un-padded 2025-08-26T20:22:08.2798029Z >>> T = 50 # Input sequence length 2025-08-26T20:22:08.2798177Z >>> C = 20 # Number of classes (including blank) 2025-08-26T20:22:08.2798273Z >>> N = 16 # Batch size 2025-08-26T20:22:08.2798363Z >>> 2025-08-26T20:22:08.2798562Z >>> # Initialize random batch of input vectors, for *size = (T,N,C) 2025-08-26T20:22:08.2798773Z >>> input = torch.randn(T, N, C).log_softmax(2).detach().requires_grad_() 2025-08-26T20:22:08.2799000Z >>> input_lengths = torch.full(size=(N,), fill_value=T, dtype=torch.long) 2025-08-26T20:22:08.2799082Z >>> 2025-08-26T20:22:08.2799288Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-26T20:22:08.2799525Z >>> target_lengths = torch.randint(low=1, high=T, size=(N,), dtype=torch.long) 2025-08-26T20:22:08.2799631Z >>> target = torch.randint( 2025-08-26T20:22:08.2799731Z ... low=1, 2025-08-26T20:22:08.2799856Z ... high=C, 2025-08-26T20:22:08.2799982Z ... size=(sum(target_lengths),), 2025-08-26T20:22:08.2800082Z ... dtype=torch.long, 2025-08-26T20:22:08.2800162Z ... ) 2025-08-26T20:22:08.2800278Z >>> ctc_loss = nn.CTCLoss() 2025-08-26T20:22:08.2800460Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-26T20:22:08.2800575Z >>> loss.backward() 2025-08-26T20:22:08.2800657Z >>> 2025-08-26T20:22:08.2800738Z >>> 2025-08-26T20:22:08.2800963Z >>> # Target are to be un-padded and unbatched (effectively N=1) 2025-08-26T20:22:08.2801076Z >>> T = 50 # Input sequence length 2025-08-26T20:22:08.2801208Z >>> C = 20 # Number of classes (including blank) 2025-08-26T20:22:08.2801300Z >>> 2025-08-26T20:22:08.2801493Z >>> # Initialize random batch of input vectors, for *size = (T,C) 2025-08-26T20:22:08.2801641Z >>> # xdoctest: +SKIP("FIXME: error in doctest") 2025-08-26T20:22:08.2801839Z >>> input = torch.randn(T, C).log_softmax(1).detach().requires_grad_() 2025-08-26T20:22:08.2801992Z >>> input_lengths = torch.tensor(T, dtype=torch.long) 2025-08-26T20:22:08.2802085Z >>> 2025-08-26T20:22:08.2802278Z >>> # Initialize random batch of targets (0 = blank, 1:C = classes) 2025-08-26T20:22:08.2802570Z >>> target_lengths = torch.randint(low=1, high=T, size=(), dtype=torch.long) 2025-08-26T20:22:08.2802675Z >>> target = torch.randint( 2025-08-26T20:22:08.2802766Z ... low=1, 2025-08-26T20:22:08.2802864Z ... high=C, 2025-08-26T20:22:08.2802970Z ... size=(target_lengths,), 2025-08-26T20:22:08.2803084Z ... dtype=torch.long, 2025-08-26T20:22:08.2803165Z ... ) 2025-08-26T20:22:08.2803269Z >>> ctc_loss = nn.CTCLoss() 2025-08-26T20:22:08.2803462Z >>> loss = ctc_loss(input, target, input_lengths, target_lengths) 2025-08-26T20:22:08.2803559Z >>> loss.backward() 2025-08-26T20:22:08.2803652Z 2025-08-26T20:22:08.2803738Z Reference: 2025-08-26T20:22:08.2803909Z A. Graves et al.: Connectionist Temporal Classification: 2025-08-26T20:22:08.2804161Z Labelling Unsegmented Sequence Data with Recurrent Neural Networks: 2025-08-26T20:22:08.2804353Z https://www.cs.toronto.edu/~graves/icml_2006.pdf 2025-08-26T20:22:08.2804443Z 2025-08-26T20:22:08.2804525Z Note: 2025-08-26T20:22:08.2804767Z In order to use CuDNN, the following must be satisfied: :attr:`targets` must be 2025-08-26T20:22:08.2805029Z in concatenated format, all :attr:`input_lengths` must be `T`. :math:`blank=0`, 2025-08-26T20:22:08.2805246Z :attr:`target_lengths` :math:`\leq 256`, the integer arguments must be of 2025-08-26T20:22:08.2805365Z dtype :attr:`torch.int32`. 2025-08-26T20:22:08.2805443Z 2025-08-26T20:22:08.2805712Z The regular implementation uses the (more common in PyTorch) `torch.long` dtype. 2025-08-26T20:22:08.2805801Z 2025-08-26T20:22:08.2805879Z 2025-08-26T20:22:08.2805970Z Note: 2025-08-26T20:22:08.2806210Z In some circumstances when using the CUDA backend with CuDNN, this operator 2025-08-26T20:22:08.2806467Z may select a nondeterministic algorithm to increase performance. If this is 2025-08-26T20:22:08.2806718Z undesirable, you can try to make the operation deterministic (potentially at 2025-08-26T20:22:08.2806953Z a performance cost) by setting ``torch.backends.cudnn.deterministic = 2025-08-26T20:22:08.2807053Z True``. 2025-08-26T20:22:08.2807250Z Please see the notes on :doc:`/notes/randomness` for background. 2025-08-26T20:22:08.2807328Z 2025-08-26T20:22:08.2807590Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2807665Z 2025-08-26T20:22:08.2807770Z warnings.warn(msg) 2025-08-26T20:22:08.2807848Z 2025-08-26T20:22:08.2808060Z --- Parse Warning: 108 / 146 --- 2025-08-26T20:22:08.2808969Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MaxUnpool2d in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/pooling.py line=410. 2025-08-26T20:22:08.2809234Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2809429Z Computes a partial inverse of :class:`MaxPool2d`. 2025-08-26T20:22:08.2809508Z 2025-08-26T20:22:08.2809766Z :class:`MaxPool2d` is not fully invertible, since the non-maximal values are lost. 2025-08-26T20:22:08.2809858Z 2025-08-26T20:22:08.2810081Z :class:`MaxUnpool2d` takes in as input the output of :class:`MaxPool2d` 2025-08-26T20:22:08.2810331Z including the indices of the maximal values and computes a partial inverse 2025-08-26T20:22:08.2810477Z in which all non-maximal values are set to zero. 2025-08-26T20:22:08.2810557Z 2025-08-26T20:22:08.2810654Z Note: 2025-08-26T20:22:08.2810967Z This operation may behave nondeterministically when the input indices has repeat values. 2025-08-26T20:22:08.2811359Z See https://github.com/pytorch/pytorch/issues/80827 and :doc:`/notes/randomness` for more information. 2025-08-26T20:22:08.2811488Z 2025-08-26T20:22:08.2811730Z .. note:: :class:`MaxPool2d` can map several input sizes to the same output 2025-08-26T20:22:08.2811919Z sizes. Hence, the inversion process can get ambiguous. 2025-08-26T20:22:08.2812107Z To accommodate this, you can provide the needed output size 2025-08-26T20:22:08.2812329Z as an additional argument :attr:`output_size` in the forward call. 2025-08-26T20:22:08.2812453Z See the Inputs and Example below. 2025-08-26T20:22:08.2812533Z 2025-08-26T20:22:08.2812627Z Args: 2025-08-26T20:22:08.2812809Z kernel_size (int or tuple): Size of the max pooling window. 2025-08-26T20:22:08.2812987Z stride (int or tuple): Stride of the max pooling window. 2025-08-26T20:22:08.2813123Z It is set to :attr:`kernel_size` by default. 2025-08-26T20:22:08.2813330Z padding (int or tuple): Padding that was added to the input 2025-08-26T20:22:08.2813428Z 2025-08-26T20:22:08.2813513Z Inputs: 2025-08-26T20:22:08.2813649Z - `input`: the input Tensor to invert 2025-08-26T20:22:08.2813853Z - `indices`: the indices given out by :class:`~torch.nn.MaxPool2d` 2025-08-26T20:22:08.2814009Z - `output_size` (optional): the targeted output size 2025-08-26T20:22:08.2814101Z 2025-08-26T20:22:08.2814186Z Shape: 2025-08-26T20:22:08.2814382Z - Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`. 2025-08-26T20:22:08.2814589Z - Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where 2025-08-26T20:22:08.2814668Z 2025-08-26T20:22:08.2814771Z .. math:: 2025-08-26T20:22:08.2815038Z H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]} 2025-08-26T20:22:08.2815171Z 2025-08-26T20:22:08.2815259Z .. math:: 2025-08-26T20:22:08.2815516Z W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]} 2025-08-26T20:22:08.2815609Z 2025-08-26T20:22:08.2815772Z or as given by :attr:`output_size` in the call operator 2025-08-26T20:22:08.2815863Z 2025-08-26T20:22:08.2815951Z Example:: 2025-08-26T20:22:08.2816030Z 2025-08-26T20:22:08.2816204Z >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True) 2025-08-26T20:22:08.2816331Z >>> unpool = nn.MaxUnpool2d(2, stride=2) 2025-08-26T20:22:08.2816462Z >>> input = torch.tensor([[[[ 1., 2., 3., 4.], 2025-08-26T20:22:08.2816582Z [ 5., 6., 7., 8.], 2025-08-26T20:22:08.2816688Z [ 9., 10., 11., 12.], 2025-08-26T20:22:08.2816812Z [13., 14., 15., 16.]]]]) 2025-08-26T20:22:08.2816930Z >>> output, indices = pool(input) 2025-08-26T20:22:08.2817038Z >>> unpool(output, indices) 2025-08-26T20:22:08.2817153Z tensor([[[[ 0., 0., 0., 0.], 2025-08-26T20:22:08.2817281Z [ 0., 6., 0., 8.], 2025-08-26T20:22:08.2817390Z [ 0., 0., 0., 0.], 2025-08-26T20:22:08.2817490Z [ 0., 14., 0., 16.]]]]) 2025-08-26T20:22:08.2817696Z >>> # Now using output_size to resolve an ambiguous size for the inverse 2025-08-26T20:22:08.2817849Z >>> input = torch.tensor([[[[ 1., 2., 3., 4., 5.], 2025-08-26T20:22:08.2817957Z [ 6., 7., 8., 9., 10.], 2025-08-26T20:22:08.2818076Z [11., 12., 13., 14., 15.], 2025-08-26T20:22:08.2818187Z [16., 17., 18., 19., 20.]]]]) 2025-08-26T20:22:08.2818301Z >>> output, indices = pool(input) 2025-08-26T20:22:08.2818484Z >>> # This call will not work without specifying output_size 2025-08-26T20:22:08.2818687Z >>> unpool(output, indices, output_size=input.size()) 2025-08-26T20:22:08.2818809Z tensor([[[[ 0., 0., 0., 0., 0.], 2025-08-26T20:22:08.2818908Z [ 0., 7., 0., 9., 0.], 2025-08-26T20:22:08.2819004Z [ 0., 0., 0., 0., 0.], 2025-08-26T20:22:08.2819118Z [ 0., 17., 0., 19., 0.]]]]) 2025-08-26T20:22:08.2819197Z 2025-08-26T20:22:08.2819286Z 2025-08-26T20:22:08.2819367Z 2025-08-26T20:22:08.2819620Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2819712Z 2025-08-26T20:22:08.2819809Z warnings.warn(msg) 2025-08-26T20:22:08.2819899Z 2025-08-26T20:22:08.2820100Z --- Parse Warning: 109 / 146 --- 2025-08-26T20:22:08.2821089Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=EmbeddingBag in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/sparse.py line=272. 2025-08-26T20:22:08.2821415Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2821735Z Compute sums or means of 'bags' of embeddings, without instantiating the intermediate embeddings. 2025-08-26T20:22:08.2821826Z 2025-08-26T20:22:08.2822146Z For bags of constant length, no :attr:`per_sample_weights`, no indices equal to :attr:`padding_idx`, 2025-08-26T20:22:08.2822257Z and with 2D inputs, this class 2025-08-26T20:22:08.2822350Z 2025-08-26T20:22:08.2822655Z * with ``mode="sum"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.sum(dim=1)``, 2025-08-26T20:22:08.2822975Z * with ``mode="mean"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.mean(dim=1)``, 2025-08-26T20:22:08.2823270Z * with ``mode="max"`` is equivalent to :class:`~torch.nn.Embedding` followed by ``torch.max(dim=1)``. 2025-08-26T20:22:08.2823365Z 2025-08-26T20:22:08.2823718Z However, :class:`~torch.nn.EmbeddingBag` is much more time and memory efficient than using a chain of these 2025-08-26T20:22:08.2823808Z operations. 2025-08-26T20:22:08.2823900Z 2025-08-26T20:22:08.2824157Z EmbeddingBag also supports per-sample weights as an argument to the forward 2025-08-26T20:22:08.2824405Z pass. This scales the output of the Embedding before performing a weighted 2025-08-26T20:22:08.2824653Z reduction as specified by ``mode``. If :attr:`per_sample_weights` is passed, the 2025-08-26T20:22:08.2824884Z only supported ``mode`` is ``"sum"``, which computes a weighted sum according to 2025-08-26T20:22:08.2825004Z :attr:`per_sample_weights`. 2025-08-26T20:22:08.2825087Z 2025-08-26T20:22:08.2825182Z Args: 2025-08-26T20:22:08.2825364Z num_embeddings (int): size of the dictionary of embeddings 2025-08-26T20:22:08.2825539Z embedding_dim (int): the size of each embedding vector 2025-08-26T20:22:08.2825863Z max_norm (float, optional): If given, each embedding vector with norm larger than :attr:`max_norm` 2025-08-26T20:22:08.2826044Z is renormalized to have norm :attr:`max_norm`. 2025-08-26T20:22:08.2826394Z norm_type (float, optional): The p of the p-norm to compute for the :attr:`max_norm` option. Default ``2``. 2025-08-26T20:22:08.2826720Z scale_grad_by_freq (bool, optional): if given, this will scale gradients by the inverse of frequency of 2025-08-26T20:22:08.2826880Z the words in the mini-batch. Default ``False``. 2025-08-26T20:22:08.2827075Z Note: this option is not supported when ``mode="max"``. 2025-08-26T20:22:08.2827327Z mode (str, optional): ``"sum"``, ``"mean"`` or ``"max"``. Specifies the way to reduce the bag. 2025-08-26T20:22:08.2827611Z ``"sum"`` computes the weighted sum, taking :attr:`per_sample_weights` 2025-08-26T20:22:08.2827832Z into consideration. ``"mean"`` computes the average of the values 2025-08-26T20:22:08.2828021Z in the bag, ``"max"`` computes the max value over each bag. 2025-08-26T20:22:08.2828136Z Default: ``"mean"`` 2025-08-26T20:22:08.2828455Z sparse (bool, optional): if ``True``, gradient w.r.t. :attr:`weight` matrix will be a sparse tensor. See 2025-08-26T20:22:08.2828718Z Notes for more details regarding sparse gradients. Note: this option is not 2025-08-26T20:22:08.2828849Z supported when ``mode="max"``. 2025-08-26T20:22:08.2829232Z include_last_offset (bool, optional): if ``True``, :attr:`offsets` has one additional element, where the last element 2025-08-26T20:22:08.2829482Z is equivalent to the size of `indices`. This matches the CSR format. 2025-08-26T20:22:08.2829826Z padding_idx (int, optional): If specified, the entries at :attr:`padding_idx` do not contribute to the 2025-08-26T20:22:08.2830103Z gradient; therefore, the embedding vector at :attr:`padding_idx` is not updated 2025-08-26T20:22:08.2830347Z during training, i.e. it remains as a fixed "pad". For a newly constructed 2025-08-26T20:22:08.2830626Z EmbeddingBag, the embedding vector at :attr:`padding_idx` will default to all 2025-08-26T20:22:08.2830871Z zeros, but can be updated to another value to be used as the padding vector. 2025-08-26T20:22:08.2831127Z Note that the embedding vector at :attr:`padding_idx` is excluded from the 2025-08-26T20:22:08.2831240Z reduction. 2025-08-26T20:22:08.2831320Z 2025-08-26T20:22:08.2831420Z Attributes: 2025-08-26T20:22:08.2831737Z weight (Tensor): the learnable weights of the module of shape `(num_embeddings, embedding_dim)` 2025-08-26T20:22:08.2831890Z initialized from :math:`\mathcal{N}(0, 1)`. 2025-08-26T20:22:08.2831972Z 2025-08-26T20:22:08.2832065Z Examples:: 2025-08-26T20:22:08.2832156Z 2025-08-26T20:22:08.2832328Z >>> # an EmbeddingBag module containing 10 tensors of size 3 2025-08-26T20:22:08.2832496Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum') 2025-08-26T20:22:08.2832623Z >>> # a batch of 2 samples of 4 indices each 2025-08-26T20:22:08.2832806Z >>> input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long) 2025-08-26T20:22:08.2832963Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-08-26T20:22:08.2833110Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:22:08.2833239Z >>> embedding_sum(input, offsets) 2025-08-26T20:22:08.2833348Z tensor([[-0.8861, -5.4350, -0.0523], 2025-08-26T20:22:08.2833479Z [ 1.1306, -2.5798, -1.0044]]) 2025-08-26T20:22:08.2833566Z 2025-08-26T20:22:08.2833676Z >>> # Example with padding_idx 2025-08-26T20:22:08.2833892Z >>> embedding_sum = nn.EmbeddingBag(10, 3, mode='sum', padding_idx=2) 2025-08-26T20:22:08.2834075Z >>> input = torch.tensor([2, 2, 2, 2, 4, 3, 2, 9], dtype=torch.long) 2025-08-26T20:22:08.2834223Z >>> offsets = torch.tensor([0, 4], dtype=torch.long) 2025-08-26T20:22:08.2834351Z >>> embedding_sum(input, offsets) 2025-08-26T20:22:08.2834456Z tensor([[ 0.0000, 0.0000, 0.0000], 2025-08-26T20:22:08.2834569Z [-0.7082, 3.2145, -2.6251]]) 2025-08-26T20:22:08.2834650Z 2025-08-26T20:22:08.2834870Z >>> # An EmbeddingBag can be loaded from an Embedding like so 2025-08-26T20:22:08.2835029Z >>> embedding = nn.Embedding(10, 3, padding_idx=2) 2025-08-26T20:22:08.2835189Z >>> embedding_sum = nn.EmbeddingBag.from_pretrained( 2025-08-26T20:22:08.2835303Z embedding.weight, 2025-08-26T20:22:08.2835434Z padding_idx=embedding.padding_idx, 2025-08-26T20:22:08.2835526Z mode='sum') 2025-08-26T20:22:08.2835616Z 2025-08-26T20:22:08.2835868Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2835959Z 2025-08-26T20:22:08.2836055Z warnings.warn(msg) 2025-08-26T20:22:08.2836134Z 2025-08-26T20:22:08.2836360Z --- Parse Warning: 110 / 146 --- 2025-08-26T20:22:08.2837312Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Transformer.forward in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py line=186. 2025-08-26T20:22:08.2837620Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2837771Z Take in and process masked source/target sequences. 2025-08-26T20:22:08.2837851Z 2025-08-26T20:22:08.2837955Z .. note:: 2025-08-26T20:22:08.2838035Z 2025-08-26T20:22:08.2838435Z If a boolean tensor is provided for any of the [src/tgt/memory]_mask arguments, positions with a ``True`` value are 2025-08-26T20:22:08.2838580Z not allowed to participate in the attention, 2025-08-26T20:22:08.2838771Z which is the opposite of the definition for :attr:`attn_mask` 2025-08-26T20:22:08.2838975Z in :func:`torch.nn.functional.scaled_dot_product_attention`. 2025-08-26T20:22:08.2839054Z 2025-08-26T20:22:08.2839152Z Args: 2025-08-26T20:22:08.2839288Z src: the sequence to the encoder (required). 2025-08-26T20:22:08.2839426Z tgt: the sequence to the decoder (required). 2025-08-26T20:22:08.2839616Z src_mask: the additive mask for the src sequence (optional). 2025-08-26T20:22:08.2839807Z tgt_mask: the additive mask for the tgt sequence (optional). 2025-08-26T20:22:08.2840017Z memory_mask: the additive mask for the encoder output (optional). 2025-08-26T20:22:08.2840254Z src_key_padding_mask: the Tensor mask for src keys per batch (optional). 2025-08-26T20:22:08.2840478Z tgt_key_padding_mask: the Tensor mask for tgt keys per batch (optional). 2025-08-26T20:22:08.2840741Z memory_key_padding_mask: the Tensor mask for memory keys per batch (optional). 2025-08-26T20:22:08.2840944Z src_is_causal: If specified, applies a causal mask as ``src_mask``. 2025-08-26T20:22:08.2841090Z Default: ``None``; try to detect a causal mask. 2025-08-26T20:22:08.2841196Z Warning: 2025-08-26T20:22:08.2841357Z ``src_is_causal`` provides a hint that ``src_mask`` is 2025-08-26T20:22:08.2841545Z the causal mask. Providing incorrect hints can result in 2025-08-26T20:22:08.2841735Z incorrect execution, including forward and backward 2025-08-26T20:22:08.2841832Z compatibility. 2025-08-26T20:22:08.2842046Z tgt_is_causal: If specified, applies a causal mask as ``tgt_mask``. 2025-08-26T20:22:08.2842189Z Default: ``None``; try to detect a causal mask. 2025-08-26T20:22:08.2842295Z Warning: 2025-08-26T20:22:08.2842451Z ``tgt_is_causal`` provides a hint that ``tgt_mask`` is 2025-08-26T20:22:08.2842626Z the causal mask. Providing incorrect hints can result in 2025-08-26T20:22:08.2842797Z incorrect execution, including forward and backward 2025-08-26T20:22:08.2842900Z compatibility. 2025-08-26T20:22:08.2843135Z memory_is_causal: If specified, applies a causal mask as 2025-08-26T20:22:08.2843226Z ``memory_mask``. 2025-08-26T20:22:08.2843329Z Default: ``False``. 2025-08-26T20:22:08.2843431Z Warning: 2025-08-26T20:22:08.2843562Z ``memory_is_causal`` provides a hint that 2025-08-26T20:22:08.2843736Z ``memory_mask`` is the causal mask. Providing incorrect 2025-08-26T20:22:08.2843891Z hints can result in incorrect execution, including 2025-08-26T20:22:08.2844017Z forward and backward compatibility. 2025-08-26T20:22:08.2844106Z 2025-08-26T20:22:08.2844188Z Shape: 2025-08-26T20:22:08.2844447Z - src: :math:`(S, E)` for unbatched input, :math:`(S, N, E)` if `batch_first=False` or 2025-08-26T20:22:08.2844560Z `(N, S, E)` if `batch_first=True`. 2025-08-26T20:22:08.2844830Z - tgt: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-08-26T20:22:08.2844958Z `(N, T, E)` if `batch_first=True`. 2025-08-26T20:22:08.2845149Z - src_mask: :math:`(S, S)` or :math:`(N\cdot\text{num\_heads}, S, S)`. 2025-08-26T20:22:08.2845345Z - tgt_mask: :math:`(T, T)` or :math:`(N\cdot\text{num\_heads}, T, T)`. 2025-08-26T20:22:08.2845453Z - memory_mask: :math:`(T, S)`. 2025-08-26T20:22:08.2845689Z - src_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-08-26T20:22:08.2845937Z - tgt_key_padding_mask: :math:`(T)` for unbatched input otherwise :math:`(N, T)`. 2025-08-26T20:22:08.2846190Z - memory_key_padding_mask: :math:`(S)` for unbatched input otherwise :math:`(N, S)`. 2025-08-26T20:22:08.2846277Z 2025-08-26T20:22:08.2846581Z Note: [src/tgt/memory]_mask ensures that position :math:`i` is allowed to attend the unmasked 2025-08-26T20:22:08.2846803Z positions. If a BoolTensor is provided, positions with ``True`` 2025-08-26T20:22:08.2847058Z are not allowed to attend while ``False`` values will be unchanged. If a FloatTensor 2025-08-26T20:22:08.2847224Z is provided, it will be added to the attention weight. 2025-08-26T20:22:08.2847539Z [src/tgt/memory]_key_padding_mask provides specified elements in the key to be ignored by 2025-08-26T20:22:08.2847750Z the attention. If a BoolTensor is provided, the positions with the 2025-08-26T20:22:08.2848072Z value of ``True`` will be ignored while the position with the value of ``False`` will be unchanged. 2025-08-26T20:22:08.2848148Z 2025-08-26T20:22:08.2848403Z - output: :math:`(T, E)` for unbatched input, :math:`(T, N, E)` if `batch_first=False` or 2025-08-26T20:22:08.2848531Z `(N, T, E)` if `batch_first=True`. 2025-08-26T20:22:08.2848610Z 2025-08-26T20:22:08.2848865Z Note: Due to the multi-head attention architecture in the transformer model, 2025-08-26T20:22:08.2849094Z the output sequence length of a transformer is same as the input sequence 2025-08-26T20:22:08.2849243Z (i.e. target) length of the decoder. 2025-08-26T20:22:08.2849334Z 2025-08-26T20:22:08.2849650Z where :math:`S` is the source sequence length, :math:`T` is the target sequence length, :math:`N` is the 2025-08-26T20:22:08.2849798Z batch size, :math:`E` is the feature number 2025-08-26T20:22:08.2849873Z 2025-08-26T20:22:08.2849961Z Examples: 2025-08-26T20:22:08.2850074Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2850186Z >>> output = transformer_model( 2025-08-26T20:22:08.2850342Z ... src, tgt, src_mask=src_mask, tgt_mask=tgt_mask 2025-08-26T20:22:08.2850423Z ... ) 2025-08-26T20:22:08.2850503Z 2025-08-26T20:22:08.2850824Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2850902Z 2025-08-26T20:22:08.2851010Z warnings.warn(msg) 2025-08-26T20:22:08.2851086Z 2025-08-26T20:22:08.2851283Z --- Parse Warning: 111 / 146 --- 2025-08-26T20:22:08.2852309Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel.join in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=1766. 2025-08-26T20:22:08.2852566Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2852656Z 2025-08-26T20:22:08.2852889Z Context manager for training with uneven inputs across processes in DDP. 2025-08-26T20:22:08.2852966Z 2025-08-26T20:22:08.2853198Z This context manager will keep track of already-joined DDP processes, 2025-08-26T20:22:08.2853430Z and "shadow" the forward and backward passes by inserting collective 2025-08-26T20:22:08.2853674Z communication operations to match with the ones created by non-joined 2025-08-26T20:22:08.2853907Z DDP processes. This will ensure each collective call has a corresponding 2025-08-26T20:22:08.2854123Z call by already-joined DDP processes, preventing hangs or errors that 2025-08-26T20:22:08.2854333Z would otherwise happen when training with uneven inputs across 2025-08-26T20:22:08.2854567Z processes. Alternatively, if the flag ``throw_on_early_termination`` is 2025-08-26T20:22:08.2854781Z specified to be ``True``, all trainers will throw an error once one rank 2025-08-26T20:22:08.2854975Z runs out of inputs, allowing these errors to be caught and handled 2025-08-26T20:22:08.2855085Z according to application logic. 2025-08-26T20:22:08.2855173Z 2025-08-26T20:22:08.2855388Z Once all DDP processes have joined, the context manager will broadcast 2025-08-26T20:22:08.2855624Z the model corresponding to the last joined process to all processes to 2025-08-26T20:22:08.2855775Z ensure the model is the same across all processes 2025-08-26T20:22:08.2855878Z (which is guaranteed by DDP). 2025-08-26T20:22:08.2855967Z 2025-08-26T20:22:08.2856165Z To use this to enable training with uneven inputs across processes, 2025-08-26T20:22:08.2856400Z simply wrap this context manager around your training loop. No further 2025-08-26T20:22:08.2856572Z modifications to the model or data loading is required. 2025-08-26T20:22:08.2856651Z 2025-08-26T20:22:08.2856752Z .. warning:: 2025-08-26T20:22:08.2856956Z If the model or training loop this context manager is wrapped around 2025-08-26T20:22:08.2857149Z has additional distributed collective operations, such as 2025-08-26T20:22:08.2857336Z ``SyncBatchNorm`` in the model's forward pass, then the flag 2025-08-26T20:22:08.2857537Z ``throw_on_early_termination`` must be enabled. This is because this 2025-08-26T20:22:08.2857764Z context manager is not aware of non-DDP collective communication. 2025-08-26T20:22:08.2857929Z This flag will cause all ranks to throw when any one rank 2025-08-26T20:22:08.2858178Z exhausts inputs, allowing these errors to be caught and recovered 2025-08-26T20:22:08.2858278Z from across all ranks. 2025-08-26T20:22:08.2858356Z 2025-08-26T20:22:08.2858448Z Args: 2025-08-26T20:22:08.2858626Z divide_by_initial_world_size (bool): If ``True``, will divide 2025-08-26T20:22:08.2858838Z gradients by the initial ``world_size`` DDP training was launched 2025-08-26T20:22:08.2859000Z with. If ``False``, will compute the effective world size 2025-08-26T20:22:08.2859184Z (number of ranks that have not depleted their inputs yet) and 2025-08-26T20:22:08.2859342Z divide gradients by that during allreduce. Set 2025-08-26T20:22:08.2859514Z ``divide_by_initial_world_size=True`` to ensure every input 2025-08-26T20:22:08.2859780Z sample including the uneven inputs have equal weight in terms of 2025-08-26T20:22:08.2859950Z how much they contribute to the global gradient. This is 2025-08-26T20:22:08.2860120Z achieved by always dividing the gradient by the initial 2025-08-26T20:22:08.2860317Z ``world_size`` even when we encounter uneven inputs. If you set 2025-08-26T20:22:08.2860568Z this to ``False``, we divide the gradient by the remaining 2025-08-26T20:22:08.2860774Z number of nodes. This ensures parity with training on a smaller 2025-08-26T20:22:08.2860956Z ``world_size`` although it also means the uneven inputs would 2025-08-26T20:22:08.2861160Z contribute more towards the global gradient. Typically, you 2025-08-26T20:22:08.2861345Z would want to set this to ``True`` for cases where the last few 2025-08-26T20:22:08.2861535Z inputs of your training job are uneven. In extreme cases, where 2025-08-26T20:22:08.2861769Z there is a large discrepancy in the number of inputs, setting 2025-08-26T20:22:08.2861912Z this to ``False`` might provide better results. 2025-08-26T20:22:08.2862128Z enable (bool): Whether to enable uneven input detection or not. Pass 2025-08-26T20:22:08.2862297Z in ``enable=False`` to disable in cases where you know that 2025-08-26T20:22:08.2862482Z inputs are even across participating processes. Default is 2025-08-26T20:22:08.2862578Z ``True``. 2025-08-26T20:22:08.2862760Z throw_on_early_termination (bool): Whether to throw an error 2025-08-26T20:22:08.2862975Z or continue training when at least one rank has exhausted 2025-08-26T20:22:08.2863157Z inputs. If ``True``, will throw upon the first rank reaching end 2025-08-26T20:22:08.2863322Z of data. If ``False``, will continue training with a smaller 2025-08-26T20:22:08.2863525Z effective world size until all ranks are joined. Note that if 2025-08-26T20:22:08.2863649Z this flag is specified, then the flag 2025-08-26T20:22:08.2863828Z ``divide_by_initial_world_size`` would be ignored. Default 2025-08-26T20:22:08.2863921Z is ``False``. 2025-08-26T20:22:08.2863996Z 2025-08-26T20:22:08.2864084Z 2025-08-26T20:22:08.2864173Z Example:: 2025-08-26T20:22:08.2864258Z 2025-08-26T20:22:08.2864375Z >>> # xdoctest: +SKIP("Distributed") 2025-08-26T20:22:08.2864463Z >>> import torch 2025-08-26T20:22:08.2864590Z >>> import torch.distributed as dist 2025-08-26T20:22:08.2864676Z >>> import os 2025-08-26T20:22:08.2864795Z >>> import torch.multiprocessing as mp 2025-08-26T20:22:08.2864907Z >>> import torch.nn as nn 2025-08-26T20:22:08.2865006Z >>> # On each spawned worker 2025-08-26T20:22:08.2865109Z >>> def worker(rank): 2025-08-26T20:22:08.2865283Z >>> dist.init_process_group("nccl", rank=rank, world_size=2) 2025-08-26T20:22:08.2865401Z >>> torch.cuda.set_device(rank) 2025-08-26T20:22:08.2865545Z >>> model = nn.Linear(1, 1, bias=False).to(rank) 2025-08-26T20:22:08.2865746Z >>> model = torch.nn.parallel.DistributedDataParallel( 2025-08-26T20:22:08.2865894Z >>> model, device_ids=[rank], output_device=rank 2025-08-26T20:22:08.2865976Z >>> ) 2025-08-26T20:22:08.2866099Z >>> # Rank 1 gets one more input than rank 0. 2025-08-26T20:22:08.2866291Z >>> inputs = [torch.tensor([1]).float() for _ in range(10 + rank)] 2025-08-26T20:22:08.2866388Z >>> with model.join(): 2025-08-26T20:22:08.2866496Z >>> for _ in range(5): 2025-08-26T20:22:08.2866600Z >>> for inp in inputs: 2025-08-26T20:22:08.2866710Z >>> loss = model(inp).sum() 2025-08-26T20:22:08.2866827Z >>> loss.backward() 2025-08-26T20:22:08.2867017Z >>> # Without the join() API, the below synchronization will hang 2025-08-26T20:22:08.2867211Z >>> # blocking for rank 1's allreduce to complete. 2025-08-26T20:22:08.2867339Z >>> torch.cuda.synchronize(device=rank) 2025-08-26T20:22:08.2867417Z 2025-08-26T20:22:08.2867680Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2867757Z 2025-08-26T20:22:08.2867862Z warnings.warn(msg) 2025-08-26T20:22:08.2867942Z 2025-08-26T20:22:08.2868151Z --- Parse Warning: 112 / 146 --- 2025-08-26T20:22:08.2869244Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedDataParallel._register_fused_optim in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/parallel/distributed.py line=2057. 2025-08-26T20:22:08.2869505Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2869623Z 2025-08-26T20:22:08.2869934Z Register an optimizer in DDP to optimize parameter immediately after its gradient reduction. 2025-08-26T20:22:08.2870011Z 2025-08-26T20:22:08.2870226Z Registers an optimizer with DDP such that the optimization for a 2025-08-26T20:22:08.2870435Z parameter will run immediately when that parameter's gradient is 2025-08-26T20:22:08.2870645Z finished with reduction, instead of waiting for all parameters' 2025-08-26T20:22:08.2870865Z gradients to finish reduction. This can result in a training speedup 2025-08-26T20:22:08.2871080Z depending on your workload since the optimizer can run while gradient 2025-08-26T20:22:08.2871318Z reduction for other parameters are still ongoing. In addition, this has 2025-08-26T20:22:08.2871540Z the potential to reduce peak memory consumption during training, as it 2025-08-26T20:22:08.2871752Z only needs to load the per-parameter optimizer states of a single 2025-08-26T20:22:08.2871967Z parameter at a time, instead of loading all per-parameter optimizer 2025-08-26T20:22:08.2872057Z states at once. 2025-08-26T20:22:08.2872154Z 2025-08-26T20:22:08.2872238Z Args: 2025-08-26T20:22:08.2872446Z optim (Type): a ``torch.optim.Optimizer`` class to be registered 2025-08-26T20:22:08.2872551Z as a fused optimizer. 2025-08-26T20:22:08.2872719Z *args (Sequence[Any]): Arguments to forward to `optim`. 2025-08-26T20:22:08.2872946Z optim_params (Optional[Iterable[torch.Tensor]]): Set of parameters 2025-08-26T20:22:08.2873169Z to optimize, similar to `params` argument of traditional `torch.optim` 2025-08-26T20:22:08.2873387Z Optimizers. If this is omitted, all DDP model parameters will be 2025-08-26T20:22:08.2873476Z optimized. 2025-08-26T20:22:08.2873671Z **kwargs: (Dict[str, Any]): Keyword arguments to forward to `optim`. 2025-08-26T20:22:08.2873764Z 2025-08-26T20:22:08.2873856Z .. warning :: 2025-08-26T20:22:08.2874085Z _register_fused_optim should only be called once on a DDP instance, 2025-08-26T20:22:08.2874301Z and registering multiple fused optimizers for the same DDP model 2025-08-26T20:22:08.2874431Z is not currently supported. Please ping 2025-08-26T20:22:08.2874702Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-26T20:22:08.2874799Z for your use case. 2025-08-26T20:22:08.2874897Z 2025-08-26T20:22:08.2874986Z .. warning :: 2025-08-26T20:22:08.2875178Z _register_fused_optim and register_comm_hook currently do not 2025-08-26T20:22:08.2875410Z compose together, meaning that custom DDP communication hooks are 2025-08-26T20:22:08.2875582Z not supported with overlapped optimizers. Please ping 2025-08-26T20:22:08.2875823Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-26T20:22:08.2875922Z for your use case. 2025-08-26T20:22:08.2876003Z 2025-08-26T20:22:08.2876106Z .. warning :: 2025-08-26T20:22:08.2876335Z Gradient accumulation and DDP `no_sync` are currently not supported 2025-08-26T20:22:08.2876524Z with overlapped optimizer. Please ping 2025-08-26T20:22:08.2876751Z https://github.com/pytorch/pytorch/issues/71595 if this is necessary 2025-08-26T20:22:08.2876851Z for your use case. 2025-08-26T20:22:08.2876943Z 2025-08-26T20:22:08.2877032Z Example:: 2025-08-26T20:22:08.2877122Z 2025-08-26T20:22:08.2877258Z >>> # xdoctest: +SKIP("No rendezvous handler") 2025-08-26T20:22:08.2877556Z >>> torch.distributed.init_process_group(backend='nccl', world_size=4, init_method='...') 2025-08-26T20:22:08.2877764Z >>> net = torch.nn.parallel.DistributedDataParallel(model, pg) 2025-08-26T20:22:08.2877851Z >>> lr = 1e-2 2025-08-26T20:22:08.2877958Z >>> betas = (0.9, 0.99) 2025-08-26T20:22:08.2878048Z >>> eps = 1e-6 2025-08-26T20:22:08.2878268Z >>> net._register_fused_optim(torch.optim.Adam, lr, betas=betas, eps=eps) 2025-08-26T20:22:08.2878428Z >>> # Example with subset of parameters 2025-08-26T20:22:08.2878569Z >>> params_to_opt = [list(net.parameters())[0]] 2025-08-26T20:22:08.2878677Z >>> net._register_fused_optim( 2025-08-26T20:22:08.2878917Z ... torch.optim.Adam, lr, optim_params=params_to_opt, betas=betas, eps=eps 2025-08-26T20:22:08.2879000Z ... ) 2025-08-26T20:22:08.2879090Z 2025-08-26T20:22:08.2879340Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2879419Z 2025-08-26T20:22:08.2879526Z warnings.warn(msg) 2025-08-26T20:22:08.2879608Z 2025-08-26T20:22:08.2879818Z --- Parse Warning: 113 / 146 --- 2025-08-26T20:22:08.2880821Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv2d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=14. 2025-08-26T20:22:08.2881088Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2881318Z Convert ``memory_format`` of ``nn.Conv2d.weight`` to ``memory_format``. 2025-08-26T20:22:08.2881399Z 2025-08-26T20:22:08.2881691Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-08-26T20:22:08.2881963Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-08-26T20:22:08.2882231Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-08-26T20:22:08.2882536Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-08-26T20:22:08.2882615Z 2025-08-26T20:22:08.2882715Z .. note:: 2025-08-26T20:22:08.2882949Z Calling ``model.to(memory_format=torch.channels_last)`` is more aggressive 2025-08-26T20:22:08.2883184Z than the utility function ``convert_conv2d_weight_memory_format``. Any 2025-08-26T20:22:08.2883398Z layer with 4d weight will be affected by ``model.to``, which does not 2025-08-26T20:22:08.2883622Z necessarily benefit from conversion to specified ``memory_format``. 2025-08-26T20:22:08.2883887Z One place we are confident in is that NHWC(channels_last) conversion for 2025-08-26T20:22:08.2884107Z convolution in cuDNN, as it is beneficial to run convolution in NHWC, 2025-08-26T20:22:08.2884321Z even in cases where we have to apply permutation to input tensors. 2025-08-26T20:22:08.2884406Z 2025-08-26T20:22:08.2884630Z Hence our strategy here is to convert only the weight of convolution to 2025-08-26T20:22:08.2884759Z channels_last. This ensures that; 2025-08-26T20:22:08.2884980Z 1. Fast convolution kernels will be used, the benefit of which could 2025-08-26T20:22:08.2885225Z outweigh overhead of permutation (if input is not in the same format). 2025-08-26T20:22:08.2885520Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-08-26T20:22:08.2885637Z from memory_format conversion. 2025-08-26T20:22:08.2885730Z 2025-08-26T20:22:08.2885953Z The optimal case is that, layers between convolution layers are channels 2025-08-26T20:22:08.2886196Z last compatible. Input tensor would be permuted to channels last when it 2025-08-26T20:22:08.2886427Z encounters the first convolution layer and stay in that memory format. 2025-08-26T20:22:08.2886665Z Hence following convolutions will not need to permute its input tensor. 2025-08-26T20:22:08.2886757Z 2025-08-26T20:22:08.2886980Z In case where a channels last incompatible layer is between convolution 2025-08-26T20:22:08.2887205Z layers, we need to permute the input tensor back to contiguous format 2025-08-26T20:22:08.2887427Z for that layer. The input tensor will go through the remaining layers in 2025-08-26T20:22:08.2887681Z contiguous format and be permuted to channels last when it encounters 2025-08-26T20:22:08.2887904Z another convolution layer. There's no point in propagating that 2025-08-26T20:22:08.2888124Z permutation to an earlier layer, as most layers are quite agnostic to 2025-08-26T20:22:08.2888236Z ``memory_format``. 2025-08-26T20:22:08.2888318Z 2025-08-26T20:22:08.2888546Z This claim might change when PyTorch supports fusion of permutation, as 2025-08-26T20:22:08.2888779Z there might have been a better spot to fuse the permutation other than 2025-08-26T20:22:08.2888899Z immediately before a convolution. 2025-08-26T20:22:08.2888994Z 2025-08-26T20:22:08.2889078Z Args: 2025-08-26T20:22:08.2889290Z module (nn.Module): ``nn.Conv2d`` & ``nn.ConvTranspose2d`` or container 2025-08-26T20:22:08.2889408Z ``nn.Module`` 2025-08-26T20:22:08.2889559Z memory_format: user specified ``memory_format``, 2025-08-26T20:22:08.2889755Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-08-26T20:22:08.2889835Z 2025-08-26T20:22:08.2889922Z Returns: 2025-08-26T20:22:08.2890076Z The original module with updated ``nn.Conv2d`` 2025-08-26T20:22:08.2890157Z 2025-08-26T20:22:08.2890254Z Example: 2025-08-26T20:22:08.2890393Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:22:08.2890546Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-08-26T20:22:08.2890664Z >>> input = torch.randint( 2025-08-26T20:22:08.2890812Z ... 1, 10, (2, 8, 4, 4), dtype=torch.float16, device="cuda" 2025-08-26T20:22:08.2890906Z ... ) 2025-08-26T20:22:08.2891010Z >>> model = nn.Sequential( 2025-08-26T20:22:08.2891125Z >>> nn.Conv2d(8, 4, 3)).cuda().half() 2025-08-26T20:22:08.2891245Z >>> # This is identical to: 2025-08-26T20:22:08.2891489Z >>> # nn.utils.convert_conv2d_weight_memory_format(model, torch.channels_last) 2025-08-26T20:22:08.2891870Z >>> model = nn.utils.convert_conv2d_weight_memory_format( 2025-08-26T20:22:08.2892062Z ... model, torch.channels_last 2025-08-26T20:22:08.2892145Z ... ) 2025-08-26T20:22:08.2892258Z >>> out = model(input) 2025-08-26T20:22:08.2892340Z 2025-08-26T20:22:08.2892605Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2892684Z 2025-08-26T20:22:08.2892781Z warnings.warn(msg) 2025-08-26T20:22:08.2892874Z 2025-08-26T20:22:08.2893082Z --- Parse Warning: 114 / 146 --- 2025-08-26T20:22:08.2894090Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=convert_conv3d_weight_memory_format in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/memory_format.py line=93. 2025-08-26T20:22:08.2894426Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2894642Z Convert ``memory_format`` of ``nn.Conv3d.weight`` to ``memory_format`` 2025-08-26T20:22:08.2894928Z The conversion recursively applies to nested ``nn.Module``, including ``module``. 2025-08-26T20:22:08.2895198Z Note that it only changes the memory_format, but not the semantics of each dimensions. 2025-08-26T20:22:08.2895464Z This function is used to facilitate the computation to adopt NHWC kernels, which 2025-08-26T20:22:08.2895770Z provides considerable speed up for fp16 data on CUDA devices with compute capability >= 7.0 2025-08-26T20:22:08.2895850Z 2025-08-26T20:22:08.2895954Z .. note:: 2025-08-26T20:22:08.2896197Z Calling ``model.to(memory_format=torch.channels_last_3d)`` is more aggressive 2025-08-26T20:22:08.2896430Z than the utility function ``convert_conv3d_weight_memory_format``. Any 2025-08-26T20:22:08.2896678Z layer with 4d weight will be affected by ``model.to``, which does not 2025-08-26T20:22:08.2896904Z necessarily benefit from conversion to specified ``memory_format``. 2025-08-26T20:22:08.2897206Z One place we are confident in is that NDHWC(channels_last_3d) conversion for 2025-08-26T20:22:08.2897426Z convolution in cuDNN, as it is beneficial to run convolution in NDHWC, 2025-08-26T20:22:08.2897639Z even in cases where we have to apply permutation to input tensors. 2025-08-26T20:22:08.2897720Z 2025-08-26T20:22:08.2897953Z Hence our strategy here is to convert only the weight of convolution to 2025-08-26T20:22:08.2898072Z channels_last_3d. This ensures that; 2025-08-26T20:22:08.2898287Z 1. Fast convolution kernels will be used, the benefit of which could 2025-08-26T20:22:08.2898529Z outweigh overhead of permutation (if input is not in the same format). 2025-08-26T20:22:08.2898767Z 2. No unnecessary permutations are applied on layers that do not benefit 2025-08-26T20:22:08.2898891Z from memory_format conversion. 2025-08-26T20:22:08.2898973Z 2025-08-26T20:22:08.2899196Z The optimal case is that, layers between convolution layers are channels 2025-08-26T20:22:08.2899442Z last compatible. Input tensor would be permuted to channels last when it 2025-08-26T20:22:08.2899671Z encounters the first convolution layer and stay in that memory format. 2025-08-26T20:22:08.2899918Z Hence following convolutions will not need to permute its input tensor. 2025-08-26T20:22:08.2899997Z 2025-08-26T20:22:08.2900216Z In case where a channels last incompatible layer is between convolution 2025-08-26T20:22:08.2900515Z layers, we need to permute the input tensor back to contiguous format 2025-08-26T20:22:08.2900738Z for that layer. The input tensor will go through the remaining layers in 2025-08-26T20:22:08.2900981Z contiguous format and be permuted to channels last when it encounters 2025-08-26T20:22:08.2901190Z another convolution layer. There's no point in propagating that 2025-08-26T20:22:08.2901441Z permutation to an earlier layer, as most layers are quite agnostic to 2025-08-26T20:22:08.2901555Z ``memory_format``. 2025-08-26T20:22:08.2901638Z 2025-08-26T20:22:08.2901885Z This claim might change when PyTorch supports fusion of permutation, as 2025-08-26T20:22:08.2902106Z there might have been a better spot to fuse the permutation other than 2025-08-26T20:22:08.2902225Z immediately before a convolution. 2025-08-26T20:22:08.2902318Z 2025-08-26T20:22:08.2902401Z Args: 2025-08-26T20:22:08.2902630Z module (nn.Module): ``nn.Conv3d`` & ``nn.ConvTranspose3d`` or container 2025-08-26T20:22:08.2902735Z ``nn.Module`` 2025-08-26T20:22:08.2902887Z memory_format: user specified ``memory_format``, 2025-08-26T20:22:08.2903125Z e.g. ``torch.channels_last`` or ``torch.contiguous_format`` 2025-08-26T20:22:08.2903206Z 2025-08-26T20:22:08.2903306Z Returns: 2025-08-26T20:22:08.2903448Z The original module with updated ``nn.Conv3d`` 2025-08-26T20:22:08.2903528Z 2025-08-26T20:22:08.2903628Z Example: 2025-08-26T20:22:08.2903765Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) 2025-08-26T20:22:08.2903932Z >>> # xdoctest: +REQUIRES(env:CUBLAS_WORKSPACE_CONFIG) 2025-08-26T20:22:08.2904039Z >>> input = torch.randint( 2025-08-26T20:22:08.2904189Z ... 1, 10, (2, 8, 4, 4, 4), dtype=torch.float16, device="cuda" 2025-08-26T20:22:08.2904285Z ... ) 2025-08-26T20:22:08.2904390Z >>> model = nn.Sequential( 2025-08-26T20:22:08.2904562Z >>> nn.Conv3d(8, 4, 3)).cuda().half() 2025-08-26T20:22:08.2904695Z >>> # This is identical to: 2025-08-26T20:22:08.2904953Z >>> # nn.utils.convert_conv3d_weight_memory_format(model, torch.channels_last_3d) 2025-08-26T20:22:08.2905131Z >>> model = nn.utils.convert_conv3d_weight_memory_format( 2025-08-26T20:22:08.2905251Z ... model, torch.channels_last_3d 2025-08-26T20:22:08.2905347Z ... ) 2025-08-26T20:22:08.2905444Z >>> out = model(input) 2025-08-26T20:22:08.2905523Z 2025-08-26T20:22:08.2905790Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2905869Z 2025-08-26T20:22:08.2905967Z warnings.warn(msg) 2025-08-26T20:22:08.2906060Z 2025-08-26T20:22:08.2906262Z --- Parse Warning: 115 / 146 --- 2025-08-26T20:22:08.2907240Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_parametrization in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/parametrize.py line=424. 2025-08-26T20:22:08.2907510Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2907681Z Register a parametrization to a tensor in a module. 2025-08-26T20:22:08.2907763Z 2025-08-26T20:22:08.2908040Z Assume that ``tensor_name="weight"`` for simplicity. When accessing ``module.weight``, 2025-08-26T20:22:08.2908335Z the module will return the parametrized version ``parametrization(module.weight)``. 2025-08-26T20:22:08.2908595Z If the original tensor requires a gradient, the backward pass will differentiate 2025-08-26T20:22:08.2908896Z through :attr:`parametrization`, and the optimizer will update the tensor accordingly. 2025-08-26T20:22:08.2908977Z 2025-08-26T20:22:08.2909292Z The first time that a module registers a parametrization, this function will add an attribute 2025-08-26T20:22:08.2909551Z ``parametrizations`` to the module of type :class:`~ParametrizationList`. 2025-08-26T20:22:08.2909633Z 2025-08-26T20:22:08.2909907Z The list of parametrizations on the tensor ``weight`` will be accessible under 2025-08-26T20:22:08.2910038Z ``module.parametrizations.weight``. 2025-08-26T20:22:08.2910153Z 2025-08-26T20:22:08.2910303Z The original tensor will be accessible under 2025-08-26T20:22:08.2910456Z ``module.parametrizations.weight.original``. 2025-08-26T20:22:08.2910546Z 2025-08-26T20:22:08.2910810Z Parametrizations may be concatenated by registering several parametrizations 2025-08-26T20:22:08.2910911Z on the same attribute. 2025-08-26T20:22:08.2911001Z 2025-08-26T20:22:08.2911245Z The training mode of a registered parametrization is updated on registration 2025-08-26T20:22:08.2911379Z to match the training mode of the host module 2025-08-26T20:22:08.2911468Z 2025-08-26T20:22:08.2911770Z Parametrized parameters and buffers have an inbuilt caching system that can be activated 2025-08-26T20:22:08.2911913Z using the context manager :func:`cached`. 2025-08-26T20:22:08.2912089Z 2025-08-26T20:22:08.2912333Z A :attr:`parametrization` may optionally implement a method with signature 2025-08-26T20:22:08.2912423Z 2025-08-26T20:22:08.2912528Z .. code-block:: python 2025-08-26T20:22:08.2912619Z 2025-08-26T20:22:08.2912835Z def right_inverse(self, X: Tensor) -> Union[Tensor, Sequence[Tensor]] 2025-08-26T20:22:08.2912913Z 2025-08-26T20:22:08.2913190Z This method is called on the unparametrized tensor when the first parametrization 2025-08-26T20:22:08.2913399Z is registered to compute the initial value of the original tensor. 2025-08-26T20:22:08.2913715Z If this method is not implemented, the original tensor will be just the unparametrized tensor. 2025-08-26T20:22:08.2913793Z 2025-08-26T20:22:08.2914101Z If all the parametrizations registered on a tensor implement `right_inverse` it is possible 2025-08-26T20:22:08.2914432Z to initialize a parametrized tensor by assigning to it, as shown in the example below. 2025-08-26T20:22:08.2914511Z 2025-08-26T20:22:08.2914747Z It is possible for the first parametrization to depend on several inputs. 2025-08-26T20:22:08.2914992Z This may be implemented returning a tuple of tensors from ``right_inverse`` 2025-08-26T20:22:08.2915224Z (see the example implementation of a ``RankOne`` parametrization below). 2025-08-26T20:22:08.2915315Z 2025-08-26T20:22:08.2915648Z In this case, the unconstrained tensors are also located under ``module.parametrizations.weight`` 2025-08-26T20:22:08.2915790Z with names ``original0``, ``original1``,... 2025-08-26T20:22:08.2915868Z 2025-08-26T20:22:08.2915953Z .. note:: 2025-08-26T20:22:08.2916042Z 2025-08-26T20:22:08.2916309Z If unsafe=False (default) both the forward and right_inverse methods will be called 2025-08-26T20:22:08.2916471Z once to perform a number of consistency checks. 2025-08-26T20:22:08.2916738Z If unsafe=True, then right_inverse will be called if the tensor is not parametrized, 2025-08-26T20:22:08.2916864Z and nothing will be called otherwise. 2025-08-26T20:22:08.2916955Z 2025-08-26T20:22:08.2917039Z .. note:: 2025-08-26T20:22:08.2917128Z 2025-08-26T20:22:08.2917333Z In most situations, ``right_inverse`` will be a function such that 2025-08-26T20:22:08.2917455Z ``forward(right_inverse(X)) == X`` (see 2025-08-26T20:22:08.2917749Z `right inverse `_). 2025-08-26T20:22:08.2918002Z Sometimes, when the parametrization is not surjective, it may be reasonable 2025-08-26T20:22:08.2918106Z to relax this. 2025-08-26T20:22:08.2918185Z 2025-08-26T20:22:08.2918276Z .. warning:: 2025-08-26T20:22:08.2918365Z 2025-08-26T20:22:08.2918644Z If a parametrization depends on several inputs, :func:`~register_parametrization` 2025-08-26T20:22:08.2918915Z will register a number of new parameters. If such parametrization is registered 2025-08-26T20:22:08.2919227Z after the optimizer is created, these new parameters will need to be added manually 2025-08-26T20:22:08.2919424Z to the optimizer. See :meth:`torch.Optimizer.add_param_group`. 2025-08-26T20:22:08.2919513Z 2025-08-26T20:22:08.2919596Z Args: 2025-08-26T20:22:08.2919817Z module (nn.Module): module on which to register the parametrization 2025-08-26T20:22:08.2920030Z tensor_name (str): name of the parameter or buffer on which to register 2025-08-26T20:22:08.2920134Z the parametrization 2025-08-26T20:22:08.2920350Z parametrization (nn.Module): the parametrization to register 2025-08-26T20:22:08.2920441Z Keyword args: 2025-08-26T20:22:08.2920668Z unsafe (bool): a boolean flag that denotes whether the parametrization 2025-08-26T20:22:08.2921175Z may change the dtype and shape of the tensor. Default: `False` 2025-08-26T20:22:08.2921446Z Warning: the parametrization is not checked for consistency upon registration. 2025-08-26T20:22:08.2921584Z Enable this flag at your own risk. 2025-08-26T20:22:08.2921667Z 2025-08-26T20:22:08.2921768Z Raises: 2025-08-26T20:22:08.2922056Z ValueError: if the module does not have a parameter or a buffer named :attr:`tensor_name` 2025-08-26T20:22:08.2922138Z 2025-08-26T20:22:08.2922240Z Examples: 2025-08-26T20:22:08.2922390Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_LAPACK) 2025-08-26T20:22:08.2922502Z >>> import torch 2025-08-26T20:22:08.2922609Z >>> import torch.nn as nn 2025-08-26T20:22:08.2922747Z >>> import torch.nn.utils.parametrize as P 2025-08-26T20:22:08.2922843Z >>> 2025-08-26T20:22:08.2922982Z >>> class Symmetric(nn.Module): 2025-08-26T20:22:08.2923103Z >>> def forward(self, X): 2025-08-26T20:22:08.2923282Z >>> return X.triu() + X.triu(1).T # Return a symmetric matrix 2025-08-26T20:22:08.2923365Z >>> 2025-08-26T20:22:08.2923489Z >>> def right_inverse(self, A): 2025-08-26T20:22:08.2923587Z >>> return A.triu() 2025-08-26T20:22:08.2923680Z >>> 2025-08-26T20:22:08.2923778Z >>> m = nn.Linear(5, 5) 2025-08-26T20:22:08.2923947Z >>> P.register_parametrization(m, "weight", Symmetric()) 2025-08-26T20:22:08.2924196Z >>> print(torch.allclose(m.weight, m.weight.T)) # m.weight is now symmetric 2025-08-26T20:22:08.2924278Z True 2025-08-26T20:22:08.2924375Z >>> A = torch.rand(5, 5) 2025-08-26T20:22:08.2924498Z >>> A = A + A.T # A is now symmetric 2025-08-26T20:22:08.2924696Z >>> m.weight = A # Initialize the weight to be the symmetric matrix A 2025-08-26T20:22:08.2924833Z >>> print(torch.allclose(m.weight, A)) 2025-08-26T20:22:08.2924920Z True 2025-08-26T20:22:08.2924998Z 2025-08-26T20:22:08.2925115Z >>> class RankOne(nn.Module): 2025-08-26T20:22:08.2925228Z >>> def forward(self, x, y): 2025-08-26T20:22:08.2925381Z >>> # Form a rank 1 matrix multiplying two vectors 2025-08-26T20:22:08.2925515Z >>> return x.unsqueeze(-1) @ y.unsqueeze(-2) 2025-08-26T20:22:08.2925596Z >>> 2025-08-26T20:22:08.2925723Z >>> def right_inverse(self, Z): 2025-08-26T20:22:08.2925844Z >>> # Project Z onto the rank 1 matrices 2025-08-26T20:22:08.2926005Z >>> U, S, Vh = torch.linalg.svd(Z, full_matrices=False) 2025-08-26T20:22:08.2926124Z >>> # Return rescaled singular vectors 2025-08-26T20:22:08.2926245Z >>> s0_sqrt = S[0].sqrt().unsqueeze(-1) 2025-08-26T20:22:08.2926404Z >>> return U[..., :, 0] * s0_sqrt, Vh[..., 0, :] * s0_sqrt 2025-08-26T20:22:08.2926489Z >>> 2025-08-26T20:22:08.2926647Z >>> linear_rank_one = P.register_parametrization( 2025-08-26T20:22:08.2926802Z ... nn.Linear(4, 4), "weight", RankOne() 2025-08-26T20:22:08.2926884Z ... ) 2025-08-26T20:22:08.2927096Z >>> print(torch.linalg.matrix_rank(linear_rank_one.weight).item()) 2025-08-26T20:22:08.2927176Z 1 2025-08-26T20:22:08.2927265Z 2025-08-26T20:22:08.2927344Z 2025-08-26T20:22:08.2927597Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2927687Z 2025-08-26T20:22:08.2927783Z warnings.warn(msg) 2025-08-26T20:22:08.2927874Z 2025-08-26T20:22:08.2928083Z --- Parse Warning: 116 / 146 --- 2025-08-26T20:22:08.2929057Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ln_structured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=979. 2025-08-26T20:22:08.2929333Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2929644Z Prune tensor by removing channels with the lowest L\ ``n``-norm along the specified dimension. 2025-08-26T20:22:08.2929734Z 2025-08-26T20:22:08.2929965Z Prunes tensor corresponding to parameter called ``name`` in ``module`` 2025-08-26T20:22:08.2930181Z by removing the specified ``amount`` of (currently unpruned) channels 2025-08-26T20:22:08.2930366Z along the specified ``dim`` with the lowest L\ ``n``-norm. 2025-08-26T20:22:08.2930561Z Modifies module in place (and also return the modified module) 2025-08-26T20:22:08.2930656Z by: 2025-08-26T20:22:08.2930735Z 2025-08-26T20:22:08.2930941Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-26T20:22:08.2931194Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-26T20:22:08.2931410Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-26T20:22:08.2931629Z original (unpruned) parameter is stored in a new parameter named 2025-08-26T20:22:08.2931728Z ``name+'_orig'``. 2025-08-26T20:22:08.2931810Z 2025-08-26T20:22:08.2931902Z Args: 2025-08-26T20:22:08.2932079Z module (nn.Module): module containing the tensor to prune 2025-08-26T20:22:08.2932277Z name (str): parameter name within ``module`` on which pruning 2025-08-26T20:22:08.2932368Z will act. 2025-08-26T20:22:08.2932538Z amount (int or float): quantity of parameters to prune. 2025-08-26T20:22:08.2932727Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-08-26T20:22:08.2932931Z fraction of parameters to prune. If ``int``, it represents the 2025-08-26T20:22:08.2933078Z absolute number of parameters to prune. 2025-08-26T20:22:08.2933270Z n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid 2025-08-26T20:22:08.2933424Z entries for argument ``p`` in :func:`torch.norm`. 2025-08-26T20:22:08.2933634Z dim (int): index of the dim along which we define channels to prune. 2025-08-26T20:22:08.2933865Z importance_scores (torch.Tensor): tensor of importance scores (of same 2025-08-26T20:22:08.2934061Z shape as module parameter) used to compute mask for pruning. 2025-08-26T20:22:08.2934280Z The values in this tensor indicate the importance of the corresponding 2025-08-26T20:22:08.2934413Z elements in the parameter being pruned. 2025-08-26T20:22:08.2934649Z If unspecified or None, the module parameter will be used in its place. 2025-08-26T20:22:08.2934730Z 2025-08-26T20:22:08.2934826Z Returns: 2025-08-26T20:22:08.2935043Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-08-26T20:22:08.2935124Z 2025-08-26T20:22:08.2935226Z Examples: 2025-08-26T20:22:08.2935348Z >>> from torch.nn.utils import prune 2025-08-26T20:22:08.2935498Z >>> m = prune.ln_structured( 2025-08-26T20:22:08.2935680Z ... nn.Conv2d(5, 3, 2), "weight", amount=0.3, dim=1, n=float("-inf") 2025-08-26T20:22:08.2935763Z ... ) 2025-08-26T20:22:08.2935856Z 2025-08-26T20:22:08.2936109Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2936200Z 2025-08-26T20:22:08.2936298Z warnings.warn(msg) 2025-08-26T20:22:08.2936378Z 2025-08-26T20:22:08.2936586Z --- Parse Warning: 117 / 146 --- 2025-08-26T20:22:08.2937499Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=global_unstructured in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1026. 2025-08-26T20:22:08.2937825Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2937907Z 2025-08-26T20:22:08.2938338Z Globally prunes tensors corresponding to all parameters in ``parameters`` by applying the specified ``pruning_method``. 2025-08-26T20:22:08.2938433Z 2025-08-26T20:22:08.2938543Z Modifies modules in place by: 2025-08-26T20:22:08.2938635Z 2025-08-26T20:22:08.2938846Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-26T20:22:08.2939064Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-26T20:22:08.2939289Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-26T20:22:08.2939498Z original (unpruned) parameter is stored in a new parameter named 2025-08-26T20:22:08.2939604Z ``name+'_orig'``. 2025-08-26T20:22:08.2939685Z 2025-08-26T20:22:08.2939768Z Args: 2025-08-26T20:22:08.2940005Z parameters (Iterable of (module, name) tuples): parameters of 2025-08-26T20:22:08.2940201Z the model to prune in a global fashion, i.e. by aggregating all 2025-08-26T20:22:08.2940496Z weights prior to deciding which ones to prune. module must be of 2025-08-26T20:22:08.2940654Z type :class:`nn.Module`, and name must be a string. 2025-08-26T20:22:08.2940877Z pruning_method (function): a valid pruning function from this module, 2025-08-26T20:22:08.2941068Z or a custom one implemented by the user that satisfies the 2025-08-26T20:22:08.2941299Z implementation guidelines and has ``PRUNING_TYPE='unstructured'``. 2025-08-26T20:22:08.2941542Z importance_scores (dict): a dictionary mapping (module, name) tuples to 2025-08-26T20:22:08.2941764Z the corresponding parameter's importance scores tensor. The tensor 2025-08-26T20:22:08.2941974Z should be the same shape as the parameter, and is used for computing 2025-08-26T20:22:08.2942095Z mask for pruning. 2025-08-26T20:22:08.2942304Z If unspecified or None, the parameter will be used in place of its 2025-08-26T20:22:08.2942420Z importance scores. 2025-08-26T20:22:08.2942549Z kwargs: other keyword arguments such as: 2025-08-26T20:22:08.2942746Z amount (int or float): quantity of parameters to prune across the 2025-08-26T20:22:08.2942863Z specified parameters. 2025-08-26T20:22:08.2943035Z If ``float``, should be between 0.0 and 1.0 and represent the 2025-08-26T20:22:08.2943243Z fraction of parameters to prune. If ``int``, it represents the 2025-08-26T20:22:08.2943376Z absolute number of parameters to prune. 2025-08-26T20:22:08.2943458Z 2025-08-26T20:22:08.2943556Z Raises: 2025-08-26T20:22:08.2943704Z TypeError: if ``PRUNING_TYPE != 'unstructured'`` 2025-08-26T20:22:08.2943794Z 2025-08-26T20:22:08.2943876Z Note: 2025-08-26T20:22:08.2946747Z Since global structured pruning doesn't make much sense unless the 2025-08-26T20:22:08.2946986Z norm is normalized by the size of the parameter, we now limit the 2025-08-26T20:22:08.2947138Z scope of global pruning to unstructured methods. 2025-08-26T20:22:08.2947266Z 2025-08-26T20:22:08.2947364Z Examples: 2025-08-26T20:22:08.2947483Z >>> from torch.nn.utils import prune 2025-08-26T20:22:08.2947615Z >>> from collections import OrderedDict 2025-08-26T20:22:08.2947713Z >>> net = nn.Sequential( 2025-08-26T20:22:08.2947809Z ... OrderedDict( 2025-08-26T20:22:08.2947905Z ... [ 2025-08-26T20:22:08.2948027Z ... ("first", nn.Linear(10, 4)), 2025-08-26T20:22:08.2948144Z ... ("second", nn.Linear(4, 1)), 2025-08-26T20:22:08.2948240Z ... ] 2025-08-26T20:22:08.2948324Z ... ) 2025-08-26T20:22:08.2948418Z ... ) 2025-08-26T20:22:08.2948522Z >>> parameters_to_prune = ( 2025-08-26T20:22:08.2948658Z ... (net.first, "weight"), 2025-08-26T20:22:08.2948788Z ... (net.second, "weight"), 2025-08-26T20:22:08.2948882Z ... ) 2025-08-26T20:22:08.2948994Z >>> prune.global_unstructured( 2025-08-26T20:22:08.2949094Z ... parameters_to_prune, 2025-08-26T20:22:08.2949238Z ... pruning_method=prune.L1Unstructured, 2025-08-26T20:22:08.2949330Z ... amount=10, 2025-08-26T20:22:08.2949425Z ... ) 2025-08-26T20:22:08.2949643Z >>> print(sum(torch.nn.utils.parameters_to_vector(net.buffers()) == 0)) 2025-08-26T20:22:08.2949729Z tensor(10) 2025-08-26T20:22:08.2949820Z 2025-08-26T20:22:08.2949898Z 2025-08-26T20:22:08.2950163Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2950241Z 2025-08-26T20:22:08.2950337Z warnings.warn(msg) 2025-08-26T20:22:08.2950428Z 2025-08-26T20:22:08.2950664Z --- Parse Warning: 118 / 146 --- 2025-08-26T20:22:08.2951620Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=custom_from_mask in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/prune.py line=1149. 2025-08-26T20:22:08.2951884Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2952277Z Prune tensor corresponding to parameter called ``name`` in ``module`` by applying the pre-computed mask in ``mask``. 2025-08-26T20:22:08.2952368Z 2025-08-26T20:22:08.2952579Z Modifies module in place (and also return the modified module) by: 2025-08-26T20:22:08.2952673Z 2025-08-26T20:22:08.2952883Z 1) adding a named buffer called ``name+'_mask'`` corresponding to the 2025-08-26T20:22:08.2953100Z binary mask applied to the parameter ``name`` by the pruning method. 2025-08-26T20:22:08.2953319Z 2) replacing the parameter ``name`` by its pruned version, while the 2025-08-26T20:22:08.2953540Z original (unpruned) parameter is stored in a new parameter named 2025-08-26T20:22:08.2953648Z ``name+'_orig'``. 2025-08-26T20:22:08.2953726Z 2025-08-26T20:22:08.2953815Z Args: 2025-08-26T20:22:08.2954004Z module (nn.Module): module containing the tensor to prune 2025-08-26T20:22:08.2954185Z name (str): parameter name within ``module`` on which pruning 2025-08-26T20:22:08.2954286Z will act. 2025-08-26T20:22:08.2954461Z mask (Tensor): binary mask to be applied to the parameter. 2025-08-26T20:22:08.2954541Z 2025-08-26T20:22:08.2954637Z Returns: 2025-08-26T20:22:08.2954852Z module (nn.Module): modified (i.e. pruned) version of the input module 2025-08-26T20:22:08.2954944Z 2025-08-26T20:22:08.2955030Z Examples: 2025-08-26T20:22:08.2955151Z >>> from torch.nn.utils import prune 2025-08-26T20:22:08.2955275Z >>> m = prune.custom_from_mask( 2025-08-26T20:22:08.2955522Z ... nn.Linear(5, 3), name="bias", mask=torch.tensor([0, 1, 0]) 2025-08-26T20:22:08.2955605Z ... ) 2025-08-26T20:22:08.2955718Z >>> print(m.bias_mask) 2025-08-26T20:22:08.2955840Z tensor([0., 1., 0.]) 2025-08-26T20:22:08.2955930Z 2025-08-26T20:22:08.2956013Z 2025-08-26T20:22:08.2956268Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2956366Z 2025-08-26T20:22:08.2956465Z warnings.warn(msg) 2025-08-26T20:22:08.2956557Z 2025-08-26T20:22:08.2956754Z --- Parse Warning: 119 / 146 --- 2025-08-26T20:22:08.2957649Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=pad_packed_sequence in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/rnn.py line=350. 2025-08-26T20:22:08.2957923Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2958075Z Pad a packed batch of variable length sequences. 2025-08-26T20:22:08.2958193Z 2025-08-26T20:22:08.2958371Z It is an inverse operation to :func:`pack_padded_sequence`. 2025-08-26T20:22:08.2958451Z 2025-08-26T20:22:08.2958742Z The returned Tensor's data will be of size ``T x B x *`` (if :attr:`batch_first` is ``False``) 2025-08-26T20:22:08.2958983Z or ``B x T x *`` (if :attr:`batch_first` is ``True``) , where ``T`` is the length of the longest 2025-08-26T20:22:08.2959117Z sequence and ``B`` is the batch size. 2025-08-26T20:22:08.2959197Z 2025-08-26T20:22:08.2959283Z Example: 2025-08-26T20:22:08.2959540Z >>> from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence 2025-08-26T20:22:08.2959687Z >>> seq = torch.tensor([[1, 2, 0], [3, 0, 0], [4, 5, 6]]) 2025-08-26T20:22:08.2959795Z >>> lens = [2, 1, 3] 2025-08-26T20:22:08.2959911Z >>> packed = pack_padded_sequence( 2025-08-26T20:22:08.2960094Z ... seq, lens, batch_first=True, enforce_sorted=False 2025-08-26T20:22:08.2960187Z ... ) 2025-08-26T20:22:08.2960274Z >>> packed 2025-08-26T20:22:08.2960530Z PackedSequence(data=tensor([4, 1, 3, 5, 2, 6]), batch_sizes=tensor([3, 2, 1]), 2025-08-26T20:22:08.2960746Z sorted_indices=tensor([2, 0, 1]), unsorted_indices=tensor([1, 2, 0])) 2025-08-26T20:22:08.2960991Z >>> seq_unpacked, lens_unpacked = pad_packed_sequence(packed, batch_first=True) 2025-08-26T20:22:08.2961095Z >>> seq_unpacked 2025-08-26T20:22:08.2961190Z tensor([[1, 2, 0], 2025-08-26T20:22:08.2961289Z [3, 0, 0], 2025-08-26T20:22:08.2961377Z [4, 5, 6]]) 2025-08-26T20:22:08.2961468Z >>> lens_unpacked 2025-08-26T20:22:08.2961572Z tensor([2, 1, 3]) 2025-08-26T20:22:08.2961649Z 2025-08-26T20:22:08.2961756Z .. note:: 2025-08-26T20:22:08.2961905Z :attr:`total_length` is useful to implement the 2025-08-26T20:22:08.2962133Z ``pack sequence -> recurrent network -> unpack sequence`` pattern in a 2025-08-26T20:22:08.2962363Z :class:`~torch.nn.Module` wrapped in :class:`~torch.nn.DataParallel`. 2025-08-26T20:22:08.2962593Z See :ref:`this FAQ section ` for 2025-08-26T20:22:08.2962691Z details. 2025-08-26T20:22:08.2962771Z 2025-08-26T20:22:08.2962855Z Args: 2025-08-26T20:22:08.2962996Z sequence (PackedSequence): batch to pad 2025-08-26T20:22:08.2963229Z batch_first (bool, optional): if ``True``, the output will be in ``B x T x *`` 2025-08-26T20:22:08.2963355Z format, ``T x B x *`` otherwise. 2025-08-26T20:22:08.2963545Z padding_value (float, optional): values for padded elements. 2025-08-26T20:22:08.2963772Z total_length (int, optional): if not ``None``, the output will be padded to 2025-08-26T20:22:08.2964070Z have length :attr:`total_length`. This method will throw :class:`ValueError` 2025-08-26T20:22:08.2964258Z if :attr:`total_length` is less than the max sequence length in 2025-08-26T20:22:08.2964402Z :attr:`sequence`. 2025-08-26T20:22:08.2964482Z 2025-08-26T20:22:08.2964565Z Returns: 2025-08-26T20:22:08.2964767Z Tuple of Tensor containing the padded sequence, and a Tensor 2025-08-26T20:22:08.2964962Z containing the list of lengths of each sequence in the batch. 2025-08-26T20:22:08.2965199Z Batch elements will be re-ordered as they were ordered originally when 2025-08-26T20:22:08.2965414Z the batch was passed to ``pack_padded_sequence`` or ``pack_sequence``. 2025-08-26T20:22:08.2965495Z 2025-08-26T20:22:08.2965760Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2965838Z 2025-08-26T20:22:08.2965947Z warnings.warn(msg) 2025-08-26T20:22:08.2966028Z 2025-08-26T20:22:08.2966253Z --- Parse Warning: 120 / 146 --- 2025-08-26T20:22:08.2967168Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SequentialLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=808. 2025-08-26T20:22:08.2967430Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2967766Z Contains a list of schedulers expected to be called sequentially during the optimization process. 2025-08-26T20:22:08.2967846Z 2025-08-26T20:22:08.2968216Z Specifically, the schedulers will be called according to the milestone points, which should provide exact 2025-08-26T20:22:08.2968447Z intervals by which each scheduler should be called at a given epoch. 2025-08-26T20:22:08.2968527Z 2025-08-26T20:22:08.2968624Z Args: 2025-08-26T20:22:08.2968791Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:08.2968939Z schedulers (list): List of chained schedulers. 2025-08-26T20:22:08.2969168Z milestones (list): List of integers that reflects milestone points. 2025-08-26T20:22:08.2969336Z last_epoch (int): The index of last epoch. Default: -1. 2025-08-26T20:22:08.2969435Z 2025-08-26T20:22:08.2969522Z Example: 2025-08-26T20:22:08.2969634Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2969788Z >>> # Assuming optimizer uses lr = 0.05 for all groups 2025-08-26T20:22:08.2969907Z >>> # lr = 0.005 if epoch == 0 2025-08-26T20:22:08.2970012Z >>> # lr = 0.005 if epoch == 1 2025-08-26T20:22:08.2970118Z >>> # lr = 0.005 if epoch == 2 2025-08-26T20:22:08.2970218Z >>> # ... 2025-08-26T20:22:08.2970326Z >>> # lr = 0.05 if epoch == 20 2025-08-26T20:22:08.2970446Z >>> # lr = 0.045 if epoch == 21 2025-08-26T20:22:08.2970557Z >>> # lr = 0.0405 if epoch == 22 2025-08-26T20:22:08.2970759Z >>> scheduler1 = ConstantLR(optimizer, factor=0.1, total_iters=20) 2025-08-26T20:22:08.2970934Z >>> scheduler2 = ExponentialLR(optimizer, gamma=0.9) 2025-08-26T20:22:08.2971047Z >>> scheduler = SequentialLR( 2025-08-26T20:22:08.2971156Z ... optimizer, 2025-08-26T20:22:08.2971292Z ... schedulers=[scheduler1, scheduler2], 2025-08-26T20:22:08.2971394Z ... milestones=[20], 2025-08-26T20:22:08.2971491Z ... ) 2025-08-26T20:22:08.2971599Z >>> for epoch in range(100): 2025-08-26T20:22:08.2971692Z >>> train(...) 2025-08-26T20:22:08.2971801Z >>> validate(...) 2025-08-26T20:22:08.2971905Z >>> scheduler.step() 2025-08-26T20:22:08.2972001Z 2025-08-26T20:22:08.2972183Z .. image:: ../scripts/lr_scheduler_images/SequentialLR.png 2025-08-26T20:22:08.2972269Z 2025-08-26T20:22:08.2972572Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.2972652Z 2025-08-26T20:22:08.2972764Z warnings.warn(msg) 2025-08-26T20:22:08.2972871Z 2025-08-26T20:22:08.2973062Z --- Parse Warning: 121 / 146 --- 2025-08-26T20:22:08.2974013Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ReduceLROnPlateau in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1233. 2025-08-26T20:22:08.2974261Z Caused by: DoctestParseError('Failed to parse doctest in _package_groups') 2025-08-26T20:22:08.2974458Z Reduce learning rate when a metric has stopped improving. 2025-08-26T20:22:08.2974539Z 2025-08-26T20:22:08.2974744Z Models often benefit from reducing the learning rate by a factor 2025-08-26T20:22:08.2974946Z of 2-10 once learning stagnates. This scheduler reads a metrics 2025-08-26T20:22:08.2975178Z quantity and if no improvement is seen for a 'patience' number 2025-08-26T20:22:08.2975319Z of epochs, the learning rate is reduced. 2025-08-26T20:22:08.2975401Z 2025-08-26T20:22:08.2975484Z Args: 2025-08-26T20:22:08.2975634Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:08.2975789Z mode (str): One of `min`, `max`. In `min` mode, lr will 2025-08-26T20:22:08.2975961Z be reduced when the quantity monitored has stopped 2025-08-26T20:22:08.2976122Z decreasing; in `max` mode it will be reduced when the 2025-08-26T20:22:08.2976314Z quantity monitored has stopped increasing. Default: 'min'. 2025-08-26T20:22:08.2976500Z factor (float): Factor by which the learning rate will be 2025-08-26T20:22:08.2976641Z reduced. new_lr = lr * factor. Default: 0.1. 2025-08-26T20:22:08.2976870Z patience (int): The number of allowed epochs with no improvement after 2025-08-26T20:22:08.2977034Z which the learning rate will be reduced. 2025-08-26T20:22:08.2977262Z For example, consider the case of having no patience (`patience = 0`). 2025-08-26T20:22:08.2977639Z In the first epoch, a baseline is established and is always considered good as there's no previous baseline. 2025-08-26T20:22:08.2977844Z In the second epoch, if the performance is worse than the baseline, 2025-08-26T20:22:08.2978006Z we have what is considered an intolerable epoch. 2025-08-26T20:22:08.2978271Z Since the count of intolerable epochs (1) is greater than the patience level (0), 2025-08-26T20:22:08.2978451Z the learning rate is reduced at the end of this epoch. 2025-08-26T20:22:08.2978771Z From the third epoch onwards, the learning rate continues to be reduced at the end of each epoch 2025-08-26T20:22:08.2979089Z if the performance is worse than the baseline. If the performance improves or remains the same, 2025-08-26T20:22:08.2979227Z the learning rate is not adjusted. 2025-08-26T20:22:08.2979322Z Default: 10. 2025-08-26T20:22:08.2979524Z threshold (float): Threshold for measuring the new optimum, 2025-08-26T20:22:08.2979685Z to only focus on significant changes. Default: 1e-4. 2025-08-26T20:22:08.2979855Z threshold_mode (str): One of `rel`, `abs`. In `rel` mode, 2025-08-26T20:22:08.2980028Z dynamic_threshold = best * ( 1 + threshold ) in 'max' 2025-08-26T20:22:08.2980169Z mode or best * ( 1 - threshold ) in `min` mode. 2025-08-26T20:22:08.2980339Z In `abs` mode, dynamic_threshold = best + threshold in 2025-08-26T20:22:08.2980611Z `max` mode or best - threshold in `min` mode. Default: 'rel'. 2025-08-26T20:22:08.2980784Z cooldown (int): Number of epochs to wait before resuming 2025-08-26T20:22:08.2981012Z normal operation after lr has been reduced. Default: 0. 2025-08-26T20:22:08.2981180Z min_lr (float or list): A scalar or a list of scalars. A 2025-08-26T20:22:08.2981357Z lower bound on the learning rate of all param groups 2025-08-26T20:22:08.2981517Z or each group respectively. Default: 0. 2025-08-26T20:22:08.2981697Z eps (float): Minimal decay applied to lr. If the difference 2025-08-26T20:22:08.2981887Z between new and old lr is smaller than eps, the update is 2025-08-26T20:22:08.2981997Z ignored. Default: 1e-8. 2025-08-26T20:22:08.2982092Z 2025-08-26T20:22:08.2982177Z Example: 2025-08-26T20:22:08.2982274Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2982513Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-08-26T20:22:08.2982668Z >>> scheduler = ReduceLROnPlateau(optimizer, "min") 2025-08-26T20:22:08.2982789Z >>> for epoch in range(10): 2025-08-26T20:22:08.2982884Z >>> train(...) 2025-08-26T20:22:08.2983031Z >>> val_loss = validate(...) 2025-08-26T20:22:08.2983195Z >>> # Note that step should be called after validate() 2025-08-26T20:22:08.2983310Z >>> scheduler.step(val_loss) 2025-08-26T20:22:08.2983401Z 2025-08-26T20:22:08.2983596Z .. image:: ../scripts/lr_scheduler_images/ReduceLROnPlateau.png 2025-08-26T20:22:08.2983708Z 2025-08-26T20:22:08.2984133Z Original Error: IndentationError('unexpected indent', ('', 8, 4, ' scheduler.step(val_loss)\n', 8, -1)) 2025-08-26T20:22:08.2984213Z 2025-08-26T20:22:08.2984333Z scheduler.step(val_loss) 2025-08-26T20:22:08.2984416Z ^ 2025-08-26T20:22:08.2984517Z warnings.warn(msg) 2025-08-26T20:22:08.2984613Z 2025-08-26T20:22:08.2984819Z --- Parse Warning: 122 / 146 --- 2025-08-26T20:22:08.2985717Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CyclicLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1430. 2025-08-26T20:22:08.2986014Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.2986337Z Sets the learning rate of each parameter group according to cyclical learning rate policy (CLR). 2025-08-26T20:22:08.2986435Z 2025-08-26T20:22:08.2986718Z The policy cycles the learning rate between two boundaries with a constant frequency, 2025-08-26T20:22:08.2986997Z as detailed in the paper `Cyclical Learning Rates for Training Neural Networks`_. 2025-08-26T20:22:08.2987229Z The distance between the two boundaries can be scaled on a per-iteration 2025-08-26T20:22:08.2987329Z or per-cycle basis. 2025-08-26T20:22:08.2987422Z 2025-08-26T20:22:08.2987666Z Cyclical learning rate policy changes the learning rate after every batch. 2025-08-26T20:22:08.2987880Z `step` should be called after a batch has been used for training. 2025-08-26T20:22:08.2987962Z 2025-08-26T20:22:08.2988169Z This class has three built-in policies, as put forth in the paper: 2025-08-26T20:22:08.2988265Z 2025-08-26T20:22:08.2988469Z * "triangular": A basic triangular cycle without amplitude scaling. 2025-08-26T20:22:08.2988775Z * "triangular2": A basic triangular cycle that scales initial amplitude by half each cycle. 2025-08-26T20:22:08.2989095Z * "exp_range": A cycle that scales initial amplitude by :math:`\text{gamma}^{\text{cycle iterations}}` 2025-08-26T20:22:08.2989197Z at each cycle iteration. 2025-08-26T20:22:08.2989288Z 2025-08-26T20:22:08.2989525Z This implementation was adapted from the github repo: `bckenstler/CLR`_ 2025-08-26T20:22:08.2989618Z 2025-08-26T20:22:08.2989702Z Args: 2025-08-26T20:22:08.2989839Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:08.2990061Z base_lr (float or list): Initial learning rate which is the 2025-08-26T20:22:08.2990233Z lower boundary in the cycle for each parameter group. 2025-08-26T20:22:08.2990468Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-08-26T20:22:08.2990599Z for each parameter group. Functionally, 2025-08-26T20:22:08.2990754Z it defines the cycle amplitude (max_lr - base_lr). 2025-08-26T20:22:08.2990894Z The lr at any cycle is the sum of base_lr 2025-08-26T20:22:08.2991031Z and some scaling of the amplitude; therefore 2025-08-26T20:22:08.2991191Z max_lr may not actually be reached depending on 2025-08-26T20:22:08.2991291Z scaling function. 2025-08-26T20:22:08.2991459Z step_size_up (int): Number of training iterations in the 2025-08-26T20:22:08.2991603Z increasing half of a cycle. Default: 2000 2025-08-26T20:22:08.2991964Z step_size_down (int): Number of training iterations in the 2025-08-26T20:22:08.2992217Z decreasing half of a cycle. If step_size_down is None, 2025-08-26T20:22:08.2992348Z it is set to step_size_up. Default: None 2025-08-26T20:22:08.2992518Z mode (str): One of {triangular, triangular2, exp_range}. 2025-08-26T20:22:08.2992679Z Values correspond to policies detailed above. 2025-08-26T20:22:08.2992830Z If scale_fn is not None, this argument is ignored. 2025-08-26T20:22:08.2992948Z Default: 'triangular' 2025-08-26T20:22:08.2993113Z gamma (float): Constant in 'exp_range' scaling function: 2025-08-26T20:22:08.2993220Z gamma**(cycle iterations) 2025-08-26T20:22:08.2993327Z Default: 1.0 2025-08-26T20:22:08.2993517Z scale_fn (function): Custom scaling policy defined by a single 2025-08-26T20:22:08.2993648Z argument lambda function, where 2025-08-26T20:22:08.2993808Z 0 <= scale_fn(x) <= 1 for all x >= 0. 2025-08-26T20:22:08.2993932Z If specified, then 'mode' is ignored. 2025-08-26T20:22:08.2994040Z Default: None 2025-08-26T20:22:08.2994169Z scale_mode (str): {'cycle', 'iterations'}. 2025-08-26T20:22:08.2994310Z Defines whether scale_fn is evaluated on 2025-08-26T20:22:08.2994443Z cycle number or cycle iterations (training 2025-08-26T20:22:08.2994562Z iterations since start of cycle). 2025-08-26T20:22:08.2994670Z Default: 'cycle' 2025-08-26T20:22:08.2994867Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-08-26T20:22:08.2995064Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-08-26T20:22:08.2995158Z Default: True 2025-08-26T20:22:08.2995373Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-08-26T20:22:08.2995597Z for each parameter group. Note that momentum is cycled inversely 2025-08-26T20:22:08.2995761Z to learning rate; at the peak of a cycle, momentum is 2025-08-26T20:22:08.2995917Z 'base_momentum' and learning rate is 'max_lr'. 2025-08-26T20:22:08.2996012Z Default: 0.8 2025-08-26T20:22:08.2996224Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-08-26T20:22:08.2996368Z for each parameter group. Functionally, 2025-08-26T20:22:08.2996569Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-08-26T20:22:08.2996765Z The momentum at any cycle is the difference of max_momentum 2025-08-26T20:22:08.2996904Z and some scaling of the amplitude; therefore 2025-08-26T20:22:08.2997088Z base_momentum may not actually be reached depending on 2025-08-26T20:22:08.2997271Z scaling function. Note that momentum is cycled inversely 2025-08-26T20:22:08.2997530Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-08-26T20:22:08.2997660Z and learning rate is 'base_lr' 2025-08-26T20:22:08.2997791Z Default: 0.9 2025-08-26T20:22:08.2998024Z last_epoch (int): The index of the last batch. This parameter is used when 2025-08-26T20:22:08.2998243Z resuming a training job. Since `step()` should be invoked after each 2025-08-26T20:22:08.2998458Z batch instead of after each epoch, this number represents the total 2025-08-26T20:22:08.2998691Z number of *batches* computed, not the total number of epochs computed. 2025-08-26T20:22:08.2998883Z When last_epoch=-1, the schedule is started from the beginning. 2025-08-26T20:22:08.2998990Z Default: -1 2025-08-26T20:22:08.2999069Z 2025-08-26T20:22:08.2999155Z Example: 2025-08-26T20:22:08.2999266Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.2999519Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) 2025-08-26T20:22:08.2999689Z >>> scheduler = torch.optim.lr_scheduler.CyclicLR( 2025-08-26T20:22:08.2999786Z ... optimizer, 2025-08-26T20:22:08.2999879Z ... base_lr=0.01, 2025-08-26T20:22:08.2999977Z ... max_lr=0.1, 2025-08-26T20:22:08.3006675Z ... step_size_up=10, 2025-08-26T20:22:08.3006783Z ... ) 2025-08-26T20:22:08.3006957Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-08-26T20:22:08.3007080Z >>> for epoch in range(10): 2025-08-26T20:22:08.3007194Z >>> for batch in data_loader: 2025-08-26T20:22:08.3007298Z >>> train_batch(...) 2025-08-26T20:22:08.3007418Z >>> scheduler.step() 2025-08-26T20:22:08.3007500Z 2025-08-26T20:22:08.3007686Z .. image:: ../scripts/lr_scheduler_images/CyclicLR.png 2025-08-26T20:22:08.3007862Z 2025-08-26T20:22:08.3008184Z .. _Cyclical Learning Rates for Training Neural Networks: https://arxiv.org/abs/1506.01186 2025-08-26T20:22:08.3008372Z .. _bckenstler/CLR: https://github.com/bckenstler/CLR 2025-08-26T20:22:08.3008460Z 2025-08-26T20:22:08.3008717Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3008844Z 2025-08-26T20:22:08.3008943Z warnings.warn(msg) 2025-08-26T20:22:08.3009036Z 2025-08-26T20:22:08.3009279Z --- Parse Warning: 123 / 146 --- 2025-08-26T20:22:08.3010279Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CosineAnnealingWarmRestarts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1722. 2025-08-26T20:22:08.3010557Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3010814Z Set the learning rate of each parameter group using a cosine annealing schedule. 2025-08-26T20:22:08.3010910Z 2025-08-26T20:22:08.3011095Z The :math:`\eta_{max}` is set to the initial lr, :math:`T_{cur}` 2025-08-26T20:22:08.3011349Z is the number of epochs since the last restart and :math:`T_{i}` is the number 2025-08-26T20:22:08.3011488Z of epochs between two warm restarts in SGDR: 2025-08-26T20:22:08.3011568Z 2025-08-26T20:22:08.3011670Z .. math:: 2025-08-26T20:22:08.3011850Z \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})\left(1 + 2025-08-26T20:22:08.3012003Z \cos\left(\frac{T_{cur}}{T_{i}}\pi\right)\right) 2025-08-26T20:22:08.3012082Z 2025-08-26T20:22:08.3012249Z When :math:`T_{cur}=T_{i}`, set :math:`\eta_t = \eta_{min}`. 2025-08-26T20:22:08.3012451Z When :math:`T_{cur}=0` after restart, set :math:`\eta_t=\eta_{max}`. 2025-08-26T20:22:08.3012530Z 2025-08-26T20:22:08.3012635Z It has been proposed in 2025-08-26T20:22:08.3012871Z `SGDR: Stochastic Gradient Descent with Warm Restarts`_. 2025-08-26T20:22:08.3012955Z 2025-08-26T20:22:08.3013050Z Args: 2025-08-26T20:22:08.3013192Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:08.3013388Z T_0 (int): Number of iterations until the first restart. 2025-08-26T20:22:08.3013688Z T_mult (int, optional): A factor by which :math:`T_{i}` increases after a restart. Default: 1. 2025-08-26T20:22:08.3013878Z eta_min (float, optional): Minimum learning rate. Default: 0. 2025-08-26T20:22:08.3014102Z last_epoch (int, optional): The index of the last epoch. Default: -1. 2025-08-26T20:22:08.3014183Z 2025-08-26T20:22:08.3014349Z .. _SGDR\: Stochastic Gradient Descent with Warm Restarts: 2025-08-26T20:22:08.3014488Z https://arxiv.org/abs/1608.03983 2025-08-26T20:22:08.3014569Z 2025-08-26T20:22:08.3014655Z Example: 2025-08-26T20:22:08.3014772Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3014991Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.05) 2025-08-26T20:22:08.3015233Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts( 2025-08-26T20:22:08.3015339Z ... optimizer, T_0=20 2025-08-26T20:22:08.3015423Z ... ) 2025-08-26T20:22:08.3015541Z >>> for epoch in range(100): 2025-08-26T20:22:08.3015636Z >>> train(...) 2025-08-26T20:22:08.3015743Z >>> validate(...) 2025-08-26T20:22:08.3015844Z >>> scheduler.step() 2025-08-26T20:22:08.3015924Z 2025-08-26T20:22:08.3016171Z .. image:: ../scripts/lr_scheduler_images/CosineAnnealingWarmRestarts.png 2025-08-26T20:22:08.3016257Z 2025-08-26T20:22:08.3016523Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3016604Z 2025-08-26T20:22:08.3016702Z warnings.warn(msg) 2025-08-26T20:22:08.3016825Z 2025-08-26T20:22:08.3017021Z --- Parse Warning: 124 / 146 --- 2025-08-26T20:22:08.3017919Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=OneCycleLR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/lr_scheduler.py line=1872. 2025-08-26T20:22:08.3018196Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3018495Z Sets the learning rate of each parameter group according to the 1cycle learning rate policy. 2025-08-26T20:22:08.3018589Z 2025-08-26T20:22:08.3018888Z The 1cycle policy anneals the learning rate from an initial learning rate to some maximum 2025-08-26T20:22:08.3019191Z learning rate and then from that maximum learning rate to some minimum learning rate much 2025-08-26T20:22:08.3019311Z lower than the initial learning rate. 2025-08-26T20:22:08.3019535Z This policy was initially described in the paper `Super-Convergence: 2025-08-26T20:22:08.3019761Z Very Fast Training of Neural Networks Using Large Learning Rates`_. 2025-08-26T20:22:08.3019840Z 2025-08-26T20:22:08.3020094Z The 1cycle learning rate policy changes the learning rate after every batch. 2025-08-26T20:22:08.3020291Z `step` should be called after a batch has been used for training. 2025-08-26T20:22:08.3020462Z 2025-08-26T20:22:08.3020590Z This scheduler is not chainable. 2025-08-26T20:22:08.3020670Z 2025-08-26T20:22:08.3020921Z Note also that the total number of steps in the cycle can be determined in one 2025-08-26T20:22:08.3021057Z of two ways (listed in order of precedence): 2025-08-26T20:22:08.3021136Z 2025-08-26T20:22:08.3021299Z #. A value for total_steps is explicitly provided. 2025-08-26T20:22:08.3021483Z #. A number of epochs (epochs) and a number of steps per epoch 2025-08-26T20:22:08.3021606Z (steps_per_epoch) are provided. 2025-08-26T20:22:08.3021770Z In this case, the number of total steps is inferred by 2025-08-26T20:22:08.3021936Z total_steps = epochs * steps_per_epoch 2025-08-26T20:22:08.3022030Z 2025-08-26T20:22:08.3022270Z You must either provide a value for total_steps or provide a value for both 2025-08-26T20:22:08.3022429Z epochs and steps_per_epoch. 2025-08-26T20:22:08.3022507Z 2025-08-26T20:22:08.3022809Z The default behaviour of this scheduler follows the fastai implementation of 1cycle, which 2025-08-26T20:22:08.3023112Z claims that "unpublished work has shown even better results by using only two phases". To 2025-08-26T20:22:08.3023355Z mimic the behaviour of the original paper instead, set ``three_phase=True``. 2025-08-26T20:22:08.3023445Z 2025-08-26T20:22:08.3023527Z Args: 2025-08-26T20:22:08.3023663Z optimizer (Optimizer): Wrapped optimizer. 2025-08-26T20:22:08.3023872Z max_lr (float or list): Upper learning rate boundaries in the cycle 2025-08-26T20:22:08.3023986Z for each parameter group. 2025-08-26T20:22:08.3024227Z total_steps (int): The total number of steps in the cycle. Note that 2025-08-26T20:22:08.3024440Z if a value is not provided here, then it must be inferred by providing 2025-08-26T20:22:08.3024571Z a value for epochs and steps_per_epoch. 2025-08-26T20:22:08.3024678Z Default: None 2025-08-26T20:22:08.3024876Z epochs (int): The number of epochs to train for. This is used along 2025-08-26T20:22:08.3025133Z with steps_per_epoch in order to infer the total number of steps in the cycle 2025-08-26T20:22:08.3025269Z if a value for total_steps is not provided. 2025-08-26T20:22:08.3025361Z Default: None 2025-08-26T20:22:08.3025595Z steps_per_epoch (int): The number of steps per epoch to train for. This is 2025-08-26T20:22:08.3025823Z used along with epochs in order to infer the total number of steps in the 2025-08-26T20:22:08.3026014Z cycle if a value for total_steps is not provided. 2025-08-26T20:22:08.3026109Z Default: None 2025-08-26T20:22:08.3026335Z pct_start (float): The percentage of the cycle (in number of steps) spent 2025-08-26T20:22:08.3026466Z increasing the learning rate. 2025-08-26T20:22:08.3026561Z Default: 0.3 2025-08-26T20:22:08.3026700Z anneal_strategy (str): {'cos', 'linear'} 2025-08-26T20:22:08.3026947Z Specifies the annealing strategy: "cos" for cosine annealing, "linear" for 2025-08-26T20:22:08.3027044Z linear annealing. 2025-08-26T20:22:08.3027153Z Default: 'cos' 2025-08-26T20:22:08.3027350Z cycle_momentum (bool): If ``True``, momentum is cycled inversely 2025-08-26T20:22:08.3027545Z to learning rate between 'base_momentum' and 'max_momentum'. 2025-08-26T20:22:08.3027644Z Default: True 2025-08-26T20:22:08.3027861Z base_momentum (float or list): Lower momentum boundaries in the cycle 2025-08-26T20:22:08.3028079Z for each parameter group. Note that momentum is cycled inversely 2025-08-26T20:22:08.3028242Z to learning rate; at the peak of a cycle, momentum is 2025-08-26T20:22:08.3028398Z 'base_momentum' and learning rate is 'max_lr'. 2025-08-26T20:22:08.3028491Z Default: 0.85 2025-08-26T20:22:08.3028703Z max_momentum (float or list): Upper momentum boundaries in the cycle 2025-08-26T20:22:08.3028847Z for each parameter group. Functionally, 2025-08-26T20:22:08.3029040Z it defines the cycle amplitude (max_momentum - base_momentum). 2025-08-26T20:22:08.3029175Z Note that momentum is cycled inversely 2025-08-26T20:22:08.3029384Z to learning rate; at the start of a cycle, momentum is 'max_momentum' 2025-08-26T20:22:08.3029500Z and learning rate is 'base_lr' 2025-08-26T20:22:08.3029633Z Default: 0.95 2025-08-26T20:22:08.3029823Z div_factor (float): Determines the initial learning rate via 2025-08-26T20:22:08.3029973Z initial_lr = max_lr/div_factor 2025-08-26T20:22:08.3030066Z Default: 25 2025-08-26T20:22:08.3030272Z final_div_factor (float): Determines the minimum learning rate via 2025-08-26T20:22:08.3030403Z min_lr = initial_lr/final_div_factor 2025-08-26T20:22:08.3030496Z Default: 1e4 2025-08-26T20:22:08.3030755Z three_phase (bool): If ``True``, use a third phase of the schedule to annihilate the 2025-08-26T20:22:08.3031009Z learning rate according to 'final_div_factor' instead of modifying the second 2025-08-26T20:22:08.3031247Z phase (the first two phases will be symmetrical about the step indicated by 2025-08-26T20:22:08.3031354Z 'pct_start'). 2025-08-26T20:22:08.3031580Z last_epoch (int): The index of the last batch. This parameter is used when 2025-08-26T20:22:08.3031834Z resuming a training job. Since `step()` should be invoked after each 2025-08-26T20:22:08.3032053Z batch instead of after each epoch, this number represents the total 2025-08-26T20:22:08.3032274Z number of *batches* computed, not the total number of epochs computed. 2025-08-26T20:22:08.3032477Z When last_epoch=-1, the schedule is started from the beginning. 2025-08-26T20:22:08.3032576Z Default: -1 2025-08-26T20:22:08.3032668Z 2025-08-26T20:22:08.3032752Z Example: 2025-08-26T20:22:08.3032850Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3033015Z >>> data_loader = torch.utils.data.DataLoader(...) 2025-08-26T20:22:08.3033248Z >>> optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9) 2025-08-26T20:22:08.3033418Z >>> scheduler = torch.optim.lr_scheduler.OneCycleLR( 2025-08-26T20:22:08.3033659Z ... optimizer, max_lr=0.01, steps_per_epoch=len(data_loader), epochs=10 2025-08-26T20:22:08.3033742Z ... ) 2025-08-26T20:22:08.3033861Z >>> for epoch in range(10): 2025-08-26T20:22:08.3033970Z >>> for batch in data_loader: 2025-08-26T20:22:08.3034082Z >>> train_batch(...) 2025-08-26T20:22:08.3034186Z >>> optimizer.step() 2025-08-26T20:22:08.3034286Z >>> scheduler.step() 2025-08-26T20:22:08.3034376Z 2025-08-26T20:22:08.3034546Z .. image:: ../scripts/lr_scheduler_images/OneCycleLR.png 2025-08-26T20:22:08.3034636Z 2025-08-26T20:22:08.3034924Z .. _Super-Convergence\: Very Fast Training of Neural Networks Using Large Learning Rates: 2025-08-26T20:22:08.3035045Z https://arxiv.org/abs/1708.07120 2025-08-26T20:22:08.3035140Z 2025-08-26T20:22:08.3035390Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3035481Z 2025-08-26T20:22:08.3035582Z warnings.warn(msg) 2025-08-26T20:22:08.3035659Z 2025-08-26T20:22:08.3035883Z --- Parse Warning: 125 / 146 --- 2025-08-26T20:22:08.3036826Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=Optimizer.load_state_dict in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/optimizer.py line=867. 2025-08-26T20:22:08.3037098Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3037201Z Load the optimizer state. 2025-08-26T20:22:08.3037279Z 2025-08-26T20:22:08.3037375Z Args: 2025-08-26T20:22:08.3037573Z state_dict (dict): optimizer state. Should be an object returned 2025-08-26T20:22:08.3037710Z from a call to :meth:`state_dict`. 2025-08-26T20:22:08.3037793Z 2025-08-26T20:22:08.3037888Z .. warning:: 2025-08-26T20:22:08.3038268Z Make sure this method is called after initializing :class:`torch.optim.lr_scheduler.LRScheduler`, 2025-08-26T20:22:08.3038481Z as calling it beforehand will overwrite the loaded learning rates. 2025-08-26T20:22:08.3038599Z 2025-08-26T20:22:08.3038686Z .. note:: 2025-08-26T20:22:08.3038975Z The names of the parameters (if they exist under the "param_names" key of each param group 2025-08-26T20:22:08.3039159Z in :meth:`state_dict`) will not affect the loading process. 2025-08-26T20:22:08.3039483Z To use the parameters' names for custom cases (such as when the parameters in the loaded state dict 2025-08-26T20:22:08.3039650Z differ from those initialized in the optimizer), 2025-08-26T20:22:08.3039953Z a custom ``register_load_state_dict_pre_hook`` should be implemented to adapt the loaded dict 2025-08-26T20:22:08.3040051Z accordingly. 2025-08-26T20:22:08.3040377Z If ``param_names`` exist in loaded state dict ``param_groups`` they will be saved and override 2025-08-26T20:22:08.3040696Z the current names, if present, in the optimizer state. If they do not exist in loaded state dict, 2025-08-26T20:22:08.3040870Z the optimizer ``param_names`` will remain unchanged. 2025-08-26T20:22:08.3040950Z 2025-08-26T20:22:08.3041035Z Example: 2025-08-26T20:22:08.3041148Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3041269Z >>> model = torch.nn.Linear(10, 10) 2025-08-26T20:22:08.3041447Z >>> optim = torch.optim.SGD(model.parameters(), lr=3e-4) 2025-08-26T20:22:08.3041608Z >>> scheduler1 = torch.optim.lr_scheduler.LinearLR( 2025-08-26T20:22:08.3041701Z ... optim, 2025-08-26T20:22:08.3041815Z ... start_factor=0.1, 2025-08-26T20:22:08.3041913Z ... end_factor=1, 2025-08-26T20:22:08.3042048Z ... total_iters=20, 2025-08-26T20:22:08.3042137Z ... ) 2025-08-26T20:22:08.3042325Z >>> scheduler2 = torch.optim.lr_scheduler.CosineAnnealingLR( 2025-08-26T20:22:08.3042430Z ... optim, 2025-08-26T20:22:08.3042523Z ... T_max=80, 2025-08-26T20:22:08.3042631Z ... eta_min=3e-5, 2025-08-26T20:22:08.3042715Z ... ) 2025-08-26T20:22:08.3042863Z >>> lr = torch.optim.lr_scheduler.SequentialLR( 2025-08-26T20:22:08.3042965Z ... optim, 2025-08-26T20:22:08.3043098Z ... schedulers=[scheduler1, scheduler2], 2025-08-26T20:22:08.3043212Z ... milestones=[20], 2025-08-26T20:22:08.3043296Z ... ) 2025-08-26T20:22:08.3043445Z >>> lr.load_state_dict(torch.load("./save_seq.pt")) 2025-08-26T20:22:08.3043660Z >>> # now load the optimizer checkpoint after loading the LRScheduler 2025-08-26T20:22:08.3043831Z >>> optim.load_state_dict(torch.load("./save_optim.pt")) 2025-08-26T20:22:08.3043922Z 2025-08-26T20:22:08.3044004Z 2025-08-26T20:22:08.3044257Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3044347Z 2025-08-26T20:22:08.3044445Z warnings.warn(msg) 2025-08-26T20:22:08.3044523Z 2025-08-26T20:22:08.3044736Z --- Parse Warning: 126 / 146 --- 2025-08-26T20:22:08.3045625Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=AveragedModel in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=120. 2025-08-26T20:22:08.3045901Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3046263Z Implements averaged model for Stochastic Weight Averaging (SWA) and Exponential Moving Average (EMA). 2025-08-26T20:22:08.3046358Z 2025-08-26T20:22:08.3046632Z Stochastic Weight Averaging was proposed in `Averaging Weights Leads to 2025-08-26T20:22:08.3046850Z Wider Optima and Better Generalization`_ by Pavel Izmailov, Dmitrii 2025-08-26T20:22:08.3047109Z Podoprikhin, Timur Garipov, Dmitry Vetrov and Andrew Gordon Wilson 2025-08-26T20:22:08.3047197Z (UAI 2018). 2025-08-26T20:22:08.3047287Z 2025-08-26T20:22:08.3047503Z Exponential Moving Average is a variation of `Polyak averaging`_, 2025-08-26T20:22:08.3047744Z but using exponential weights instead of equal weights across iterations. 2025-08-26T20:22:08.3047836Z 2025-08-26T20:22:08.3048072Z AveragedModel class creates a copy of the provided module :attr:`model` 2025-08-26T20:22:08.3048315Z on the device :attr:`device` and allows to compute running averages of the 2025-08-26T20:22:08.3048427Z parameters of the :attr:`model`. 2025-08-26T20:22:08.3048507Z 2025-08-26T20:22:08.3048605Z Args: 2025-08-26T20:22:08.3048760Z model (torch.nn.Module): model to use with SWA/EMA 2025-08-26T20:22:08.3049032Z device (torch.device, optional): if provided, the averaged model will be 2025-08-26T20:22:08.3049145Z stored on the :attr:`device` 2025-08-26T20:22:08.3049346Z avg_fn (function, optional): the averaging function used to update 2025-08-26T20:22:08.3049559Z parameters; the function must take in the current value of the 2025-08-26T20:22:08.3049781Z :class:`AveragedModel` parameter, the current value of :attr:`model` 2025-08-26T20:22:08.3049989Z parameter, and the number of models already averaged; if None, 2025-08-26T20:22:08.3050149Z an equally weighted average is used (default: None) 2025-08-26T20:22:08.3050374Z multi_avg_fn (function, optional): the averaging function used to update 2025-08-26T20:22:08.3050622Z parameters inplace; the function must take in the current values of the 2025-08-26T20:22:08.3050948Z :class:`AveragedModel` parameters as a list, the current values of :attr:`model` 2025-08-26T20:22:08.3051188Z parameters as a list, and the number of models already averaged; if None, 2025-08-26T20:22:08.3051350Z an equally weighted average is used (default: None) 2025-08-26T20:22:08.3051550Z use_buffers (bool): if ``True``, it will compute running averages for 2025-08-26T20:22:08.3051781Z both the parameters and the buffers of the model. (default: ``False``) 2025-08-26T20:22:08.3051861Z 2025-08-26T20:22:08.3051957Z Example: 2025-08-26T20:22:08.3052087Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.3052215Z >>> loader, optimizer, model, loss_fn = ... 2025-08-26T20:22:08.3052393Z >>> swa_model = torch.optim.swa_utils.AveragedModel(model) 2025-08-26T20:22:08.3052616Z >>> scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 2025-08-26T20:22:08.3052745Z >>> T_max=300) 2025-08-26T20:22:08.3052843Z >>> swa_start = 160 2025-08-26T20:22:08.3052986Z >>> swa_scheduler = SWALR(optimizer, swa_lr=0.05) 2025-08-26T20:22:08.3053095Z >>> for i in range(300): 2025-08-26T20:22:08.3053212Z >>> for input, target in loader: 2025-08-26T20:22:08.3053333Z >>> optimizer.zero_grad() 2025-08-26T20:22:08.3053469Z >>> loss_fn(model(input), target).backward() 2025-08-26T20:22:08.3053574Z >>> optimizer.step() 2025-08-26T20:22:08.3053684Z >>> if i > swa_start: 2025-08-26T20:22:08.3053816Z >>> swa_model.update_parameters(model) 2025-08-26T20:22:08.3053937Z >>> swa_scheduler.step() 2025-08-26T20:22:08.3054025Z >>> else: 2025-08-26T20:22:08.3054127Z >>> scheduler.step() 2025-08-26T20:22:08.3054220Z >>> 2025-08-26T20:22:08.3054413Z >>> # Update bn statistics for the swa_model at the end 2025-08-26T20:22:08.3054586Z >>> torch.optim.swa_utils.update_bn(loader, swa_model) 2025-08-26T20:22:08.3054690Z 2025-08-26T20:22:08.3054987Z You can also use custom averaging functions with the `avg_fn` or `multi_avg_fn` parameters. 2025-08-26T20:22:08.3055194Z If no averaging function is provided, the default is to compute 2025-08-26T20:22:08.3055341Z equally-weighted average of the weights (SWA). 2025-08-26T20:22:08.3055430Z 2025-08-26T20:22:08.3055515Z Example: 2025-08-26T20:22:08.3055643Z >>> # xdoctest: +SKIP("undefined variables") 2025-08-26T20:22:08.3055861Z >>> # Compute exponential moving averages of the weights and buffers 2025-08-26T20:22:08.3056030Z >>> ema_model = torch.optim.swa_utils.AveragedModel(model, 2025-08-26T20:22:08.3056259Z >>> torch.optim.swa_utils.get_ema_multi_avg_fn(0.9), use_buffers=True) 2025-08-26T20:22:08.3056343Z 2025-08-26T20:22:08.3056458Z .. note:: 2025-08-26T20:22:08.3056695Z When using SWA/EMA with models containing Batch Normalization you may 2025-08-26T20:22:08.3056902Z need to update the activation statistics for Batch Normalization. 2025-08-26T20:22:08.3057148Z This can be done either by using the :meth:`torch.optim.swa_utils.update_bn` 2025-08-26T20:22:08.3057372Z or by setting :attr:`use_buffers` to `True`. The first approach updates the 2025-08-26T20:22:08.3057611Z statistics in a post-training step by passing data through the model. The 2025-08-26T20:22:08.3057860Z second does it during the parameter update phase by averaging all buffers. 2025-08-26T20:22:08.3058106Z Empirical evidence has shown that updating the statistics in normalization 2025-08-26T20:22:08.3058344Z layers increases accuracy, but you may wish to empirically test which 2025-08-26T20:22:08.3058530Z approach yields the best results in your problem. 2025-08-26T20:22:08.3058613Z 2025-08-26T20:22:08.3058714Z .. note:: 2025-08-26T20:22:08.3058967Z :attr:`avg_fn` and `multi_avg_fn` are not saved in the :meth:`state_dict` of the model. 2025-08-26T20:22:08.3059056Z 2025-08-26T20:22:08.3059141Z .. note:: 2025-08-26T20:22:08.3059339Z When :meth:`update_parameters` is called for the first time (i.e. 2025-08-26T20:22:08.3059534Z :attr:`n_averaged` is `0`) the parameters of `model` are copied 2025-08-26T20:22:08.3059737Z to the parameters of :class:`AveragedModel`. For every subsequent 2025-08-26T20:22:08.3059932Z call of :meth:`update_parameters` the function `avg_fn` is used 2025-08-26T20:22:08.3060040Z to update the parameters. 2025-08-26T20:22:08.3060118Z 2025-08-26T20:22:08.3060428Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-08-26T20:22:08.3060561Z https://arxiv.org/abs/1803.05407 2025-08-26T20:22:08.3060812Z .. _There Are Many Consistent Explanations of Unlabeled Data: Why You Should 2025-08-26T20:22:08.3060900Z Average: 2025-08-26T20:22:08.3061019Z https://arxiv.org/abs/1806.05594 2025-08-26T20:22:08.3061229Z .. _SWALP: Stochastic Weight Averaging in Low-Precision Training: 2025-08-26T20:22:08.3061345Z https://arxiv.org/abs/1904.11943 2025-08-26T20:22:08.3061584Z .. _Stochastic Weight Averaging in Parallel: Large-Batch Training That 2025-08-26T20:22:08.3061686Z Generalizes Well: 2025-08-26T20:22:08.3061800Z https://arxiv.org/abs/2001.02312 2025-08-26T20:22:08.3061911Z .. _Polyak averaging: 2025-08-26T20:22:08.3062081Z https://paperswithcode.com/method/polyak-averaging 2025-08-26T20:22:08.3062175Z 2025-08-26T20:22:08.3062425Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3062506Z 2025-08-26T20:22:08.3062654Z warnings.warn(msg) 2025-08-26T20:22:08.3062734Z 2025-08-26T20:22:08.3062960Z --- Parse Warning: 127 / 146 --- 2025-08-26T20:22:08.3063833Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SWALR in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/optim/swa_utils.py line=375. 2025-08-26T20:22:08.3064095Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3064325Z Anneals the learning rate in each parameter group to a fixed value. 2025-08-26T20:22:08.3064408Z 2025-08-26T20:22:08.3064649Z This learning rate scheduler is meant to be used with Stochastic Weight 2025-08-26T20:22:08.3064859Z Averaging (SWA) method (see `torch.optim.swa_utils.AveragedModel`). 2025-08-26T20:22:08.3064939Z 2025-08-26T20:22:08.3065037Z Args: 2025-08-26T20:22:08.3065211Z optimizer (torch.optim.Optimizer): wrapped optimizer 2025-08-26T20:22:08.3065455Z swa_lrs (float or list): the learning rate value for all param groups 2025-08-26T20:22:08.3065589Z together or separately for each group. 2025-08-26T20:22:08.3065787Z annealing_epochs (int): number of epochs in the annealing phase 2025-08-26T20:22:08.3065893Z (default: 10) 2025-08-26T20:22:08.3066105Z annealing_strategy (str): "cos" or "linear"; specifies the annealing 2025-08-26T20:22:08.3066321Z strategy: "cos" for cosine annealing, "linear" for linear annealing 2025-08-26T20:22:08.3066418Z (default: "cos") 2025-08-26T20:22:08.3066595Z last_epoch (int): the index of the last epoch (default: -1) 2025-08-26T20:22:08.3066683Z 2025-08-26T20:22:08.3066859Z The :class:`SWALR` scheduler can be used together with other 2025-08-26T20:22:08.3067087Z schedulers to switch to a constant learning rate late in the training 2025-08-26T20:22:08.3067220Z as in the example below. 2025-08-26T20:22:08.3067299Z 2025-08-26T20:22:08.3067400Z Example: 2025-08-26T20:22:08.3067532Z >>> # xdoctest: +SKIP("Undefined variables") 2025-08-26T20:22:08.3067658Z >>> loader, optimizer, model = ... 2025-08-26T20:22:08.3067769Z >>> lr_lambda = lambda epoch: 0.9 2025-08-26T20:22:08.3067989Z >>> scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, 2025-08-26T20:22:08.3068107Z >>> lr_lambda=lr_lambda) 2025-08-26T20:22:08.3068277Z >>> swa_scheduler = torch.optim.swa_utils.SWALR(optimizer, 2025-08-26T20:22:08.3068464Z >>> anneal_strategy="linear", anneal_epochs=20, swa_lr=0.05) 2025-08-26T20:22:08.3068558Z >>> swa_start = 160 2025-08-26T20:22:08.3068656Z >>> for i in range(300): 2025-08-26T20:22:08.3068780Z >>> for input, target in loader: 2025-08-26T20:22:08.3068897Z >>> optimizer.zero_grad() 2025-08-26T20:22:08.3069045Z >>> loss_fn(model(input), target).backward() 2025-08-26T20:22:08.3069149Z >>> optimizer.step() 2025-08-26T20:22:08.3069249Z >>> if i > swa_start: 2025-08-26T20:22:08.3069368Z >>> swa_scheduler.step() 2025-08-26T20:22:08.3069455Z >>> else: 2025-08-26T20:22:08.3069567Z >>> scheduler.step() 2025-08-26T20:22:08.3069647Z 2025-08-26T20:22:08.3069867Z .. _Averaging Weights Leads to Wider Optima and Better Generalization: 2025-08-26T20:22:08.3069994Z https://arxiv.org/abs/1803.05407 2025-08-26T20:22:08.3070075Z 2025-08-26T20:22:08.3070336Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3070414Z 2025-08-26T20:22:08.3070508Z warnings.warn(msg) 2025-08-26T20:22:08.3070598Z 2025-08-26T20:22:08.3070792Z --- Parse Warning: 128 / 146 --- 2025-08-26T20:22:08.3071754Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=assert_close in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/testing/_comparison.py line=1331. 2025-08-26T20:22:08.3072047Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3072194Z Asserts that ``actual`` and ``expected`` are close. 2025-08-26T20:22:08.3072286Z 2025-08-26T20:22:08.3072652Z If ``actual`` and ``expected`` are strided, non-quantized, real-valued, and finite, they are considered close if 2025-08-26T20:22:08.3072744Z 2025-08-26T20:22:08.3072835Z .. math:: 2025-08-26T20:22:08.3072915Z 2025-08-26T20:22:08.3073288Z \lvert \text{actual} - \text{expected} \rvert \le \texttt{atol} + \texttt{rtol} \cdot \lvert \text{expected} \rvert 2025-08-26T20:22:08.3073373Z 2025-08-26T20:22:08.3073759Z Non-finite values (``-inf`` and ``inf``) are only considered close if and only if they are equal. ``NaN``'s are 2025-08-26T20:22:08.3073966Z only considered equal to each other if ``equal_nan`` is ``True``. 2025-08-26T20:22:08.3074048Z 2025-08-26T20:22:08.3074262Z In addition, they are only considered close if they have the same 2025-08-26T20:22:08.3074341Z 2025-08-26T20:22:08.3074543Z - :attr:`~torch.Tensor.device` (if ``check_device`` is ``True``), 2025-08-26T20:22:08.3074679Z - ``dtype`` (if ``check_dtype`` is ``True``), 2025-08-26T20:22:08.3074834Z - ``layout`` (if ``check_layout`` is ``True``), and 2025-08-26T20:22:08.3074961Z - stride (if ``check_stride`` is ``True``). 2025-08-26T20:22:08.3075054Z 2025-08-26T20:22:08.3075352Z If either ``actual`` or ``expected`` is a meta tensor, only the attribute checks will be performed. 2025-08-26T20:22:08.3075431Z 2025-08-26T20:22:08.3075801Z If ``actual`` and ``expected`` are sparse (either having COO, CSR, CSC, BSR, or BSC layout), their strided members are 2025-08-26T20:22:08.3076203Z checked individually. Indices, namely ``indices`` for COO, ``crow_indices`` and ``col_indices`` for CSR and BSR, 2025-08-26T20:22:08.3076443Z or ``ccol_indices`` and ``row_indices`` for CSC and BSC layouts, respectively, 2025-08-26T20:22:08.3076828Z are always checked for equality whereas the values are checked for closeness according to the definition above. 2025-08-26T20:22:08.3076909Z 2025-08-26T20:22:08.3077199Z If ``actual`` and ``expected`` are quantized, they are considered close if they have the same 2025-08-26T20:22:08.3077551Z :meth:`~torch.Tensor.qscheme` and the result of :meth:`~torch.Tensor.dequantize` is close according to the 2025-08-26T20:22:08.3077660Z definition above. 2025-08-26T20:22:08.3077742Z 2025-08-26T20:22:08.3078043Z ``actual`` and ``expected`` can be :class:`~torch.Tensor`'s or any tensor-or-scalar-likes from which 2025-08-26T20:22:08.3078431Z :class:`torch.Tensor`'s can be constructed with :func:`torch.as_tensor`. Except for Python scalars the input types 2025-08-26T20:22:08.3078797Z have to be directly related. In addition, ``actual`` and ``expected`` can be :class:`~collections.abc.Sequence`'s 2025-08-26T20:22:08.3079185Z or :class:`~collections.abc.Mapping`'s in which case they are considered close if their structure matches and all 2025-08-26T20:22:08.3079416Z their elements are considered close according to the above definition. 2025-08-26T20:22:08.3079507Z 2025-08-26T20:22:08.3079593Z .. note:: 2025-08-26T20:22:08.3079672Z 2025-08-26T20:22:08.3080015Z Python scalars are an exception to the type relation requirement, because their :func:`type`, i.e. 2025-08-26T20:22:08.3080335Z :class:`int`, :class:`float`, and :class:`complex`, is equivalent to the ``dtype`` of a tensor-like. Thus, 2025-08-26T20:22:08.3080652Z Python scalars of different types can be checked, but require ``check_dtype=False``. 2025-08-26T20:22:08.3080734Z 2025-08-26T20:22:08.3080816Z Args: 2025-08-26T20:22:08.3080935Z actual (Any): Actual input. 2025-08-26T20:22:08.3081075Z expected (Any): Expected input. 2025-08-26T20:22:08.3081446Z allow_subclasses (bool): If ``True`` (default) and except for Python scalars, inputs of directly related types 2025-08-26T20:22:08.3081605Z are allowed. Otherwise type equality is required. 2025-08-26T20:22:08.3081970Z rtol (Optional[float]): Relative tolerance. If specified ``atol`` must also be specified. If omitted, default 2025-08-26T20:22:08.3082241Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-08-26T20:22:08.3082602Z atol (Optional[float]): Absolute tolerance. If specified ``rtol`` must also be specified. If omitted, default 2025-08-26T20:22:08.3082876Z values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. 2025-08-26T20:22:08.3083144Z equal_nan (Union[bool, str]): If ``True``, two ``NaN`` values will be considered equal. 2025-08-26T20:22:08.3083430Z check_device (bool): If ``True`` (default), asserts that corresponding tensors are on the same 2025-08-26T20:22:08.3083688Z :attr:`~torch.Tensor.device`. If this check is disabled, tensors on different 2025-08-26T20:22:08.3083920Z :attr:`~torch.Tensor.device`'s are moved to the CPU before being compared. 2025-08-26T20:22:08.3084277Z check_dtype (bool): If ``True`` (default), asserts that corresponding tensors have the same ``dtype``. If this 2025-08-26T20:22:08.3084622Z check is disabled, tensors with different ``dtype``'s are promoted to a common ``dtype`` (according to 2025-08-26T20:22:08.3084795Z :func:`torch.promote_types`) before being compared. 2025-08-26T20:22:08.3085190Z check_layout (bool): If ``True`` (default), asserts that corresponding tensors have the same ``layout``. If this 2025-08-26T20:22:08.3085524Z check is disabled, tensors with different ``layout``'s are converted to strided tensors before being 2025-08-26T20:22:08.3085627Z compared. 2025-08-26T20:22:08.3085989Z check_stride (bool): If ``True`` and corresponding tensors are strided, asserts that they have the same stride. 2025-08-26T20:22:08.3086355Z msg (Optional[Union[str, Callable[[str], str]]]): Optional error message to use in case a failure occurs during 2025-08-26T20:22:08.3086709Z the comparison. Can also passed as callable in which case it will be called with the generated message and 2025-08-26T20:22:08.3086835Z should return the new message. 2025-08-26T20:22:08.3086914Z 2025-08-26T20:22:08.3086998Z Raises: 2025-08-26T20:22:08.3087242Z ValueError: If no :class:`torch.Tensor` can be constructed from an input. 2025-08-26T20:22:08.3087414Z ValueError: If only ``rtol`` or ``atol`` is specified. 2025-08-26T20:22:08.3087748Z AssertionError: If corresponding inputs are not Python scalars and are not directly related. 2025-08-26T20:22:08.3088115Z AssertionError: If ``allow_subclasses`` is ``False``, but corresponding inputs are not Python scalars and have 2025-08-26T20:22:08.3088216Z different types. 2025-08-26T20:22:08.3088588Z AssertionError: If the inputs are :class:`~collections.abc.Sequence`'s, but their length does not match. 2025-08-26T20:22:08.3088950Z AssertionError: If the inputs are :class:`~collections.abc.Mapping`'s, but their set of keys do not match. 2025-08-26T20:22:08.3089276Z AssertionError: If corresponding tensors do not have the same :attr:`~torch.Tensor.shape`. 2025-08-26T20:22:08.3089575Z AssertionError: If ``check_layout`` is ``True``, but corresponding tensors do not have the same 2025-08-26T20:22:08.3089728Z :attr:`~torch.Tensor.layout`. 2025-08-26T20:22:08.3089959Z AssertionError: If only one of corresponding tensors is quantized. 2025-08-26T20:22:08.3090374Z AssertionError: If corresponding tensors are quantized, but have different :meth:`~torch.Tensor.qscheme`'s. 2025-08-26T20:22:08.3090676Z AssertionError: If ``check_device`` is ``True``, but corresponding tensors are not on the same 2025-08-26T20:22:08.3090792Z :attr:`~torch.Tensor.device`. 2025-08-26T20:22:08.3091134Z AssertionError: If ``check_dtype`` is ``True``, but corresponding tensors do not have the same ``dtype``. 2025-08-26T20:22:08.3091488Z AssertionError: If ``check_stride`` is ``True``, but corresponding strided tensors do not have the same stride. 2025-08-26T20:22:08.3092041Z AssertionError: If the values of corresponding tensors are not close according to the definition above. 2025-08-26T20:22:08.3092135Z 2025-08-26T20:22:08.3092572Z The following table displays the default ``rtol`` and ``atol`` for different ``dtype``'s. In case of mismatching 2025-08-26T20:22:08.3092739Z ``dtype``'s, the maximum of both tolerances is used. 2025-08-26T20:22:08.3092823Z 2025-08-26T20:22:08.3092953Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3093095Z | ``dtype`` | ``rtol`` | ``atol`` | 2025-08-26T20:22:08.3093206Z +===========================+============+==========+ 2025-08-26T20:22:08.3093357Z | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | 2025-08-26T20:22:08.3093480Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3093617Z | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | 2025-08-26T20:22:08.3093754Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3093887Z | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:08.3094060Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3094195Z | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | 2025-08-26T20:22:08.3094315Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3094466Z | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | 2025-08-26T20:22:08.3094585Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3094732Z | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:08.3094850Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3094986Z | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | 2025-08-26T20:22:08.3095118Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3095250Z | :attr:`~torch.quint8` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:08.3095385Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3095521Z | :attr:`~torch.quint2x4` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:08.3095648Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3095797Z | :attr:`~torch.quint4x2` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:08.3095923Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3096068Z | :attr:`~torch.qint8` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:08.3096191Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3096334Z | :attr:`~torch.qint32` | ``1.3e-6`` | ``1e-5`` | 2025-08-26T20:22:08.3096456Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3096575Z | other | ``0.0`` | ``0.0`` | 2025-08-26T20:22:08.3096708Z +---------------------------+------------+----------+ 2025-08-26T20:22:08.3096788Z 2025-08-26T20:22:08.3096877Z .. note:: 2025-08-26T20:22:08.3096970Z 2025-08-26T20:22:08.3097412Z :func:`~torch.testing.assert_close` is highly configurable with strict default settings. Users are encouraged 2025-08-26T20:22:08.3097778Z to :func:`~functools.partial` it to fit their use case. For example, if an equality check is needed, one might 2025-08-26T20:22:08.3098074Z define an ``assert_equal`` that uses zero tolerances for every ``dtype`` by default: 2025-08-26T20:22:08.3098165Z 2025-08-26T20:22:08.3098263Z >>> import functools 2025-08-26T20:22:08.3098520Z >>> assert_equal = functools.partial(torch.testing.assert_close, rtol=0, atol=0) 2025-08-26T20:22:08.3098640Z >>> assert_equal(1e-9, 1e-10) 2025-08-26T20:22:08.3098759Z Traceback (most recent call last): 2025-08-26T20:22:08.3098854Z ... 2025-08-26T20:22:08.3098981Z AssertionError: Scalars are not equal! 2025-08-26T20:22:08.3099071Z 2025-08-26T20:22:08.3099219Z Expected 1e-10 but got 1e-09. 2025-08-26T20:22:08.3099348Z Absolute difference: 9.000000000000001e-10 2025-08-26T20:22:08.3099479Z Relative difference: 9.0 2025-08-26T20:22:08.3099570Z 2025-08-26T20:22:08.3099654Z Examples: 2025-08-26T20:22:08.3099782Z >>> # tensor to tensor comparison 2025-08-26T20:22:08.3099913Z >>> expected = torch.tensor([1e0, 1e-1, 1e-2]) 2025-08-26T20:22:08.3100052Z >>> actual = torch.acos(torch.cos(expected)) 2025-08-26T20:22:08.3100208Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:08.3100288Z 2025-08-26T20:22:08.3100485Z >>> # scalar to scalar comparison 2025-08-26T20:22:08.3100583Z >>> import math 2025-08-26T20:22:08.3100687Z >>> expected = math.sqrt(2.0) 2025-08-26T20:22:08.3100811Z >>> actual = 2.0 / math.sqrt(2.0) 2025-08-26T20:22:08.3100953Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:08.3101046Z 2025-08-26T20:22:08.3101208Z >>> # numpy array to numpy array comparison 2025-08-26T20:22:08.3101313Z >>> import numpy as np 2025-08-26T20:22:08.3101451Z >>> expected = np.array([1e0, 1e-1, 1e-2]) 2025-08-26T20:22:08.3101576Z >>> actual = np.arccos(np.cos(expected)) 2025-08-26T20:22:08.3101733Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:08.3101811Z 2025-08-26T20:22:08.3101929Z >>> # sequence to sequence comparison 2025-08-26T20:22:08.3102039Z >>> import numpy as np 2025-08-26T20:22:08.3102288Z >>> # The types of the sequences do not have to match. They only have to have the same 2025-08-26T20:22:08.3102433Z >>> # length and their elements have to match. 2025-08-26T20:22:08.3102586Z >>> expected = [torch.tensor([1.0]), 2.0, np.array(3.0)] 2025-08-26T20:22:08.3102692Z >>> actual = tuple(expected) 2025-08-26T20:22:08.3102845Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:08.3102929Z 2025-08-26T20:22:08.3103051Z >>> # mapping to mapping comparison 2025-08-26T20:22:08.3103184Z >>> from collections import OrderedDict 2025-08-26T20:22:08.3103283Z >>> import numpy as np 2025-08-26T20:22:08.3103396Z >>> foo = torch.tensor(1.0) 2025-08-26T20:22:08.3103485Z >>> bar = 2.0 2025-08-26T20:22:08.3103583Z >>> baz = np.array(3.0) 2025-08-26T20:22:08.3103845Z >>> # The types and a possible ordering of mappings do not have to match. They only 2025-08-26T20:22:08.3104046Z >>> # have to have the same set of keys and their elements have to match. 2025-08-26T20:22:08.3104261Z >>> expected = OrderedDict([("foo", foo), ("bar", bar), ("baz", baz)]) 2025-08-26T20:22:08.3104396Z >>> actual = {"baz": baz, "bar": bar, "foo": foo} 2025-08-26T20:22:08.3104541Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:08.3104636Z 2025-08-26T20:22:08.3104759Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-08-26T20:22:08.3104905Z >>> actual = expected.clone() 2025-08-26T20:22:08.3105073Z >>> # By default, directly related instances can be compared 2025-08-26T20:22:08.3105317Z >>> torch.testing.assert_close(torch.nn.Parameter(actual), expected) 2025-08-26T20:22:08.3105519Z >>> # This check can be made more strict with allow_subclasses=False 2025-08-26T20:22:08.3105634Z >>> torch.testing.assert_close( 2025-08-26T20:22:08.3105846Z ... torch.nn.Parameter(actual), expected, allow_subclasses=False 2025-08-26T20:22:08.3105927Z ... ) 2025-08-26T20:22:08.3106044Z Traceback (most recent call last): 2025-08-26T20:22:08.3106139Z ... 2025-08-26T20:22:08.3106343Z TypeError: No comparison pair was able to handle inputs of type 2025-08-26T20:22:08.3106574Z and . 2025-08-26T20:22:08.3106830Z >>> # If the inputs are not directly related, they are never considered close 2025-08-26T20:22:08.3107015Z >>> torch.testing.assert_close(actual.numpy(), expected) 2025-08-26T20:22:08.3107136Z Traceback (most recent call last): 2025-08-26T20:22:08.3107220Z ... 2025-08-26T20:22:08.3107524Z TypeError: No comparison pair was able to handle inputs of type 2025-08-26T20:22:08.3107632Z and . 2025-08-26T20:22:08.3107904Z >>> # Exceptions to these rules are Python scalars. They can be checked regardless of 2025-08-26T20:22:08.3108025Z >>> # their type if check_dtype=False. 2025-08-26T20:22:08.3108193Z >>> torch.testing.assert_close(1.0, 1, check_dtype=False) 2025-08-26T20:22:08.3108287Z 2025-08-26T20:22:08.3108391Z >>> # NaN != NaN by default. 2025-08-26T20:22:08.3108517Z >>> expected = torch.tensor(float("Nan")) 2025-08-26T20:22:08.3108667Z >>> actual = expected.clone() 2025-08-26T20:22:08.3108817Z >>> torch.testing.assert_close(actual, expected) 2025-08-26T20:22:08.3108947Z Traceback (most recent call last): 2025-08-26T20:22:08.3109032Z ... 2025-08-26T20:22:08.3109157Z AssertionError: Scalars are not close! 2025-08-26T20:22:08.3109259Z 2025-08-26T20:22:08.3109365Z Expected nan but got nan. 2025-08-26T20:22:08.3109521Z Absolute difference: nan (up to 1e-05 allowed) 2025-08-26T20:22:08.3109667Z Relative difference: nan (up to 1.3e-06 allowed) 2025-08-26T20:22:08.3109865Z >>> torch.testing.assert_close(actual, expected, equal_nan=True) 2025-08-26T20:22:08.3109956Z 2025-08-26T20:22:08.3110079Z >>> expected = torch.tensor([1.0, 2.0, 3.0]) 2025-08-26T20:22:08.3110211Z >>> actual = torch.tensor([1.0, 4.0, 5.0]) 2025-08-26T20:22:08.3110356Z >>> # The default error message can be overwritten. 2025-08-26T20:22:08.3110472Z >>> torch.testing.assert_close( 2025-08-26T20:22:08.3110664Z ... actual, expected, msg="Argh, the tensors are not close!" 2025-08-26T20:22:08.3110750Z ... ) 2025-08-26T20:22:08.3110878Z Traceback (most recent call last): 2025-08-26T20:22:08.3110960Z ... 2025-08-26T20:22:08.3111107Z AssertionError: Argh, the tensors are not close! 2025-08-26T20:22:08.3111389Z >>> # If msg is a callable, it can be used to augment the generated message with 2025-08-26T20:22:08.3111486Z >>> # extra information 2025-08-26T20:22:08.3111611Z >>> torch.testing.assert_close( 2025-08-26T20:22:08.3111807Z ... actual, expected, msg=lambda msg: f"Header\n\n{msg}\n\nFooter" 2025-08-26T20:22:08.3111889Z ... ) 2025-08-26T20:22:08.3112017Z Traceback (most recent call last): 2025-08-26T20:22:08.3112100Z ... 2025-08-26T20:22:08.3112216Z AssertionError: Header 2025-08-26T20:22:08.3112331Z 2025-08-26T20:22:08.3112440Z Tensor-likes are not close! 2025-08-26T20:22:08.3112566Z 2025-08-26T20:22:08.3112678Z Mismatched elements: 2 / 3 (66.7%) 2025-08-26T20:22:08.3112916Z Greatest absolute difference: 2.0 at index (1,) (up to 1e-05 allowed) 2025-08-26T20:22:08.3113144Z Greatest relative difference: 1.0 at index (1,) (up to 1.3e-06 allowed) 2025-08-26T20:22:08.3113233Z 2025-08-26T20:22:08.3113328Z Footer 2025-08-26T20:22:08.3113409Z 2025-08-26T20:22:08.3113659Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3113752Z 2025-08-26T20:22:08.3113847Z warnings.warn(msg) 2025-08-26T20:22:08.3113937Z 2025-08-26T20:22:08.3114165Z --- Parse Warning: 129 / 146 --- 2025-08-26T20:22:08.3115132Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=register_pytree_node in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/_cxx_pytree.py line=134. 2025-08-26T20:22:08.3115409Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3115553Z Register a container-like type as pytree node. 2025-08-26T20:22:08.3115643Z 2025-08-26T20:22:08.3115727Z Args: 2025-08-26T20:22:08.3115913Z cls (type): A Python type to treat as an internal pytree node. 2025-08-26T20:22:08.3116193Z flatten_fn (callable): A function to be used during flattening, taking an instance of 2025-08-26T20:22:08.3116438Z ``cls`` and returning a pair, with (1) an iterable for the children to be flattened 2025-08-26T20:22:08.3116740Z recursively, and (2) some hashable auxiliary data to be stored in the treespec and to be 2025-08-26T20:22:08.3116890Z passed to the ``unflatten_fn``. 2025-08-26T20:22:08.3117182Z unflatten_fn (callable): A function taking two arguments: the auxiliary data that was 2025-08-26T20:22:08.3117441Z returned by ``flatten_fn`` and stored in the treespec, and the unflattened children. 2025-08-26T20:22:08.3117597Z The function should return an instance of ``cls``. 2025-08-26T20:22:08.3117871Z serialized_type_name (str, optional): A keyword argument used to specify the fully 2025-08-26T20:22:08.3118033Z qualified name used when serializing the tree spec. 2025-08-26T20:22:08.3118353Z to_dumpable_context (callable, optional): An optional keyword argument to custom specify how 2025-08-26T20:22:08.3118629Z to convert the context of the pytree to a custom json dumpable representation. This is 2025-08-26T20:22:08.3118899Z used for json serialization, which is being used in :mod:`torch.export` right now. 2025-08-26T20:22:08.3119217Z from_dumpable_context (callable, optional): An optional keyword argument to custom specify 2025-08-26T20:22:08.3119480Z how to convert the custom json dumpable representation of the context back to the 2025-08-26T20:22:08.3119816Z original context. This is used for json deserialization, which is being used in 2025-08-26T20:22:08.3119933Z :mod:`torch.export` right now. 2025-08-26T20:22:08.3120023Z 2025-08-26T20:22:08.3120119Z Example:: 2025-08-26T20:22:08.3120200Z 2025-08-26T20:22:08.3120312Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3120456Z >>> # Registry a Python type with lambda functions 2025-08-26T20:22:08.3120561Z >>> register_pytree_node( 2025-08-26T20:22:08.3120663Z ... set, 2025-08-26T20:22:08.3120786Z ... lambda s: (sorted(s), None, None), 2025-08-26T20:22:08.3120924Z ... lambda children, _: set(children), 2025-08-26T20:22:08.3121006Z ... ) 2025-08-26T20:22:08.3121088Z 2025-08-26T20:22:08.3121381Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3121463Z 2025-08-26T20:22:08.3121596Z warnings.warn(msg) 2025-08-26T20:22:08.3121674Z 2025-08-26T20:22:08.3121871Z --- Parse Warning: 130 / 146 --- 2025-08-26T20:22:08.3122857Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SelectiveCheckpointContext in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1218. 2025-08-26T20:22:08.3123119Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3123209Z 2025-08-26T20:22:08.3123422Z Context passed to policy function during selective checkpointing. 2025-08-26T20:22:08.3123505Z 2025-08-26T20:22:08.3123743Z This class is used to pass relevant metadata to the policy function during 2025-08-26T20:22:08.3124037Z selective checkpointing. The metadata includes whether the current invocation 2025-08-26T20:22:08.3124210Z of the policy function is during recomputation or not. 2025-08-26T20:22:08.3124292Z 2025-08-26T20:22:08.3124377Z Example: 2025-08-26T20:22:08.3124489Z >>> # xdoctest: +SKIP(stub) 2025-08-26T20:22:08.3124571Z >>> 2025-08-26T20:22:08.3124711Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-08-26T20:22:08.3124819Z >>> print(ctx.is_recompute) 2025-08-26T20:22:08.3124900Z >>> 2025-08-26T20:22:08.3125184Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-08-26T20:22:08.3125265Z >>> 2025-08-26T20:22:08.3125419Z >>> out = torch.utils.checkpoint.checkpoint( 2025-08-26T20:22:08.3125506Z >>> fn, x, y, 2025-08-26T20:22:08.3125670Z >>> use_reentrant=False, 2025-08-26T20:22:08.3125786Z >>> context_fn=context_fn, 2025-08-26T20:22:08.3125897Z >>> ) 2025-08-26T20:22:08.3125977Z 2025-08-26T20:22:08.3126244Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3126325Z 2025-08-26T20:22:08.3126432Z warnings.warn(msg) 2025-08-26T20:22:08.3126510Z 2025-08-26T20:22:08.3126697Z --- Parse Warning: 131 / 146 --- 2025-08-26T20:22:08.3127699Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=create_selective_checkpoint_contexts in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/checkpoint.py line=1358. 2025-08-26T20:22:08.3127961Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3128054Z 2025-08-26T20:22:08.3128290Z Helper to avoid recomputing certain ops during activation checkpointing. 2025-08-26T20:22:08.3128368Z 2025-08-26T20:22:08.3128592Z Use this with `torch.utils.checkpoint.checkpoint` to control which 2025-08-26T20:22:08.3128757Z operations are recomputed during the backward pass. 2025-08-26T20:22:08.3128850Z 2025-08-26T20:22:08.3128932Z Args: 2025-08-26T20:22:08.3129067Z policy_fn_or_list (Callable or List): 2025-08-26T20:22:08.3129243Z - If a policy function is provided, it should accept a 2025-08-26T20:22:08.3129485Z :class:`SelectiveCheckpointContext`, the :class:`OpOverload`, args and 2025-08-26T20:22:08.3129700Z kwargs to the op, and return a :class:`CheckpointPolicy` enum value 2025-08-26T20:22:08.3129940Z indicating whether the execution of the op should be recomputed or not. 2025-08-26T20:22:08.3130142Z - If a list of operations is provided, it is equivalent to a policy 2025-08-26T20:22:08.3130335Z returning `CheckpointPolicy.MUST_SAVE` for the specified 2025-08-26T20:22:08.3130553Z operations and `CheckpointPolicy.PREFER_RECOMPUTE` for all other 2025-08-26T20:22:08.3130660Z operations. 2025-08-26T20:22:08.3130903Z allow_cache_entry_mutation (bool, optional): By default, an error is 2025-08-26T20:22:08.3131126Z raised if any tensors cached by selective activation checkpoint are 2025-08-26T20:22:08.3131364Z mutated in order to ensure correctness. If set to `True`, this check 2025-08-26T20:22:08.3131454Z is disabled. 2025-08-26T20:22:08.3131550Z Returns: 2025-08-26T20:22:08.3131660Z A tuple of two context managers. 2025-08-26T20:22:08.3131740Z 2025-08-26T20:22:08.3131837Z Example: 2025-08-26T20:22:08.3131943Z >>> # xdoctest: +REQUIRES(LINUX) 2025-08-26T20:22:08.3132052Z >>> import functools 2025-08-26T20:22:08.3132132Z >>> 2025-08-26T20:22:08.3132257Z >>> x = torch.rand(10, 10, requires_grad=True) 2025-08-26T20:22:08.3132392Z >>> y = torch.rand(10, 10, requires_grad=True) 2025-08-26T20:22:08.3132472Z >>> 2025-08-26T20:22:08.3132576Z >>> ops_to_save = [ 2025-08-26T20:22:08.3132708Z >>> torch.ops.aten.mm.default, 2025-08-26T20:22:08.3132815Z >>> ] 2025-08-26T20:22:08.3132897Z >>> 2025-08-26T20:22:08.3133033Z >>> def policy_fn(ctx, op, *args, **kwargs): 2025-08-26T20:22:08.3133135Z >>> if op in ops_to_save: 2025-08-26T20:22:08.3133274Z >>> return CheckpointPolicy.MUST_SAVE 2025-08-26T20:22:08.3133359Z >>> else: 2025-08-26T20:22:08.3133501Z >>> return CheckpointPolicy.PREFER_RECOMPUTE 2025-08-26T20:22:08.3133595Z >>> 2025-08-26T20:22:08.3133868Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, policy_fn) 2025-08-26T20:22:08.3133960Z >>> 2025-08-26T20:22:08.3134058Z >>> # or equivalently 2025-08-26T20:22:08.3134332Z >>> context_fn = functools.partial(create_selective_checkpoint_contexts, ops_to_save) 2025-08-26T20:22:08.3134425Z >>> 2025-08-26T20:22:08.3134519Z >>> def fn(x, y): 2025-08-26T20:22:08.3134756Z >>> return torch.sigmoid(torch.matmul(torch.matmul(x, y), y)) * y 2025-08-26T20:22:08.3134840Z >>> 2025-08-26T20:22:08.3134982Z >>> out = torch.utils.checkpoint.checkpoint( 2025-08-26T20:22:08.3135084Z >>> fn, x, y, 2025-08-26T20:22:08.3135186Z >>> use_reentrant=False, 2025-08-26T20:22:08.3135297Z >>> context_fn=context_fn, 2025-08-26T20:22:08.3135380Z >>> ) 2025-08-26T20:22:08.3135458Z 2025-08-26T20:22:08.3135717Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3135795Z 2025-08-26T20:22:08.3135888Z warnings.warn(msg) 2025-08-26T20:22:08.3135977Z 2025-08-26T20:22:08.3136171Z --- Parse Warning: 132 / 146 --- 2025-08-26T20:22:08.3137087Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CppExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1159. 2025-08-26T20:22:08.3137353Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3137446Z 2025-08-26T20:22:08.3137592Z Create a :class:`setuptools.Extension` for C++. 2025-08-26T20:22:08.3137674Z 2025-08-26T20:22:08.3137924Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-26T20:22:08.3138143Z bare minimum (but often sufficient) arguments to build a C++ extension. 2025-08-26T20:22:08.3138223Z 2025-08-26T20:22:08.3138435Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-26T20:22:08.3138585Z constructor. Full list arguments can be found at 2025-08-26T20:22:08.3138917Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-08-26T20:22:08.3138996Z 2025-08-26T20:22:08.3139086Z .. warning:: 2025-08-26T20:22:08.3139323Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-26T20:22:08.3139564Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-26T20:22:08.3139770Z the user's responsibility in their library to not use APIs from 2025-08-26T20:22:08.3140026Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-26T20:22:08.3140245Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-26T20:22:08.3140550Z example, to give access to custom ops from python, the library should 2025-08-26T20:22:08.3140685Z register the ops through the dispatcher. 2025-08-26T20:22:08.3140770Z 2025-08-26T20:22:08.3140990Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-26T20:22:08.3141202Z as a compile flag when py_limited_api is specified as an option for 2025-08-26T20:22:08.3141408Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-26T20:22:08.3141618Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-26T20:22:08.3141878Z safety, and sanity in order to encourage best practices. To target a 2025-08-26T20:22:08.3142093Z different version, set min_supported_cpython to the hexcode of the 2025-08-26T20:22:08.3142209Z CPython version of choice. 2025-08-26T20:22:08.3142290Z 2025-08-26T20:22:08.3142372Z Example: 2025-08-26T20:22:08.3142482Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3142629Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:08.3142737Z >>> from setuptools import setup 2025-08-26T20:22:08.3142964Z >>> from torch.utils.cpp_extension import BuildExtension, CppExtension 2025-08-26T20:22:08.3143047Z >>> setup( 2025-08-26T20:22:08.3143161Z ... name='extension', 2025-08-26T20:22:08.3143253Z ... ext_modules=[ 2025-08-26T20:22:08.3143344Z ... CppExtension( 2025-08-26T20:22:08.3143459Z ... name='extension', 2025-08-26T20:22:08.3143611Z ... sources=['extension.cpp'], 2025-08-26T20:22:08.3143743Z ... extra_compile_args=['-g'], 2025-08-26T20:22:08.3143896Z ... extra_link_args=['-Wl,--no-as-needed', '-lm']) 2025-08-26T20:22:08.3143973Z ... ], 2025-08-26T20:22:08.3144072Z ... cmdclass={ 2025-08-26T20:22:08.3144184Z ... 'build_ext': BuildExtension 2025-08-26T20:22:08.3144279Z ... }) 2025-08-26T20:22:08.3144357Z 2025-08-26T20:22:08.3144608Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3144698Z 2025-08-26T20:22:08.3144793Z warnings.warn(msg) 2025-08-26T20:22:08.3144878Z 2025-08-26T20:22:08.3145074Z --- Parse Warning: 133 / 146 --- 2025-08-26T20:22:08.3145977Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=CUDAExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1229. 2025-08-26T20:22:08.3146252Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3146331Z 2025-08-26T20:22:08.3146497Z Create a :class:`setuptools.Extension` for CUDA/C++. 2025-08-26T20:22:08.3146575Z 2025-08-26T20:22:08.3146813Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-26T20:22:08.3147023Z bare minimum (but often sufficient) arguments to build a CUDA/C++ 2025-08-26T20:22:08.3147252Z extension. This includes the CUDA include path, library path and runtime 2025-08-26T20:22:08.3147348Z library. 2025-08-26T20:22:08.3147429Z 2025-08-26T20:22:08.3147634Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-26T20:22:08.3147795Z constructor. Full list arguments can be found at 2025-08-26T20:22:08.3148115Z https://setuptools.pypa.io/en/latest/userguide/ext_modules.html#extension-api-reference 2025-08-26T20:22:08.3148206Z 2025-08-26T20:22:08.3148325Z .. warning:: 2025-08-26T20:22:08.3148552Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-26T20:22:08.3148809Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-26T20:22:08.3149002Z the user's responsibility in their library to not use APIs from 2025-08-26T20:22:08.3149242Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-26T20:22:08.3149456Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-26T20:22:08.3149663Z example, to give access to custom ops from python, the library should 2025-08-26T20:22:08.3149801Z register the ops through the dispatcher. 2025-08-26T20:22:08.3149878Z 2025-08-26T20:22:08.3150107Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-26T20:22:08.3150308Z as a compile flag when py_limited_api is specified as an option for 2025-08-26T20:22:08.3150542Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-26T20:22:08.3150772Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-26T20:22:08.3150984Z safety, and sanity in order to encourage best practices. To target a 2025-08-26T20:22:08.3151209Z different version, set min_supported_cpython to the hexcode of the 2025-08-26T20:22:08.3151313Z CPython version of choice. 2025-08-26T20:22:08.3151386Z 2025-08-26T20:22:08.3151482Z Example: 2025-08-26T20:22:08.3151579Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3151737Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:08.3151847Z >>> from setuptools import setup 2025-08-26T20:22:08.3152065Z >>> from torch.utils.cpp_extension import BuildExtension, CUDAExtension 2025-08-26T20:22:08.3152159Z >>> setup( 2025-08-26T20:22:08.3152259Z ... name='cuda_extension', 2025-08-26T20:22:08.3152394Z ... ext_modules=[ 2025-08-26T20:22:08.3152493Z ... CUDAExtension( 2025-08-26T20:22:08.3152605Z ... name='cuda_extension', 2025-08-26T20:22:08.3152780Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-26T20:22:08.3152906Z ... extra_compile_args={'cxx': ['-g'], 2025-08-26T20:22:08.3153034Z ... 'nvcc': ['-O2']}, 2025-08-26T20:22:08.3153187Z ... extra_link_args=['-Wl,--no-as-needed', '-lcuda']) 2025-08-26T20:22:08.3153265Z ... ], 2025-08-26T20:22:08.3153367Z ... cmdclass={ 2025-08-26T20:22:08.3153478Z ... 'build_ext': BuildExtension 2025-08-26T20:22:08.3153557Z ... }) 2025-08-26T20:22:08.3153648Z 2025-08-26T20:22:08.3153744Z Compute capabilities: 2025-08-26T20:22:08.3153838Z 2025-08-26T20:22:08.3154136Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-08-26T20:22:08.3154431Z building process of the extension, plus PTX. If down the road a new card is installed the 2025-08-26T20:22:08.3154737Z extension may need to be recompiled. If a visible card has a compute capability (CC) that's 2025-08-26T20:22:08.3155034Z newer than the newest version for which your nvcc can build fully-compiled binaries, PyTorch 2025-08-26T20:22:08.3155330Z will make nvcc fall back to building kernels with the newest version of PTX your nvcc does 2025-08-26T20:22:08.3155448Z support (see below for details on PTX). 2025-08-26T20:22:08.3155524Z 2025-08-26T20:22:08.3155846Z You can override the default behavior using `TORCH_CUDA_ARCH_LIST` to explicitly specify which 2025-08-26T20:22:08.3155973Z CCs you want the extension to support: 2025-08-26T20:22:08.3156063Z 2025-08-26T20:22:08.3156247Z ``TORCH_CUDA_ARCH_LIST="6.1 8.6" python build_my_extension.py`` 2025-08-26T20:22:08.3156507Z ``TORCH_CUDA_ARCH_LIST="5.2 6.0 6.1 7.0 7.5 8.0 8.6+PTX" python build_my_extension.py`` 2025-08-26T20:22:08.3156605Z 2025-08-26T20:22:08.3156921Z The +PTX option causes extension kernel binaries to include PTX instructions for the specified 2025-08-26T20:22:08.3157275Z CC. PTX is an intermediate representation that allows kernels to runtime-compile for any CC >= 2025-08-26T20:22:08.3157574Z the specified CC (for example, 8.6+PTX generates PTX that can runtime-compile for any GPU with 2025-08-26T20:22:08.3157877Z CC >= 8.6). This improves your binary's forward compatibility. However, relying on older PTX to 2025-08-26T20:22:08.3158198Z provide forward compat by runtime-compiling for newer CCs can modestly reduce performance on 2025-08-26T20:22:08.3158468Z those newer CCs. If you know exact CC(s) of the GPUs you want to target, you're always better 2025-08-26T20:22:08.3158802Z off specifying them individually. For example, if you want your extension to run on 8.0 and 8.6, 2025-08-26T20:22:08.3159139Z "8.0+PTX" would work functionally because it includes PTX that can runtime-compile for 8.6, but 2025-08-26T20:22:08.3159243Z "8.0 8.6" would be better. 2025-08-26T20:22:08.3159324Z 2025-08-26T20:22:08.3159621Z Note that while it's possible to include all supported archs, the more archs get included the 2025-08-26T20:22:08.3159923Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-08-26T20:22:08.3160000Z 2025-08-26T20:22:08.3160340Z Note that CUDA-11.5 nvcc will hit internal compiler error while parsing torch/extension.h on Windows. 2025-08-26T20:22:08.3160551Z To workaround the issue, move python binding logic to pure C++ file. 2025-08-26T20:22:08.3160630Z 2025-08-26T20:22:08.3160727Z Example use: 2025-08-26T20:22:08.3160822Z #include 2025-08-26T20:22:08.3160988Z at::Tensor SigmoidAlphaBlendForwardCuda(....) 2025-08-26T20:22:08.3161094Z 2025-08-26T20:22:08.3161175Z Instead of: 2025-08-26T20:22:08.3161295Z #include 2025-08-26T20:22:08.3161454Z torch::Tensor SigmoidAlphaBlendForwardCuda(...) 2025-08-26T20:22:08.3161543Z 2025-08-26T20:22:08.3161816Z Currently open issue for nvcc bug: https://github.com/pytorch/pytorch/issues/69460 2025-08-26T20:22:08.3162326Z Complete workaround code example: https://github.com/facebookresearch/pytorch3d/commit/cb170ac024a949f1f9614ffe6af1c38d972f7d48 2025-08-26T20:22:08.3162416Z 2025-08-26T20:22:08.3162523Z Relocatable device code linking: 2025-08-26T20:22:08.3162611Z 2025-08-26T20:22:08.3162890Z If you want to reference device symbols across compilation units (across object files), 2025-08-26T20:22:08.3163140Z the object files need to be built with `relocatable device code` (-rdc=true or -dc). 2025-08-26T20:22:08.3163509Z An exception to this rule is "dynamic parallelism" (nested kernel launches) which is not used a lot anymore. 2025-08-26T20:22:08.3163840Z `Relocatable device code` is less optimized so it needs to be used only on object files that need it. 2025-08-26T20:22:08.3164169Z Using `-dlto` (Device Link Time Optimization) at the device code compilation step and `dlink` step 2025-08-26T20:22:08.3164353Z helps reduce the protentional perf degradation of `-rdc`. 2025-08-26T20:22:08.3164518Z Note that it needs to be used at both steps to be useful. 2025-08-26T20:22:08.3164610Z 2025-08-26T20:22:08.3164979Z If you have `rdc` objects you need to have an extra `-dlink` (device linking) step before the CPU symbol linking step. 2025-08-26T20:22:08.3165165Z There is also a case where `-dlink` is used without `-rdc`: 2025-08-26T20:22:08.3165421Z when an extension is linked against a static lib containing rdc-compiled objects 2025-08-26T20:22:08.3165633Z like the [NVSHMEM library](https://developer.nvidia.com/nvshmem). 2025-08-26T20:22:08.3165728Z 2025-08-26T20:22:08.3165932Z Note: Ninja is required to build a CUDA Extension with RDC linking. 2025-08-26T20:22:08.3166028Z 2025-08-26T20:22:08.3166143Z Example: 2025-08-26T20:22:08.3166242Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3166405Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:08.3166530Z >>> CUDAExtension( 2025-08-26T20:22:08.3166649Z ... name='cuda_extension', 2025-08-26T20:22:08.3166809Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-26T20:22:08.3166906Z ... dlink=True, 2025-08-26T20:22:08.3167041Z ... dlink_libraries=["dlink_lib"], 2025-08-26T20:22:08.3167167Z ... extra_compile_args={'cxx': ['-g'], 2025-08-26T20:22:08.3167310Z ... 'nvcc': ['-O2', '-rdc=true']}) 2025-08-26T20:22:08.3167392Z 2025-08-26T20:22:08.3167645Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3167741Z 2025-08-26T20:22:08.3167843Z warnings.warn(msg) 2025-08-26T20:22:08.3167939Z 2025-08-26T20:22:08.3168174Z --- Parse Warning: 134 / 146 --- 2025-08-26T20:22:08.3169090Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=SyclExtension in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1420. 2025-08-26T20:22:08.3169369Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3169451Z 2025-08-26T20:22:08.3169631Z Creates a :class:`setuptools.Extension` for SYCL/C++. 2025-08-26T20:22:08.3169712Z 2025-08-26T20:22:08.3169954Z Convenience method that creates a :class:`setuptools.Extension` with the 2025-08-26T20:22:08.3170169Z bare minimum (but often sufficient) arguments to build a SYCL/C++ 2025-08-26T20:22:08.3170258Z extension. 2025-08-26T20:22:08.3170351Z 2025-08-26T20:22:08.3170554Z All arguments are forwarded to the :class:`setuptools.Extension` 2025-08-26T20:22:08.3170673Z constructor. 2025-08-26T20:22:08.3170768Z 2025-08-26T20:22:08.3170861Z .. warning:: 2025-08-26T20:22:08.3171089Z The PyTorch python API (as provided in libtorch_python) cannot be built 2025-08-26T20:22:08.3171315Z with the flag ``py_limited_api=True``. When this flag is passed, it is 2025-08-26T20:22:08.3171513Z the user's responsibility in their library to not use APIs from 2025-08-26T20:22:08.3171756Z libtorch_python (in particular pytorch/python bindings) and to only use 2025-08-26T20:22:08.3171972Z APIs from libtorch (aten objects, operators and the dispatcher). For 2025-08-26T20:22:08.3172195Z example, to give access to custom ops from python, the library should 2025-08-26T20:22:08.3172325Z register the ops through the dispatcher. 2025-08-26T20:22:08.3172404Z 2025-08-26T20:22:08.3172641Z Contrary to CPython setuptools, who does not define -DPy_LIMITED_API 2025-08-26T20:22:08.3172847Z as a compile flag when py_limited_api is specified as an option for 2025-08-26T20:22:08.3173066Z the "bdist_wheel" command in ``setup``, PyTorch does! We will specify 2025-08-26T20:22:08.3173280Z -DPy_LIMITED_API=min_supported_cpython to best enforce consistency, 2025-08-26T20:22:08.3173490Z safety, and sanity in order to encourage best practices. To target a 2025-08-26T20:22:08.3173716Z different version, set min_supported_cpython to the hexcode of the 2025-08-26T20:22:08.3173821Z CPython version of choice. 2025-08-26T20:22:08.3173911Z 2025-08-26T20:22:08.3173995Z Example: 2025-08-26T20:22:08.3174092Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3174254Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:08.3174476Z >>> from torch.utils.cpp_extension import BuildExtension, SyclExtension 2025-08-26T20:22:08.3174572Z >>> setup( 2025-08-26T20:22:08.3174676Z ... name='xpu_extension', 2025-08-26T20:22:08.3174771Z ... ext_modules=[ 2025-08-26T20:22:08.3174906Z ... SyclExtension( 2025-08-26T20:22:08.3175023Z ... name='xpu_extension', 2025-08-26T20:22:08.3175218Z ... sources=['extension.cpp', 'extension_kernel.cpp'], 2025-08-26T20:22:08.3175411Z ... extra_compile_args={'cxx': ['-g', '-std=c++20', '-fPIC']}) 2025-08-26T20:22:08.3175493Z ... ], 2025-08-26T20:22:08.3175599Z ... cmdclass={ 2025-08-26T20:22:08.3175713Z ... 'build_ext': BuildExtension 2025-08-26T20:22:08.3175794Z ... }) 2025-08-26T20:22:08.3175886Z 2025-08-26T20:22:08.3176188Z By default the extension will be compiled to run on all archs of the cards visible during the 2025-08-26T20:22:08.3176456Z building process of the extension. If down the road a new card is installed the 2025-08-26T20:22:08.3176709Z extension may need to be recompiled. You can override the default behavior using 2025-08-26T20:22:08.3177048Z `TORCH_XPU_ARCH_LIST` to explicitly specify which device architectures you want the extension 2025-08-26T20:22:08.3177150Z to support: 2025-08-26T20:22:08.3177231Z 2025-08-26T20:22:08.3177443Z ``TORCH_XPU_ARCH_LIST="pvc,xe-lpg" python build_my_extension.py`` 2025-08-26T20:22:08.3177523Z 2025-08-26T20:22:08.3177821Z Note that while it's possible to include all supported archs, the more archs get included the 2025-08-26T20:22:08.3178124Z slower the building process will be, as it will build a separate kernel image for each arch. 2025-08-26T20:22:08.3178205Z 2025-08-26T20:22:08.3178356Z Note: Ninja is required to build SyclExtension. 2025-08-26T20:22:08.3178436Z 2025-08-26T20:22:08.3178684Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3178775Z 2025-08-26T20:22:08.3178872Z warnings.warn(msg) 2025-08-26T20:22:08.3178965Z 2025-08-26T20:22:08.3179190Z --- Parse Warning: 135 / 146 --- 2025-08-26T20:22:08.3180058Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1597. 2025-08-26T20:22:08.3180336Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3180492Z 2025-08-26T20:22:08.3180656Z Load a PyTorch C++ extension just-in-time (JIT). 2025-08-26T20:22:08.3180739Z 2025-08-26T20:22:08.3180947Z To load an extension, a Ninja build file is emitted, which is used to 2025-08-26T20:22:08.3181169Z compile the given sources into a dynamic library. This library is 2025-08-26T20:22:08.3181392Z subsequently loaded into the current Python process as a module and 2025-08-26T20:22:08.3181537Z returned from this function, ready for use. 2025-08-26T20:22:08.3181620Z 2025-08-26T20:22:08.3181829Z By default, the directory to which the build file is emitted and the 2025-08-26T20:22:08.3182088Z resulting library compiled to is ``/torch_extensions/``, where 2025-08-26T20:22:08.3182294Z ```` is the temporary folder on the current platform and ```` 2025-08-26T20:22:08.3182525Z the name of the extension. This location can be overridden in two ways. 2025-08-26T20:22:08.3182734Z First, if the ``TORCH_EXTENSIONS_DIR`` environment variable is set, it 2025-08-26T20:22:08.3182951Z replaces ``/torch_extensions`` and all extensions will be compiled 2025-08-26T20:22:08.3183181Z into subfolders of this directory. Second, if the ``build_directory`` 2025-08-26T20:22:08.3183410Z argument to this function is supplied, it overrides the entire path, i.e. 2025-08-26T20:22:08.3183587Z the library will be compiled into that folder directly. 2025-08-26T20:22:08.3183668Z 2025-08-26T20:22:08.3183878Z To compile the sources, the default system compiler (``c++``) is used, 2025-08-26T20:22:08.3184178Z which can be overridden by setting the ``CXX`` environment variable. To pass 2025-08-26T20:22:08.3184406Z additional arguments to the compilation process, ``extra_cflags`` or 2025-08-26T20:22:08.3184665Z ``extra_ldflags`` can be provided. For example, to compile your extension 2025-08-26T20:22:08.3184875Z with optimizations, pass ``extra_cflags=['-O3']``. You can also use 2025-08-26T20:22:08.3185046Z ``extra_cflags`` to pass further include directories. 2025-08-26T20:22:08.3185131Z 2025-08-26T20:22:08.3185366Z CUDA support with mixed compilation is provided. Simply pass CUDA source 2025-08-26T20:22:08.3185565Z files (``.cu`` or ``.cuh``) along with other sources. Such files will be 2025-08-26T20:22:08.3185805Z detected and compiled with nvcc rather than the C++ compiler. This includes 2025-08-26T20:22:08.3186032Z passing the CUDA lib64 directory as a library directory, and linking 2025-08-26T20:22:08.3186183Z ``cudart``. You can pass additional flags to nvcc via 2025-08-26T20:22:08.3186418Z ``extra_cuda_cflags``, just like with ``extra_cflags`` for C++. Various 2025-08-26T20:22:08.3186665Z heuristics for finding the CUDA install directory are used, which usually 2025-08-26T20:22:08.3186878Z work fine. If not, setting the ``CUDA_HOME`` environment variable is the 2025-08-26T20:22:08.3186980Z safest option. 2025-08-26T20:22:08.3187060Z 2025-08-26T20:22:08.3187292Z SYCL support with mixed compilation is provided. Simply pass SYCL source 2025-08-26T20:22:08.3187504Z files (``.sycl``) along with other sources. Such files will be detected 2025-08-26T20:22:08.3187720Z and compiled with SYCL compiler (such as Intel DPC++ Compiler) rather 2025-08-26T20:22:08.3187938Z than the C++ compiler. You can pass additional flags to SYCL compiler 2025-08-26T20:22:08.3188127Z via ``extra_sycl_cflags``, just like with ``extra_cflags`` for C++. 2025-08-26T20:22:08.3188333Z SYCL compiler is expected to be found via system PATH environment 2025-08-26T20:22:08.3188457Z variable. 2025-08-26T20:22:08.3188537Z 2025-08-26T20:22:08.3188634Z Args: 2025-08-26T20:22:08.3188841Z name: The name of the extension to build. This MUST be the same as the 2025-08-26T20:22:08.3188952Z name of the pybind11 module! 2025-08-26T20:22:08.3189164Z sources: A list of relative or absolute paths to C++ source files. 2025-08-26T20:22:08.3189388Z extra_cflags: optional list of compiler flags to forward to the build. 2025-08-26T20:22:08.3189615Z extra_cuda_cflags: optional list of compiler flags to forward to nvcc 2025-08-26T20:22:08.3189720Z when building CUDA sources. 2025-08-26T20:22:08.3189936Z extra_sycl_cflags: optional list of compiler flags to forward to SYCL 2025-08-26T20:22:08.3190067Z compiler when building SYCL sources. 2025-08-26T20:22:08.3190285Z extra_ldflags: optional list of linker flags to forward to the build. 2025-08-26T20:22:08.3190513Z extra_include_paths: optional list of include directories to forward 2025-08-26T20:22:08.3190609Z to the build. 2025-08-26T20:22:08.3190786Z build_directory: optional path to use as build workspace. 2025-08-26T20:22:08.3190978Z verbose: If ``True``, turns on verbose logging of load steps. 2025-08-26T20:22:08.3191198Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-08-26T20:22:08.3191371Z the build. If set to ``None`` (default), this value is 2025-08-26T20:22:08.3191573Z automatically determined based on the existence of ``.cu`` or 2025-08-26T20:22:08.3191914Z ``.cuh`` in ``sources``. Set it to `True`` to force CUDA headers 2025-08-26T20:22:08.3192041Z and libraries to be included. 2025-08-26T20:22:08.3192259Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-08-26T20:22:08.3192428Z the build. If set to ``None`` (default), this value is 2025-08-26T20:22:08.3192641Z automatically determined based on the existence of ``.sycl`` in 2025-08-26T20:22:08.3192877Z ``sources``. Set it to `True`` to force SYCL headers and 2025-08-26T20:22:08.3193004Z libraries to be included. 2025-08-26T20:22:08.3193244Z is_python_module: If ``True`` (default), imports the produced shared 2025-08-26T20:22:08.3193445Z library as a Python module. If ``False``, behavior depends on 2025-08-26T20:22:08.3193545Z ``is_standalone``. 2025-08-26T20:22:08.3193749Z is_standalone: If ``False`` (default) loads the constructed extension 2025-08-26T20:22:08.3193960Z into the process as a plain dynamic library. If ``True``, build a 2025-08-26T20:22:08.3194065Z standalone executable. 2025-08-26T20:22:08.3194162Z 2025-08-26T20:22:08.3194249Z Returns: 2025-08-26T20:22:08.3194364Z If ``is_python_module`` is ``True``: 2025-08-26T20:22:08.3194556Z Returns the loaded PyTorch extension as a Python module. 2025-08-26T20:22:08.3194639Z 2025-08-26T20:22:08.3194891Z If ``is_python_module`` is ``False`` and ``is_standalone`` is ``False``: 2025-08-26T20:22:08.3195104Z Returns nothing. (The shared library is loaded into the process as 2025-08-26T20:22:08.3195201Z a side effect.) 2025-08-26T20:22:08.3195293Z 2025-08-26T20:22:08.3195400Z If ``is_standalone`` is ``True``. 2025-08-26T20:22:08.3195613Z Return the path to the executable. (On Windows, TORCH_LIB_PATH is 2025-08-26T20:22:08.3195787Z added to the PATH environment variable as a side effect.) 2025-08-26T20:22:08.3195866Z 2025-08-26T20:22:08.3195964Z Example: 2025-08-26T20:22:08.3196063Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3196214Z >>> from torch.utils.cpp_extension import load 2025-08-26T20:22:08.3196308Z >>> module = load( 2025-08-26T20:22:08.3196407Z ... name='extension', 2025-08-26T20:22:08.3196580Z ... sources=['extension.cpp', 'extension_kernel.cu'], 2025-08-26T20:22:08.3196722Z ... extra_cflags=['-O2'], 2025-08-26T20:22:08.3196819Z ... verbose=True) 2025-08-26T20:22:08.3196910Z 2025-08-26T20:22:08.3197174Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3197266Z 2025-08-26T20:22:08.3197363Z warnings.warn(msg) 2025-08-26T20:22:08.3197445Z 2025-08-26T20:22:08.3197673Z --- Parse Warning: 136 / 146 --- 2025-08-26T20:22:08.3198567Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=load_inline in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/cpp_extension.py line=1882. 2025-08-26T20:22:08.3198843Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3198925Z 2025-08-26T20:22:08.3199135Z Load a PyTorch C++ extension just-in-time (JIT) from string sources. 2025-08-26T20:22:08.3199232Z 2025-08-26T20:22:08.3199465Z This function behaves exactly like :func:`load`, but takes its sources as 2025-08-26T20:22:08.3199716Z strings rather than filenames. These strings are stored to files in the 2025-08-26T20:22:08.3199929Z build directory, after which the behavior of :func:`load_inline` is 2025-08-26T20:22:08.3200033Z identical to :func:`load`. 2025-08-26T20:22:08.3200129Z 2025-08-26T20:22:08.3200213Z See `the 2025-08-26T20:22:08.3200553Z tests `_ 2025-08-26T20:22:08.3200682Z for good examples of using this function. 2025-08-26T20:22:08.3200765Z 2025-08-26T20:22:08.3201017Z Sources may omit two required parts of a typical non-inline C++ extension: 2025-08-26T20:22:08.3201259Z the necessary header includes, as well as the (pybind11) binding code. More 2025-08-26T20:22:08.3201512Z precisely, strings passed to ``cpp_sources`` are first concatenated into a 2025-08-26T20:22:08.3201729Z single ``.cpp`` file. This file is then prepended with ``#include 2025-08-26T20:22:08.3201835Z `` 2025-08-26T20:22:08.3201928Z 2025-08-26T20:22:08.3202182Z Furthermore, if the ``functions`` argument is supplied, bindings will be 2025-08-26T20:22:08.3202425Z automatically generated for each function specified. ``functions`` can 2025-08-26T20:22:08.3202645Z either be a list of function names, or a dictionary mapping from function 2025-08-26T20:22:08.3202872Z names to docstrings. If a list is given, the name of each function is used 2025-08-26T20:22:08.3202975Z as its docstring. 2025-08-26T20:22:08.3203052Z 2025-08-26T20:22:08.3203273Z The sources in ``cuda_sources`` are concatenated into a separate ``.cu`` 2025-08-26T20:22:08.3203448Z file and prepended with ``torch/types.h``, ``cuda.h`` and 2025-08-26T20:22:08.3203652Z ``cuda_runtime.h`` includes. The ``.cpp`` and ``.cu`` files are compiled 2025-08-26T20:22:08.3203889Z separately, but ultimately linked into a single library. Note that no 2025-08-26T20:22:08.3204143Z bindings are generated for functions in ``cuda_sources`` per se. To bind 2025-08-26T20:22:08.3204377Z to a CUDA kernel, you must create a C++ function that calls it, and either 2025-08-26T20:22:08.3204589Z declare or define this C++ function in one of the ``cpp_sources`` (and 2025-08-26T20:22:08.3204700Z include its name in ``functions``). 2025-08-26T20:22:08.3204789Z 2025-08-26T20:22:08.3205005Z The sources in ``sycl_sources`` are concatenated into a separate ``.sycl`` 2025-08-26T20:22:08.3205231Z file and prepended with ``torch/types.h``, ``sycl/sycl.hpp`` includes. 2025-08-26T20:22:08.3205425Z The ``.cpp`` and ``.sycl`` files are compiled separately, but ultimately 2025-08-26T20:22:08.3205638Z linked into a single library. Note that no bindings are generated for 2025-08-26T20:22:08.3205859Z functions in ``sycl_sources`` per se. To bind to a SYCL kernel, you must 2025-08-26T20:22:08.3206128Z create a C++ function that calls it, and either declare or define this 2025-08-26T20:22:08.3206332Z C++ function in one of the ``cpp_sources`` (and include its name 2025-08-26T20:22:08.3206425Z in ``functions``). 2025-08-26T20:22:08.3206503Z 2025-08-26T20:22:08.3206592Z 2025-08-26T20:22:08.3206669Z 2025-08-26T20:22:08.3206865Z See :func:`load` for a description of arguments omitted below. 2025-08-26T20:22:08.3206943Z 2025-08-26T20:22:08.3207025Z Args: 2025-08-26T20:22:08.3207250Z cpp_sources: A string, or list of strings, containing C++ source code. 2025-08-26T20:22:08.3207473Z cuda_sources: A string, or list of strings, containing CUDA source code. 2025-08-26T20:22:08.3207704Z sycl_sources: A string, or list of strings, containing SYCL source code. 2025-08-26T20:22:08.3207906Z functions: A list of function names for which to generate function 2025-08-26T20:22:08.3208118Z bindings. If a dictionary is given, it should map function names to 2025-08-26T20:22:08.3208318Z docstrings (which are otherwise just the function names). 2025-08-26T20:22:08.3208539Z with_cuda: Determines whether CUDA headers and libraries are added to 2025-08-26T20:22:08.3208710Z the build. If set to ``None`` (default), this value is 2025-08-26T20:22:08.3208914Z automatically determined based on whether ``cuda_sources`` is 2025-08-26T20:22:08.3209065Z provided. Set it to ``True`` to force CUDA headers 2025-08-26T20:22:08.3209184Z and libraries to be included. 2025-08-26T20:22:08.3209402Z with_sycl: Determines whether SYCL headers and libraries are added to 2025-08-26T20:22:08.3209568Z the build. If set to ``None`` (default), this value is 2025-08-26T20:22:08.3209771Z automatically determined based on whether ``sycl_sources`` is 2025-08-26T20:22:08.3209920Z provided. Set it to ``True`` to force SYCL headers 2025-08-26T20:22:08.3210046Z and libraries to be included. 2025-08-26T20:22:08.3210278Z with_pytorch_error_handling: Determines whether pytorch error and 2025-08-26T20:22:08.3210492Z warning macros are handled by pytorch instead of pybind. To do 2025-08-26T20:22:08.3210730Z this, each function ``foo`` is called via an intermediary ``_safe_foo`` 2025-08-26T20:22:08.3210940Z function. This redirection might cause issues in obscure cases 2025-08-26T20:22:08.3211123Z of cpp. This flag should be set to ``False`` when this redirect 2025-08-26T20:22:08.3211218Z causes issues. 2025-08-26T20:22:08.3211478Z no_implicit_headers: If ``True``, skips automatically adding headers, most notably 2025-08-26T20:22:08.3211703Z ``#include `` and ``#include `` lines. 2025-08-26T20:22:08.3211873Z Use this option to improve cold start times when you 2025-08-26T20:22:08.3212132Z already include the necessary headers in your source code. Default: ``False``. 2025-08-26T20:22:08.3212240Z 2025-08-26T20:22:08.3212337Z Example: 2025-08-26T20:22:08.3212486Z >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CPP_EXT) 2025-08-26T20:22:08.3212655Z >>> from torch.utils.cpp_extension import load_inline 2025-08-26T20:22:08.3212747Z >>> source = """ 2025-08-26T20:22:08.3212896Z at::Tensor sin_add(at::Tensor x, at::Tensor y) { 2025-08-26T20:22:08.3213008Z return x.sin() + y.sin(); 2025-08-26T20:22:08.3213090Z } 2025-08-26T20:22:08.3213170Z """ 2025-08-26T20:22:08.3213319Z >>> module = load_inline(name='inline_extension', 2025-08-26T20:22:08.3213438Z ... cpp_sources=[source], 2025-08-26T20:22:08.3213569Z ... functions=['sin_add']) 2025-08-26T20:22:08.3213648Z 2025-08-26T20:22:08.3213733Z .. note:: 2025-08-26T20:22:08.3213982Z Since load_inline will just-in-time compile the source code, please ensure 2025-08-26T20:22:08.3214252Z that you have the right toolchains installed in the runtime. For example, 2025-08-26T20:22:08.3214481Z when loading C++, make sure a C++ compiler is available. If you're loading 2025-08-26T20:22:08.3214725Z a CUDA extension, you will need to additionally install the corresponding CUDA 2025-08-26T20:22:08.3214983Z toolkit (nvcc and any other dependencies your code has). Compiling toolchains 2025-08-26T20:22:08.3215219Z are not included when you install torch and must be additionally installed. 2025-08-26T20:22:08.3215298Z 2025-08-26T20:22:08.3215562Z During compiling, by default, the Ninja backend uses #CPUS + 2 workers to build 2025-08-26T20:22:08.3215777Z the extension. This may use up too many resources on some systems. One 2025-08-26T20:22:08.3216007Z can control the number of workers by setting the `MAX_JOBS` environment 2025-08-26T20:22:08.3216126Z variable to a non-negative number. 2025-08-26T20:22:08.3216205Z 2025-08-26T20:22:08.3216470Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3216549Z 2025-08-26T20:22:08.3216646Z warnings.warn(msg) 2025-08-26T20:22:08.3216739Z 2025-08-26T20:22:08.3216943Z --- Parse Warning: 137 / 146 --- 2025-08-26T20:22:08.3217928Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ThroughputBenchmark in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/throughput_benchmark.py line=61. 2025-08-26T20:22:08.3218188Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3218281Z 2025-08-26T20:22:08.3218573Z This class is a wrapper around a c++ component throughput_benchmark::ThroughputBenchmark. 2025-08-26T20:22:08.3218652Z 2025-08-26T20:22:08.3218959Z This wrapper on the throughput_benchmark::ThroughputBenchmark component is responsible 2025-08-26T20:22:08.3219238Z for executing a PyTorch module (nn.Module or ScriptModule) under an inference 2025-08-26T20:22:08.3219480Z server like load. It can emulate multiple calling threads to a single module 2025-08-26T20:22:08.3219746Z provided. In the future we plan to enhance this component to support inter and 2025-08-26T20:22:08.3219987Z intra-op parallelism as well as multiple models running in a single process. 2025-08-26T20:22:08.3220078Z 2025-08-26T20:22:08.3220328Z Please note that even though nn.Module is supported, it might incur an overhead 2025-08-26T20:22:08.3220644Z from the need to hold GIL every time we execute Python code or pass around 2025-08-26T20:22:08.3220888Z inputs as Python objects. As soon as you have a ScriptModule version of your 2025-08-26T20:22:08.3221124Z model for inference deployment it is better to switch to using it in this 2025-08-26T20:22:08.3221226Z benchmark. 2025-08-26T20:22:08.3221308Z 2025-08-26T20:22:08.3221410Z Example:: 2025-08-26T20:22:08.3221492Z 2025-08-26T20:22:08.3221642Z >>> # xdoctest: +SKIP("undefined vars") 2025-08-26T20:22:08.3221797Z >>> from torch.utils import ThroughputBenchmark 2025-08-26T20:22:08.3221928Z >>> bench = ThroughputBenchmark(my_module) 2025-08-26T20:22:08.3222099Z >>> # Pre-populate benchmark's data set with the inputs 2025-08-26T20:22:08.3222198Z >>> for input in inputs: 2025-08-26T20:22:08.3222414Z ... # Both args and kwargs work, same as any PyTorch Module / ScriptModule 2025-08-26T20:22:08.3222557Z ... bench.add_input(input[0], x2=input[1]) 2025-08-26T20:22:08.3222750Z >>> # Inputs supplied above are randomly used during the execution 2025-08-26T20:22:08.3222867Z >>> stats = bench.benchmark( 2025-08-26T20:22:08.3222971Z ... num_calling_threads=4, 2025-08-26T20:22:08.3223073Z ... num_warmup_iters = 100, 2025-08-26T20:22:08.3223206Z ... num_iters = 1000, 2025-08-26T20:22:08.3223290Z ... ) 2025-08-26T20:22:08.3223483Z >>> print("Avg latency (ms): {}".format(stats.latency_avg_ms)) 2025-08-26T20:22:08.3223655Z >>> print("Number of iterations: {}".format(stats.num_iters)) 2025-08-26T20:22:08.3223739Z 2025-08-26T20:22:08.3223999Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3224076Z 2025-08-26T20:22:08.3224186Z warnings.warn(msg) 2025-08-26T20:22:08.3224265Z 2025-08-26T20:22:08.3224466Z --- Parse Warning: 138 / 146 --- 2025-08-26T20:22:08.3225418Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DistributedSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/distributed.py line=18. 2025-08-26T20:22:08.3225681Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3225896Z Sampler that restricts data loading to a subset of the dataset. 2025-08-26T20:22:08.3225977Z 2025-08-26T20:22:08.3226112Z It is especially useful in conjunction with 2025-08-26T20:22:08.3226375Z :class:`torch.nn.parallel.DistributedDataParallel`. In such a case, each 2025-08-26T20:22:08.3226634Z process can pass a :class:`~torch.utils.data.DistributedSampler` instance as a 2025-08-26T20:22:08.3226876Z :class:`~torch.utils.data.DataLoader` sampler, and load a subset of the 2025-08-26T20:22:08.3227008Z original dataset that is exclusive to it. 2025-08-26T20:22:08.3227087Z 2025-08-26T20:22:08.3227187Z .. note:: 2025-08-26T20:22:08.3227423Z Dataset is assumed to be of constant size and that any instance of it always 2025-08-26T20:22:08.3227575Z returns the same elements in the same order. 2025-08-26T20:22:08.3227655Z 2025-08-26T20:22:08.3227736Z Args: 2025-08-26T20:22:08.3227870Z dataset: Dataset used for sampling. 2025-08-26T20:22:08.3228116Z num_replicas (int, optional): Number of processes participating in 2025-08-26T20:22:08.3228375Z distributed training. By default, :attr:`world_size` is retrieved from the 2025-08-26T20:22:08.3228555Z current distributed group. 2025-08-26T20:22:08.3228793Z rank (int, optional): Rank of the current process within :attr:`num_replicas`. 2025-08-26T20:22:08.3229001Z By default, :attr:`rank` is retrieved from the current distributed 2025-08-26T20:22:08.3229086Z group. 2025-08-26T20:22:08.3229320Z shuffle (bool, optional): If ``True`` (default), sampler will shuffle the 2025-08-26T20:22:08.3229409Z indices. 2025-08-26T20:22:08.3229597Z seed (int, optional): random seed used to shuffle the sampler if 2025-08-26T20:22:08.3229804Z :attr:`shuffle=True`. This number should be identical across all 2025-08-26T20:22:08.3229968Z processes in the distributed group. Default: ``0``. 2025-08-26T20:22:08.3230220Z drop_last (bool, optional): if ``True``, then the sampler will drop the 2025-08-26T20:22:08.3230416Z tail of the data to make it evenly divisible across the number of 2025-08-26T20:22:08.3230613Z replicas. If ``False``, the sampler will add extra indices to make 2025-08-26T20:22:08.3230834Z the data evenly divisible across the replicas. Default: ``False``. 2025-08-26T20:22:08.3230914Z 2025-08-26T20:22:08.3231019Z .. warning:: 2025-08-26T20:22:08.3231207Z In distributed mode, calling the :meth:`set_epoch` method at 2025-08-26T20:22:08.3231459Z the beginning of each epoch **before** creating the :class:`DataLoader` iterator 2025-08-26T20:22:08.3231732Z is necessary to make shuffling work properly across multiple epochs. Otherwise, 2025-08-26T20:22:08.3231859Z the same ordering will be always used. 2025-08-26T20:22:08.3231979Z 2025-08-26T20:22:08.3232068Z Example:: 2025-08-26T20:22:08.3232149Z 2025-08-26T20:22:08.3232264Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3232485Z >>> sampler = DistributedSampler(dataset) if is_distributed else None 2025-08-26T20:22:08.3232674Z >>> loader = DataLoader(dataset, shuffle=(sampler is None), 2025-08-26T20:22:08.3232795Z ... sampler=sampler) 2025-08-26T20:22:08.3232931Z >>> for epoch in range(start_epoch, n_epochs): 2025-08-26T20:22:08.3233049Z ... if is_distributed: 2025-08-26T20:22:08.3233167Z ... sampler.set_epoch(epoch) 2025-08-26T20:22:08.3233277Z ... train(loader) 2025-08-26T20:22:08.3233359Z 2025-08-26T20:22:08.3233613Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3233714Z 2025-08-26T20:22:08.3233811Z warnings.warn(msg) 2025-08-26T20:22:08.3233906Z 2025-08-26T20:22:08.3234097Z --- Parse Warning: 139 / 146 --- 2025-08-26T20:22:08.3235054Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=WeightedRandomSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=227. 2025-08-26T20:22:08.3235333Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3235588Z Samples elements from ``[0,..,len(weights)-1]`` with given probabilities (weights). 2025-08-26T20:22:08.3235684Z 2025-08-26T20:22:08.3235770Z Args: 2025-08-26T20:22:08.3236012Z weights (sequence) : a sequence of weights, not necessary summing up to one 2025-08-26T20:22:08.3236168Z num_samples (int): number of samples to draw 2025-08-26T20:22:08.3236381Z replacement (bool): if ``True``, samples are drawn with replacement. 2025-08-26T20:22:08.3236597Z If not, they are drawn without replacement, which means that when a 2025-08-26T20:22:08.3236852Z sample index is drawn for a row, it cannot be drawn again for that row. 2025-08-26T20:22:08.3237054Z generator (Generator): Generator used in sampling. 2025-08-26T20:22:08.3237171Z 2025-08-26T20:22:08.3237257Z Example: 2025-08-26T20:22:08.3237414Z >>> # xdoctest: +IGNORE_WANT("non-deterministic") 2025-08-26T20:22:08.3237498Z >>> list( 2025-08-26T20:22:08.3237611Z ... WeightedRandomSampler( 2025-08-26T20:22:08.3237768Z ... [0.1, 0.9, 0.4, 0.7, 3.0, 0.6], 5, replacement=True 2025-08-26T20:22:08.3237855Z ... ) 2025-08-26T20:22:08.3237947Z ... ) 2025-08-26T20:22:08.3238034Z [4, 4, 1, 4, 5] 2025-08-26T20:22:08.3238117Z >>> list( 2025-08-26T20:22:08.3238245Z ... WeightedRandomSampler( 2025-08-26T20:22:08.3238388Z ... [0.9, 0.4, 0.05, 0.2, 0.3, 0.1], 5, replacement=False 2025-08-26T20:22:08.3238484Z ... ) 2025-08-26T20:22:08.3238589Z ... ) 2025-08-26T20:22:08.3238675Z [0, 1, 4, 3, 2] 2025-08-26T20:22:08.3238766Z 2025-08-26T20:22:08.3239020Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3239098Z 2025-08-26T20:22:08.3239202Z warnings.warn(msg) 2025-08-26T20:22:08.3239279Z 2025-08-26T20:22:08.3239477Z --- Parse Warning: 140 / 146 --- 2025-08-26T20:22:08.3240376Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=BatchSampler in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/sampler.py line=300. 2025-08-26T20:22:08.3240637Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3240818Z Wraps another sampler to yield a mini-batch of indices. 2025-08-26T20:22:08.3240926Z 2025-08-26T20:22:08.3241021Z Args: 2025-08-26T20:22:08.3241275Z sampler (Sampler or Iterable): Base sampler. Can be any iterable object 2025-08-26T20:22:08.3241397Z batch_size (int): Size of mini-batch. 2025-08-26T20:22:08.3241610Z drop_last (bool): If ``True``, the sampler will drop the last batch if 2025-08-26T20:22:08.3241740Z its size would be less than ``batch_size`` 2025-08-26T20:22:08.3241837Z 2025-08-26T20:22:08.3241921Z Example: 2025-08-26T20:22:08.3242009Z >>> list( 2025-08-26T20:22:08.3242121Z ... BatchSampler( 2025-08-26T20:22:08.3242323Z ... SequentialSampler(range(10)), batch_size=3, drop_last=False 2025-08-26T20:22:08.3242418Z ... ) 2025-08-26T20:22:08.3242501Z ... ) 2025-08-26T20:22:08.3242603Z [[0, 1, 2], [3, 4, 5], [6, 7, 8], [9]] 2025-08-26T20:22:08.3242699Z >>> list( 2025-08-26T20:22:08.3242957Z ... BatchSampler(SequentialSampler(range(10)), batch_size=3, drop_last=True) 2025-08-26T20:22:08.3243059Z ... ) 2025-08-26T20:22:08.3243160Z [[0, 1, 2], [3, 4, 5], [6, 7, 8]] 2025-08-26T20:22:08.3243244Z 2025-08-26T20:22:08.3243510Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3243590Z 2025-08-26T20:22:08.3243699Z warnings.warn(msg) 2025-08-26T20:22:08.3243778Z 2025-08-26T20:22:08.3243961Z --- Parse Warning: 141 / 146 --- 2025-08-26T20:22:08.3244924Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=IterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/datapipe.py line=56. 2025-08-26T20:22:08.3245184Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3245275Z 2025-08-26T20:22:08.3245379Z Iterable-style DataPipe. 2025-08-26T20:22:08.3245460Z 2025-08-26T20:22:08.3245759Z All DataPipes that represent an iterable of data samples should subclass this. 2025-08-26T20:22:08.3246016Z This style of DataPipes is particularly useful when data come from a stream, or 2025-08-26T20:22:08.3246415Z when the number of samples is too large to fit them all in memory. ``IterDataPipe`` is lazily initialized and its 2025-08-26T20:22:08.3246709Z elements are computed only when ``next()`` is called on the iterator of an ``IterDataPipe``. 2025-08-26T20:22:08.3246788Z 2025-08-26T20:22:08.3247029Z All subclasses should overwrite :meth:`__iter__`, which would return an 2025-08-26T20:22:08.3247379Z iterator of samples in this DataPipe. Calling ``__iter__`` of an ``IterDataPipe`` automatically invokes its 2025-08-26T20:22:08.3247754Z method ``reset()``, which by default performs no operation. When writing a custom ``IterDataPipe``, users should 2025-08-26T20:22:08.3248031Z override ``reset()`` if necessary. The common usages include resetting buffers, pointers, 2025-08-26T20:22:08.3248271Z and various state variables within the custom ``IterDataPipe``. 2025-08-26T20:22:08.3248363Z 2025-08-26T20:22:08.3248447Z Note: 2025-08-26T20:22:08.3248672Z Only `one` iterator can be valid for each ``IterDataPipe`` at a time, 2025-08-26T20:22:08.3249019Z and the creation a second iterator will invalidate the first one. This constraint is necessary because 2025-08-26T20:22:08.3249396Z some ``IterDataPipe`` have internal buffers, whose states can become invalid if there are multiple iterators. 2025-08-26T20:22:08.3249675Z The code example below presents details on how this constraint looks in practice. 2025-08-26T20:22:08.3250039Z If you have any feedback related to this constraint, please see `GitHub IterDataPipe Single Iterator Issue`_. 2025-08-26T20:22:08.3250130Z 2025-08-26T20:22:08.3250414Z These DataPipes can be invoked in two ways, using the class constructor or applying their 2025-08-26T20:22:08.3250816Z functional form onto an existing ``IterDataPipe`` (recommended, available to most but not all DataPipes). 2025-08-26T20:22:08.3251124Z You can chain multiple `IterDataPipe` together to form a pipeline that will perform multiple 2025-08-26T20:22:08.3251232Z operations in succession. 2025-08-26T20:22:08.3251324Z 2025-08-26T20:22:08.3251461Z .. _GitHub IterDataPipe Single Iterator Issue: 2025-08-26T20:22:08.3251610Z https://github.com/pytorch/data/issues/45 2025-08-26T20:22:08.3251688Z 2025-08-26T20:22:08.3251770Z Note: 2025-08-26T20:22:08.3252011Z When a subclass is used with :class:`~torch.utils.data.DataLoader`, each 2025-08-26T20:22:08.3252274Z item in the DataPipe will be yielded from the :class:`~torch.utils.data.DataLoader` 2025-08-26T20:22:08.3252502Z iterator. When :attr:`num_workers > 0`, each worker process will have a 2025-08-26T20:22:08.3252735Z different copy of the DataPipe object, so it is often desired to configure 2025-08-26T20:22:08.3252976Z each copy independently to avoid having duplicate data returned from the 2025-08-26T20:22:08.3253223Z workers. :func:`~torch.utils.data.get_worker_info`, when called in a worker 2025-08-26T20:22:08.3253475Z process, returns information about the worker. It can be used in either the 2025-08-26T20:22:08.3253733Z dataset's :meth:`__iter__` method or the :class:`~torch.utils.data.DataLoader` 's 2025-08-26T20:22:08.3253925Z :attr:`worker_init_fn` option to modify each copy's behavior. 2025-08-26T20:22:08.3254005Z 2025-08-26T20:22:08.3254101Z Examples: 2025-08-26T20:22:08.3254192Z General Usage: 2025-08-26T20:22:08.3254303Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3254511Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-08-26T20:22:08.3254631Z >>> dp = IterableWrapper(range(10)) 2025-08-26T20:22:08.3254828Z >>> map_dp_1 = Mapper(dp, lambda x: x + 1) # Using class constructor 2025-08-26T20:22:08.3254956Z >>> map_dp_2 = dp.map( 2025-08-26T20:22:08.3255070Z ... lambda x: x + 1 2025-08-26T20:22:08.3255203Z ... ) # Using functional form (recommended) 2025-08-26T20:22:08.3255327Z >>> list(map_dp_1) 2025-08-26T20:22:08.3255436Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-08-26T20:22:08.3255530Z >>> list(map_dp_2) 2025-08-26T20:22:08.3255635Z [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] 2025-08-26T20:22:08.3255782Z >>> filter_dp = map_dp_1.filter(lambda x: x % 2 == 0) 2025-08-26T20:22:08.3255877Z >>> list(filter_dp) 2025-08-26T20:22:08.3255975Z [2, 4, 6, 8, 10] 2025-08-26T20:22:08.3256091Z Single Iterator Constraint Example: 2025-08-26T20:22:08.3256296Z >>> from torchdata.datapipes.iter import IterableWrapper, Mapper 2025-08-26T20:22:08.3256437Z >>> source_dp = IterableWrapper(range(10)) 2025-08-26T20:22:08.3256539Z >>> it1 = iter(source_dp) 2025-08-26T20:22:08.3256640Z >>> list(it1) 2025-08-26T20:22:08.3256758Z [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] 2025-08-26T20:22:08.3256858Z >>> it1 = iter(source_dp) 2025-08-26T20:22:08.3256965Z >>> it2 = iter( 2025-08-26T20:22:08.3257057Z ... source_dp 2025-08-26T20:22:08.3257226Z ... ) # The creation of a new iterator invalidates `it1` 2025-08-26T20:22:08.3257315Z >>> next(it2) 2025-08-26T20:22:08.3257396Z 0 2025-08-26T20:22:08.3257589Z >>> next(it1) # Further usage of `it1` will raise a `RunTimeError` 2025-08-26T20:22:08.3257668Z 2025-08-26T20:22:08.3257931Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3258012Z 2025-08-26T20:22:08.3258108Z warnings.warn(msg) 2025-08-26T20:22:08.3258199Z 2025-08-26T20:22:08.3258393Z --- Parse Warning: 142 / 146 --- 2025-08-26T20:22:08.3259514Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=DemultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=375. 2025-08-26T20:22:08.3259778Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3259857Z 2025-08-26T20:22:08.3260328Z Splits the input DataPipe into multiple child DataPipes, using the given classification function (functional name: ``demux``). 2025-08-26T20:22:08.3260486Z 2025-08-26T20:22:08.3260692Z A list of the child DataPipes is returned from this operation. 2025-08-26T20:22:08.3260773Z 2025-08-26T20:22:08.3260857Z Args: 2025-08-26T20:22:08.3261003Z datapipe: Iterable DataPipe being filtered 2025-08-26T20:22:08.3261194Z num_instances: number of instances of the DataPipe to create 2025-08-26T20:22:08.3261580Z classifier_fn: a function that maps values to an integer within the range ``[0, num_instances - 1]`` or ``None`` 2025-08-26T20:22:08.3261903Z drop_none: defaults to ``False``, if ``True``, the function will skip over elements classified as ``None`` 2025-08-26T20:22:08.3262221Z buffer_size: this defines the maximum number of inputs that the buffer can hold across all child 2025-08-26T20:22:08.3262410Z DataPipes while waiting for their values to be yielded. 2025-08-26T20:22:08.3262580Z Defaults to ``1000``. Use ``-1`` for the unlimited buffer. 2025-08-26T20:22:08.3262673Z 2025-08-26T20:22:08.3262758Z Examples: 2025-08-26T20:22:08.3262881Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-26T20:22:08.3263064Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-26T20:22:08.3263162Z >>> def odd_or_even(n): 2025-08-26T20:22:08.3263267Z ... return n % 2 2025-08-26T20:22:08.3263393Z >>> source_dp = IterableWrapper(range(5)) 2025-08-26T20:22:08.3263639Z >>> dp1, dp2 = source_dp.demux(num_instances=2, classifier_fn=odd_or_even) 2025-08-26T20:22:08.3263746Z >>> list(dp1) 2025-08-26T20:22:08.3263830Z [0, 2, 4] 2025-08-26T20:22:08.3263954Z >>> list(dp2) 2025-08-26T20:22:08.3264038Z [1, 3] 2025-08-26T20:22:08.3264277Z >>> # It can also filter out any element that gets `None` from the `classifier_fn` 2025-08-26T20:22:08.3264398Z >>> def odd_or_even_no_zero(n): 2025-08-26T20:22:08.3264516Z ... return n % 2 if n != 0 else None 2025-08-26T20:22:08.3264636Z >>> dp1, dp2 = source_dp.demux( 2025-08-26T20:22:08.3264852Z ... num_instances=2, classifier_fn=odd_or_even_no_zero, drop_none=True 2025-08-26T20:22:08.3264934Z ... ) 2025-08-26T20:22:08.3265036Z >>> list(dp1) 2025-08-26T20:22:08.3265119Z [2, 4] 2025-08-26T20:22:08.3265221Z >>> list(dp2) 2025-08-26T20:22:08.3265302Z [1, 3] 2025-08-26T20:22:08.3265384Z 2025-08-26T20:22:08.3265658Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3265766Z 2025-08-26T20:22:08.3265865Z warnings.warn(msg) 2025-08-26T20:22:08.3265958Z 2025-08-26T20:22:08.3266162Z --- Parse Warning: 143 / 146 --- 2025-08-26T20:22:08.3267237Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=MultiplexerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=594. 2025-08-26T20:22:08.3267502Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3267596Z 2025-08-26T20:22:08.3267906Z Yields one element at a time from each of the input Iterable DataPipes (functional name: ``mux``). 2025-08-26T20:22:08.3267988Z 2025-08-26T20:22:08.3268349Z As in, one element from the 1st input DataPipe, then one element from the 2nd DataPipe in the next iteration, 2025-08-26T20:22:08.3268586Z and so on. It ends when the shortest input DataPipe is exhausted. 2025-08-26T20:22:08.3268681Z 2025-08-26T20:22:08.3268765Z Args: 2025-08-26T20:22:08.3269167Z datapipes: Iterable DataPipes that will take turn to yield their elements, until the shortest DataPipe is exhausted 2025-08-26T20:22:08.3269262Z 2025-08-26T20:22:08.3269347Z Example: 2025-08-26T20:22:08.3269476Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-26T20:22:08.3269668Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-26T20:22:08.3269764Z >>> dp1, dp2, dp3 = ( 2025-08-26T20:22:08.3269892Z ... IterableWrapper(range(3)), 2025-08-26T20:22:08.3270016Z ... IterableWrapper(range(10, 15)), 2025-08-26T20:22:08.3270135Z ... IterableWrapper(range(20, 25)), 2025-08-26T20:22:08.3270233Z ... ) 2025-08-26T20:22:08.3270336Z >>> list(dp1.mux(dp2, dp3)) 2025-08-26T20:22:08.3270446Z [0, 10, 20, 1, 11, 21, 2, 12, 22] 2025-08-26T20:22:08.3270528Z 2025-08-26T20:22:08.3270781Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3270897Z 2025-08-26T20:22:08.3270994Z warnings.warn(msg) 2025-08-26T20:22:08.3271084Z 2025-08-26T20:22:08.3271266Z --- Parse Warning: 144 / 146 --- 2025-08-26T20:22:08.3272286Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=ZipperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/combining.py line=665. 2025-08-26T20:22:08.3272560Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3272639Z 2025-08-26T20:22:08.3272956Z Aggregates elements into a tuple from each of the input DataPipes (functional name: ``zip``). 2025-08-26T20:22:08.3273037Z 2025-08-26T20:22:08.3273263Z The output is stopped as soon as the shortest input DataPipe is exhausted. 2025-08-26T20:22:08.3273355Z 2025-08-26T20:22:08.3273469Z Args: 2025-08-26T20:22:08.3273630Z *datapipes: Iterable DataPipes being aggregated 2025-08-26T20:22:08.3273735Z 2025-08-26T20:22:08.3273820Z Example: 2025-08-26T20:22:08.3273956Z >>> # xdoctest: +REQUIRES(module:torchdata) 2025-08-26T20:22:08.3274129Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-26T20:22:08.3274236Z >>> dp1, dp2, dp3 = ( 2025-08-26T20:22:08.3274348Z ... IterableWrapper(range(5)), 2025-08-26T20:22:08.3274467Z ... IterableWrapper(range(10, 15)), 2025-08-26T20:22:08.3274645Z ... IterableWrapper(range(20, 25)), 2025-08-26T20:22:08.3274726Z ... ) 2025-08-26T20:22:08.3274830Z >>> list(dp1.zip(dp2, dp3)) 2025-08-26T20:22:08.3274978Z [(0, 10, 20), (1, 11, 21), (2, 12, 22), (3, 13, 23), (4, 14, 24)] 2025-08-26T20:22:08.3275062Z 2025-08-26T20:22:08.3275331Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3275413Z 2025-08-26T20:22:08.3275535Z warnings.warn(msg) 2025-08-26T20:22:08.3275628Z 2025-08-26T20:22:08.3275812Z --- Parse Warning: 145 / 146 --- 2025-08-26T20:22:08.3276878Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=FileOpenerIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/fileopener.py line=18. 2025-08-26T20:22:08.3277140Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3277220Z 2025-08-26T20:22:08.3277605Z Given pathnames, opens files and yield pathname and file stream in a tuple (functional name: ``open_files``). 2025-08-26T20:22:08.3277683Z 2025-08-26T20:22:08.3277777Z Args: 2025-08-26T20:22:08.3277940Z datapipe: Iterable datapipe that provides pathnames 2025-08-26T20:22:08.3278151Z mode: An optional string that specifies the mode in which 2025-08-26T20:22:08.3278377Z the file is opened by ``open()``. It defaults to ``r``, other options are 2025-08-26T20:22:08.3278538Z ``b`` for reading in binary mode and ``t`` for text mode. 2025-08-26T20:22:08.3278749Z encoding: An optional string that specifies the encoding of the 2025-08-26T20:22:08.3279009Z underlying file. It defaults to ``None`` to match the default encoding of ``open``. 2025-08-26T20:22:08.3279143Z length: Nominal length of the datapipe 2025-08-26T20:22:08.3279224Z 2025-08-26T20:22:08.3279306Z Note: 2025-08-26T20:22:08.3279588Z The opened file handles will be closed by Python's GC periodically. Users can choose 2025-08-26T20:22:08.3279692Z to close them explicitly. 2025-08-26T20:22:08.3279772Z 2025-08-26T20:22:08.3279869Z Example: 2025-08-26T20:22:08.3279964Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3280112Z >>> from torchdata.datapipes.iter import ( 2025-08-26T20:22:08.3280206Z ... FileLister, 2025-08-26T20:22:08.3280301Z ... FileOpener, 2025-08-26T20:22:08.3280405Z ... StreamReader, 2025-08-26T20:22:08.3280496Z ... ) 2025-08-26T20:22:08.3280723Z >>> dp = FileLister(root=".").filter(lambda fname: fname.endswith(".txt")) 2025-08-26T20:22:08.3280822Z >>> dp = FileOpener(dp) 2025-08-26T20:22:08.3280924Z >>> dp = StreamReader(dp) 2025-08-26T20:22:08.3281021Z >>> list(dp) 2025-08-26T20:22:08.3281116Z [('./abc.txt', 'abc')] 2025-08-26T20:22:08.3281208Z 2025-08-26T20:22:08.3281463Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3281543Z 2025-08-26T20:22:08.3281652Z warnings.warn(msg) 2025-08-26T20:22:08.3281730Z 2025-08-26T20:22:08.3281913Z --- Parse Warning: 146 / 146 --- 2025-08-26T20:22:08.3282982Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/xdoctest/core.py:423: UserWarning: Cannot scrape callname=GrouperIterDataPipe in modpath=/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/utils/data/datapipes/iter/grouping.py line=155. 2025-08-26T20:22:08.3283286Z Caused by: DoctestParseError('Failed to parse doctest in _label_docsrc_lines') 2025-08-26T20:22:08.3283376Z 2025-08-26T20:22:08.3283782Z Groups data from IterDataPipe by keys from ``group_key_fn``, yielding a ``DataChunk`` with batch size up to ``group_size``. 2025-08-26T20:22:08.3283874Z 2025-08-26T20:22:08.3283980Z (functional name: ``groupby``). 2025-08-26T20:22:08.3284058Z 2025-08-26T20:22:08.3284452Z The samples are read sequentially from the source ``datapipe``, and a batch of samples belonging to the same group 2025-08-26T20:22:08.3284744Z will be yielded as soon as the size of the batch reaches ``group_size``. When the buffer is full, 2025-08-26T20:22:08.3285060Z the DataPipe will yield the largest batch with the same key, provided that its size is larger 2025-08-26T20:22:08.3285410Z than ``guaranteed_group_size``. If its size is smaller, it will be dropped if ``drop_remaining=True``. 2025-08-26T20:22:08.3285490Z 2025-08-26T20:22:08.3285881Z After iterating through the entirety of source ``datapipe``, everything not dropped due to the buffer capacity 2025-08-26T20:22:08.3286196Z will be yielded from the buffer, even if the group sizes are smaller than ``guaranteed_group_size``. 2025-08-26T20:22:08.3286286Z 2025-08-26T20:22:08.3286366Z Args: 2025-08-26T20:22:08.3286497Z datapipe: Iterable datapipe to be grouped 2025-08-26T20:22:08.3286783Z group_key_fn: Function used to generate group key from the data of the source datapipe 2025-08-26T20:22:08.3287016Z keep_key: Option to yield the matching key along with the items in a tuple, 2025-08-26T20:22:08.3287205Z resulting in `(key, [items])` otherwise returning [items] 2025-08-26T20:22:08.3287355Z buffer_size: The size of buffer for ungrouped data 2025-08-26T20:22:08.3287666Z group_size: The max size of each group, a batch is yielded as soon as it reaches this size 2025-08-26T20:22:08.3288010Z guaranteed_group_size: The guaranteed minimum group size to be yielded in case the buffer is full 2025-08-26T20:22:08.3288364Z drop_remaining: Specifies if the group smaller than ``guaranteed_group_size`` will be dropped from buffer 2025-08-26T20:22:08.3288480Z when the buffer is full 2025-08-26T20:22:08.3288559Z 2025-08-26T20:22:08.3288644Z Example: 2025-08-26T20:22:08.3288743Z >>> import os 2025-08-26T20:22:08.3288841Z >>> # xdoctest: +SKIP 2025-08-26T20:22:08.3289027Z >>> from torchdata.datapipes.iter import IterableWrapper 2025-08-26T20:22:08.3289125Z >>> def group_fn(file): 2025-08-26T20:22:08.3289268Z ... return os.path.basename(file).split(".")[0] 2025-08-26T20:22:08.3289389Z >>> source_dp = IterableWrapper( 2025-08-26T20:22:08.3289551Z ... ["a.png", "b.png", "a.json", "b.json", "a.jpg", "c.json"] 2025-08-26T20:22:08.3289647Z ... ) 2025-08-26T20:22:08.3289788Z >>> dp0 = source_dp.groupby(group_key_fn=group_fn) 2025-08-26T20:22:08.3289877Z >>> list(dp0) 2025-08-26T20:22:08.3290048Z [['a.png', 'a.json', 'a.jpg'], ['b.png', 'b.json'], ['c.json']] 2025-08-26T20:22:08.3290234Z >>> # A group is yielded as soon as its size equals to `group_size` 2025-08-26T20:22:08.3290432Z >>> dp1 = source_dp.groupby(group_key_fn=group_fn, group_size=2) 2025-08-26T20:22:08.3290520Z >>> list(dp1) 2025-08-26T20:22:08.3290677Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-08-26T20:22:08.3291034Z >>> # Scenario where `buffer` is full, and group 'a' needs to be yielded since its size > `guaranteed_group_size` 2025-08-26T20:22:08.3291139Z >>> dp2 = source_dp.groupby( 2025-08-26T20:22:08.3291258Z ... group_key_fn=group_fn, 2025-08-26T20:22:08.3291354Z ... buffer_size=3, 2025-08-26T20:22:08.3291476Z ... group_size=3, 2025-08-26T20:22:08.3291598Z ... guaranteed_group_size=2, 2025-08-26T20:22:08.3291891Z ... ) 2025-08-26T20:22:08.3292063Z >>> list(dp2) 2025-08-26T20:22:08.3292225Z [['a.png', 'a.json'], ['b.png', 'b.json'], ['a.jpg'], ['c.json']] 2025-08-26T20:22:08.3292309Z 2025-08-26T20:22:08.3292575Z Original Error: TokenError('unexpected EOF in multi-line statement', (1, 0)) 2025-08-26T20:22:08.3292655Z 2025-08-26T20:22:08.3292768Z warnings.warn(msg) 2025-08-26T20:22:08.3292901Z 2025-08-26T20:22:08.3293022Z  2025-08-26T20:22:08.3293210Z === Found 8 run-time warnings === 2025-08-26T20:22:08.3293386Z --- Runtime Warning: 1 / 8 --- 2025-08-26T20:22:08.3293647Z example = 2025-08-26T20:22:08.3295046Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/_tensor.py:1351: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /var/lib/jenkins/workspace/c10/core/TensorImpl.h:1974.) 2025-08-26T20:22:08.3295167Z return super().refine_names(names) 2025-08-26T20:22:08.3295259Z 2025-08-26T20:22:08.3295434Z --- Runtime Warning: 2 / 8 --- 2025-08-26T20:22:08.3295753Z example = 2025-08-26T20:22:08.3296374Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/library.py:282: UserWarning: Warning only once for all operators, other operators may also be overridden. 2025-08-26T20:22:08.3296687Z Overriding a previously registered kernel for the same operator and the same dispatch key 2025-08-26T20:22:08.3296908Z operator: aten::div.Tensor(Tensor self, Tensor other) -> Tensor 2025-08-26T20:22:08.3297279Z registered at /var/lib/jenkins/workspace/build/aten/src/ATen/RegisterSchema.cpp:6 2025-08-26T20:22:08.3297390Z dispatch key: CPU 2025-08-26T20:22:08.3297820Z previous kernel: registered at /var/lib/jenkins/workspace/aten/src/ATen/LegacyBatchingRegistrations.cpp:1079 2025-08-26T20:22:08.3298380Z new kernel: registered at /dev/null:811 (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/core/dispatch/OperatorEntry.cpp:225.) 2025-08-26T20:22:08.3305123Z impl_fn(self.ns, name.split("::")[-1], dispatch_key) 2025-08-26T20:22:08.3305250Z 2025-08-26T20:22:08.3305496Z --- Runtime Warning: 3 / 8 --- 2025-08-26T20:22:08.3305733Z example = 2025-08-26T20:22:08.3307625Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nested/__init__.py:117: UserWarning: The PyTorch API of nested tensors is in prototype stage and will change in the near future. We recommend specifying layout=torch.jagged when constructing a nested tensor, as this layout receives active development, has better operator coverage, and works with torch.compile. (Triggered internally at /var/lib/jenkins/workspace/aten/src/ATen/NestedTensorImpl.cpp:182.) 2025-08-26T20:22:08.3307879Z return torch._nested_tensor_from_tensor_list(ts, dtype, None, device, None) 2025-08-26T20:22:08.3307975Z 2025-08-26T20:22:08.3308156Z --- Runtime Warning: 4 / 8 --- 2025-08-26T20:22:08.3308463Z example = 2025-08-26T20:22:08.3309985Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/fx/experimental/const_fold.py:271: UserWarning: Attempted to insert a get_attr Node with no underlying reference in the owning GraphModule! Call GraphModule.add_submodule to add the necessary submodule, GraphModule.add_parameter to add the necessary Parameter, or nn.Module.register_buffer to add the necessary buffer 2025-08-26T20:22:08.3310157Z new_node = root_const_gm.graph.get_attr(in_node.target) 2025-08-26T20:22:08.3310377Z 2025-08-26T20:22:08.3310564Z --- Runtime Warning: 5 / 8 --- 2025-08-26T20:22:08.3310910Z example = 2025-08-26T20:22:08.3311991Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:392: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2025-08-26T20:22:08.3312086Z warnings.warn( 2025-08-26T20:22:08.3312181Z 2025-08-26T20:22:08.3312362Z --- Runtime Warning: 6 / 8 --- 2025-08-26T20:22:08.3312706Z example = 2025-08-26T20:22:08.3313805Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/modules/transformer.py:392: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance) 2025-08-26T20:22:08.3313916Z warnings.warn( 2025-08-26T20:22:08.3313996Z 2025-08-26T20:22:08.3314171Z --- Runtime Warning: 7 / 8 --- 2025-08-26T20:22:08.3314461Z example = 2025-08-26T20:22:08.3315270Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2025-08-26T20:22:08.3315406Z WeightNorm.apply(module, name, dim) 2025-08-26T20:22:08.3315486Z 2025-08-26T20:22:08.3315661Z --- Runtime Warning: 8 / 8 --- 2025-08-26T20:22:08.3315981Z example = 2025-08-26T20:22:08.3316813Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`. 2025-08-26T20:22:08.3316948Z WeightNorm.apply(module, name, dim) 2025-08-26T20:22:08.3317028Z 2025-08-26T20:22:08.3317325Z === 338 passed, 393 skipped, 154 warnings in 14.46 seconds === 2025-08-26T20:22:08.3317564Z Running dynamo/test_fake_distributed 1/1 ... [2025-08-26 20:22:08.084956] 2025-08-26T20:22:08.3317678Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:22:08.3318574Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_fake_distributed.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:22:08.085378] 2025-08-26T20:22:11.3650115Z 2025-08-26T20:22:11.3651189Z dynamo/test_fake_distributed 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_fake_distributed_1.1_0afced6d62044f2f_.log 2025-08-26T20:22:11.3651963Z 2025-08-26T20:22:11.3654942Z Running test_utils 1/1 ... [2025-08-26 20:22:11.365356] 2025-08-26T20:22:11.3655408Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:22:11.3659109Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:22:11.365687] 2025-08-26T20:27:47.6211602Z 2025-08-26T20:27:47.6212532Z test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_utils_1.1_4b8d401e50198a5f_.log 2025-08-26T20:27:47.8372745Z Running 6049 items in this shard: test/test_utils.py::TestCheckpoint::test_checkpoint, test/test_utils.py::TestCheckpoint::test_checkpoint_module_list, test/test_utils.py::TestCheckpoint::test_checkpoint_no_tensors, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor, test/test_utils.py::TestCheckpoint::test_checkpoint_non_tensor_inputs_outputs, test/test_utils.py::TestCheckpoint::test_checkpoint_not_preserve_rng_state_and_without_reentrant, test/test_utils.py::TestCheckpoint::test_checkpoint_partial_grad, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cpu, test/test_utils.py::TestCheckpoint::test_checkpoint_rng_cuda, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_multiple_args, test/test_utils.py::TestCheckpoint::test_checkpoint_sequential_deprecated_no_args, test/test_utils.py::TestCheckpoint::test_checkpoint_trigger, test/test_utils.py::TestCheckpoint::test_checkpoint_valid, test/test_utils.py::TestCheckpoint::test_checkpointing_without_reentrant_early_free, test/test_utils.py::TestCheckpoint::test_get_device_states_recursive, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_meta, test/test_utils.py::TestCheckpoint::test_infer_device_state_recursive_multi_cuda, test/test_utils.py::TestDataLoaderUtils::test_multi_drop, test/test_utils.py::TestDataLoaderUtils::test_multi_keep, test/test_utils.py::TestDataLoaderUtils::test_random_seed, test/test_utils.py::TestDataLoaderUtils::test_single_drop, test/test_utils.py::TestDataLoaderUtils::test_single_keep, test/test_utils.py::TestBottleneck::test_bottleneck_cpu_only, test/test_utils.py::TestBottleneck::test_bottleneck_cuda, test/test_utils.py::TestCollectEnv::test_smoke, test/test_utils.py::TestONNXUtils::test_check_onnx_broadcast, test/test_utils.py::TestONNXUtils::test_prepare_onnx_paddings, test/test_utils.py::TestHipify::test_import_hipify, test/test_utils.py::TestHipifyTrie::test_add_and_search_trie, test/test_utils.py::TestHipifyTrie::test_add_multiple_and_search_trie, test/test_utils.py::TestHipifyTrie::test_char_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_prefix_words_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_quote_escape, test/test_utils.py::TestHipifyTrie::test_single_export_trie_to_regex, test/test_utils.py::TestHipifyTrie::test_special_char_export_trie_to_regex, test/test_utils.py::TestAssert::test_assert_scriptable, test/test_utils.py::TestAssert::test_assert_true, test/test_utils.py::TestStandaloneCPPJIT::test_load_standalone, test/test_utils.py::TestRenderUtils::test_basic, test/test_utils.py::TestDeviceUtilsCPU::test_basic_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_decorator_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_decorator_generator_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_H_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_T_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___getitem___cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops___radd___cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmm_decomposed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addmv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_addr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_alias_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_all_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_allclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_aminmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_angle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_any_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_arange_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argsort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_argwhere_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_partial_views_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_as_strided_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_asin_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_broadcast_to_cpu_bool, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cauchy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cdouble_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ceil_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cfloat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chalf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_char_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_inverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cholesky_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_max_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clamp_min_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_clone_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_column_stack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_combinations_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_conj_physical_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_constant_pad_nd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_contiguous_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_copysign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_corrcoef_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cos_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cosh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_count_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cov_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cummin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumprod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_cumulative_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_deg2rad_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diag_embed_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagflat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diagonal_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_diff_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_digamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_floor_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_no_rounding_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_div_trunc_rounding_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_double_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_dstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_einsum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_permuted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eq_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_equal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_erfinv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expand_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_expm1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_float8_e5m2fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_eye_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_fftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_hfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ifftshift_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_ihfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_irfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfft_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fft_rfftn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flip_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fliplr_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_flipud_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_float_power_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_floor_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_fmod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_frexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_full_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gather_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gcd_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ge_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geometric_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_geqrf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gradient_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_grid_sampler_3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_gt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_half_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hash_tensor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_heaviside_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogram_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_histogramdd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_hypot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_i0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_igammac_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_imag_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_fill_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_index_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_inner_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_int_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isclose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isfinite_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isnan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isneginf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isposinf_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_isreal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_istft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_item_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_2inputs_2outputs_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_4inputs_with_extra_args_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_binary_return_by_ref_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_jiterator_unary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kron_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_kthvalue_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lcm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ldexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_le_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lerp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lgamma_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cholesky_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cond_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_cross_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_det_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_diagonal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eig_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_eigvalsh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_householder_product_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_inv_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_ldl_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lstsq_grad_oriented_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_factor_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_power_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_matrix_rank_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_multi_dot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_norm_subgradients_at_zero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_hermitian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_pinv_singular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_slogdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_ex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_solve_triangular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_svdvals_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorinv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_tensorsolve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vander_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vecdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linalg_vector_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_linspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log10_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log1p_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_log_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logcumsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logdet_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_and_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logical_not_cpu_int32, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logspace_tensor_overload_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_logsumexp_cpu_int16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_long_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_lu_unpack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mH_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mT_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_amax_cpu_uint8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_argmin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_cumprod_cpu_float64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logaddexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_logsumexp_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_masked_mean_cpu_float64, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matmul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_matrix_exp_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_pool2d_with_indices_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_max_reduction_no_dim_cpu_int8, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_median_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_list_of_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_meshgrid_variadic_tensors_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_binary_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_no_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_min_reduction_with_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_minimum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mm_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mode_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_movedim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_msort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mul_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_multinomial_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mv_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_mvlgamma_mvlgamma_p_5_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nan_to_num_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanmedian_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nanquantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nansum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_narrow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_dropout_backward_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_native_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ne_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_empty_strided_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_full_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_new_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nextafter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_adaptive_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_alpha_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_avg_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_batch_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_binary_cross_entropy_with_logits_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_celu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_channel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose1d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_conv_transpose3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_embedding_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cosine_similarity_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_cross_entropy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_ctc_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_dropout_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_elu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_bag_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_embedding_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_with_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_feature_alpha_dropout_without_train_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_fractional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gaussian_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_gelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_glu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_grid_sample_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_group_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardswish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hardtanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_hinge_embedding_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_huber_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_instance_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_area_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bicubic_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest-exact_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_interpolate_trilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_kl_div_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_layer_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_leaky_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_linear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_local_response_norm_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_logsigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_margin_ranking_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool2d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_pool3d_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool1d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool2d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_max_unpool3d_grad_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mish_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_mse_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_head_attention_forward_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multi_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_multilabel_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_normalize_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_one_hot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_circular_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_constant_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_reflect_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pad_replicate_negative_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pairwise_distance_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pdist_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_shuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_pixel_unshuffle_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_poisson_nll_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_prelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu6_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_relu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rms_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_rrelu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_scaled_dot_product_attention_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_selu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_complex_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_silu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_smooth_l1_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_soft_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softmin_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softplus_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_softsign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_tanhshrink_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_threshold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_triplet_margin_with_distance_loss_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_bilinear_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nn_functional_upsample_nearest_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_nonzero_static_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_fro_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_inf_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_norm_nuc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_in_place_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_normal_number_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ones_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ormqr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_outer_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pca_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_permute_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pinverse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polar_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_0_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_1_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_2_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_3_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_polygamma_polygamma_n_4_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_positive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_pow_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_put_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_qr_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_quantile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rad2deg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rand_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randint_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_randn_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_ravel_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_real_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reciprocal_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_remainder_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_renorm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_repeat_interleave_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_reshape_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resize_as__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_conj_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_resolve_neg_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_roll_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rot90_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_0_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_round_decimals_neg_3_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsqrt_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_rsub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scalar_tensor_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_add_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amax_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_amin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_mean_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_prod_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_scatter_reduce_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_searchsorted_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_select_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sgn_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_short_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sigmoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sign_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_bartlett_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_blackman_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_exponential_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_gaussian_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_cosine_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_general_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hamming_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_hann_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_kaiser_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signal_windows_nuttall_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_signbit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sin_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sinh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_slice_scatter_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_softmax_with_dtype_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sort_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_mm_reduce_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sparse_sampled_addmm_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_special_airy_ai_cpu_bool, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_split_with_sizes_cpu_bfloat16, 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test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_std_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_stft_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sub_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_sum_to_size_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_svd_lowrank_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_t_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_along_dim_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_take_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tan_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tanh_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensor_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tensordot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tile_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_to_sparse_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_topk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e4m3fn, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e4m3fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e5m2, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch__scaled_mm_cpu_float8_e5m2fnuz, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_torch_ops_aten__safe_softmax_default_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trace_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_transpose_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapezoid_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trapz_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triangular_solve_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_tril_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_triu_indices_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_true_divide_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_trunc_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unbind_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unflatten_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unfold_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_uniform_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_consecutive_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unique_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unravel_index_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_chunk_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsafe_split_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_unsqueeze_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_mean_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_var_unbiased_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vdot_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_complex_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_as_real_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_copy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_view_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vsplit_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_vstack_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_where_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_xlogy_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zero__cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bfloat16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_bool, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex128, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_complex64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_float64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int16, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int32, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int64, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_int8, test/test_utils.py::TestDeviceUtilsCPU::test_device_mode_ops_zeros_like_cpu_uint8, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_get_default_device_more_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_nn_module_cpu, test/test_utils.py::TestDeviceUtilsCPU::test_set_default_device_cpu, test/test_utils.py::TestCppExtensionUtils::test_cc_compiler_is_ok, test/test_utils.py::TestCppExtensionUtils::test_cpp_compiler_is_ok, test/test_utils.py::TestTraceback::test_basic, test/test_utils.py::TestTraceback::test_captured_traceback, test/test_utils.py::TestTraceback::test_captured_traceback_format_all, test/test_utils.py::TestTraceback::test_captured_traceback_format_all_cached, test/test_utils.py::TestTraceback::test_format_traceback_short, test/test_utils.py::TestTryImport::test_import_existing, test/test_utils.py::TestTryImport::test_import_imported, test/test_utils.py::TestTryImport::test_import_missing, test/test_utils.py::TestDeprecate::test_deprecated 2025-08-26T20:27:48.0980403Z 2025-08-26T20:27:48.0980604Z Running test_fx 1/1 ... [2025-08-26 20:27:47.629265] 2025-08-26T20:27:48.0981007Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:27:48.0982040Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fx.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:27:47.629617] 2025-08-26T20:32:40.2492549Z 2025-08-26T20:32:40.2493328Z test_fx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fx_1.1_86b99f913d275e4e_.log 2025-08-26T20:32:40.2979544Z Running 1261 items in this shard: test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationInput_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationMetadata_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_MutationTorchTensorCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_Mutation_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_ReturnList_cpu, test/test_fx.py::TestCommonPass::test_correctness_CSEPass_TakeList_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_FactoryFunctionCall_cpu, test/test_fx.py::TestCommonPass::test_correctness_factory_CSEPass_MutationFactory_cpu, test/test_fx.py::TestCSEPass::test_banned_list, test/test_fx.py::TestCSEPass::test_empty, test/test_fx.py::TestCSEPass::test_immutable_list_multiple_entries, test/test_fx.py::TestCSEPass::test_immutable_list_type, test/test_fx.py::TestCSEPass::test_kwarg, test/test_fx.py::TestCSEPass::test_nested_immutable_list_type, test/test_fx.py::TestCSEPass::test_nochange, test/test_fx.py::TestCSEPass::test_rand_like, test/test_fx.py::TestCSEPass::test_rand_n, test/test_fx.py::TestCSEPass::test_random, test/test_fx.py::TestCSEPass::test_simple, test/test_fx.py::TestCSEPass::test_simple_2, test/test_fx.py::TestCSEPass::test_simple_multiple_same_ops, test/test_fx.py::TestCSEPass::test_two_args, test/test_fx.py::TestCSEPass::test_two_args_default, test/test_fx.py::TestDCE::test_dead_chain, test/test_fx.py::TestDCE::test_dead_getattr, test/test_fx.py::TestDCE::test_dead_placeholder, test/test_fx.py::TestDCE::test_dead_placeholder_with_user, test/test_fx.py::TestDCE::test_impure_custom, test/test_fx.py::TestDCE::test_impure_kwargs, test/test_fx.py::TestDCE::test_impure_nodes_args, test/test_fx.py::TestDCE::test_impure_random, test/test_fx.py::TestDCE::test_keep_collectives, test/test_fx.py::TestDCE::test_keep_collectives_no_overload, test/test_fx.py::TestDCE::test_keep_module_with_side_effects, test/test_fx.py::TestDCE::test_keep_setitem, test/test_fx.py::TestDCE::test_keep_torch_assert, test/test_fx.py::TestDCE::test_simple, test/test_fx.py::TestConstFold::test_check_inline_non_const, test/test_fx.py::TestConstFold::test_check_inline_non_const_mult_return, test/test_fx.py::TestConstFold::test_check_skip_folding_quant_dequant_pattern, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_one_attr_no_name_collision, test/test_fx.py::TestConstFold::test_const_fold_basic_placeholder_reordered, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr, test/test_fx.py::TestConstFold::test_const_fold_basic_two_attr_three_input, test/test_fx.py::TestConstFold::test_const_fold_has_inlined_call_module_node, test/test_fx.py::TestConstFold::test_const_fold_module_attr, test/test_fx.py::TestConstFold::test_const_fold_multi_const_folded_attrs, test/test_fx.py::TestConstFold::test_const_fold_noop, test/test_fx.py::TestConstFold::test_const_fold_submod_hierarchy, test/test_fx.py::TestConstFold::test_const_fold_tensor_meta, test/test_fx.py::TestConstFold::test_const_fold_unused_placeholder, test/test_fx.py::TestConstFold::test_dict_output, test/test_fx.py::TestConstFold::test_fold_module, test/test_fx.py::TestConstFold::test_retain_node_meta, test/test_fx.py::TestConstFold::test_three_outputs, test/test_fx.py::TestConstFold::test_two_outputs, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_dim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_ndim_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_nelement_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_numel_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_shape_const, test/test_fx.py::TestConstParamShapeInControlFlow::test_param_size_const, test/test_fx.py::AnnotationsTest::test_annotate, test/test_fx.py::AnnotationsTest::test_annotations, test/test_fx.py::AnnotationsTest::test_broadcasting1, test/test_fx.py::AnnotationsTest::test_broadcasting2, test/test_fx.py::AnnotationsTest::test_broadcasting3, test/test_fx.py::AnnotationsTest::test_consistency, test/test_fx.py::AnnotationsTest::test_precision, test/test_fx.py::TypeCheckerTest::test_flatten_fully_static, test/test_fx.py::TypeCheckerTest::test_resnet50, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_symbolic_add_with_broadcast_2, test/test_fx.py::TypeCheckerTest::test_type_check_add_false, test/test_fx.py::TypeCheckerTest::test_type_check_add_true, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_add_with_scalar, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_broadcast, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_2D_false, test/test_fx.py::TypeCheckerTest::test_type_check_batch_norm_symbolic, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_2_fully_static, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_conv2D_types, test/test_fx.py::TypeCheckerTest::test_type_check_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_flatten3, test/test_fx.py::TypeCheckerTest::test_type_check_flatten_2, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_true, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_dyn_true_param_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_false, test/test_fx.py::TypeCheckerTest::test_type_check_reshape_true, test/test_fx.py::TypeCheckerTest::test_type_check_symbolic_inferenceconv2D_maxpool2d_flatten, test/test_fx.py::TypeCheckerTest::test_type_check_transpose_False, test/test_fx.py::TypeCheckerTest::test_type_check_transpose_true, test/test_fx.py::TypeCheckerTest::test_type_maxpool2d_fully_static, test/test_fx.py::TypeCheckerTest::test_type_typechecl_maxpool2d_3dinput, test/test_fx.py::TypeCheckerTest::test_typecheck_basicblock, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_function, test/test_fx.py::TestMatcher::test_matcher_with_name_node_map_module, test/test_fx.py::TestMatcher::test_split_to_graph_and_name_node_map, test/test_fx.py::TestMatcher::test_subgraph_matcher_ignore_literals, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_attributes, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list, test/test_fx.py::TestMatcher::test_subgraph_matcher_with_list_bad, test/test_fx.py::TestMatcher::test_variatic_arg_matching, test/test_fx.py::TestPassManager::test_pass_manager, test/test_fx.py::TestPassManager::test_pass_manager_bad_checks, test/test_fx.py::TestPassManager::test_pass_manager_checks, test/test_fx.py::TestPassManager::test_pass_manager_error, test/test_fx.py::TestPassManager::test_this_before_that_pass_constraint, test/test_fx.py::TestPassManager::test_topological_sort, test/test_fx.py::TestSourceMatcher::test_legalize_slice, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_conv_relu_maxpool_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_conv_relu_conv_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_functional_linear_relu_linear_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear_torch_fn_export_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_linear_relu_linear_torch_fn_export_strict_True, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_False, test/test_fx.py::TestSourceMatcher::test_module_partitioner_weight_tied_strict_True, test/test_fx.py::TestSubgraphRewriter::test_matching_pattern_with_list_type_arg, test/test_fx.py::TestSubgraphRewriter::test_matching_variable_arguments, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_callback, test/test_fx.py::TestSubgraphRewriter::test_replace_pattern_with_filters, test/test_fx.py::TestSubgraphRewriter::test_replaced_nodes, test/test_fx.py::TestSubgraphRewriter::test_replacement_with_attrs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_annotations_int, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_call_method, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_correct_output_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_graph_argument_order, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_internal_pattern_nodes_cannot_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_local_revert, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_multiple_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_nodes_with_kwargs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_is_entire_graph, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_pattern_output_pattern_node_can_have_users_that_are_not_matched, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_placeholder_matching, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_consecutive_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_duplicated_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replace_with_multiple_outputs, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_replaces_referenced_submodules, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_single_pattern_match, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_traced_as_callable, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_oneliner_pattern, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_overlapping_matches, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_trivial_replacement, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_args, test/test_fx.py::TestSubgraphRewriter::test_subgraph_rewriter_with_unused_results, test/test_fx.py::TestFX::test_all_input_nodes, test/test_fx.py::TestFX::test_annotation_with_future, test/test_fx.py::TestFX::test_annotations_empty_tuple, test/test_fx.py::TestFX::test_annotations_with_forward_references, test/test_fx.py::TestFX::test_annotations_with_no_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_internal_forward_references, test/test_fx.py::TestFX::test_annotations_with_non_torch_reference_and_no_internal_forward_references, test/test_fx.py::TestFX::test_args_kwargs, test/test_fx.py::TestFX::test_args_kwargs_no_self, test/test_fx.py::TestFX::test_assert, test/test_fx.py::TestFX::test_ast_rewriter_reassigns_submodules, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert, test/test_fx.py::TestFX::test_ast_rewriter_rewrites_assert_with_message, test/test_fx.py::TestFX::test_ast_rewriter_wrap, test/test_fx.py::TestFX::test_ast_rewriter_wrap_fn_directly, test/test_fx.py::TestFX::test_ast_rewriter_wrap_with_submodule, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator, test/test_fx.py::TestFX::test_ast_rewriter_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_autowrap_functions, test/test_fx.py::TestFX::test_concrete_arg_none_assert, test/test_fx.py::TestFX::test_construct_root_dict, test/test_fx.py::TestFX::test_control_flow_tracing, test/test_fx.py::TestFX::test_copy_it, test/test_fx.py::TestFX::test_copy_no_remap, test/test_fx.py::TestFX::test_ctx_mgr, test/test_fx.py::TestFX::test_custom_codegen, test/test_fx.py::TestFX::test_custom_codegen_with_transformer, test/test_fx.py::TestFX::test_custom_import, test/test_fx.py::TestFX::test_custom_proxy_dynamic_value, test/test_fx.py::TestFX::test_custom_proxy_input_dependent_control_flow, test/test_fx.py::TestFX::test_custom_proxy_type, test/test_fx.py::TestFX::test_custom_proxy_type_literal, test/test_fx.py::TestFX::test_custom_traceback_not_raised_when_exception_source_is_submodule, test/test_fx.py::TestFX::test_custom_traceback_raised_when_exception_source_is_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graph_with_tracer_cls, test/test_fx.py::TestFX::test_deepcopy_graphmodule, test/test_fx.py::TestFX::test_deepcopy_graphmodule_with_transform, test/test_fx.py::TestFX::test_deepcopy_no_recursion, test/test_fx.py::TestFX::test_deepcopy_recursion_depth, test/test_fx.py::TestFX::test_deepcopy_tracer, test/test_fx.py::TestFX::test_deepcopy_with_submods_params, test/test_fx.py::TestFX::test_delete_unused_submodules_leaf, test/test_fx.py::TestFX::test_delete_unused_values, test/test_fx.py::TestFX::test_dict, test/test_fx.py::TestFX::test_direct_param_use, test/test_fx.py::TestFX::test_disallow_override, test/test_fx.py::TestFX::test_ellipsis, test/test_fx.py::TestFX::test_empty_graph_codegen, test/test_fx.py::TestFX::test_enum, test/test_fx.py::TestFX::test_erase_node_error, test/test_fx.py::TestFX::test_example_shape_prop, test/test_fx.py::TestFX::test_find_uses, test/test_fx.py::TestFX::test_fn_type_annotation_empty, test/test_fx.py::TestFX::test_fn_type_annotations, test/test_fx.py::TestFX::test_fx_and_or, test/test_fx.py::TestFX::test_fx_create_arg, test/test_fx.py::TestFX::test_fx_shifts, test/test_fx.py::TestFX::test_fx_stateless, test/test_fx.py::TestFX::test_get_torch_func_signature, test/test_fx.py::TestFX::test_getitem, test/test_fx.py::TestFX::test_getitem_subproc, test/test_fx.py::TestFX::test_graph_edit_with_proxy, test/test_fx.py::TestFX::test_graph_fns, test/test_fx.py::TestFX::test_graph_module, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_dict_init, test/test_fx.py::TestFX::test_graph_module_init_buffer_param_copied_mod_init, test/test_fx.py::TestFX::test_graph_module_replicate_for_dp, test/test_fx.py::TestFX::test_graph_unique_names, test/test_fx.py::TestFX::test_graph_unique_names_manual, test/test_fx.py::TestFX::test_immutable_dict_pytree_ops, test/test_fx.py::TestFX::test_immutable_list_pytree_ops, test/test_fx.py::TestFX::test_imul_code_print, test/test_fx.py::TestFX::test_inf_nan, test/test_fx.py::TestFX::test_inf_nan_kwds, test/test_fx.py::TestFX::test_informative_co_filename, test/test_fx.py::TestFX::test_inline_graph, test/test_fx.py::TestFX::test_insert_arg, test/test_fx.py::TestFX::test_insertion_point, test/test_fx.py::TestFX::test_interpreter, test/test_fx.py::TestFX::test_interpreter_default_args, test/test_fx.py::TestFX::test_interpreter_gc_values, test/test_fx.py::TestFX::test_interpreter_noop_resnet18, test/test_fx.py::TestFX::test_interpreter_not_enough_args, test/test_fx.py::TestFX::test_interpreter_onthefly_swap, test/test_fx.py::TestFX::test_interpreter_other_graph, test/test_fx.py::TestFX::test_interpreter_partial_eval, test/test_fx.py::TestFX::test_interpreter_run_node_override, test/test_fx.py::TestFX::test_interpreter_star_args, test/test_fx.py::TestFX::test_interpreter_with_codegen, test/test_fx.py::TestFX::test_layout, test/test_fx.py::TestFX::test_leaf_module, test/test_fx.py::TestFX::test_lineno_map, test/test_fx.py::TestFX::test_matmul_tracing, test/test_fx.py::TestFX::test_metadata_on_ph, test/test_fx.py::TestFX::test_module_deepcopy_edit_nodes, test/test_fx.py::TestFX::test_move_before, test/test_fx.py::TestFX::test_multi_insert_point, test/test_fx.py::TestFX::test_multiple_default_args, test/test_fx.py::TestFX::test_named_tuple_inlined, test/test_fx.py::TestFX::test_namedtuple_return_qualname, test/test_fx.py::TestFX::test_namedtuple_return_trace, test/test_fx.py::TestFX::test_native_callable, test/test_fx.py::TestFX::test_nn_module_stack, test/test_fx.py::TestFX::test_no_mutation, test/test_fx.py::TestFX::test_node_tagging, test/test_fx.py::TestFX::test_nonetype_annotation, test/test_fx.py::TestFX::test_partial_trace, test/test_fx.py::TestFX::test_pickle_custom_import, test/test_fx.py::TestFX::test_pickle_graphmodule, test/test_fx.py::TestFX::test_pickle_nonetype_annotation, test/test_fx.py::TestFX::test_pickle_torch_custom_ops, test/test_fx.py::TestFX::test_prepend_self, test/test_fx.py::TestFX::test_pretty_print, test/test_fx.py::TestFX::test_pretty_print_graph, test/test_fx.py::TestFX::test_pretty_print_node, test/test_fx.py::TestFX::test_pretty_print_targets, test/test_fx.py::TestFX::test_print_graph, test/test_fx.py::TestFX::test_profiler_ranges_side_effect, test/test_fx.py::TestFX::test_proxy_deepcopy_with_tracer, test/test_fx.py::TestFX::test_proxy_deepcopy_without_tracer, test/test_fx.py::TestFX::test_pytree, test/test_fx.py::TestFX::test_pytree_concrete, test/test_fx.py::TestFX::test_reassign_args_kwargs_uses, test/test_fx.py::TestFX::test_regular_and_default_args, test/test_fx.py::TestFX::test_remove_uses, test/test_fx.py::TestFX::test_remove_uses_with_custom_filter, test/test_fx.py::TestFX::test_replace_input, test/test_fx.py::TestFX::test_replace_uses, test/test_fx.py::TestFX::test_reserved_getattr, test/test_fx.py::TestFX::test_return_tuple, test/test_fx.py::TestFX::test_return_type_exists, test/test_fx.py::TestFX::test_return_type_exists_pre_pep585, test/test_fx.py::TestFX::test_script_method_trace, test/test_fx.py::TestFX::test_script_tensor_constant, test/test_fx.py::TestFX::test_sequential, test/test_fx.py::TestFX::test_shape_prop_aggregate, test/test_fx.py::TestFX::test_shape_prop_layout, test/test_fx.py::TestFX::test_shape_prop_layout_3d, test/test_fx.py::TestFX::test_shape_prop_unbacked_sym, test/test_fx.py::TestFX::test_single_default_arg, test/test_fx.py::TestFX::test_snake_case, test/test_fx.py::TestFX::test_sqrt, test/test_fx.py::TestFX::test_stack_traces, test/test_fx.py::TestFX::test_stack_traces_with_transformer, test/test_fx.py::TestFX::test_string_literal_return, test/test_fx.py::TestFX::test_submodule_manipulation_API, test/test_fx.py::TestFX::test_symbolic_trace_assert, test/test_fx.py::TestFX::test_symbolic_trace_sequential, test/test_fx.py::TestFX::test_tensor_attribute, test/test_fx.py::TestFX::test_tensor_attribute_coalseced, test/test_fx.py::TestFX::test_tensor_constant, test/test_fx.py::TestFX::test_throw_out_variant, test/test_fx.py::TestFX::test_torch_custom_ops, test/test_fx.py::TestFX::test_torch_fx_getattr, test/test_fx.py::TestFX::test_torch_fx_len, test/test_fx.py::TestFX::test_torch_op_overloads, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx, test/test_fx.py::TestFX::test_torchbind_class_attribute_in_fx_tensor_arg, test/test_fx.py::TestFX::test_trace_buffer_slice, test/test_fx.py::TestFX::test_trace_dict_int_keys, test/test_fx.py::TestFX::test_trace_dict_proxy_keys, test/test_fx.py::TestFX::test_trace_fn_constant, test/test_fx.py::TestFX::test_trace_function, test/test_fx.py::TestFX::test_trace_multiple_funcs, test/test_fx.py::TestFX::test_trace_return_dataclass, test/test_fx.py::TestFX::test_trace_return_dataclass_nested, test/test_fx.py::TestFX::test_trace_return_namedtuple, test/test_fx.py::TestFX::test_tracing_graphmodules_as_leaf_submodules, test/test_fx.py::TestFX::test_transformer_multi_outputs, test/test_fx.py::TestFX::test_transformer_noop, test/test_fx.py::TestFX::test_transformer_op_swap, test/test_fx.py::TestFX::test_transformer_preserves_nn_module_stack_for_get_attr, test/test_fx.py::TestFX::test_tuple_no_subscript, test/test_fx.py::TestFX::test_typename_print, test/test_fx.py::TestFX::test_typename_print_pre_pep585, test/test_fx.py::TestFX::test_unpack, test/test_fx.py::TestFX::test_unpack_dict_better_error, test/test_fx.py::TestFX::test_unpack_list_better_error, test/test_fx.py::TestFX::test_update_args_api, test/test_fx.py::TestFX::test_update_args_kwargs_yells_at_you, test/test_fx.py::TestFX::test_update_kwargs_api, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_function, test/test_fx.py::TestFX::test_user_friendly_call_provenance_with_module, test/test_fx.py::TestFX::test_varargs_concrete, test/test_fx.py::TestFX::test_wrap, test/test_fx.py::TestFX::test_wrap_decorated_function, test/test_fx.py::TestFX::test_wrap_fn_directly, test/test_fx.py::TestFX::test_wrap_with_submodule, test/test_fx.py::TestFX::test_wrapped_method, test/test_fx.py::TestFX::test_wrapped_retrace, test/test_fx.py::TestFX::test_wrapped_via_decorator, test/test_fx.py::TestFX::test_wrapped_via_decorator_and_transformed, test/test_fx.py::TestFX::test_wrong_target_type, test/test_fx.py::TestFX::test_wrong_topo, test/test_fx.py::TestFXAPIBackwardCompatibility::test_adding_side_effect_function, test/test_fx.py::TestFXAPIBackwardCompatibility::test_class_member_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_function_back_compat, test/test_fx.py::TestFXAPIBackwardCompatibility::test_preserve_unused_attr_after_unpickle, test/test_fx.py::TestFXAPIBackwardCompatibility::test_public_api_surface, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_adaptive_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_affine_grid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_avg_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_batch_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_binary_cross_entropy_with_logits, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_celu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_tbc, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_conv_transpose3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cosine_similarity, test/test_fx.py::TestFunctionalTracing::test_nn_functional_cross_entropy, test/test_fx.py::TestFunctionalTracing::test_nn_functional_ctc_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_dropout3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_elu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding, test/test_fx.py::TestFunctionalTracing::test_nn_functional_embedding_bag, test/test_fx.py::TestFunctionalTracing::test_nn_functional_feature_alpha_dropout, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_fractional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gaussian_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_glu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_grid_sample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_group_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_gumbel_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardswish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hardtanh_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_hinge_embedding_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_huber_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_instance_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_interpolate, test/test_fx.py::TestFunctionalTracing::test_nn_functional_kl_div, test/test_fx.py::TestFunctionalTracing::test_nn_functional_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_layer_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_leaky_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_linear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_local_response_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_log_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_logsigmoid, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_lp_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_margin_ranking_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool1d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool2d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_pool3d_with_indices, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool1d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool2d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_max_unpool3d, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mish, test/test_fx.py::TestFunctionalTracing::test_nn_functional_mse_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_head_attention_forward, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multi_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_multilabel_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_native_channel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_normalize, test/test_fx.py::TestFunctionalTracing::test_nn_functional_one_hot, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pad, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pairwise_distance, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pdist, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_shuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_pixel_unshuffle, test/test_fx.py::TestFunctionalTracing::test_nn_functional_poisson_nll_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_prelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu6, test/test_fx.py::TestFunctionalTracing::test_nn_functional_relu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rms_norm, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_rrelu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_scaled_dot_product_attention, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_selu_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_silu, test/test_fx.py::TestFunctionalTracing::test_nn_functional_smooth_l1_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_soft_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmax, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softmin, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softplus, test/test_fx.py::TestFunctionalTracing::test_nn_functional_softshrink, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_threshold_, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_triplet_margin_with_distance_loss, test/test_fx.py::TestFunctionalTracing::test_nn_functional_unfold, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_bilinear, test/test_fx.py::TestFunctionalTracing::test_nn_functional_upsample_nearest, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_H_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_T_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___getitem___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___radd___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rdiv___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmatmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmod___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rmul___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rpow___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive___rsub___cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__batch_norm_with_update_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__chunk_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__native_batch_norm_legit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_lengths_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__segment_reduce_offsets_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__softmax_backward_data_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__unsafe_masked_index_put_accumulate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive__upsample_bilinear2d_aa_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_abs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_acosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcdiv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addcmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmm_decomposed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addmv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_addr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_alias_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_all_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_allclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_aminmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_angle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_any_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_arange_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argsort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_argwhere_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_partial_views_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_as_strided_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_asinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_atleast_3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_baddbmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bernoulli_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bfloat16_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_block_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bool_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_shapes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_broadcast_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_bucketize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_byte_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cartesian_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cauchy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cdouble_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ceil_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cfloat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chalf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_char_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_inverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cholesky_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_max_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clamp_min_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_clone_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_column_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_combinations_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_conj_physical_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_constant_pad_nd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_contiguous_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_copysign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_corrcoef_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cos_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cosh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_count_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cov_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cummin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_cumulative_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_deg2rad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diag_embed_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagflat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diagonal_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_diff_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_digamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_floor_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_no_rounding_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_div_trunc_rounding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_double_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_dstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_einsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_permuted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_equal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_erfinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expand_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_expm1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_eye_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_fftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_hfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ifftshift_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_ihfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_irfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fft_rfftn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flip_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fliplr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_flipud_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_float_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_floor_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_fmod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_frexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_full_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gather_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ge_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geometric_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_geqrf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gradient_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_grid_sampler_2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_grid_sampler_3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_gt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_half_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hash_tensor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_heaviside_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histogram_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_histogramdd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_hypot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_i0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_igamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_igammac_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_put_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_reduce_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_index_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_inner_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_int_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isclose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isfinite_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isinf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isnan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isneginf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isposinf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_isreal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_item_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_2inputs_2outputs_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_4inputs_with_extra_args_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_binary_return_by_ref_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_jiterator_unary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_kron_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_kthvalue_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ldexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_le_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lerp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lgamma_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cholesky_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cholesky_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cond_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_cross_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_det_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_diagonal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eig_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigvals_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_eigvalsh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_householder_product_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_inv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_inv_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_factor_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_ldl_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lstsq_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lstsq_grad_oriented_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_factor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_factor_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_lu_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_power_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_matrix_rank_hermitian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_multi_dot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_norm_subgradients_at_zero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_hermitian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_pinv_singular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_qr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_slogdet_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_ex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_solve_triangular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_svd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_svdvals_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_tensorinv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_tensorsolve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vander_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vecdot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linalg_vector_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linspace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_linspace_tensor_overload_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log10_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log1p_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_normal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_log_softmax_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logaddexp2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logaddexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logcumsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logdet_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_and_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_not_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_or_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logical_xor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logspace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logspace_tensor_overload_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_logsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_long_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_lu_unpack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mH_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mT_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_argmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_argmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_cumprod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_cumsum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_fill_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_log_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_logaddexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_logsumexp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_median_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_normalize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_softmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_std_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_masked_var_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_matmul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_matrix_exp_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_pool2d_with_indices_backward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_reduction_no_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_max_reduction_with_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_maximum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_median_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_meshgrid_list_of_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_meshgrid_variadic_tensors_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_binary_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_reduction_no_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_min_reduction_with_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_minimum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mode_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_movedim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_msort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mul_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_multinomial_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mv_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_mvlgamma_mvlgamma_p_5_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nan_to_num_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanmean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanmedian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nanquantile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nansum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_narrow_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_narrow_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_batch_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_dropout_backward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_native_layer_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ne_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_neg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_empty_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_empty_strided_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_full_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_ones_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_new_zeros_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nextafter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_avg_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_adaptive_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_alpha_dropout_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_avg_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_batch_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_binary_cross_entropy_with_logits_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_celu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_channel_shuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_conv_transpose3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cosine_embedding_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cosine_similarity_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_cross_entropy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_ctc_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_dropout_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_elu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_embedding_bag_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_embedding_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_with_train_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_feature_alpha_dropout_without_train_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_fractional_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_gaussian_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_gelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_glu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_grid_sample_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_group_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardsigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardswish_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hardtanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_hinge_embedding_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_huber_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_instance_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_area_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bicubic_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_linear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest-exact_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_nearest_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_interpolate_trilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_kl_div_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_l1_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_layer_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_leaky_relu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_linear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_local_response_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_logsigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_margin_ranking_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_pool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool1d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool2d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_max_unpool3d_grad_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_mish_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_mse_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multi_head_attention_forward_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multi_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_multilabel_soft_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_normalize_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_circular_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_constant_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_reflect_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pad_replicate_negative_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pairwise_distance_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pdist_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pixel_shuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_pixel_unshuffle_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_poisson_nll_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_prelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_relu6_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_relu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_rms_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_rrelu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_scaled_dot_product_attention_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_selu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_silu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_smooth_l1_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_soft_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softmin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softmin_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softplus_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_softsign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_tanhshrink_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_threshold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_triplet_margin_with_distance_loss_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_unfold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_upsample_bilinear_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nn_functional_upsample_nearest_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nonzero_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_nonzero_static_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_fro_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_inf_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_norm_nuc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_in_place_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_normal_number_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ones_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ones_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ormqr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_outer_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pca_lowrank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_permute_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_permute_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pinverse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polar_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_2_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_polygamma_polygamma_n_4_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_positive_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_pow_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_put_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_qr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_quantile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rad2deg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rand_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randint_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randint_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_randn_like_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_ravel_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_real_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reciprocal_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_remainder_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_renorm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_repeat_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_repeat_interleave_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reshape_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_reshape_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resize__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resize_as__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resolve_conj_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_resolve_neg_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_roll_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rot90_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_round_decimals_neg_3_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rsqrt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_rsub_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scalar_tensor_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_add_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_amax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_amin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_prod_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_scatter_reduce_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_searchsorted_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_select_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_select_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sgn_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_short_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sigmoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sign_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_bartlett_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_blackman_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_cosine_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_exponential_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_gaussian_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_general_cosine_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_general_hamming_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_hamming_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_hann_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_kaiser_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signal_windows_nuttall_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_signbit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sin_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sinc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sinh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_slice_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_slice_scatter_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_softmax_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_softmax_with_dtype_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sort_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sparse_mm_reduce_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sparse_sampled_addmm_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_airy_ai_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_j0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_j1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_y0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_bessel_y1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_u_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_v_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_chebyshev_polynomial_w_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_entr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_erfcx_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_h_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_hermite_polynomial_he_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i0e_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_i1e_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_laguerre_polynomial_l_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_legendre_polynomial_p_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_log_ndtr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_i0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_i1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_k0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_modified_bessel_k1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_ndtr_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_ndtri_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_polygamma_special_polygamma_n_0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_scaled_modified_bessel_k1_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_u_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_v_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_shifted_chebyshev_polynomial_w_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_spherical_bessel_j0_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_xlog1py_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_special_zeta_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_list_args_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_with_sizes_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_split_with_sizes_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sqrt_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_square_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_squeeze_multiple_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_stack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_mean_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_std_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_stft_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sub_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sum_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_sum_to_size_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_svd_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_svd_lowrank_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_t_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_t_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_take_along_dim_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_take_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tan_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tanh_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tensor_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tensordot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tile_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_to_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_to_sparse_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_topk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_torch_ops_aten__safe_softmax_default_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trace_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_transpose_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_transpose_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trapezoid_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trapz_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_triangular_solve_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_tril_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_triu_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_true_divide_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_trunc_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unbind_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unbind_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unflatten_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unfold_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unfold_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_uniform_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unique_consecutive_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unique_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsafe_chunk_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsafe_split_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsqueeze_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_unsqueeze_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_mean_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_mean_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_var_unbiased_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vdot_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_as_complex_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_as_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_copy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_view_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vsplit_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_vstack_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_where_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_xlogy_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zero__cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zeros_cpu_float32, test/test_fx.py::TestOperatorSignaturesCPU::test_get_torch_func_signature_exhaustive_zeros_like_cpu_float32, test/test_fx.py::TestVisionTracing::test_torchvision_models_alexnet, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_base, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_small, test/test_fx.py::TestVisionTracing::test_torchvision_models_convnext_tiny, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet121, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet161, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet169, test/test_fx.py::TestVisionTracing::test_torchvision_models_densenet201, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_320_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_mobilenet_v3_large_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fasterrcnn_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_fcos_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_keypointrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_maskrcnn_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_retinanet_resnet50_fpn_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_ssd300_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_detection_ssdlite320_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b0, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b1, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b2, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b3, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b4, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b5, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b6, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_b7, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_l, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_m, test/test_fx.py::TestVisionTracing::test_torchvision_models_efficientnet_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_googlenet, test/test_fx.py::TestVisionTracing::test_torchvision_models_inception_v3, test/test_fx.py::TestVisionTracing::test_torchvision_models_maxvit_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet0_75, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_mnasnet1_3, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v2, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_mobilenet_v3_small, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_3_2gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_x_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_128gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_16gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_1_6gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_32gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_3_2gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_400mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_800mf, test/test_fx.py::TestVisionTracing::test_torchvision_models_regnet_y_8gf, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet152, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet18, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet34, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_32x8d, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext101_64x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_resnext50_32x4d, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_deeplabv3_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet101, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_fcn_resnet50, test/test_fx.py::TestVisionTracing::test_torchvision_models_segmentation_lraspp_mobilenet_v3_large, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x0_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x1_5, test/test_fx.py::TestVisionTracing::test_torchvision_models_shufflenet_v2_x2_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_0, test/test_fx.py::TestVisionTracing::test_torchvision_models_squeezenet1_1, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_swin_v2_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg11_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg13_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg16_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19, test/test_fx.py::TestVisionTracing::test_torchvision_models_vgg19_bn, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mc3_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v1_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_mvit_v2_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r2plus1d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_r3d_18, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_s3d, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_b, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_s, test/test_fx.py::TestVisionTracing::test_torchvision_models_video_swin3d_t, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_b_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_h_14, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_16, test/test_fx.py::TestVisionTracing::test_torchvision_models_vit_l_32, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet101_2, test/test_fx.py::TestVisionTracing::test_torchvision_models_wide_resnet50_2 2025-08-26T20:32:40.3448671Z 2025-08-26T20:32:40.3448926Z Running test_transformers_privateuse1 1/1 ... [2025-08-26 20:32:40.251527] 2025-08-26T20:32:41.7028352Z -- The CXX compiler identification is Clang 12.0.1 2025-08-26T20:32:41.7872356Z -- The C compiler identification is Clang 12.0.1 2025-08-26T20:32:41.8108444Z -- Detecting CXX compiler ABI info 2025-08-26T20:32:42.0155642Z -- Detecting CXX compiler ABI info - done 2025-08-26T20:32:42.0291397Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped 2025-08-26T20:32:42.0300692Z -- Detecting CXX compile features 2025-08-26T20:32:42.0313122Z -- Detecting CXX compile features - done 2025-08-26T20:32:42.0516709Z -- Detecting C compiler ABI info 2025-08-26T20:32:42.1690086Z -- Detecting C compiler ABI info - done 2025-08-26T20:32:42.1818456Z -- Check for working C compiler: /opt/cache/bin/cc - skipped 2025-08-26T20:32:42.1821749Z -- Detecting C compile features 2025-08-26T20:32:42.1826174Z -- Detecting C compile features - done 2025-08-26T20:32:42.3149084Z CMake Warning at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message): 2025-08-26T20:32:42.3150606Z static library kineto_LIBRARY-NOTFOUND not found. 2025-08-26T20:32:42.3151359Z Call Stack (most recent call first): 2025-08-26T20:32:42.3152954Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found) 2025-08-26T20:32:42.3154490Z CMakeLists.txt:29 (find_package) 2025-08-26T20:32:42.3154899Z 2025-08-26T20:32:42.3155140Z  2025-08-26T20:32:42.3159500Z -- Found Torch: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so 2025-08-26T20:32:42.3216536Z -- Configuring done (0.8s) 2025-08-26T20:32:42.3380205Z -- Generating done (0.0s) 2025-08-26T20:32:42.3393208Z -- Build files have been written to: /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/build 2025-08-26T20:32:42.4829507Z [ 5%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o 2025-08-26T20:32:42.4831413Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o 2025-08-26T20:32:42.5847011Z [ 17%] Linking CXX shared library libopenreg.so 2025-08-26T20:32:42.6640404Z [ 17%] Built target openreg 2025-08-26T20:32:42.6739129Z [ 23%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o 2025-08-26T20:32:42.6740884Z [ 35%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o 2025-08-26T20:32:42.6744942Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o 2025-08-26T20:32:42.6746481Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o 2025-08-26T20:32:42.6753715Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o 2025-08-26T20:32:42.6758516Z [ 52%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o 2025-08-26T20:32:42.6760412Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o 2025-08-26T20:32:42.6762334Z [ 64%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o 2025-08-26T20:32:42.8677572Z [ 70%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o 2025-08-26T20:32:42.9101465Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o 2025-08-26T20:32:42.9157361Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o 2025-08-26T20:32:43.3949384Z [ 88%] Linking CXX shared library libtorch_openreg.so 2025-08-26T20:32:43.9161886Z [ 88%] Built target torch_openreg 2025-08-26T20:32:43.9246918Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o 2025-08-26T20:32:44.5755727Z [100%] Linking CXX shared library libtorch_bindings.so 2025-08-26T20:32:44.9044127Z [100%] Built target torch_bindings 2025-08-26T20:32:44.9109434Z Install the project... 2025-08-26T20:32:44.9134439Z -- Install configuration: "" 2025-08-26T20:32:44.9747778Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated 2025-08-26T20:32:44.9749037Z !! 2025-08-26T20:32:44.9749222Z 2025-08-26T20:32:44.9749444Z ******************************************************************************** 2025-08-26T20:32:44.9750605Z 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). 2025-08-26T20:32:44.9751349Z 2025-08-26T20:32:44.9751577Z By 2026-Feb-18, you need to update your project and remove deprecated calls 2025-08-26T20:32:44.9752073Z or your builds will no longer be supported. 2025-08-26T20:32:44.9752322Z 2025-08-26T20:32:44.9752712Z See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. 2025-08-26T20:32:44.9753944Z ******************************************************************************** 2025-08-26T20:32:44.9754195Z 2025-08-26T20:32:44.9754278Z !! 2025-08-26T20:32:44.9754510Z corresp(dist, value, root_dir) 2025-08-26T20:32:45.0193395Z running install 2025-08-26T20:32:45.0205914Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-08-26T20:32:45.0206719Z !! 2025-08-26T20:32:45.0206832Z 2025-08-26T20:32:45.0206957Z ******************************************************************************** 2025-08-26T20:32:45.0207353Z Please avoid running ``setup.py`` directly. 2025-08-26T20:32:45.0207769Z Instead, use pypa/build, pypa/installer or other 2025-08-26T20:32:45.0208147Z standards-based tools. 2025-08-26T20:32:45.0208333Z 2025-08-26T20:32:45.0208563Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-08-26T20:32:45.0209062Z or your builds will no longer be supported. 2025-08-26T20:32:45.0209323Z 2025-08-26T20:32:45.0209771Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-08-26T20:32:45.0210318Z ******************************************************************************** 2025-08-26T20:32:45.0210561Z 2025-08-26T20:32:45.0210663Z !! 2025-08-26T20:32:45.0210875Z self.initialize_options() 2025-08-26T20:32:45.0333875Z running build 2025-08-26T20:32:45.0334126Z running build_py 2025-08-26T20:32:45.0411490Z creating build/lib.linux-x86_64-cpython-313/torch_openreg 2025-08-26T20:32:45.0413452Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg 2025-08-26T20:32:45.0421081Z copying torch_openreg/_utils.py -> build/lib.linux-x86_64-cpython-313/torch_openreg 2025-08-26T20:32:45.0429471Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-08-26T20:32:45.0430611Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-08-26T20:32:45.0437732Z copying torch_openreg/openreg/meta.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-08-26T20:32:45.0444852Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-08-26T20:32:45.0459343Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-08-26T20:32:45.0460842Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-08-26T20:32:45.0474475Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-08-26T20:32:45.0568623Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-08-26T20:32:45.0608382Z running build_ext 2025-08-26T20:32:45.0719988Z building 'torch_openreg._C' extension 2025-08-26T20:32:45.0721818Z creating build/temp.linux-x86_64-cpython-313/torch_openreg/csrc 2025-08-26T20:32:45.0726744Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/include/python3.13 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-313/torch_openreg/csrc/stub.o -Wall -Wextra -Wno-strict-overflow -Wno-unused-parameter -Wno-missing-field-initializers -Wno-unknown-pragmas -fno-strict-aliasing 2025-08-26T20:32:45.1653807Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/torch_openreg/csrc/stub.o -L/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib 2025-08-26T20:32:45.1869191Z running install_lib 2025-08-26T20:32:45.1946447Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-08-26T20:32:45.1949953Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-08-26T20:32:45.1951612Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-08-26T20:32:45.1952981Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/_utils.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-08-26T20:32:45.1954420Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg 2025-08-26T20:32:45.1956023Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/openreg/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg 2025-08-26T20:32:45.1957680Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/openreg/meta.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg 2025-08-26T20:32:45.1959185Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/openreg/random.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg 2025-08-26T20:32:45.1960435Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-08-26T20:32:45.1961520Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-08-26T20:32:45.1963324Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-08-26T20:32:45.1979937Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-08-26T20:32:45.1986223Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-08-26T20:32:45.1992204Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/__init__.py to __init__.cpython-313.pyc 2025-08-26T20:32:45.1996115Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/_utils.py to _utils.cpython-313.pyc 2025-08-26T20:32:45.2002028Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg/__init__.py to __init__.cpython-313.pyc 2025-08-26T20:32:45.2007591Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg/meta.py to meta.cpython-313.pyc 2025-08-26T20:32:45.2010289Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/openreg/random.py to random.cpython-313.pyc 2025-08-26T20:32:45.2015449Z running install_egg_info 2025-08-26T20:32:45.2168182Z running egg_info 2025-08-26T20:32:45.2240498Z creating torch_openreg.egg-info 2025-08-26T20:32:45.2241763Z writing torch_openreg.egg-info/PKG-INFO 2025-08-26T20:32:45.2246107Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt 2025-08-26T20:32:45.2248123Z writing requirements to torch_openreg.egg-info/requires.txt 2025-08-26T20:32:45.2249204Z writing top-level names to torch_openreg.egg-info/top_level.txt 2025-08-26T20:32:45.2250434Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-08-26T20:32:45.2331576Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-08-26T20:32:45.2339367Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-08-26T20:32:45.2340869Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg-0.0.1-py3.13.egg-info 2025-08-26T20:32:45.2346855Z running install_scripts 2025-08-26T20:32:45.5734452Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:32:45.5736962Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_transformers_privateuse1.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:32:45.573445] 2025-08-26T20:32:50.4444757Z 2025-08-26T20:32:50.4445819Z test_transformers_privateuse1 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_transformers_privateuse1_1.1_c539cf5a43ff94bf_.log 2025-08-26T20:32:50.4448302Z 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 2025-08-26T20:32:50.4450027Z 2025-08-26T20:32:50.4450434Z Running test_cpp_api_parity 1/1 ... [2025-08-26 20:32:50.444732] 2025-08-26T20:32:50.4450863Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:32:50.4453143Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_api_parity.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:32:50.445089] 2025-08-26T20:38:23.2776330Z 2025-08-26T20:38:23.2777422Z test_cpp_api_parity 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_api_parity_1.1_6a534ae330357197_.log 2025-08-26T20:38:23.2971247Z Running 488 items in this shard: test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCELoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_mean_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_none_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_BCEWithLogitsLoss_no_batch_dim_sum_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad1size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad2size1_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_reflect_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_stride_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv1d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_padded_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_depthwise_with_multiplier_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_thnn, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_groups_thnn_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_padding, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_padding_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_reflect_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_reflect_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv2d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_1x1x1_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_1x1x1_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_circular_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_circular_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_dilated_strided, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_dilated_strided_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_same, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_same_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_same_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_same_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_valid, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_pad_valid_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_replicate_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_replicate_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_stride, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_stride_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_stride_padding, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_stride_padding_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_zero_batch, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_zero_batch_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_zeros_stride2_pad2, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_Conv3d_zeros_stride2_pad2_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_no_bias, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose1d_no_bias_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_dilated, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_dilated_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_groups, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_groups_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_ConvTranspose2d_no_bias, 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test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_1d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_2d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_nearest_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_3d_zero_dim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_scale_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_align_corners_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_interpolate_trilinear_tuple_3d_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_log_softmax_spatial_special_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_multimarginloss_1d_input_0d_target_no_reduce_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_has_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_sample_functional_no_parity_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim0_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_dim3_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_functional_scalar_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_lastdim_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_dtype_cuda, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special, test/test_cpp_api_parity.py::TestCppApiParity::test_torch_nn_functional_softmax_spatial_special_cuda 2025-08-26T20:38:23.3158917Z 2025-08-26T20:38:23.3159147Z Running test_extension_utils 1/1 ... [2025-08-26 20:38:23.278754] 2025-08-26T20:38:23.3159573Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:38:23.3160641Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_extension_utils.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:38:23.279135] 2025-08-26T20:38:28.1001335Z 2025-08-26T20:38:28.1002443Z test_extension_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_extension_utils_1.1_59b18d1ffa3f95d4_.log 2025-08-26T20:38:28.1003991Z Running 2 items in this shard: test/test_extension_utils.py::TestExtensionUtils::test_external_module_register, test/test_extension_utils.py::TestExtensionUtils::test_external_module_register_with_renamed_backend 2025-08-26T20:38:28.1005212Z 2025-08-26T20:38:28.1005383Z Running test_openreg 1/1 ... [2025-08-26 20:38:28.100359] 2025-08-26T20:38:29.5288882Z -- The CXX compiler identification is Clang 12.0.1 2025-08-26T20:38:29.6022507Z -- The C compiler identification is Clang 12.0.1 2025-08-26T20:38:29.6102569Z -- Detecting CXX compiler ABI info 2025-08-26T20:38:29.7972894Z -- Detecting CXX compiler ABI info - done 2025-08-26T20:38:29.8110310Z -- Check for working CXX compiler: /opt/cache/bin/c++ - skipped 2025-08-26T20:38:29.8114015Z -- Detecting CXX compile features 2025-08-26T20:38:29.8120795Z -- Detecting CXX compile features - done 2025-08-26T20:38:29.8235294Z -- Detecting C compiler ABI info 2025-08-26T20:38:29.9401455Z -- Detecting C compiler ABI info - done 2025-08-26T20:38:29.9528932Z -- Check for working C compiler: /opt/cache/bin/cc - skipped 2025-08-26T20:38:29.9531714Z -- Detecting C compile features 2025-08-26T20:38:29.9536170Z -- Detecting C compile features - done 2025-08-26T20:38:30.0617434Z CMake Warning at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:22 (message): 2025-08-26T20:38:30.0618236Z static library kineto_LIBRARY-NOTFOUND not found. 2025-08-26T20:38:30.0618622Z Call Stack (most recent call first): 2025-08-26T20:38:30.0619336Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/share/cmake/Torch/TorchConfig.cmake:125 (append_torchlib_if_found) 2025-08-26T20:38:30.0620063Z CMakeLists.txt:29 (find_package) 2025-08-26T20:38:30.0620271Z 2025-08-26T20:38:30.0620381Z  2025-08-26T20:38:30.0625525Z -- Found Torch: /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so 2025-08-26T20:38:30.0652535Z -- Configuring done (0.7s) 2025-08-26T20:38:30.0775572Z -- Generating done (0.0s) 2025-08-26T20:38:30.0788554Z -- Build files have been written to: /var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/build 2025-08-26T20:38:30.2247099Z [ 5%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/device.cpp.o 2025-08-26T20:38:30.2248797Z [ 11%] Building CXX object third_party/openreg/CMakeFiles/openreg.dir/csrc/memory.cpp.o 2025-08-26T20:38:30.3225519Z [ 17%] Linking CXX shared library libopenreg.so 2025-08-26T20:38:30.4027570Z [ 17%] Built target openreg 2025-08-26T20:38:30.4124188Z [ 23%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegExtra.cpp.o 2025-08-26T20:38:30.4125719Z [ 29%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/OpenRegMinimal.cpp.o 2025-08-26T20:38:30.4127211Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Minimal.cpp.o 2025-08-26T20:38:30.4128722Z [ 41%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/aten/native/Extra.cpp.o 2025-08-26T20:38:30.4134803Z [ 47%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegDeviceAllocator.cpp.o 2025-08-26T20:38:30.4137028Z [ 52%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegFunctions.cpp.o 2025-08-26T20:38:30.4138547Z [ 58%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGenerator.cpp.o 2025-08-26T20:38:30.4163665Z [ 64%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegGuard.cpp.o 2025-08-26T20:38:30.5945444Z [ 70%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHooks.cpp.o 2025-08-26T20:38:30.6180849Z [ 76%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegHostAllocator.cpp.o 2025-08-26T20:38:30.6512066Z [ 82%] Building CXX object csrc/CMakeFiles/torch_openreg.dir/runtime/OpenRegSerialization.cpp.o 2025-08-26T20:38:31.1687719Z [ 88%] Linking CXX shared library libtorch_openreg.so 2025-08-26T20:38:31.6976033Z [ 88%] Built target torch_openreg 2025-08-26T20:38:31.7065748Z [ 94%] Building CXX object torch_openreg/csrc/CMakeFiles/torch_bindings.dir/Module.cpp.o 2025-08-26T20:38:32.3281319Z [100%] Linking CXX shared library libtorch_bindings.so 2025-08-26T20:38:32.6579194Z [100%] Built target torch_bindings 2025-08-26T20:38:32.6644510Z Install the project... 2025-08-26T20:38:32.6670458Z -- Install configuration: "" 2025-08-26T20:38:32.7220933Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/config/_apply_pyprojecttoml.py:82: SetuptoolsDeprecationWarning: `project.license` as a TOML table is deprecated 2025-08-26T20:38:32.7222816Z !! 2025-08-26T20:38:32.7223030Z 2025-08-26T20:38:32.7223263Z ******************************************************************************** 2025-08-26T20:38:32.7225199Z 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). 2025-08-26T20:38:32.7226611Z 2025-08-26T20:38:32.7227031Z By 2026-Feb-18, you need to update your project and remove deprecated calls 2025-08-26T20:38:32.7227974Z or your builds will no longer be supported. 2025-08-26T20:38:32.7228427Z 2025-08-26T20:38:32.7229119Z See https://packaging.python.org/en/latest/guides/writing-pyproject-toml/#license for details. 2025-08-26T20:38:32.7230229Z ******************************************************************************** 2025-08-26T20:38:32.7230675Z 2025-08-26T20:38:32.7230814Z !! 2025-08-26T20:38:32.7231181Z corresp(dist, value, root_dir) 2025-08-26T20:38:32.7534920Z running install 2025-08-26T20:38:32.7536759Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-08-26T20:38:32.7538443Z !! 2025-08-26T20:38:32.7538647Z 2025-08-26T20:38:32.7538886Z ******************************************************************************** 2025-08-26T20:38:32.7539549Z Please avoid running ``setup.py`` directly. 2025-08-26T20:38:32.7540302Z Instead, use pypa/build, pypa/installer or other 2025-08-26T20:38:32.7541062Z standards-based tools. 2025-08-26T20:38:32.7541401Z 2025-08-26T20:38:32.7541825Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-08-26T20:38:32.7542742Z or your builds will no longer be supported. 2025-08-26T20:38:32.7543221Z 2025-08-26T20:38:32.7543796Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-08-26T20:38:32.7544821Z ******************************************************************************** 2025-08-26T20:38:32.7545298Z 2025-08-26T20:38:32.7545461Z !! 2025-08-26T20:38:32.7545867Z self.initialize_options() 2025-08-26T20:38:32.7672157Z running build 2025-08-26T20:38:32.7672656Z running build_py 2025-08-26T20:38:32.7754154Z creating build/lib.linux-x86_64-cpython-313/torch_openreg 2025-08-26T20:38:32.7755571Z copying torch_openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg 2025-08-26T20:38:32.7758914Z copying torch_openreg/_utils.py -> build/lib.linux-x86_64-cpython-313/torch_openreg 2025-08-26T20:38:32.7761746Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-08-26T20:38:32.7763003Z copying torch_openreg/openreg/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-08-26T20:38:32.7765249Z copying torch_openreg/openreg/meta.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-08-26T20:38:32.7767282Z copying torch_openreg/openreg/random.py -> build/lib.linux-x86_64-cpython-313/torch_openreg/openreg 2025-08-26T20:38:32.7776759Z creating build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-08-26T20:38:32.7777915Z copying torch_openreg/lib/libopenreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-08-26T20:38:32.7790059Z copying torch_openreg/lib/libtorch_openreg.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-08-26T20:38:32.7884476Z copying torch_openreg/lib/libtorch_bindings.so -> build/lib.linux-x86_64-cpython-313/torch_openreg/lib 2025-08-26T20:38:32.7925779Z running build_ext 2025-08-26T20:38:32.8018866Z building 'torch_openreg._C' extension 2025-08-26T20:38:32.8020385Z creating build/temp.linux-x86_64-cpython-313/torch_openreg/csrc 2025-08-26T20:38:32.8025961Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/include/python3.13 -c torch_openreg/csrc/stub.c -o build/temp.linux-x86_64-cpython-313/torch_openreg/csrc/stub.o -Wall -Wextra -Wno-strict-overflow -Wno-unused-parameter -Wno-missing-field-initializers -Wno-unknown-pragmas -fno-strict-aliasing 2025-08-26T20:38:32.8922088Z gcc -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/torch_openreg/csrc/stub.o -L/var/lib/jenkins/workspace/test/cpp_extensions/open_registration_extension/torch_openreg/torch_openreg/lib -ltorch_bindings -o build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -Wl,-rpath,$ORIGIN/lib 2025-08-26T20:38:32.9139753Z running install_lib 2025-08-26T20:38:32.9223548Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libopenreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-08-26T20:38:32.9226290Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_openreg.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-08-26T20:38:32.9245639Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/lib/libtorch_bindings.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg/lib 2025-08-26T20:38:32.9252969Z copying build/lib.linux-x86_64-cpython-313/torch_openreg/_C.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg 2025-08-26T20:38:32.9259825Z running install_egg_info 2025-08-26T20:38:32.9414372Z running egg_info 2025-08-26T20:38:32.9487804Z writing torch_openreg.egg-info/PKG-INFO 2025-08-26T20:38:32.9492135Z writing dependency_links to torch_openreg.egg-info/dependency_links.txt 2025-08-26T20:38:32.9494455Z writing requirements to torch_openreg.egg-info/requires.txt 2025-08-26T20:38:32.9495794Z writing top-level names to torch_openreg.egg-info/top_level.txt 2025-08-26T20:38:32.9577555Z reading manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-08-26T20:38:32.9587643Z writing manifest file 'torch_openreg.egg-info/SOURCES.txt' 2025-08-26T20:38:32.9589147Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg-0.0.1-py3.13.egg-info' (and everything under it) 2025-08-26T20:38:32.9590842Z Copying torch_openreg.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_openreg-0.0.1-py3.13.egg-info 2025-08-26T20:38:32.9597745Z running install_scripts 2025-08-26T20:38:33.2793217Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:38:33.2796336Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_openreg.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:38:33.279398] 2025-08-26T20:38:47.5638455Z 2025-08-26T20:38:47.5639346Z test_openreg 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_openreg_1.1_dcf76942ec0fb034_.log 2025-08-26T20:38:47.5652286Z Running 44 items in this shard: test/test_openreg.py::TestPrivateUse1::test_backend_dispatchstub, test/test_openreg.py::TestPrivateUse1::test_backend_generate_methods, test/test_openreg.py::TestPrivateUse1::test_backend_module_function, test/test_openreg.py::TestPrivateUse1::test_backend_module_methods, test/test_openreg.py::TestPrivateUse1::test_backend_module_registration, test/test_openreg.py::TestPrivateUse1::test_backend_name, test/test_openreg.py::TestPrivateUse1::test_backend_operator_registration, test/test_openreg.py::TestPrivateUse1::test_backend_packed_sequence_methods, test/test_openreg.py::TestPrivateUse1::test_backend_storage_methods, test/test_openreg.py::TestPrivateUse1::test_backend_tensor_methods, test/test_openreg.py::TestPrivateUse1::test_backend_tensor_type, test/test_openreg.py::TestPrivateUse1::test_backend_type_methods, test/test_openreg.py::TestOpenReg::test_autograd_init, test/test_openreg.py::TestOpenReg::test_compile_autograd_function_aliasing, test/test_openreg.py::TestOpenReg::test_compile_autograd_function_returns_self, test/test_openreg.py::TestOpenReg::test_copy_same_device, test/test_openreg.py::TestOpenReg::test_cross_device_copy, test/test_openreg.py::TestOpenReg::test_cross_diff_devices_copy, test/test_openreg.py::TestOpenReg::test_data_dependent_output, test/test_openreg.py::TestOpenReg::test_event_elapsed_time, test/test_openreg.py::TestOpenReg::test_event_wait_stream, test/test_openreg.py::TestOpenReg::test_expand, test/test_openreg.py::TestOpenReg::test_factory, test/test_openreg.py::TestOpenReg::test_fake_tensor, test/test_openreg.py::TestOpenReg::test_generator, test/test_openreg.py::TestOpenReg::test_manual_seed, test/test_openreg.py::TestOpenReg::test_named_tensor, test/test_openreg.py::TestOpenReg::test_open_device_cpu_serialization, test/test_openreg.py::TestOpenReg::test_open_device_dlpack, test/test_openreg.py::TestOpenReg::test_open_device_numpy_serialization, test/test_openreg.py::TestOpenReg::test_pin_memory, test/test_openreg.py::TestOpenReg::test_printing, test/test_openreg.py::TestOpenReg::test_quantize, test/test_openreg.py::TestOpenReg::test_record_event, test/test_openreg.py::TestOpenReg::test_resize, test/test_openreg.py::TestOpenReg::test_rewrapped_storage, test/test_openreg.py::TestOpenReg::test_rng_state, test/test_openreg.py::TestOpenReg::test_scalar_type_fallback, test/test_openreg.py::TestOpenReg::test_serialization, test/test_openreg.py::TestOpenReg::test_stream_synchronize, test/test_openreg.py::TestOpenReg::test_stream_wait_event, test/test_openreg.py::TestOpenReg::test_stream_wait_stream, test/test_openreg.py::TestOpenReg::test_tensor_type_fallback, test/test_openreg.py::TestOpenReg::test_tensorlist_type_fallback 2025-08-26T20:38:47.5663684Z 2025-08-26T20:38:47.5663886Z Running test_show_pickle 1/1 ... [2025-08-26 20:38:47.564186] 2025-08-26T20:38:47.5664300Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:38:47.5665338Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_show_pickle.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:38:47.564552] 2025-08-26T20:38:52.1350924Z 2025-08-26T20:38:52.1351942Z test_show_pickle 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_show_pickle_1.1_e92d65f23de98801_.log 2025-08-26T20:38:52.1353525Z Running 1 items in this shard: test/test_show_pickle.py::TestShowPickle::test_scripted_model 2025-08-26T20:38:52.1354071Z 2025-08-26T20:38:52.1355538Z Running test_torch 1/2 ... [2025-08-26 20:38:52.135345] 2025-08-26T20:38:52.1356202Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:38:52.1359255Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_torch.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:38:52.135676] 2025-08-26T20:46:49.3931213Z 2025-08-26T20:46:49.3932330Z test_torch 1/2 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_1.2_43b3bf5ab98b9559_.log 2025-08-26T20:46:49.4081471Z Running 472 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals_read_write, test/test_torch.py::TestBasicVitalSigns::test_dataloader_vitals, test/test_torch.py::TestTorch::test_RNG_after_pickle, test/test_torch.py::TestTorch::test_Size, test/test_torch.py::TestTorch::test_Size_concat_non_tuple_sequence, test/test_torch.py::TestTorch::test_Size_concat_wildcard, test/test_torch.py::TestTorch::test_Size_scalar, test/test_torch.py::TestTorch::test_allow_tensor_metadata_change, test/test_torch.py::TestTorch::test_backward_hooks_traverse, test/test_torch.py::TestTorch::test_batch_norm_cpu_inference, test/test_torch.py::TestTorch::test_bf16_supported_on_cpu, test/test_torch.py::TestTorch::test_chunk_neg_dim, test/test_torch.py::TestTorch::test_copy_dtypes, test/test_torch.py::TestTorch::test_copy_float16, test/test_torch.py::TestTorch::test_cummax_neg_dim, test/test_torch.py::TestTorch::test_cxx_flags, test/test_torch.py::TestTorch::test_deterministic_flag, test/test_torch.py::TestTorch::test_dim_order, test/test_torch.py::TestTorch::test_doc, test/test_torch.py::TestTorch::test_dot_data_use, test/test_torch.py::TestTorch::test_dtype_is_signed, test/test_torch.py::TestTorch::test_empty_storage_view, test/test_torch.py::TestTorch::test_equal, test/test_torch.py::TestTorch::test_error_msg_type_translation, test/test_torch.py::TestTorch::test_fill_diagonal, test/test_torch.py::TestTorch::test_format_scalar_meta, test/test_torch.py::TestTorch::test_gather_neg_dim, test/test_torch.py::TestTorch::test_generator_cpu, test/test_torch.py::TestTorch::test_index_add_all_dtypes, test/test_torch.py::TestTorch::test_index_add_cornercase, test/test_torch.py::TestTorch::test_index_add_correctness, test/test_torch.py::TestTorch::test_index_add_neg_dim, test/test_torch.py::TestTorch::test_index_copy_neg_dim, test/test_torch.py::TestTorch::test_index_fill_neg_dim, test/test_torch.py::TestTorch::test_index_select_neg_dim, test/test_torch.py::TestTorch::test_invalid_generator_raises, test/test_torch.py::TestTorch::test_is_nonzero, test/test_torch.py::TestTorch::test_is_same_size, test/test_torch.py::TestTorch::test_iter, test/test_torch.py::TestTorch::test_manual_seed, test/test_torch.py::TestTorch::test_median_neg_dim, test/test_torch.py::TestTorch::test_memory_format_contiguous_returns_same_tensor_if_already_satisfies, test/test_torch.py::TestTorch::test_memory_format_empty, test/test_torch.py::TestTorch::test_multinomial_invalid_probs, test/test_torch.py::TestTorch::test_nanmedian_neg_dim, test/test_torch.py::TestTorch::test_narrow_neg_dim, test/test_torch.py::TestTorch::test_nbytes, test/test_torch.py::TestTorch::test_ndim, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_copy_transpose_math_view_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_corrcoef_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_cpp_warnings_have_python_context_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_cublas_config_nondeterministic_alert_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_deepcopy_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_deterministic_empty_cpu_int8, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_diff_noncontig_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_discontiguous_out_cumsum_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_dtypetensor_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_exponential_kstest_cpu_bfloat16, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_binary_op_no_materialize_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_lazy_clone_view_materialize_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_log_normal_cpu_float16, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_cpu_and_cuda_ops_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_empty_like_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_factory_like_functions_preserve_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_operators_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_cpu_float16, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_FractionalMaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad1d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReflectionPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad1d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_bincount_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_linear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_trilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_median_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_put_accumulate_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_put_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_and_graph_partition_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_and_graph_partition_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_and_graph_partition_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_pdist_norm_large_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pickle_gradscaler_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pin_memory_from_constructor_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_repeat_interleave_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scalar_check_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_bool_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_to_large_input_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_zero_size_index_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_serialization_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_shift_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_skip_xla_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_False_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_strides_propagation_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_set_errors_multigpu_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_type_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_untyped_storage_meta_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_warn_always_caught_cpu 2025-08-26T20:46:49.4225299Z 2025-08-26T20:46:49.9433881Z Uploading artifacts took 0.55 seconds 2025-08-26T20:46:49.9437583Z Running test_torch 2/2 ... [2025-08-26 20:46:49.943546] 2025-08-26T20:46:49.9438204Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:46:49.9442901Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_torch.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:46:49.943966] 2025-08-26T20:53:38.0701581Z 2025-08-26T20:53:38.0702420Z test_torch 2/2 was successful, full logs can be found in artifacts with path test/test-reports/test_torch_2.2_c2a56fa1ebbbeab9_.log 2025-08-26T20:53:38.0864156Z Running 481 items in this shard: test/test_torch.py::TestBasicVitalSigns::test_basic_vitals, test/test_torch.py::TestTorch::test_RNGState, test/test_torch.py::TestTorch::test_RNGStateAliasing, test/test_torch.py::TestTorch::test_Size_iter, test/test_torch.py::TestTorch::test_add_meta_scalar, test/test_torch.py::TestTorch::test_apply, test/test_torch.py::TestTorch::test_as_subclass, test/test_torch.py::TestTorch::test_assert_async, test/test_torch.py::TestTorch::test_bmm_multithreaded, test/test_torch.py::TestTorch::test_boxMullerState, test/test_torch.py::TestTorch::test_cat_neg_dim, test/test_torch.py::TestTorch::test_check, test/test_torch.py::TestTorch::test_conj_neg_tolist, test/test_torch.py::TestTorch::test_conj_physical_meta_stride, test/test_torch.py::TestTorch::test_contains, test/test_torch.py::TestTorch::test_copy_broadcast, test/test_torch.py::TestTorch::test_copy_many_to_one, test/test_torch.py::TestTorch::test_copy_transpose, test/test_torch.py::TestTorch::test_cuda_not_built, test/test_torch.py::TestTorch::test_cummin_neg_dim, test/test_torch.py::TestTorch::test_cumprod_neg_dim, test/test_torch.py::TestTorch::test_cumsum_neg_dim, test/test_torch.py::TestTorch::test_data_ptr_of_empty_tensor_with_storage, test/test_torch.py::TestTorch::test_data_ptr_of_empty_view_with_storage, test/test_torch.py::TestTorch::test_deepcopy_gradient, test/test_torch.py::TestTorch::test_deepcopy_parameter, test/test_torch.py::TestTorch::test_deterministic_fill_uninitialized_memory, test/test_torch.py::TestTorch::test_device, test/test_torch.py::TestTorch::test_dir, test/test_torch.py::TestTorch::test_doc_template, test/test_torch.py::TestTorch::test_element_size, test/test_torch.py::TestTorch::test_empty_meta, test/test_torch.py::TestTorch::test_from_buffer, test/test_torch.py::TestTorch::test_from_file, test/test_torch.py::TestTorch::test_get_cpu_capability, test/test_torch.py::TestTorch::test_has_internal_overlap, test/test_torch.py::TestTorch::test_has_storage, test/test_torch.py::TestTorch::test_index_add, test/test_torch.py::TestTorch::test_invalid_arg_error_handling, test/test_torch.py::TestTorch::test_kthvalue_neg_dim, test/test_torch.py::TestTorch::test_linspace_logspace, test/test_torch.py::TestTorch::test_logcumsumexp_neg_dim, test/test_torch.py::TestTorch::test_map, test/test_torch.py::TestTorch::test_map2, test/test_torch.py::TestTorch::test_max_neg_dim, test/test_torch.py::TestTorch::test_mean_neg_dim, test/test_torch.py::TestTorch::test_memory_format, test/test_torch.py::TestTorch::test_min_neg_dim, test/test_torch.py::TestTorch::test_mode_neg_dim, test/test_torch.py::TestTorch::test_new, test/test_torch.py::TestTorch::test_normal_shape, test/test_torch.py::TestTorch::test_numel, test/test_torch.py::TestTorch::test_parsing_int64, test/test_torch.py::TestTorch::test_parsing_intlist, test/test_torch.py::TestTorch::test_permute, test/test_torch.py::TestTorch::test_pickle_dtype, test/test_torch.py::TestTorch::test_pickle_generator, test/test_torch.py::TestTorch::test_pickle_parameter, test/test_torch.py::TestTorch::test_pickle_parameter_no_requires_grad, test/test_torch.py::TestTorch::test_pyobj_preserved, test/test_torch.py::TestTorch::test_renorm_neg_dim, test/test_torch.py::TestTorch::test_set_flush_denormal, test/test_torch.py::TestTorch::test_size_neg_dim, test/test_torch.py::TestTorch::test_size_stride, test/test_torch.py::TestTorch::test_sizeof, test/test_torch.py::TestTorch::test_slow_test, test/test_torch.py::TestTorch::test_sobolengine_bounds, test/test_torch.py::TestTorch::test_sobolengine_bounds_scrambled, test/test_torch.py::TestTorch::test_sobolengine_default_dtype, test/test_torch.py::TestTorch::test_sobolengine_distribution_scrambled, test/test_torch.py::TestTorch::test_sobolengine_draw_base2, test/test_torch.py::TestTorch::test_sobolengine_draw_base2_scrambled, test/test_torch.py::TestTorch::test_sobolengine_fast_forward, test/test_torch.py::TestTorch::test_sobolengine_fast_forward_scrambled, test/test_torch.py::TestTorch::test_sobolengine_high_dim, test/test_torch.py::TestTorch::test_sobolengine_raise, test/test_torch.py::TestTorch::test_sobolengine_reset, test/test_torch.py::TestTorch::test_squeeze_neg_dim, test/test_torch.py::TestTorch::test_std_neg_dim, test/test_torch.py::TestTorch::test_storage_base_new, test/test_torch.py::TestTorch::test_storage_byteswap, test/test_torch.py::TestTorch::test_storage_cycle_via_dict, test/test_torch.py::TestTorch::test_storage_dead_weak_ref, test/test_torch.py::TestTorch::test_storage_dealloc, test/test_torch.py::TestTorch::test_storage_dict_dealloc, test/test_torch.py::TestTorch::test_storage_error, 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test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_masked_select_discontiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_clone_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_consistency_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_preserved_after_permute_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_propagation_rules_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_to_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_memory_format_type_shortcuts_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_module_share_memory_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_deterministic_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_device_constrain_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_multinomial_empty_wo_replacement_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_copy_non_contiguous_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_narrow_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_no_nondeterministic_alert_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveAvgPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveAvgPool3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_AdaptiveMaxPool2d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_CTCLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool1d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool2d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_MaxUnpool3d_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_NLLLoss_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_ReplicationPad3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_grid_sample_3d_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_histc_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_bicubic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_interpolate_bilinear_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_alert_kthvalue_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint2x4, test/test_torch.py::TestTorchDeviceTypeCPU::test_nondeterministic_resize_quantized_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_normal_kstest_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_nullary_op_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_pairwise_distance_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_parallel_cow_materialize_error_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_AdamW_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_Adam_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_params_invalidated_with_grads_invalidated_between_unscale_and_step_SGD_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_pdist_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_accumulate_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_put_mem_overlap_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_reduced_type_float_copy_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_non_unique_index_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_one_dim_deterministic_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_add_to_large_input_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_bool_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_multiply_unsupported_dtypes_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_non_unique_index_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_operations_to_large_input_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_scatter_reduce_scalar_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_default_tensor_type_warnings_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_set_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_all_devices_non_blocking_True_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_errors_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_from_tensor_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_meta_ok_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_qint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_quint4x2, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_setitem_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_storage_use_count_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_sync_warning_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_int32, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_take_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_bool, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_complex64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_from_storage_cpu_uint8, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_shape_empty_cpu, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_tensor_storage_type_cpu_int64, test/test_torch.py::TestTorchDeviceTypeCPU::test_ternary_op_mem_overlap_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_complex128, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_float64, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int16, test/test_torch.py::TestTorchDeviceTypeCPU::test_typed_storage_meta_cpu_int8, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_bfloat16, test/test_torch.py::TestTorchDeviceTypeCPU::test_uniform_kstest_cpu_float32, test/test_torch.py::TestTorchDeviceTypeCPU::test_where_scalar_handcrafted_values_cpu 2025-08-26T20:53:38.1011089Z 2025-08-26T20:53:38.1011307Z Running test_tensorexpr 1/1 ... [2025-08-26 20:53:38.072200] 2025-08-26T20:53:38.1011730Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:53:38.1012782Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_tensorexpr.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:53:38.072611] 2025-08-26T20:53:41.5421616Z 2025-08-26T20:53:41.5422750Z test_tensorexpr 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_tensorexpr_1.1_d09a3f61a8aebf4f_.log 2025-08-26T20:53:41.5445867Z Running 74 items in this shard: test/test_tensorexpr.py::TestTensorExprFuser::test_add_const_rhs, test/test_tensorexpr.py::TestTensorExprFuser::test_add_sub, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_input_and_module, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_alias_analysis_module, test/test_tensorexpr.py::TestTensorExprFuser::test_all_combos, test/test_tensorexpr.py::TestTensorExprFuser::test_alpha, test/test_tensorexpr.py::TestTensorExprFuser::test_binary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_bitwise_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast3, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_2, test/test_tensorexpr.py::TestTensorExprFuser::test_broadcast_big2, test/test_tensorexpr.py::TestTensorExprFuser::test_cat, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_empty_tensors, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_negative_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_only, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_promote_inputs, test/test_tensorexpr.py::TestTensorExprFuser::test_cat_with_constant_dim, test/test_tensorexpr.py::TestTensorExprFuser::test_char, test/test_tensorexpr.py::TestTensorExprFuser::test_chunk, test/test_tensorexpr.py::TestTensorExprFuser::test_clamp, test/test_tensorexpr.py::TestTensorExprFuser::test_constant, test/test_tensorexpr.py::TestTensorExprFuser::test_double, test/test_tensorexpr.py::TestTensorExprFuser::test_double_intrinsics, test/test_tensorexpr.py::TestTensorExprFuser::test_dynamic_shape, test/test_tensorexpr.py::TestTensorExprFuser::test_easy, test/test_tensorexpr.py::TestTensorExprFuser::test_eq, test/test_tensorexpr.py::TestTensorExprFuser::test_exp_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_four_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_ge, test/test_tensorexpr.py::TestTensorExprFuser::test_gt, test/test_tensorexpr.py::TestTensorExprFuser::test_guard_fails, test/test_tensorexpr.py::TestTensorExprFuser::test_half_bn_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_half_gelu, test/test_tensorexpr.py::TestTensorExprFuser::test_int64_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_int_output, test/test_tensorexpr.py::TestTensorExprFuser::test_le, test/test_tensorexpr.py::TestTensorExprFuser::test_loop, test/test_tensorexpr.py::TestTensorExprFuser::test_lt, test/test_tensorexpr.py::TestTensorExprFuser::test_mask, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction2, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1, test/test_tensorexpr.py::TestTensorExprFuser::test_min_max_reduction_dim1_2, test/test_tensorexpr.py::TestTensorExprFuser::test_multi_rand, test/test_tensorexpr.py::TestTensorExprFuser::test_multioutput, test/test_tensorexpr.py::TestTensorExprFuser::test_multiple_outputs, test/test_tensorexpr.py::TestTensorExprFuser::test_nans, test/test_tensorexpr.py::TestTensorExprFuser::test_ne, test/test_tensorexpr.py::TestTensorExprFuser::test_promotion, test/test_tensorexpr.py::TestTensorExprFuser::test_propagated_mem_layout, test/test_tensorexpr.py::TestTensorExprFuser::test_rand_like, test/test_tensorexpr.py::TestTensorExprFuser::test_rank_two, test/test_tensorexpr.py::TestTensorExprFuser::test_relu, test/test_tensorexpr.py::TestTensorExprFuser::test_remainder, test/test_tensorexpr.py::TestTensorExprFuser::test_reps, test/test_tensorexpr.py::TestTensorExprFuser::test_round_2, test/test_tensorexpr.py::TestTensorExprFuser::test_scalar, test/test_tensorexpr.py::TestTensorExprFuser::test_short, test/test_tensorexpr.py::TestTensorExprFuser::test_simple_add, test/test_tensorexpr.py::TestTensorExprFuser::test_sin_pow, test/test_tensorexpr.py::TestTensorExprFuser::test_slice, test/test_tensorexpr.py::TestTensorExprFuser::test_sliced_stride, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cpu, test/test_tensorexpr.py::TestTensorExprFuser::test_softmax_cuda, test/test_tensorexpr.py::TestTensorExprFuser::test_strided_output_preserved, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg, test/test_tensorexpr.py::TestTensorExprFuser::test_three_arg2, test/test_tensorexpr.py::TestTensorExprFuser::test_transpose, test/test_tensorexpr.py::TestTensorExprFuser::test_unary_ops, test/test_tensorexpr.py::TestTensorExprFuser::test_unsqueeze, test/test_tensorexpr.py::TestTensorExprFuser::test_where 2025-08-26T20:53:41.5467663Z 2025-08-26T20:53:41.5467906Z Running test_namedtuple_return_api 1/1 ... [2025-08-26 20:53:41.542328] 2025-08-26T20:53:41.5468367Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:53:41.5469450Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_namedtuple_return_api.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:53:41.542628] 2025-08-26T20:53:48.4669048Z 2025-08-26T20:53:48.4670299Z test_namedtuple_return_api 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_namedtuple_return_api_1.1_c8e32001193e8cc5_.log 2025-08-26T20:53:48.4672151Z Running 3 items in this shard: test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_import_return_types, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_namedtuple_return, test/test_namedtuple_return_api.py::TestNamedTupleAPI::test_native_functions_yaml 2025-08-26T20:53:48.4673723Z 2025-08-26T20:53:48.4673939Z Running test_multiprocessing 1/1 ... [2025-08-26 20:53:48.467183] 2025-08-26T20:53:48.4674371Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:53:48.4677808Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:53:48.467551] 2025-08-26T20:55:26.5822551Z 2025-08-26T20:55:26.5824209Z test_multiprocessing 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_1.1_6be91ed6a23c4aa8_.log 2025-08-26T20:55:26.5840093Z 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 2025-08-26T20:55:26.5854038Z 2025-08-26T20:55:26.5854267Z Running test_autograd_fallback 1/1 ... [2025-08-26 20:55:26.582609] 2025-08-26T20:55:26.5854704Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:55:26.5855761Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autograd_fallback.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:55:26.583014] 2025-08-26T20:55:39.8664702Z 2025-08-26T20:55:39.8665571Z test_autograd_fallback 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autograd_fallback_1.1_e8243f7bba56ebe0_.log 2025-08-26T20:55:39.8678734Z Running 28 items in this shard: test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_base_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_composite_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_cpu_return_self_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_autograd_function_registered_to_cpu_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_inplace_on_tensor_that_does_not_require_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_inplace_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_autograd_kernel_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_no_grad_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_leaf_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_post_autograd_returns_mix_of_requires_grad_tensors_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_supports_tensor_lists_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_grads_mode_warn, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_nothing, test/test_autograd_fallback.py::TestAutogradFallback::test_undefined_inputs_outputs_mode_warn 2025-08-26T20:55:39.8690625Z 2025-08-26T20:55:39.8690828Z Running test_fake_tensor 1/1 ... [2025-08-26 20:55:39.866756] 2025-08-26T20:55:39.8691221Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:55:39.8692520Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_fake_tensor.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:55:39.867086] 2025-08-26T20:56:36.6313180Z 2025-08-26T20:56:36.6315042Z test_fake_tensor 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_fake_tensor_1.1_e691602f0eb21410_.log 2025-08-26T20:56:36.6432598Z Running 290 items in this shard: test/test_fake_tensor.py::FakeTensorTest::test__adaptive_avg_pool2d_backward, test/test_fake_tensor.py::FakeTensorTest::test_alias_call, test/test_fake_tensor.py::FakeTensorTest::test_allow_meta, test/test_fake_tensor.py::FakeTensorTest::test_aten_copy_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_index_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_aten_slice_scatter_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_basic, test/test_fake_tensor.py::FakeTensorTest::test_batch_tensor, test/test_fake_tensor.py::FakeTensorTest::test_binary_op_type_promotion, test/test_fake_tensor.py::FakeTensorTest::test_constructor, test/test_fake_tensor.py::FakeTensorTest::test_convert_fake_to_real, test/test_fake_tensor.py::FakeTensorTest::test_cpu_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cuda_initialized, test/test_fake_tensor.py::FakeTensorTest::test_cuda_lstm, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_with_fallback, test/test_fake_tensor.py::FakeTensorTest::test_cudnn_rnn_without_fallback, test/test_fake_tensor.py::FakeTensorTest::test_custom_op_fallback, test/test_fake_tensor.py::FakeTensorTest::test_data_dependent_operator, test/test_fake_tensor.py::FakeTensorTest::test_deepcopy, test/test_fake_tensor.py::FakeTensorTest::test_device_inplace_copy, test/test_fake_tensor.py::FakeTensorTest::test_embedding_bag_meta, test/test_fake_tensor.py::FakeTensorTest::test_export_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fake_device, test/test_fake_tensor.py::FakeTensorTest::test_fake_dispatch_keys, test/test_fake_tensor.py::FakeTensorTest::test_fake_grad_copy, test/test_fake_tensor.py::FakeTensorTest::test_fake_mode_error, test/test_fake_tensor.py::FakeTensorTest::test_fast_div, test/test_fake_tensor.py::FakeTensorTest::test_from_numpy, test/test_fake_tensor.py::FakeTensorTest::test_fsdp_flat_param, test/test_fake_tensor.py::FakeTensorTest::test_full, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex128, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_complex64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int16, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int32, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int64, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_int8, test/test_fake_tensor.py::FakeTensorTest::test_index_cuda_with_cpu_uint8, test/test_fake_tensor.py::FakeTensorTest::test_index_put_error, test/test_fake_tensor.py::FakeTensorTest::test_jagged_fake_to_fake_preserved, test/test_fake_tensor.py::FakeTensorTest::test_like_constructor, test/test_fake_tensor.py::FakeTensorTest::test_mixed_real_and_fake_inputs, test/test_fake_tensor.py::FakeTensorTest::test_mode, test/test_fake_tensor.py::FakeTensorTest::test_nan_to_num, test/test_fake_tensor.py::FakeTensorTest::test_nanmean_out, test/test_fake_tensor.py::FakeTensorTest::test_new, test/test_fake_tensor.py::FakeTensorTest::test_no_tag_func, test/test_fake_tensor.py::FakeTensorTest::test_non_kwarg_device, test/test_fake_tensor.py::FakeTensorTest::test_non_overlapping_stride_zero, test/test_fake_tensor.py::FakeTensorTest::test_non_parameter_grad, test/test_fake_tensor.py::FakeTensorTest::test_normalize_device, test/test_fake_tensor.py::FakeTensorTest::test_op_with_zero_dim_bypassed, test/test_fake_tensor.py::FakeTensorTest::test_out_multi_device, test/test_fake_tensor.py::FakeTensorTest::test_parameter_instantiation, test/test_fake_tensor.py::FakeTensorTest::test_parameter_view, test/test_fake_tensor.py::FakeTensorTest::test_print_in_fake_mode, test/test_fake_tensor.py::FakeTensorTest::test_randperm, test/test_fake_tensor.py::FakeTensorTest::test_recursive_invocation, test/test_fake_tensor.py::FakeTensorTest::test_repr, test/test_fake_tensor.py::FakeTensorTest::test_same_shape_env_preserved, test/test_fake_tensor.py::FakeTensorTest::test_scalar_inputs, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_False, test/test_fake_tensor.py::FakeTensorTest::test_scan_reverse_True, test/test_fake_tensor.py::FakeTensorTest::test_setitem, test/test_fake_tensor.py::FakeTensorTest::test_shape_take_not_device, test/test_fake_tensor.py::FakeTensorTest::test_split_return_self, test/test_fake_tensor.py::FakeTensorTest::test_throw, test/test_fake_tensor.py::FakeTensorTest::test_tolist, test/test_fake_tensor.py::FakeTensorTest::test_type_as, test/test_fake_tensor.py::FakeTensorTest::test_unbind_copy_out, test/test_fake_tensor.py::FakeTensorTest::test_unsqueeze_copy, test/test_fake_tensor.py::FakeTensorTest::test_upsample_bilinear_small_channels, test/test_fake_tensor.py::FakeTensorTest::test_zero_dim, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test__adaptive_avg_pool2d_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_alias_call_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_allow_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_copy_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_index_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_aten_slice_scatter_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_basic_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_batch_tensor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_binary_op_type_promotion_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_convert_fake_to_real_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cpu_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_initialized_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cuda_lstm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_with_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_cudnn_rnn_without_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_custom_op_fallback_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_data_dependent_operator_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_deepcopy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_device_inplace_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_embedding_bag_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_export_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_dispatch_keys_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_grad_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fake_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fast_div_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_from_numpy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_fsdp_flat_param_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_full_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex128_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_complex64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fn_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e4m3fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_float8_e5m2fnuz_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int16_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int32_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int64_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_int8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_cuda_with_cpu_uint8_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_index_put_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_jagged_fake_to_fake_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_like_constructor_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mixed_real_and_fake_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_nanmean_out_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_no_tag_func_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_overlapping_stride_zero_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_non_parameter_grad_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_normalize_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_op_with_zero_dim_bypassed_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_out_multi_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_instantiation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_parameter_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_print_in_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_randperm_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_recursive_invocation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_repr_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_same_shape_env_preserved_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scalar_inputs_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_False_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_scan_reverse_True_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_setitem_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_shape_take_not_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_split_return_self_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_throw_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_tolist_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_type_as_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unbind_copy_out_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_unsqueeze_copy_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_upsample_bilinear_small_channels_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorTest::test_zero_dim_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorConstHandling::test_aliased_const_write, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_constant_propagate_through_functions, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_batch_norm_cpu, test/test_fake_tensor.py::FakeTensorConstHandling::test_fake_tensor_in_intlist_repro, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_add, test/test_fake_tensor.py::FakeTensorConstHandling::test_inplace_view_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storage_invalidation, test/test_fake_tensor.py::FakeTensorConstHandling::test_shared_storages, test/test_fake_tensor.py::FakeTensorConstHandling::test_simple, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_aliased_const_write_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_constant_propagate_through_functions_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_batch_norm_cpu_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_fake_tensor_in_intlist_repro_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_add_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_inplace_view_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storage_invalidation_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_shared_storages_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConstHandling::test_simple_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorOpInfoTestCPU::test_fake_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCatCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyCubeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyMulScalarCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNMSCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyNonzeroCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySortCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyTakeCustomOp_cpu_float32, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOpInfoTestCPU::test_fake_propagate_real_tensors_NumpyViewCopyCustomOp_cpu_float32, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_key, test/test_fake_tensor.py::FakeTensorConverterTest::test_dead_weak_ref, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_from_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_memoized_conversion_to_meta, test/test_fake_tensor.py::FakeTensorConverterTest::test_multiple_modes, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_active_mode, test/test_fake_tensor.py::FakeTensorConverterTest::test_no_ref_cycle, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_mode_error, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_non_view, test/test_fake_tensor.py::FakeTensorConverterTest::test_separate_tensor_storages_view, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_key_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_dead_weak_ref_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_from_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_memoized_conversion_to_meta_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_multiple_modes_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_active_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_no_ref_cycle_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_mode_error_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_non_view_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorConverterTest::test_separate_tensor_storages_view_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_conv_c1_backward, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_cross_entropy_loss, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_embedding_bag_private, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_fake_gpu_no_init, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_flash_attention, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_like_ops, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_module_to, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_no_dispatch_with_like_function, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_non_kwarg_only_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_sparse_new, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_str_storage, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device, test/test_fake_tensor.py::FakeTensorOperatorInvariants::test_tensor_new, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_conv_c1_backward_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_cross_entropy_loss_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_embedding_bag_private_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_fake_gpu_no_init_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_flash_attention_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_like_ops_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_module_to_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_no_dispatch_with_like_function_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_non_kwarg_only_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_sparse_new_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_str_storage_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_constructors_all_have_kwarg_device_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorOperatorInvariants::test_tensor_new_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module, test/test_fake_tensor.py::FakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args, test/test_fake_tensor.py::FakeTensorPropTest::test_nan_to_num, test/test_fake_tensor.py::FakeTensorPropTest::test_nonzero_stride, test/test_fake_tensor.py::FakeTensorPropTest::test_torch_load_with_fake_mode, test/test_fake_tensor.py::FakeTensorPropTest::test_unbacked_shape_realloc, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_fake_tensor_prop_on_nn_module_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_fake_tensor_prop_on_nn_module_with_optional_args_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_nan_to_num_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_nonzero_stride_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_torch_load_with_fake_mode_propagate_real_tensors, test/test_fake_tensor.py::PropagateRealTensorsFakeTensorPropTest::test_unbacked_shape_realloc_propagate_real_tensors, test/test_fake_tensor.py::FakeTensorSerialization::test_serialization, test/test_fake_tensor.py::FakeTensorSerialization::test_serialization_with_tracing, test/test_fake_tensor.py::FakeTensorDispatchCache::test__upsample_bilinear2d_aa_backward_dynamic_shapes, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_aten_index, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_bypass, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_default_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_dispatch_key_set, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_hit, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_inplace_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_constants, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_device, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_dtype, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_conj, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_inference, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_is_neg, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_memory_format, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_requires_grad, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_shape, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_storage_offset, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_key_stride, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_tuple_outputs, test/test_fake_tensor.py::FakeTensorDispatchCache::test_cache_view_op, test/test_fake_tensor.py::FakeTensorDispatchCache::test_fft_hfft2_issue145522, test/test_fake_tensor.py::FakeTensorDispatchCache::test_from_buffer, test/test_fake_tensor.py::FakeTensorDispatchCache::test_inference_mode, test/test_fake_tensor.py::FakeTensorDispatchCache::test_invoke_subgraph, test/test_fake_tensor.py::FakeTensorDispatchCache::test_invoke_subgraph_cacheable_inplace, test/test_fake_tensor.py::FakeTensorDispatchCache::test_meta_tensor_to_fake_cpu, test/test_fake_tensor.py::FakeTensorDispatchCache::test_shape_env_settings, test/test_fake_tensor.py::FakeTensorDispatchCache::test_unbacked_output, test/test_fake_tensor.py::FakeTensorDispatchCache::test_wrapper_tensor_subclass_different_device, test/test_fake_tensor.py::FakeTensorPreferDeviceType::test_fake_tensor_prefer_device_type, test/test_fake_tensor.py::FakeTensorPreferDeviceType::test_fake_tensor_prefer_device_type_cpu_only 2025-08-26T20:56:36.6548046Z 2025-08-26T20:56:36.6548256Z Running test_python_dispatch 1/1 ... [2025-08-26 20:56:36.632078] 2025-08-26T20:56:36.6548688Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:56:36.6549771Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_python_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:56:36.632418] 2025-08-26T20:57:22.9704839Z 2025-08-26T20:57:22.9706005Z test_python_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_python_dispatch_1.1_bab38d8c021a889a_.log 2025-08-26T20:57:22.9751741Z Running 118 items in this shard: test/test_python_dispatch.py::TestDispatcherPythonBindings::test_call_boxed, test/test_python_dispatch.py::TestPythonRegistration::test_alias_analysis, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_no_existing, test/test_python_dispatch.py::TestPythonRegistration::test_create_new_library_fragment_with_existing, test/test_python_dispatch.py::TestPythonRegistration::test_dispatchkeyset_eq, test/test_python_dispatch.py::TestPythonRegistration::test_dispatchkeyset_pickle, test/test_python_dispatch.py::TestPythonRegistration::test_error_for_unsupported_ns_or_kind, test/test_python_dispatch.py::TestPythonRegistration::test_error_if_fn_not_callable, test/test_python_dispatch.py::TestPythonRegistration::test_extend_library_with_dispatch_key_arg, test/test_python_dispatch.py::TestPythonRegistration::test_fallback, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_fallback_keyset, test/test_python_dispatch.py::TestPythonRegistration::test_fallthrough_for_dense_key_with_meta_in_tls, test/test_python_dispatch.py::TestPythonRegistration::test_finalizer, test/test_python_dispatch.py::TestPythonRegistration::test_override_aten_ops_with_multiple_libraries, test/test_python_dispatch.py::TestPythonRegistration::test_override_cpu_sum, test/test_python_dispatch.py::TestPythonRegistration::test_override_cuda_with_jiterator, test/test_python_dispatch.py::TestPythonRegistration::test_register_fallthrough, test/test_python_dispatch.py::TestPythonRegistration::test_returning_symint, test/test_python_dispatch.py::TestPythonDispatch::test_all_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_autograd_in_attr, test/test_python_dispatch.py::TestPythonDispatch::test_basic, test/test_python_dispatch.py::TestPythonDispatch::test_capture_logs_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_construct_int_tensor, test/test_python_dispatch.py::TestPythonDispatch::test_custom_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_custom_dispatch_mode_not_supports_higher_order_operators, test/test_python_dispatch.py::TestPythonDispatch::test_custom_dispatch_mode_supports_higher_order_operators, test/test_python_dispatch.py::TestPythonDispatch::test_custom_size_policy_dynamic_shapes, test/test_python_dispatch.py::TestPythonDispatch::test_data_ptr_respects_numel_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_non_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_deepcopy_wrapper_subclass_with_clone_returning_different_type, test/test_python_dispatch.py::TestPythonDispatch::test_detach_appears_twice_when_called_once, test/test_python_dispatch.py::TestPythonDispatch::test_device_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dim_slowpath, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_call_list_arg, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_super_dont_autograd, test/test_python_dispatch.py::TestPythonDispatch::test_dispatch_uint64, test/test_python_dispatch.py::TestPythonDispatch::test_error_using_class_method_on_mode, test/test_python_dispatch.py::TestPythonDispatch::test_exception_handling, test/test_python_dispatch.py::TestPythonDispatch::test_fancy_strides, test/test_python_dispatch.py::TestPythonDispatch::test_format, test/test_python_dispatch.py::TestPythonDispatch::test_get_cur_mode, test/test_python_dispatch.py::TestPythonDispatch::test_get_mode_stack, test/test_python_dispatch.py::TestPythonDispatch::test_index_put_where_only_index_is_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_invalid_ret, test/test_python_dispatch.py::TestPythonDispatch::test_is_contiguous_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only, test/test_python_dispatch.py::TestPythonDispatch::test_kwarg_only_and_positional_default, test/test_python_dispatch.py::TestPythonDispatch::test_layout_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_like, test/test_python_dispatch.py::TestPythonDispatch::test_list_ret, test/test_python_dispatch.py::TestPythonDispatch::test_make_fx_with_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_make_subclass_with_modes, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_noalloc, test/test_python_dispatch.py::TestPythonDispatch::test_make_wrapper_subclass_propagates_metadata, test/test_python_dispatch.py::TestPythonDispatch::test_maybe_tuple_bug, test/test_python_dispatch.py::TestPythonDispatch::test_mode_detection, test/test_python_dispatch.py::TestPythonDispatch::test_mode_with_make_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_multiple_ops_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_nested_push_logging_tensor_mode, test/test_python_dispatch.py::TestPythonDispatch::test_nesting_same_mode, test/test_python_dispatch.py::TestPythonDispatch::test_new_ones, test/test_python_dispatch.py::TestPythonDispatch::test_none_wrapping, test/test_python_dispatch.py::TestPythonDispatch::test_notimplemented_mode, test/test_python_dispatch.py::TestPythonDispatch::test_optional_tensor_list, test/test_python_dispatch.py::TestPythonDispatch::test_out, test/test_python_dispatch.py::TestPythonDispatch::test_produce_real_type, test/test_python_dispatch.py::TestPythonDispatch::test_record_stream, test/test_python_dispatch.py::TestPythonDispatch::test_return_and_correct_aliasing_gives_correct_stride, test/test_python_dispatch.py::TestPythonDispatch::test_return_stream, test/test_python_dispatch.py::TestPythonDispatch::test_set_data, test/test_python_dispatch.py::TestPythonDispatch::test_shallow_copy_and_detach, test/test_python_dispatch.py::TestPythonDispatch::test_sizes_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_standard_is_not_subclass, test/test_python_dispatch.py::TestPythonDispatch::test_storage, test/test_python_dispatch.py::TestPythonDispatch::test_storage_can_be_converted_to_python_object, test/test_python_dispatch.py::TestPythonDispatch::test_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_creation, test/test_python_dispatch.py::TestPythonDispatch::test_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_sym_sizes_strides_slow_path, test/test_python_dispatch.py::TestPythonDispatch::test_tolist_numpy_with_torch_dispatch_mode, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_basic, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_respects_no_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_subclass_priority, test/test_python_dispatch.py::TestPythonDispatch::test_torch_dispatch_mode_unrelated_tensors, test/test_python_dispatch.py::TestPythonDispatch::test_version, test/test_python_dispatch.py::TestPythonDispatch::test_view_returns_alias_under_torch_dispatch, test/test_python_dispatch.py::TestPythonDispatch::test_with_mode_created_separately, test/test_python_dispatch.py::TestPythonDispatch::test_with_nested_modes, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_extra_dispatch_keys, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_multiprocessing_preserves_dtype, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_reentrant_dispatch_with_mode, test/test_python_dispatch.py::TestPythonDispatch::test_wrapper_subclass_serializes, test/test_python_dispatch.py::TestPythonDispatcher::test_basic, test/test_python_dispatch.py::TestPythonDispatcher::test_lstsq, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_cat_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_conv2d_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCatCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyCubeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyMulScalarCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNMSCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyNonzeroCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySortCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpySplitCopyWithIntCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyTakeCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_custom_NumpyViewCopyCustomOp_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_fft_fft2_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_mul_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_native_batch_norm_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_out_op_cpu, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_split_list_args_cpu_float32, test/test_python_dispatch.py::TestWrapperSubclassAliasingCPU::test_wrapper_subclass_aliasing_view_cpu_float32 2025-08-26T20:57:22.9796216Z 2025-08-26T20:57:22.9796404Z Running test_autocast 1/1 ... [2025-08-26 20:57:22.970891] 2025-08-26T20:57:22.9796804Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:57:22.9797839Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_autocast.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:57:22.971284] 2025-08-26T20:57:36.9554480Z 2025-08-26T20:57:36.9555889Z test_autocast 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_autocast_1.1_307998fb5ae2e70c_.log 2025-08-26T20:57:36.9565619Z Running 20 items in this shard: test/test_autocast.py::TestAutocastCPU::test_autocast_disabled_with_fp32_dtype, test/test_autocast.py::TestAutocastCPU::test_autocast_methods_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_16, test/test_autocast.py::TestAutocastCPU::test_autocast_nn_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_rnn, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_16, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_expect_builtin_promote, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_fp32, test/test_autocast.py::TestAutocastCPU::test_autocast_torch_need_autocast_promote, test/test_autocast.py::TestAutocastCPU::test_cpu_autocast_deprecated_warning, test/test_autocast.py::TestAutocastCPU::test_generic_autocast, test/test_autocast.py::TestAutocastGPU::test_autocast_prioritize, test/test_autocast.py::TestAutocastGPU::test_cache_disabled, test/test_autocast.py::TestAutocastGPU::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_cast_cache_is_global, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_bfloat16_supported, test/test_autocast.py::TestAutocastMPS::test_mps_autocast_error_message, test/test_autocast.py::TestTorchAutocast::test_autocast_fast_dtype, test/test_autocast.py::TestTorchAutocast::test_invalid_device, test/test_autocast.py::TestTorchAutocast::test_non_string_device 2025-08-26T20:57:36.9571743Z 2025-08-26T20:57:36.9571942Z Running test_jit_disabled 1/1 ... [2025-08-26 20:57:36.955718] 2025-08-26T20:57:36.9572356Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:57:36.9573626Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jit_disabled.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:57:36.956093] 2025-08-26T20:57:41.7772179Z 2025-08-26T20:57:41.7773054Z test_jit_disabled 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jit_disabled_1.1_4231bf8fd0f5d30b_.log 2025-08-26T20:57:41.7774925Z Running 3 items in this shard: test/test_jit_disabled.py::TestJitDisabled::test_attribute, test/test_jit_disabled.py::TestJitDisabled::test_recursive_script, test/test_jit_disabled.py::TestJitDisabled::test_script_module_construction 2025-08-26T20:57:41.7776210Z 2025-08-26T20:57:41.7776395Z Running test_dispatch 1/1 ... [2025-08-26 20:57:41.777436] 2025-08-26T20:57:41.7776794Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:57:41.7779839Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_dispatch.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:57:41.777770] 2025-08-26T20:58:58.9530521Z 2025-08-26T20:58:58.9532001Z test_dispatch 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_dispatch_1.1_74c690f515dba509_.log 2025-08-26T20:58:58.9542493Z Running 32 items in this shard: test/test_dispatch.py::TestDispatch::test_all_invariants, test/test_dispatch.py::TestDispatch::test_computed_table, test/test_dispatch.py::TestDispatch::test_computed_table_with_ambiguous_autogradother, test/test_dispatch.py::TestDispatch::test_computed_table_with_autograd, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_autograd_math_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_defaultbackend, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math, test/test_dispatch.py::TestDispatch::test_computed_table_with_cpu_math_autogradcpu_fallthrough, test/test_dispatch.py::TestDispatch::test_computed_table_with_math, test/test_dispatch.py::TestDispatch::test_def, test/test_dispatch.py::TestDispatch::test_def_impl_schema_mismatch, test/test_dispatch.py::TestDispatch::test_def_only, test/test_dispatch.py::TestDispatch::test_def_with_explicit_alias, test/test_dispatch.py::TestDispatch::test_def_with_inference, test/test_dispatch.py::TestDispatch::test_dispatch_print_registrations_for_dispatch_key_invalid, test/test_dispatch.py::TestDispatch::test_find_dangling_impls, test/test_dispatch.py::TestDispatch::test_find_dangling_impls_ext, test/test_dispatch.py::TestDispatch::test_impl_only, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_defaulting, test/test_dispatch.py::TestDispatch::test_multiple_def_alias_mismatch, test/test_dispatch.py::TestDispatch::test_multiple_def_error, test/test_dispatch.py::TestDispatch::test_multiple_fallback, test/test_dispatch.py::TestDispatch::test_overwrite_math, test/test_dispatch.py::TestPythonDispatcher::test_autogradother, test/test_dispatch.py::TestPythonDispatcher::test_basic, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_defaultbackend_math, test/test_dispatch.py::TestPythonDispatcher::test_duplicate_registrations, test/test_dispatch.py::TestPythonDispatcher::test_math_autogradcpu, test/test_dispatch.py::TestPythonDispatcher::test_quantized_structured_not_implemented 2025-08-26T20:58:58.9552258Z 2025-08-26T20:58:58.9552452Z Running test_native_mha 1/1 ... [2025-08-26 20:58:58.953512] 2025-08-26T20:58:58.9552843Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:58:58.9553875Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_native_mha.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:58:58.953849] 2025-08-26T20:59:44.6844490Z 2025-08-26T20:59:44.6846552Z test_native_mha 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_native_mha_1.1_fd630053b9780af4_.log 2025-08-26T20:59:44.6871193Z Running 28 items in this shard: test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_encoder_decoder_attention_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_False_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_False_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_False_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_False_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_False_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_native_multihead_self_attention_use_nt_True_use_padding_True_pad_all_True_need_weights_False_average_attn_weights_True_fused_True_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_cpu_float32, test/test_native_mha.py::TestMHADeviceTypeCPU::test_transform_bias_rescale_qkv_nested_cpu_float32 2025-08-26T20:59:44.6894424Z 2025-08-26T20:59:44.6894639Z Running test_sort_and_select 1/1 ... [2025-08-26 20:59:44.684919] 2025-08-26T20:59:44.6895068Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T20:59:44.6896128Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_sort_and_select.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 20:59:44.685357] 2025-08-26T21:02:48.4696104Z 2025-08-26T21:02:48.4697454Z 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_2930794af7f3a6b1_.log 2025-08-26T21:02:48.4739493Z Running 113 items in this shard: test/test_sort_and_select.py::TestSortAndSelectCPU::test_complex_unsupported_cpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_devices_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_isin_different_dtypes_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_kthvalue_scalar_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_msort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_1d_parallel_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_discontiguous_slow_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_expanded_tensor_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_large_slice_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_overflow_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_restride_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_sort_stable_none_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_against_numpy_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_stable_sort_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_1d_output_discontiguous_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_4d_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_arguments_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_integral_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_lower_precision_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_noncontiguous_gpu_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_nonfinite_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_quantized_scalar_input_cpu, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_topk_zero_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_consecutive_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bfloat16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_bool, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_float64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int16, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int32, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int64, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_int8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_cpu_uint8, test/test_sort_and_select.py::TestSortAndSelectCPU::test_unique_dim_cpu 2025-08-26T21:02:48.4777757Z 2025-08-26T21:02:48.4777969Z Running test_cpp_extensions_jit 1/1 ... [2025-08-26 21:02:48.470388] 2025-08-26T21:02:48.4778419Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:02:48.4779482Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_jit.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:02:48.470748] 2025-08-26T21:03:36.0563711Z 2025-08-26T21:03:36.0565163Z test_cpp_extensions_jit 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_jit_1.1_b31a166c04e453ce_.log 2025-08-26T21:03:36.0585017Z Running 34 items in this shard: test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_aoti_torch_call_dispatcher, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_autograd_from_cpp, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_compilation_error_formatting, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_same_output_as_python, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_has_up_to_date_attributes, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cpp_frontend_module_python_inter_op_with_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_arch_flags_default_gencode, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_arch_flags_non_default_gencode, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_cuda_pluggable_allocator_include, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_compound_op_autograd, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_custom_functorch_error, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_gen_extension_h_pch, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_half_support, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_custom_op_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_cuda, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_multiple_sources_and_no_functions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_throws_when_functions_is_bad, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_dict, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_with_functions_as_list, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_inline_jit_compile_extension_xpu, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_compile_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_archflags, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cuda_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_cudnn_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_archlists, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_jit_xpu_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_lenient_flag_handling_in_jit_extensions, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_load_with_non_platform_default_encoding, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_mps_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_reload_jit_extension, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_returns_shared_library_path_when_is_python_module_is_true, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_set_default_type_also_changes_aten_default_type, test/test_cpp_extensions_jit.py::TestCppExtensionJIT::test_warning 2025-08-26T21:03:36.0599006Z 2025-08-26T21:03:36.0599222Z Running nn/test_pooling 1/1 ... [2025-08-26 21:03:36.056875] 2025-08-26T21:03:36.0599639Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:03:36.0600683Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_pooling.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:03:36.057301] 2025-08-26T21:06:49.4297386Z 2025-08-26T21:06:49.4298892Z nn/test_pooling 1/1 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_pooling_1.1_2599e4027f4cd9fe_.log 2025-08-26T21:06:49.4356914Z Running 111 items in this shard: test/nn/test_pooling.py::TestAvgPool::test_avg_pool1d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool2d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_avg_pool3d_ceil_mode, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool2d_with_divisor, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d, test/nn/test_pooling.py::TestAvgPool::test_doubletensor_avg_pool3d_with_divisor, test/nn/test_pooling.py::TestPoolingNN::test_MaxUnpool2d_output_size, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_nhwc_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_avg_pooling_overflow, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_backward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_launch_config_forward, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_avg_nhwc_non_contiguous, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_lower_precision, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_none, test/nn/test_pooling.py::TestPoolingNN::test_adaptive_pooling_size_overflow, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool2d_nhwc_cpu, test/nn/test_pooling.py::TestPoolingNN::test_max_unpool3d_input_check, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool1d_empty_kernel, test/nn/test_pooling.py::TestPoolingNN::test_quantized_max_pool3d, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AdaptiveMaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool2d_empty_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_AvgPool3d_backward_after_cat_dim1_device_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool2d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_errors_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_batch_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_out_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_FractionalMaxPool3d_zero_samples_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool1d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool2d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool3d_indices_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxPool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case10_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case4_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case5_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case6_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case7_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case8_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_index_errors_case9_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_MaxUnpool_zero_batch_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool2d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pool3d_output_size_one_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_avg_pooling_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_max_pooling_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pool_odd_size_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_empty_output_size_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_int8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_no_suppot_input_cpu_uint8, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_adaptive_pooling_zero_batch_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_avg_pool2d_reduced_floating_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool3d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_fractional_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_corner_cases_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool1d_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_corner_cases_cpu_int64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_indices_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool2d_with_indices_backward_fails_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool3d_ndhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_bfloat16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_bfloat16_half_cpu_float16, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_max_pool_nan_inf_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool3d_non_square_backward_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_maxpool_indices_no_batch_dim_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_large_size_int64_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool3d_size_one_feature_dim_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_invalid_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pool_large_size_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_bfloat16_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_large_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float32, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_max_nhwc_cpu_float64, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_avg_pooling_dims_3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_1_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_2_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_shape_kernel_max_pooling_dims_3_cpu, test/nn/test_pooling.py::TestPoolingNNDeviceTypeCPU::test_pooling_zero_stride_cpu 2025-08-26T21:06:49.4413371Z 2025-08-26T21:06:49.4413608Z Running nn/test_convolution 1/2 ... [2025-08-26 21:06:49.430332] 2025-08-26T21:06:49.4414034Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:06:49.4415094Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_convolution.py', '--shard-id=1', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:06:49.430732] 2025-08-26T21:13:36.0713672Z 2025-08-26T21:13:36.0714612Z nn/test_convolution 1/2 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_1.2_7ac7113baf004fbf_.log 2025-08-26T21:13:36.0880902Z Running 284 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_OneDNN, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_with_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types_on_GPU_without_cudnn, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_missing_argument, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_wbias, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_half_cublas_gemm, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose2d_output_size_downsample_upsample, test/nn/test_convolution.py::TestConvolutionNN::test_conv2d_discontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_conv3d_issue_120406, test/nn/test_convolution.py::TestConvolutionNN::test_conv_backcompat, test/nn/test_convolution.py::TestConvolutionNN::test_conv_invalid_groups, test/nn/test_convolution.py::TestConvolutionNN::test_conv_modules_raise_error_on_incorrect_input_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv_padding_mode, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_weight, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grouped_conv_cudnn_nhwc_support, test/nn/test_convolution.py::TestConvolutionNN::test_permute_conv2d_issue_120211, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose2d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transpose_with_output_size_and_no_batch_dim_ConvTranspose3d_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_transposed_large_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv2d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_add_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_depthwise_conv_64bit_indexing_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_convTranspose_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_group_conv_empty_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float64 2025-08-26T21:13:36.1042166Z 2025-08-26T21:13:36.9346232Z Uploading artifacts took 0.86 seconds 2025-08-26T21:13:36.9349526Z Running nn/test_convolution 2/2 ... [2025-08-26 21:13:36.934764] 2025-08-26T21:13:36.9350037Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:13:36.9354037Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'nn/test_convolution.py', '--shard-id=2', '--num-shards=2', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:13:36.935150] 2025-08-26T21:22:38.0084932Z 2025-08-26T21:22:38.0085989Z nn/test_convolution 2/2 was successful, full logs can be found in artifacts with path test/test-reports/nn.test_convolution_2.2_44838a4294c16c91_.log 2025-08-26T21:22:38.0275940Z Running 314 items in this shard: test/nn/test_convolution.py::TestConvolutionNN::test_Conv1d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_1x1, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_backward_twice, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_groups_nobias_v2, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_inconsistent_types, test/nn/test_convolution.py::TestConvolutionNN::test_Conv2d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_groups_nobias, test/nn/test_convolution.py::TestConvolutionNN::test_Conv3d_module_same_padding, test/nn/test_convolution.py::TestConvolutionNN::test_ConvTranspose3d_correct_output_size, test/nn/test_convolution.py::TestConvolutionNN::test_conv1d_issue_120547, test/nn/test_convolution.py::TestConvolutionNN::test_conv_cudnn_memory_layout_dominance, test/nn/test_convolution.py::TestConvolutionNN::test_conv_shapecheck, test/nn/test_convolution.py::TestConvolutionNN::test_conv_tbc, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_non_contiguous, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_noncontiguous_weight, test/nn/test_convolution.py::TestConvolutionNN::test_cudnn_not_mutate_stride, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv, test/nn/test_convolution.py::TestConvolutionNN::test_functional_grad_conv2d, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv1d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv2d_input, test/nn/test_convolution.py::TestConvolutionNN::test_grad_conv3d_weight, 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test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_dilated_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_False_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow2d_transposed_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cpu_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_False_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_cuda_has_bias_True_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_False_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_backend_slow3d_dilated_has_bias_False_strided_True_contiguous_True_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_cudnn_mismatch_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_double_backward_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_empty_channel_cpu_complex64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_large_batch_1_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_noncontig_weights_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float32, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_conv_thnn_nhwc_cpu_float64, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_convert_conv3d_weight_memory_format_cpu, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_cudnn_convolution_relu_cpu_float16, test/nn/test_convolution.py::TestConvolutionNNDeviceTypeCPU::test_noncontig_conv_grad_cpu_float32 2025-08-26T21:22:38.0454431Z 2025-08-26T21:22:38.0454719Z Running test_multiprocessing_spawn 1/1 ... [2025-08-26 21:22:38.009879] 2025-08-26T21:22:38.0455197Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:22:38.0456294Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_multiprocessing_spawn.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:22:38.010261] 2025-08-26T21:25:11.9959441Z 2025-08-26T21:25:11.9960741Z test_multiprocessing_spawn 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_multiprocessing_spawn_1.1_cc144c534f4fde88_.log 2025-08-26T21:25:11.9973111Z Running 31 items in this shard: test/test_multiprocessing_spawn.py::SpawnTest::test_exception_all, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_exception_single, test/test_multiprocessing_spawn.py::SpawnTest::test_first_argument_index, test/test_multiprocessing_spawn.py::SpawnTest::test_signal_raises, test/test_multiprocessing_spawn.py::SpawnTest::test_success, test/test_multiprocessing_spawn.py::SpawnTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::SpawnTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::SpawnTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ForkTest::test_exception_all, test/test_multiprocessing_spawn.py::ForkTest::test_exception_single, test/test_multiprocessing_spawn.py::ForkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ForkTest::test_success, test/test_multiprocessing_spawn.py::ForkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ForkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::ForkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_all, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_exception_single, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_first_argument_index, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_first_then_exception, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_success_non_blocking, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period0, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_exit_grace_period_20, test/test_multiprocessing_spawn.py::ParallelForkServerShouldWorkTest::test_terminate_signal, test/test_multiprocessing_spawn.py::ParallelForkServerPerfTest::test_forkserver_perf, test/test_multiprocessing_spawn.py::ErrorTest::test_errors_pickleable 2025-08-26T21:25:11.9985165Z 2025-08-26T21:25:11.9985414Z Running test_cuda_primary_ctx 1/1 ... [2025-08-26 21:25:11.996280] 2025-08-26T21:25:11.9985877Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:25:11.9987181Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_primary_ctx.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:25:11.996653] 2025-08-26T21:25:15.3456815Z 2025-08-26T21:25:15.3458193Z test_cuda_primary_ctx 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_primary_ctx_1.1_46e78728fe332554_.log 2025-08-26T21:25:15.3459613Z Running 0 items in this shard: 2025-08-26T21:25:15.3459917Z 2025-08-26T21:25:15.3462930Z Running test_mobile_optimizer 1/1 ... [2025-08-26 21:25:15.346022] 2025-08-26T21:25:15.3463409Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:25:15.3466691Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_mobile_optimizer.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:25:15.346403] 2025-08-26T21:25:30.3829311Z 2025-08-26T21:25:30.3830968Z test_mobile_optimizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_mobile_optimizer_1.1_9312bbcbf1060e95_.log 2025-08-26T21:25:30.3836263Z 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 2025-08-26T21:25:30.3840537Z 2025-08-26T21:25:30.3840890Z Running test_cuda_trace 1/1 ... [2025-08-26 21:25:30.383298] 2025-08-26T21:25:30.3841303Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:25:30.3842628Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_trace.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:25:30.383730] 2025-08-26T21:25:33.7168747Z 2025-08-26T21:25:33.7170025Z test_cuda_trace 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_trace_1.1_446a2936e5bd4427_.log 2025-08-26T21:25:33.7171345Z Running 0 items in this shard: 2025-08-26T21:25:33.7171682Z 2025-08-26T21:25:33.7173926Z Running test_cuda_nvml_based_avail 1/1 ... [2025-08-26 21:25:33.717202] 2025-08-26T21:25:33.7174690Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:25:33.7179418Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_nvml_based_avail.py', '--shard-id=1', '--num-shards=1', '-v', '--subprocess', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:25:33.717624] 2025-08-26T21:25:37.0318950Z 2025-08-26T21:25:37.0320419Z test_cuda_nvml_based_avail 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_nvml_based_avail_1.1_6f058a6efdd1fb93_.log 2025-08-26T21:25:37.0321942Z Running 0 items in this shard: 2025-08-26T21:25:37.0322216Z 2025-08-26T21:25:37.0324489Z Running test_spectral_ops 1/1 ... [2025-08-26 21:25:37.032222] 2025-08-26T21:25:37.0324971Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:25:37.0328062Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_spectral_ops.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:25:37.032583] 2025-08-26T21:26:49.4158467Z 2025-08-26T21:26:49.4159601Z test_spectral_ops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_spectral_ops_1.1_949d0c52701f5c24_.log 2025-08-26T21:26:49.4252154Z Running 281 items in this shard: test/test_spectral_ops.py::TestFFTCPU::test_batch_istft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_istft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_definition_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_onesided_cpu, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_real_equiv_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_complex_stft_roundtrip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_cufft_context_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_cufft_plan_cache_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft2_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_fft_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_empty_ifft_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft2_fftn_equivalence_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft2_invalid_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft2_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors__refs_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_fftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ifftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_irfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft2_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfft_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_bfloat16, test/test_spectral_ops.py::TestFFTCPU::test_fft_half_and_bfloat16_errors_fft_rfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_ifft_rfft_irfft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_input_modification_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_invalid_dtypes_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_plan_repeatable_cpu, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fft_type_promotion_cpu_int8, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftfreq_out_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid__refs_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_fftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ifftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_irfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_invalid_fft_rfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_noop_transform_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftn_round_trip_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_frequencies_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_fftshift_numpy_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_hfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float16, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_ihfftn_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_against_librosa_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_linearity_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_of_sine_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_simple_cases_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_various_params_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_round_trip_with_padding_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_istft_throws_cpu, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d__refs_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_fft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_hfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ifft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_ihfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_irfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_1d_fft_rfft_cpu_float32, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd__refs_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_fftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_hfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_ifftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_reference_nd_fft_irfftn_cpu_complex64, test/test_spectral_ops.py::TestFFTCPU::test_stft_align_to_window_only_requires_non_center_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_complex_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_requires_window_cpu, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_complex128, test/test_spectral_ops.py::TestFFTCPU::test_stft_roundtrip_complex_window_cpu_float64, test/test_spectral_ops.py::TestFFTCPU::test_stft_window_device_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_fftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_hfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ifftshift_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_ihfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_irfftn_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft2_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfft_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftfreq_cpu, test/test_spectral_ops.py::TestFFTDocExamplesCPU::test_rfftn_cpu 2025-08-26T21:26:49.4343733Z 2025-08-26T21:26:49.4344006Z Running distributions/test_distributions 1/3 ... [2025-08-26 21:26:49.416616] 2025-08-26T21:26:49.4344515Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:26:49.4345707Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=1', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:26:49.416970] 2025-08-26T21:31:51.7177001Z 2025-08-26T21:31:51.7178017Z distributions/test_distributions 1/3 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_1.3_8a5a4ff2d55fe6c4_.log 2025-08-26T21:31:51.7207904Z Running 71 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_argmax_relaxed_categorical, test/distributions/test_distributions.py::TestDistributions::test_binomial_extreme_vals, test/distributions/test_distributions.py::TestDistributions::test_binomial_half, test/distributions/test_distributions.py::TestDistributions::test_binomial_sample, test/distributions/test_distributions.py::TestDistributions::test_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob_zero, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_mode, test/distributions/test_distributions.py::TestDistributions::test_exponential_sample, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor_sample, test/distributions/test_distributions.py::TestDistributions::test_generalized_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_geometric_sample, test/distributions/test_distributions.py::TestDistributions::test_halfcauchy, test/distributions/test_distributions.py::TestDistributions::test_halfnormal, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_has_examples, test/distributions/test_distributions.py::TestDistributions::test_independent_expand, test/distributions/test_distributions.py::TestDistributions::test_invalid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_shape, test/distributions/test_distributions.py::TestDistributions::test_lkj_cholesky_log_prob, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lognormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_sample, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_multinomial_sequential_draw, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_poisson_forward_ad, test/distributions/test_distributions.py::TestDistributions::test_poisson_shape, test/distributions/test_distributions.py::TestDistributions::test_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_2d, test/distributions/test_distributions.py::TestDistributions::test_sample_detached, test/distributions/test_distributions.py::TestDistributions::test_studentT, test/distributions/test_distributions.py::TestDistributions::test_torch_binomial_dtype_errors, test/distributions/test_distributions.py::TestDistributions::test_wishart_log_prob, test/distributions/test_distributions.py::TestDistributions::test_wishart_moments, test/distributions/test_distributions.py::TestDistributions::test_wishart_shape, test/distributions/test_distributions.py::TestDistributions::test_wishart_stable_with_precision_matrix, test/distributions/test_distributions.py::TestRsample::test_gamma, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_one_hot_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_weibull_scale_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_tensor_params, test/distributions/test_distributions.py::TestKL::test_entropy_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_infinite, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob, test/distributions/test_distributions.py::TestNumericalStability::test_categorical_log_prob_with_logits, test/distributions/test_distributions.py::TestAgainstScipy::test_icdf, test/distributions/test_distributions.py::TestAgainstScipy::test_variance_stddev, test/distributions/test_distributions.py::TestFunctors::test_cat_transform, test/distributions/test_distributions.py::TestValidation::test_invalid, test/distributions/test_distributions.py::TestJit::test_mean, test/distributions/test_distributions.py::TestJit::test_rsample, test/distributions/test_distributions.py::TestJit::test_variance 2025-08-26T21:31:51.7236253Z 2025-08-26T21:31:51.7236564Z Running distributions/test_distributions 2/3 ... [2025-08-26 21:31:51.718018] 2025-08-26T21:31:51.7237063Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:31:51.7238201Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=2', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:31:51.718369] 2025-08-26T21:45:30.9448662Z 2025-08-26T21:45:30.9449715Z distributions/test_distributions 2/3 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_2.3_c9ac31bfd0581fc8_.log 2025-08-26T21:45:30.9482959Z Running 78 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_beta_log_prob, test/distributions/test_distributions.py::TestDistributions::test_beta_sample, test/distributions/test_distributions.py::TestDistributions::test_beta_shape, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow_gpu, test/distributions/test_distributions.py::TestDistributions::test_binomial_bfloat16, test/distributions/test_distributions.py::TestDistributions::test_binomial_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_binomial_stable, test/distributions/test_distributions.py::TestDistributions::test_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_chi2_sample, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_continuous_bernoulli_3d, test/distributions/test_distributions.py::TestDistributions::test_distribution_expand, test/distributions/test_distributions.py::TestDistributions::test_distribution_subclass_expand, test/distributions/test_distributions.py::TestDistributions::test_exponential, test/distributions/test_distributions.py::TestDistributions::test_gamma_log_prob_at_boundary, test/distributions/test_distributions.py::TestDistributions::test_gamma_sample, test/distributions/test_distributions.py::TestDistributions::test_geometric, test/distributions/test_distributions.py::TestDistributions::test_inversegamma_sample, test/distributions/test_distributions.py::TestDistributions::test_laplace, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal, test/distributions/test_distributions.py::TestDistributions::test_logisticnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_stable_with_precision_matrix, test/distributions/test_distributions.py::TestDistributions::test_normal, test/distributions/test_distributions.py::TestDistributions::test_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_pareto, test/distributions/test_distributions.py::TestDistributions::test_poisson_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_log_prob, test/distributions/test_distributions.py::TestDistributions::test_repr, test/distributions/test_distributions.py::TestDistributions::test_rsample_requires_grad, test/distributions/test_distributions.py::TestDistributions::test_studentT_sample, test/distributions/test_distributions.py::TestDistributions::test_support_attributes, test/distributions/test_distributions.py::TestDistributions::test_valid_parameter_broadcasting, test/distributions/test_distributions.py::TestDistributions::test_vonmises_logprob, test/distributions/test_distributions.py::TestDistributions::test_wishart_sample, test/distributions/test_distributions.py::TestDistributions::test_zero_excluded_binomial, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_alpha, test/distributions/test_distributions.py::TestRsample::test_chi2, test/distributions/test_distributions.py::TestRsample::test_dirichlet_multivariate, test/distributions/test_distributions.py::TestRsample::test_dirichlet_on_diagonal, test/distributions/test_distributions.py::TestRsample::test_dirichlet_tangent_field, test/distributions/test_distributions.py::TestDistributionShapes::test_beta_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_categorical_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_entropy_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_tensor_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gumbel_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_halfcauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_multinomial_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_normal_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_vonmises_shape_scalar_params, test/distributions/test_distributions.py::TestKL::test_entropy_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_exponential_family, test/distributions/test_distributions.py::TestKL::test_kl_lowrank_multivariate_normal, test/distributions/test_distributions.py::TestKL::test_kl_monte_carlo, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched_broadcasted, test/distributions/test_distributions.py::TestConstraints::test_params_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_gradient, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_overflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob_with_logits, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_logits_initialization, test/distributions/test_distributions.py::TestFunctors::test_cat_event_dim, test/distributions/test_distributions.py::TestFunctors::test_stack_transform, test/distributions/test_distributions.py::TestValidation::test_invalid_log_probs_arg, test/distributions/test_distributions.py::TestValidation::test_valid, test/distributions/test_distributions.py::TestJit::test_cdf, test/distributions/test_distributions.py::TestJit::test_entropy, test/distributions/test_distributions.py::TestJit::test_enumerate_support, test/distributions/test_distributions.py::TestJit::test_log_prob, test/distributions/test_distributions.py::TestJit::test_sample 2025-08-26T21:45:30.9513813Z 2025-08-26T21:45:31.4622623Z Uploading artifacts took 0.52 seconds 2025-08-26T21:45:31.4626133Z Running distributions/test_distributions 3/3 ... [2025-08-26 21:45:31.462408] 2025-08-26T21:45:31.4626649Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:45:31.4630337Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'distributions/test_distributions.py', '--shard-id=3', '--num-shards=3', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:45:31.462801] 2025-08-26T21:48:33.6391812Z 2025-08-26T21:48:33.6393424Z distributions/test_distributions 3/3 was successful, full logs can be found in artifacts with path test/test-reports/distributions.test_distributions_3.3_454562b27bed4d1f_.log 2025-08-26T21:48:33.6427177Z Running 81 items in this shard: test/distributions/test_distributions.py::TestDistributions::test_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_bernoulli_enumerate_support, test/distributions/test_distributions.py::TestDistributions::test_beta_underflow, test/distributions/test_distributions.py::TestDistributions::test_binomial, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_binomial_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_cauchy, test/distributions/test_distributions.py::TestDistributions::test_cdf_icdf_inverse, test/distributions/test_distributions.py::TestDistributions::test_cdf_log_prob, test/distributions/test_distributions.py::TestDistributions::test_chi2_shape, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_log_prob, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_sample, test/distributions/test_distributions.py::TestDistributions::test_dirichlet_shape, test/distributions/test_distributions.py::TestDistributions::test_enumerate_support_type, test/distributions/test_distributions.py::TestDistributions::test_fishersnedecor, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_sample, test/distributions/test_distributions.py::TestDistributions::test_gamma_gpu_shape, test/distributions/test_distributions.py::TestDistributions::test_gamma_shape, test/distributions/test_distributions.py::TestDistributions::test_generalized_pareto, test/distributions/test_distributions.py::TestDistributions::test_geometric_log_prob_and_entropy, test/distributions/test_distributions.py::TestDistributions::test_gumbel, test/distributions/test_distributions.py::TestDistributions::test_gumbel_sample, test/distributions/test_distributions.py::TestDistributions::test_halfnormal_sample, test/distributions/test_distributions.py::TestDistributions::test_independent_shape, test/distributions/test_distributions.py::TestDistributions::test_inversegamma, test/distributions/test_distributions.py::TestDistributions::test_kumaraswamy_mean_variance, test/distributions/test_distributions.py::TestDistributions::test_laplace_sample, test/distributions/test_distributions.py::TestDistributions::test_lazy_property_grad, test/distributions/test_distributions.py::TestDistributions::test_lognormal, test/distributions/test_distributions.py::TestDistributions::test_lognormal_logprob, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_moments, test/distributions/test_distributions.py::TestDistributions::test_lowrank_multivariate_normal_shape, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_mixture_same_family_shape, test/distributions/test_distributions.py::TestDistributions::test_mode, test/distributions/test_distributions.py::TestDistributions::test_multinomial_1d, test/distributions/test_distributions.py::TestDistributions::test_multinomial_2d, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_log_prob, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_properties, test/distributions/test_distributions.py::TestDistributions::test_multivariate_normal_sample, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial, test/distributions/test_distributions.py::TestDistributions::test_negative_binomial_log_prob_vectorized_count, test/distributions/test_distributions.py::TestDistributions::test_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_pareto_sample, test/distributions/test_distributions.py::TestDistributions::test_poisson_sample, test/distributions/test_distributions.py::TestDistributions::test_relaxed_one_hot_categorical_1d, test/distributions/test_distributions.py::TestDistributions::test_rounded_relaxed_bernoulli, test/distributions/test_distributions.py::TestDistributions::test_studentT_log_prob, test/distributions/test_distributions.py::TestDistributions::test_uniform, test/distributions/test_distributions.py::TestDistributions::test_vonmises_sample, test/distributions/test_distributions.py::TestDistributions::test_wishart_properties, test/distributions/test_distributions.py::TestRsample::test_beta_wrt_beta, test/distributions/test_distributions.py::TestDistributionShapes::test_bernoulli_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_binomial_shape_vectorized_n, test/distributions/test_distributions.py::TestDistributionShapes::test_cauchy_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_chi2_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_continuous_bernoulli_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_exponential_shape_scalar_param, test/distributions/test_distributions.py::TestDistributionShapes::test_gamma_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_geometric_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_kumaraswamy_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_laplace_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_mixture_same_family_mean_shape, test/distributions/test_distributions.py::TestDistributionShapes::test_pareto_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_studentT_shape_tensor_params, test/distributions/test_distributions.py::TestDistributionShapes::test_uniform_shape_scalar_params, test/distributions/test_distributions.py::TestDistributionShapes::test_wishart_shape_scalar_params, test/distributions/test_distributions.py::TestKL::test_kl_edgecases, test/distributions/test_distributions.py::TestKL::test_kl_multivariate_normal_batched, test/distributions/test_distributions.py::TestKL::test_kl_shape, test/distributions/test_distributions.py::TestKL::test_kl_transformed, test/distributions/test_distributions.py::TestConstraints::test_support_constraints, test/distributions/test_distributions.py::TestNumericalStability::test_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_continuous_bernoulli_with_logits_underflow, test/distributions/test_distributions.py::TestNumericalStability::test_multinomial_log_prob, test/distributions/test_distributions.py::TestLazyLogitsInitialization::test_lazy_probs_initialization, test/distributions/test_distributions.py::TestAgainstScipy::test_cdf, test/distributions/test_distributions.py::TestAgainstScipy::test_mean, test/distributions/test_distributions.py::TestFunctors::test_cat_transform_non_uniform, test/distributions/test_distributions.py::TestValidation::test_warning_unimplemented_constraints 2025-08-26T21:48:33.6459527Z 2025-08-26T21:48:33.6459785Z Running test_cpp_extensions_aot_no_ninja 1/1 ... [2025-08-26 21:48:33.639475] 2025-08-26T21:48:36.0701687Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:48:36.0704548Z import pkg_resources 2025-08-26T21:48:36.1355395Z running install 2025-08-26T21:48:36.1357249Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-08-26T21:48:36.1358963Z !! 2025-08-26T21:48:36.1359173Z 2025-08-26T21:48:36.1359413Z ******************************************************************************** 2025-08-26T21:48:36.1360041Z Please avoid running ``setup.py`` directly. 2025-08-26T21:48:36.1360760Z Instead, use pypa/build, pypa/installer or other 2025-08-26T21:48:36.1361449Z standards-based tools. 2025-08-26T21:48:36.1361785Z 2025-08-26T21:48:36.1362223Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-08-26T21:48:36.1363126Z or your builds will no longer be supported. 2025-08-26T21:48:36.1363606Z 2025-08-26T21:48:36.1364171Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-08-26T21:48:36.1365171Z ******************************************************************************** 2025-08-26T21:48:36.1365606Z 2025-08-26T21:48:36.1365770Z !! 2025-08-26T21:48:36.1366154Z self.initialize_options() 2025-08-26T21:48:36.1499768Z running build 2025-08-26T21:48:36.1500274Z running build_py 2025-08-26T21:48:36.1588107Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-08-26T21:48:36.1589435Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-08-26T21:48:36.1600580Z running build_ext 2025-08-26T21:48:36.1616882Z building 'torch_test_cpp_extension.cpp' extension 2025-08-26T21:48:36.1618245Z creating build/temp.linux-x86_64-cpython-313 2025-08-26T21:48:36.1624104Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=cpp -std=c++17 2025-08-26T21:48:37.4113477Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-08-26T21:48:37.4114660Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-08-26T21:48:37.4115529Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-08-26T21:48:37.4116086Z from extension.cpp:1: 2025-08-26T21:48:37.4156985Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-08-26T21:48:37.4157969Z extension.cpp:45:53: required from here 2025-08-26T21:48:37.4159810Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:2041:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-08-26T21:48:37.4161190Z 2041 | class class_ : public detail::generic_type { 2025-08-26T21:48:37.4161559Z | ^~~~~~ 2025-08-26T21:48:37.4163174Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-08-26T21:48:37.4164621Z extension.cpp:45:53: required from here 2025-08-26T21:48:37.4166952Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:2139:28: warning: ‘pybind11::class_::class_<>(pybind11::handle, const char*)::’ declared with greater visibility than the type of its field ‘pybind11::class_::class_<>(pybind11::handle, const char*)::::’ [-Wattributes] 2025-08-26T21:48:37.4169028Z 2139 | with_internals([&](internals &internals) { 2025-08-26T21:48:37.4169421Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:37.4169945Z 2140 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-08-26T21:48:37.4170555Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:37.4171015Z 2141 | : internals.registered_types_cpp; 2025-08-26T21:48:37.4171429Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:37.4171854Z 2142 | instances[std::type_index(typeid(type_alias))] 2025-08-26T21:48:37.4172267Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:37.4172666Z 2143 | = instances[std::type_index(typeid(type))]; 2025-08-26T21:48:37.4173065Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:37.4173400Z 2144 | }); 2025-08-26T21:48:37.4173653Z | ~ 2025-08-26T21:48:37.4177085Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:48:38.1466218Z building 'torch_test_cpp_extension.maia' extension 2025-08-26T21:48:38.1469985Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=maia -std=c++17 2025-08-26T21:48:39.2627962Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:48:39.9608122Z building 'torch_test_cpp_extension.rng' extension 2025-08-26T21:48:39.9611901Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=rng -std=c++17 2025-08-26T21:48:41.2720490Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:48:41.2722073Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:48:41.2723345Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:48:41.2724682Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:48:41.2725705Z from rng_extension.cpp:6: 2025-08-26T21:48:41.2727538Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1478: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:48:41.2728858Z 1478 | #pragma unroll 2025-08-26T21:48:41.2729231Z | 2025-08-26T21:48:41.2730149Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-08-26T21:48:41.2731587Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1530, 2025-08-26T21:48:41.2733200Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:48:41.2734642Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:48:41.2736115Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:48:41.2738075Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:48:41.2739339Z from rng_extension.cpp:6: 2025-08-26T21:48:41.2741243Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:48:41.2742771Z 59 | #pragma unroll 2025-08-26T21:48:41.2743226Z | 2025-08-26T21:48:41.2744762Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:48:41.2746186Z 72 | #pragma unroll 2025-08-26T21:48:41.2746628Z | 2025-08-26T21:48:41.2747934Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:48:41.2748716Z 87 | #pragma unroll 2025-08-26T21:48:41.2748963Z | 2025-08-26T21:48:41.2749500Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1531, 2025-08-26T21:48:41.2750488Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:48:41.2751307Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:48:41.2752099Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:48:41.2753010Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:48:41.2753670Z from rng_extension.cpp:6: 2025-08-26T21:48:41.2754622Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:48:41.2755426Z 160 | #pragma unroll 2025-08-26T21:48:41.2755740Z | 2025-08-26T21:48:41.2756246Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.2757184Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.2758488Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.2759280Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.2760033Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.2760856Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.2761773Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.2762670Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.2763559Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.2764498Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.2765741Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.2766614Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.2767183Z from rng_extension.cpp:1: 2025-08-26T21:48:41.2768281Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.2770010Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.2771813Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.2773643Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.2775531Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.2777623Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.2784005Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.2794937Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.2800955Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.2801988Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.2802382Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.2808802Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.2815337Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.2816618Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.2817047Z | ^~~~~~~~ 2025-08-26T21:48:41.2817737Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.2818738Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.2819712Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.2820662Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.2821428Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.2822264Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.2823320Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.2824454Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.2825609Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.2826731Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.2827744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.2828698Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.2829543Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.2830093Z from rng_extension.cpp:1: 2025-08-26T21:48:41.2830992Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.2831771Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.2832124Z | ^~~~ 2025-08-26T21:48:41.2832690Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.2833555Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.2834377Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.2835167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.2835917Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.2836787Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.2837714Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.2838605Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.2839496Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.2840436Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.2852037Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.2852909Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.2853480Z from rng_extension.cpp:1: 2025-08-26T21:48:41.2854615Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.2856204Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.2857927Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.2859689Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.2861702Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.2863745Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.2869701Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.2879670Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.2885467Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.2886409Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.2886785Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.2892978Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.2899275Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.2900661Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.2901086Z | ^~~~~~~~ 2025-08-26T21:48:41.2901834Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.2902889Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.2903783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.2904588Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.2905357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.2906229Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.2907307Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.2908515Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.2909677Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.2910791Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.2911800Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.2912747Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.2913689Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.2914257Z from rng_extension.cpp:1: 2025-08-26T21:48:41.2915130Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.2915911Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.2916319Z | ^~~~ 2025-08-26T21:48:41.2916902Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.2917765Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.2918571Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.2919375Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.2920129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.2920981Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.2921913Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.2922796Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.2923682Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.2924612Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.2925579Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.2926478Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.2927023Z from rng_extension.cpp:1: 2025-08-26T21:48:41.2928085Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.2929694Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.2931420Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.2933201Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.2935141Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.2937195Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.2943499Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.2954034Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.2959951Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.2960931Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.2961373Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.2967641Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.2974152Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.2975437Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.2975860Z | ^~~~~~~~ 2025-08-26T21:48:41.2976491Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.2977492Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.2978372Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.2979191Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.2979958Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.2980887Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.2981946Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.2983093Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.2984241Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.2985358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.2986357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.2987377Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.2988222Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.2988832Z from rng_extension.cpp:1: 2025-08-26T21:48:41.2989681Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.2990413Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.2990766Z | ^~~~ 2025-08-26T21:48:41.2991346Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.2992551Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.2993372Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.2994270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.2995024Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.2995862Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.2996787Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.2997683Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.2998555Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.2999583Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3000548Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3001385Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3001944Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3002955Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3004516Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3006267Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3008026Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3010031Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3012341Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3018743Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3029469Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3035481Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3036419Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3036810Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3043082Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3049627Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3050951Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3051368Z | ^~~~~~~~ 2025-08-26T21:48:41.3051993Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3053004Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3053915Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3054775Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3055549Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3056393Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3057453Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3058603Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3059753Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3060981Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3061992Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3062938Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3063783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3064342Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3065176Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3065920Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3066272Z | ^~~~ 2025-08-26T21:48:41.3066846Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3067711Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3068520Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3069311Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3070055Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3070898Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3071821Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3072700Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3073636Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3074605Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3075562Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3076407Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3076948Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3077946Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3079563Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3081282Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3083053Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3084938Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3087021Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3093357Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3104057Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3110014Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3110945Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3125551Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3132206Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3138852Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3140140Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3140642Z | ^~~~~~~~ 2025-08-26T21:48:41.3141274Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3142285Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3143180Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3143999Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3144750Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3145585Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3146637Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3147779Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3148973Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3150130Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3151122Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3152061Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3152908Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3153461Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3154306Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3155073Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3155429Z | ^~~~ 2025-08-26T21:48:41.3156011Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3156877Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3157692Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3158475Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3159220Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3160098Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3161024Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3161903Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3162790Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3163721Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3164679Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3165522Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3166072Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3167077Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3168647Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3170369Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3172130Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3174061Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3176100Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3182091Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3192069Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3197817Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3198753Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3199148Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3205089Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3211312Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3212606Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3213021Z | ^~~~~~~~ 2025-08-26T21:48:41.3213657Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3214695Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3215586Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3216401Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3217162Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3218007Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3219055Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3220248Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3221475Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3222598Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3223602Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3224547Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3225375Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3225931Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3226768Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3227517Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3227919Z | ^~~~ 2025-08-26T21:48:41.3228484Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3229341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3230155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3230946Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3231691Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3232522Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3233515Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3234439Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3235333Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3236275Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3237222Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3238071Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3238633Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3239667Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3241246Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3242973Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3244726Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3246674Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3248697Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3254545Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3264556Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3270236Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3271171Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3271581Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3277439Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3283788Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3285079Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3285489Z | ^~~~~~~~ 2025-08-26T21:48:41.3286116Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3287117Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3288007Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3288824Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3289573Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3290407Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3291462Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3292974Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3294119Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3295275Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3296266Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3297207Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3298046Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3298602Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3299453Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3300228Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3300707Z | ^~~~ 2025-08-26T21:48:41.3301292Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3302152Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3302953Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3303746Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3304553Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3305393Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3306320Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3307209Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3308083Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3309013Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3309970Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3310815Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3311366Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3312370Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3313937Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3315663Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3317428Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3319367Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3321435Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3327269Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3337092Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3342873Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3343812Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3344200Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3350112Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3356300Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3357581Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3358000Z | ^~~~~~~~ 2025-08-26T21:48:41.3358637Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3359676Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3360559Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3361381Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3362142Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3362984Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3364043Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3365237Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3366378Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3367494Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3368499Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3369440Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3370283Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3370828Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3371666Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3372411Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3372765Z | ^~~~ 2025-08-26T21:48:41.3373332Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3374195Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3375008Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3375790Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3376540Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3377428Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3378335Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3379255Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3380138Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3381156Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3382111Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3382943Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3383501Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3384542Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3386107Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3387816Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3389576Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3391504Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3393812Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3399599Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3409456Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3415120Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3416091Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3416478Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3422391Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3428762Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3430056Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3430476Z | ^~~~~~~~ 2025-08-26T21:48:41.3431098Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3432097Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3432993Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3433809Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3434577Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3435413Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3436472Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3437645Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3438786Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3439929Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3440937Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3441887Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3442730Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3443333Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3444226Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3444970Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3445346Z | ^~~~ 2025-08-26T21:48:41.3445916Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3446775Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3447595Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3448386Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3449172Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3450020Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3450928Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3451819Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3452702Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3453638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3454594Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3455427Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3455979Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3456965Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3458527Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3460237Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3462082Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3464011Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3466064Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3471904Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3481638Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3487310Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3488245Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3488630Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3494796Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3501099Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3502388Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3502808Z | ^~~~~~~~ 2025-08-26T21:48:41.3503499Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3504499Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3505396Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3506215Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3506984Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3507816Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3508920Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3510060Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3511203Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3512321Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3513326Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3514246Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3515088Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3515638Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3516476Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3517219Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3517556Z | ^~~~ 2025-08-26T21:48:41.3518134Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3518989Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3519796Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3520571Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3521318Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3522194Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3523146Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3524034Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3524915Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3525837Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3526798Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3527640Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3528222Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3529216Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3530769Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3532483Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3534283Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3536166Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3538188Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3544073Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3553899Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3559642Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3560561Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3560951Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3566798Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3573026Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3574300Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3574718Z | ^~~~~~~~ 2025-08-26T21:48:41.3575353Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3576359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3577246Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3578048Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3578807Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3579640Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3581302Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3582458Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3583620Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3584737Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3585739Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3586675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3587560Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3588117Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3588988Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3589735Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3590083Z | ^~~~ 2025-08-26T21:48:41.3590659Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3591503Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3593280Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3594216Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3594969Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3595811Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3596735Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3597606Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3598493Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3599426Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3600384Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3601226Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3601763Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3602850Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3604421Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3606138Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3607906Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3609875Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3611965Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3617817Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3627676Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3633354Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3634294Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3634682Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3640622Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3646803Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3648093Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3648519Z | ^~~~~~~~ 2025-08-26T21:48:41.3649195Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3650185Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3651069Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3651881Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3652638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3653471Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3654555Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3655691Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3656837Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3657945Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3658949Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3659889Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3660801Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3661361Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3662201Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3662951Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3663285Z | ^~~~ 2025-08-26T21:48:41.3663874Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3664748Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3665561Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3666342Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3667138Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3667978Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3668933Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3669823Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3670695Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3671635Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3672602Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3673482Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3674046Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3675054Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3676607Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3678326Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3680152Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3682027Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3684052Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3690204Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3701245Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3707200Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3708131Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3708501Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3714692Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3721233Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3722516Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3722920Z | ^~~~~~~~ 2025-08-26T21:48:41.3723547Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3724540Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3725428Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3726243Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3726997Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3727884Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3728967Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3730110Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3731254Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3732358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3733363Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3734341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3735195Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3735750Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3736574Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3737316Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3737669Z | ^~~~ 2025-08-26T21:48:41.3738256Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3739155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3739968Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3740840Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3741597Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3742443Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3743368Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3744246Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3745140Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3746076Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3747030Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3747868Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3748420Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3749405Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3750974Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3752744Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3754504Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3756405Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3758421Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3764671Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3775270Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3781337Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3782259Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3782642Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3789011Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3795847Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3797122Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3797537Z | ^~~~~~~~ 2025-08-26T21:48:41.3798164Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3799277Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3800178Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3800984Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3801748Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3802587Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3803641Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3804775Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3805910Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3807024Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3808028Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3809011Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3809853Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3810410Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3811232Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3811979Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3812329Z | ^~~~ 2025-08-26T21:48:41.3812968Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3813852Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3814669Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3815461Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3816207Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3817048Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3817974Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3818897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3819794Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3820807Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3821768Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3822612Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3823155Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3824218Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3825788Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3827499Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3829261Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3831141Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3833176Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3839490Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3850127Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3856157Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3857091Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3857478Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3863867Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3870390Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3871680Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3872104Z | ^~~~~~~~ 2025-08-26T21:48:41.3872779Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3873804Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3874678Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3875491Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3876252Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3877084Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3878145Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3879309Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3880443Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3881623Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3882628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3883569Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3884444Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3884989Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3885826Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3886567Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3886914Z | ^~~~ 2025-08-26T21:48:41.3887495Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3888339Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3889161Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3889949Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3890728Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3891571Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3892710Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3893598Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3894483Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3895415Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3896370Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3897300Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3897854Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3898906Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3900543Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3902264Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3904032Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3905976Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3908008Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3914251Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3924885Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.3930899Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.3931827Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.3932212Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.3938541Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.3945186Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.3946475Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.3946931Z | ^~~~~~~~ 2025-08-26T21:48:41.3947555Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.3948550Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.3949424Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.3950259Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.3951025Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.3951862Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.3952942Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.3954068Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.3955277Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.3956400Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.3957472Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.3958421Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.3959301Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3959844Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3960723Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.3961469Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.3961823Z | ^~~~ 2025-08-26T21:48:41.3962398Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.3963264Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.3964127Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.3964916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.3965662Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.3966490Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.3967418Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.3968309Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.3969225Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.3970170Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.3971128Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.3971955Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.3972506Z from rng_extension.cpp:1: 2025-08-26T21:48:41.3973498Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.3975055Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.3976781Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.3978546Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.3980495Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.3982518Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.3988838Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.3999656Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4005697Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4006632Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4007025Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4013351Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4019990Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4021331Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4021748Z | ^~~~~~~~ 2025-08-26T21:48:41.4022376Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4023372Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4024332Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4025154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4025913Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4026749Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4027787Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4028928Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4030113Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4031229Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4032238Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4033180Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4034010Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4034563Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4035397Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4036136Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4036516Z | ^~~~ 2025-08-26T21:48:41.4037084Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4037940Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4038750Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4039537Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4040277Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4041147Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4042104Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4042994Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4043911Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4044844Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4045786Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4046628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4047229Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4048224Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4049827Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4051550Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4053305Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4055182Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4057256Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4063559Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4074158Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4080178Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4081111Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4081501Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4087787Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4094587Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4095875Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4096301Z | ^~~~~~~~ 2025-08-26T21:48:41.4096924Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4097930Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4098819Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4099634Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4100477Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4101328Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4102460Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4103610Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4104801Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4105916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4106928Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4107861Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4108709Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4109311Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4110162Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4110909Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4111244Z | ^~~~ 2025-08-26T21:48:41.4111824Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4112678Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4113488Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4114323Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4115059Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4115893Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4116813Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4117698Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4118591Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4119533Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4120475Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4121321Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4121879Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4122875Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4124432Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4126153Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4127968Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4129853Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4131924Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4138209Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4148950Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4154876Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4155807Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4156190Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4162542Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4169159Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4170449Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4170867Z | ^~~~~~~~ 2025-08-26T21:48:41.4171482Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4172479Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4173365Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4174223Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4174992Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4175817Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4176875Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4178008Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4179142Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4180256Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4181378Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4182306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4183176Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4183731Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4184566Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4185304Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4185636Z | ^~~~ 2025-08-26T21:48:41.4186217Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4187075Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4187915Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4188704Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4189433Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4190270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4191190Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4192267Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4193158Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4194079Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4195039Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4195876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4196428Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4197439Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4199101Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4200820Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4202586Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4204463Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4206492Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4212844Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4223519Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4229517Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4230520Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4230893Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4237244Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4243849Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4245132Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4245540Z | ^~~~~~~~ 2025-08-26T21:48:41.4246223Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4247272Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4248165Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4248989Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4249742Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4250585Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4251642Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4252779Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4253936Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4255054Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4256048Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4257007Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4257850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4258441Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4259273Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4260007Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4260408Z | ^~~~ 2025-08-26T21:48:41.4260986Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4261848Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4262659Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4263433Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4264176Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4265011Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4265970Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4266866Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4267769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4268704Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4269657Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4270498Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4271053Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4272109Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4273674Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4275391Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4277152Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4279033Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4281048Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4287305Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4298184Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4304390Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4305326Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4305704Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4312002Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4318606Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4319896Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4320298Z | ^~~~~~~~ 2025-08-26T21:48:41.4320928Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4321927Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4322814Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4323618Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4324365Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4325205Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4326360Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4327523Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4328667Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4329770Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4330779Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4331720Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4332613Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4333167Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4333998Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4334724Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4335068Z | ^~~~ 2025-08-26T21:48:41.4335646Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4336500Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4337301Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4338128Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4338876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4339714Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4340699Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4341591Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4342466Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4343410Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4344374Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4345216Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4345777Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4346767Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4348323Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4361186Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4363129Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4365064Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4367106Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4372967Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4382988Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4388667Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4389606Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4389999Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4396174Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4402416Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4403772Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4404199Z | ^~~~~~~~ 2025-08-26T21:48:41.4404835Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4405828Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4406718Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4407546Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4408317Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4409200Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4410271Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4411412Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4412572Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4413803Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4414800Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4415783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4416640Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4417196Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4418038Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4418770Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4419166Z | ^~~~ 2025-08-26T21:48:41.4419750Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4420700Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4421523Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4422367Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4423122Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4423999Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4424961Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4425847Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4426733Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4427658Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4428653Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4429495Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4430053Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4431051Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4432603Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4434326Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4436150Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4438029Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4440055Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4446309Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4456999Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4463156Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4464088Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4464462Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4470800Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4477375Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4478658Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4479069Z | ^~~~~~~~ 2025-08-26T21:48:41.4479701Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4480712Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4481606Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4482473Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4483224Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4484090Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4485148Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4486311Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4487482Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4488632Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4489624Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4490563Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4491401Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4492238Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4493093Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4493829Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4494265Z | ^~~~ 2025-08-26T21:48:41.4494845Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4495707Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4496525Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4497297Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4498037Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4498895Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4499816Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4500783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4501662Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4502602Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4503562Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4504401Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4504959Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4505955Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4507606Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4509336Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4511146Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4513020Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4515101Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4521364Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4531975Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4537987Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4538909Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4539339Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4545780Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4552445Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4553740Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4554186Z | ^~~~~~~~ 2025-08-26T21:48:41.4554814Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4555852Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4556747Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4557551Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4558312Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4559156Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4560220Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4561364Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4562504Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4563607Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4564614Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4565555Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4566391Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4566945Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4567813Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4568559Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4568955Z | ^~~~ 2025-08-26T21:48:41.4569529Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4570430Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4571230Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4572020Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4572767Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4573640Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4574562Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4575443Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4576332Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4577265Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4578224Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4579101Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4579648Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4580707Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4582283Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4583997Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4585751Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4587643Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4589665Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4596149Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4606918Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4612974Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4613899Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4614287Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4620699Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4627304Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4628567Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4629014Z | ^~~~~~~~ 2025-08-26T21:48:41.4629640Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4630635Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4631519Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4632321Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4633086Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4633946Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4635003Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4636138Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4637264Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4638377Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4639411Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4640356Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4641202Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4641758Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4642577Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4643315Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4643661Z | ^~~~ 2025-08-26T21:48:41.4644234Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4645077Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4645889Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4646680Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4647424Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4648261Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4649178Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4650051Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4650941Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4651882Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4652882Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4653750Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4654293Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4655280Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4656842Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4658560Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4660434Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4662332Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4664354Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4670183Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4680019Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4685678Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4686611Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4686987Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4693134Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4699443Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4700811Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4701237Z | ^~~~~~~~ 2025-08-26T21:48:41.4701868Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4702869Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4703758Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4704563Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4705329Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4706170Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4707234Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4708369Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4709511Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4710618Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4711623Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4712623Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4713463Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4714053Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4714877Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4715613Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4715958Z | ^~~~ 2025-08-26T21:48:41.4716535Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:48:41.4717392Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:48:41.4718197Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:48:41.4719022Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:48:41.4719771Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:48:41.4720611Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:48:41.4721532Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:48:41.4722410Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:48:41.4722802Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:48:41.4723265Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:48:41.4723679Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:48:41.4723995Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4724121Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4724878Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:48:41.4725741Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:48:41.4726685Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:48:41.4727617Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:48:41.4728664Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:48:41.4729774Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:48:41.4735168Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:48:41.4741275Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:48:41.4742296Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:48:41.4742458Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:48:41.4742588Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:48:41.4748696Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:48:41.4749999Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:48:41.4750221Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:48:41.4750314Z | ^~~~~~~~ 2025-08-26T21:48:41.4750778Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:48:41.4751207Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:48:41.4751561Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:48:41.4751949Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:48:41.4752247Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:48:41.4752677Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:48:41.4753193Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:48:41.4753690Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:48:41.4754269Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:48:41.4754741Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:48:41.4755166Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:48:41.4755568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:48:41.4755893Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:48:41.4756006Z from rng_extension.cpp:1: 2025-08-26T21:48:41.4756607Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:48:41.4756759Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:48:41.4756853Z | ^~~~ 2025-08-26T21:48:41.4759944Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:48:42.0088575Z running install_lib 2025-08-26T21:48:42.0219601Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-08-26T21:48:42.0222674Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:48:42.0224385Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/__init__.py -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:48:42.0226512Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:48:42.0300779Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:48:42.0376088Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:48:42.0458811Z byte-compiling ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension/__init__.py to __init__.cpython-313.pyc 2025-08-26T21:48:42.0462495Z running install_egg_info 2025-08-26T21:48:42.0663992Z running egg_info 2025-08-26T21:48:42.0745634Z creating torch_test_cpp_extension.egg-info 2025-08-26T21:48:42.0746557Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-08-26T21:48:42.0750526Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-08-26T21:48:42.0752444Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-08-26T21:48:42.0754558Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-08-26T21:48:42.0756067Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:48:42.0840569Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:48:42.0847727Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:48:42.0849382Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2025-08-26T21:48:42.0855517Z running install_scripts 2025-08-26T21:48:44.3744571Z running install 2025-08-26T21:48:44.3746249Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-08-26T21:48:44.3747127Z !! 2025-08-26T21:48:44.3747243Z 2025-08-26T21:48:44.3747382Z ******************************************************************************** 2025-08-26T21:48:44.3747765Z Please avoid running ``setup.py`` directly. 2025-08-26T21:48:44.3748177Z Instead, use pypa/build, pypa/installer or other 2025-08-26T21:48:44.3748554Z standards-based tools. 2025-08-26T21:48:44.3748746Z 2025-08-26T21:48:44.3749021Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-08-26T21:48:44.3749532Z or your builds will no longer be supported. 2025-08-26T21:48:44.3749778Z 2025-08-26T21:48:44.3750096Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-08-26T21:48:44.3750650Z ******************************************************************************** 2025-08-26T21:48:44.3750904Z 2025-08-26T21:48:44.3750997Z !! 2025-08-26T21:48:44.3751225Z self.initialize_options() 2025-08-26T21:48:44.3885052Z running build 2025-08-26T21:48:44.3885532Z running build_ext 2025-08-26T21:48:44.4387888Z building 'no_python_abi_suffix_test' extension 2025-08-26T21:48:44.4391034Z creating /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313 2025-08-26T21:48:44.7750030Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o.d -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/no_python_abi_suffix_test.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=no_python_abi_suffix_test -std=c++17 2025-08-26T21:48:44.7801668Z creating build/lib.linux-x86_64-cpython-313 2025-08-26T21:48:44.7805825Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so 2025-08-26T21:48:44.8963149Z running install_lib 2025-08-26T21:48:44.9054136Z creating install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-08-26T21:48:44.9057205Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-08-26T21:48:44.9062459Z running install_egg_info 2025-08-26T21:48:44.9254134Z running egg_info 2025-08-26T21:48:44.9327838Z creating no_python_abi_suffix_test.egg-info 2025-08-26T21:48:44.9329097Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2025-08-26T21:48:44.9333094Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2025-08-26T21:48:44.9335557Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2025-08-26T21:48:44.9336867Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-08-26T21:48:44.9416418Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-08-26T21:48:44.9423739Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-08-26T21:48:44.9425704Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info 2025-08-26T21:48:44.9430915Z running install_scripts 2025-08-26T21:48:45.4495119Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:48:45.4498024Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_no_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:48:45.449563] 2025-08-26T21:49:00.0028544Z 2025-08-26T21:49:00.0029527Z test_cpp_extensions_aot_no_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_no_ninja_1.1_9a179cf46896894c_.log 2025-08-26T21:49:00.0038486Z Running 21 items in this shard: test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_no_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_no_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_no_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_no_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_no_ninja.py::TestTorchLibrary::test_torch_library 2025-08-26T21:49:00.0046867Z 2025-08-26T21:49:00.0047117Z Running test_autoload_disable 1/1 ... [2025-08-26 21:49:00.003176] 2025-08-26T21:49:02.4377623Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:49:02.4379313Z import pkg_resources 2025-08-26T21:49:02.5032744Z running install 2025-08-26T21:49:02.5034882Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-08-26T21:49:02.5035957Z !! 2025-08-26T21:49:02.5036085Z 2025-08-26T21:49:02.5036231Z ******************************************************************************** 2025-08-26T21:49:02.5036623Z Please avoid running ``setup.py`` directly. 2025-08-26T21:49:02.5037045Z Instead, use pypa/build, pypa/installer or other 2025-08-26T21:49:02.5037414Z standards-based tools. 2025-08-26T21:49:02.5037619Z 2025-08-26T21:49:02.5037846Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-08-26T21:49:02.5038334Z or your builds will no longer be supported. 2025-08-26T21:49:02.5038580Z 2025-08-26T21:49:02.5038890Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-08-26T21:49:02.5039423Z ******************************************************************************** 2025-08-26T21:49:02.5039669Z 2025-08-26T21:49:02.5039750Z !! 2025-08-26T21:49:02.5039970Z self.initialize_options() 2025-08-26T21:49:02.5170150Z running build 2025-08-26T21:49:02.5170467Z running build_py 2025-08-26T21:49:02.5256984Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-08-26T21:49:02.5259352Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-08-26T21:49:02.5263065Z running build_ext 2025-08-26T21:49:02.5277941Z building 'torch_test_cpp_extension.cpp' extension 2025-08-26T21:49:02.5279443Z creating build/temp.linux-x86_64-cpython-313 2025-08-26T21:49:02.5283927Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=cpp -std=c++17 2025-08-26T21:49:03.6733361Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-08-26T21:49:03.6735368Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-08-26T21:49:03.6736846Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-08-26T21:49:03.6737699Z from extension.cpp:1: 2025-08-26T21:49:03.6739009Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-08-26T21:49:03.6739863Z extension.cpp:45:53: required from here 2025-08-26T21:49:03.6741508Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:2041:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-08-26T21:49:03.6742979Z 2041 | class class_ : public detail::generic_type { 2025-08-26T21:49:03.6743378Z | ^~~~~~ 2025-08-26T21:49:03.6745025Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-08-26T21:49:03.6746401Z extension.cpp:45:53: required from here 2025-08-26T21:49:03.6748721Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:2139:28: warning: ‘pybind11::class_::class_<>(pybind11::handle, const char*)::’ declared with greater visibility than the type of its field ‘pybind11::class_::class_<>(pybind11::handle, const char*)::::’ [-Wattributes] 2025-08-26T21:49:03.6750749Z 2139 | with_internals([&](internals &internals) { 2025-08-26T21:49:03.6751134Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:03.6751664Z 2140 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-08-26T21:49:03.6752238Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:03.6752682Z 2141 | : internals.registered_types_cpp; 2025-08-26T21:49:03.6753103Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:03.6753527Z 2142 | instances[std::type_index(typeid(type_alias))] 2025-08-26T21:49:03.6753947Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:03.6754354Z 2143 | = instances[std::type_index(typeid(type))]; 2025-08-26T21:49:03.6754739Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:03.6755068Z 2144 | }); 2025-08-26T21:49:03.6755324Z | ~ 2025-08-26T21:49:03.6758652Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:04.3946463Z building 'torch_test_cpp_extension.maia' extension 2025-08-26T21:49:04.3950248Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=maia -std=c++17 2025-08-26T21:49:05.5008643Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:06.2050094Z building 'torch_test_cpp_extension.rng' extension 2025-08-26T21:49:06.2057272Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=rng -std=c++17 2025-08-26T21:49:07.5465413Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:49:07.5466838Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:49:07.5468179Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:49:07.5469762Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:49:07.5470911Z from rng_extension.cpp:6: 2025-08-26T21:49:07.5472681Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1478: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:07.5474042Z 1478 | #pragma unroll 2025-08-26T21:49:07.5474453Z | 2025-08-26T21:49:07.5475308Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-08-26T21:49:07.5476775Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1530, 2025-08-26T21:49:07.5478059Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:49:07.5479638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:49:07.5480991Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:49:07.5482501Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:49:07.5483818Z from rng_extension.cpp:6: 2025-08-26T21:49:07.5485432Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:07.5486914Z 59 | #pragma unroll 2025-08-26T21:49:07.5487338Z | 2025-08-26T21:49:07.5488955Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:07.5490575Z 72 | #pragma unroll 2025-08-26T21:49:07.5491068Z | 2025-08-26T21:49:07.5492979Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:07.5494333Z 87 | #pragma unroll 2025-08-26T21:49:07.5494739Z | 2025-08-26T21:49:07.5495619Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1531, 2025-08-26T21:49:07.5497096Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:49:07.5497926Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:49:07.5498721Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:49:07.5499618Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:49:07.5500393Z from rng_extension.cpp:6: 2025-08-26T21:49:07.5501405Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:07.5502281Z 160 | #pragma unroll 2025-08-26T21:49:07.5502541Z | 2025-08-26T21:49:07.5503052Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.5503940Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.5504752Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.5505539Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.5506618Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.5507897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.5508827Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.5509729Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.5510618Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.5511561Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.5512515Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.5513360Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5513915Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5515010Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.5516685Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.5518427Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.5520241Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.5522123Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.5524234Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.5530517Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.5541274Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.5547282Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.5548222Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.5548661Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.5555060Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.5561623Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.5562945Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.5563409Z | ^~~~~~~~ 2025-08-26T21:49:07.5564032Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.5565035Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.5566001Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.5566820Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.5567585Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.5568411Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.5569470Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.5570653Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.5571794Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.5572903Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.5573910Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.5574838Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.5575683Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5576233Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5577123Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.5577864Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.5578232Z | ^~~~ 2025-08-26T21:49:07.5578811Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.5579674Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.5580625Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.5581413Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.5582154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.5583053Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.5583980Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.5584870Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.5585763Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.5586688Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.5587648Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.5588522Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5589082Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5590099Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.5591849Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.5593589Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.5595371Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.5597283Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.5599323Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.5605216Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.5615108Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.5620890Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.5621825Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.5622204Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.5628067Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.5634270Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.5635538Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.5635950Z | ^~~~~~~~ 2025-08-26T21:49:07.5636620Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.5637617Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.5638531Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.5639331Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.5640092Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.5640933Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.5641987Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.5643148Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.5644292Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.5645396Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.5646401Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.5647337Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.5648210Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5648764Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5649584Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.5650324Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.5650719Z | ^~~~ 2025-08-26T21:49:07.5651294Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.5652152Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.5652953Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.5653747Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.5654496Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.5655343Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.5656265Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.5657146Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.5658041Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.5658974Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.5659928Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.5660933Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5661480Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5662490Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.5664096Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.5665817Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.5667583Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.5669528Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.5671558Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.5677657Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.5688141Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.5694274Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.5695322Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.5695710Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.5702026Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.5708534Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.5709825Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.5710247Z | ^~~~~~~~ 2025-08-26T21:49:07.5710878Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.5711875Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.5712744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.5713557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.5714319Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.5715161Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.5716217Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.5717357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.5718493Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.5719616Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.5720619Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.5721630Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.5722479Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5723056Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5723887Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.5724630Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.5724977Z | ^~~~ 2025-08-26T21:49:07.5725555Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.5726403Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.5727215Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.5728040Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.5728784Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.5729622Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.5730531Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.5731416Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.5732300Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.5733262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.5734221Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.5735048Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5735598Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5736588Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.5738144Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.5739947Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.5741828Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.5743708Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.5745735Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.5751904Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.5762328Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.5768282Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.5769211Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.5769599Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.5775872Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.5782437Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.5783749Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.5784164Z | ^~~~~~~~ 2025-08-26T21:49:07.5784784Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.5785769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.5786659Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.5787477Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.5788269Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.5789107Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.5790149Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.5791287Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.5792637Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.5793850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.5794858Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.5795806Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.5796638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5797201Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5798049Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.5798790Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.5799142Z | ^~~~ 2025-08-26T21:49:07.5799704Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.5800568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.5801382Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.5802172Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.5802916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.5803737Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.5804675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.5805572Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.5806558Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.5807503Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.5808494Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.5809341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5809897Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5810910Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.5812502Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.5814275Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.5816041Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.5817914Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.5819991Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.5826213Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.5836702Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.5842576Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.5843510Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.5843893Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.5850114Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.5856617Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.5857895Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.5858309Z | ^~~~~~~~ 2025-08-26T21:49:07.5858921Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.5859953Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.5860932Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.5861753Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.5862512Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.5863335Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.5864396Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.5865538Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.5866744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.5867868Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.5868913Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.5869841Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.5870680Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5871238Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5872075Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.5872869Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.5873240Z | ^~~~ 2025-08-26T21:49:07.5873820Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.5874677Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.5875490Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.5876281Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.5877014Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.5877885Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.5878811Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.5879700Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.5880588Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.5881518Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.5882474Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.5883314Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5883881Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5884874Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.5886435Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.5888135Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.5889885Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.5891988Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.5894155Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.5900066Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.5909968Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.5915672Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.5916609Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.5916983Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.5922878Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.5929081Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.5930349Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.5930764Z | ^~~~~~~~ 2025-08-26T21:49:07.5931393Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.5932428Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.5933316Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.5934121Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.5934883Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.5935722Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.5936783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.5937954Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.5939103Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.5940207Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.5941330Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.5942266Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.5943107Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5943664Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5944488Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.5945232Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.5945580Z | ^~~~ 2025-08-26T21:49:07.5946157Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.5961749Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.5962738Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.5963526Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.5964282Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.5965142Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.5966190Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.5967093Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.5968033Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.5968960Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.5969926Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.5970771Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.5971336Z from rng_extension.cpp:1: 2025-08-26T21:49:07.5972501Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.5974071Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.5975797Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.5977565Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.5979506Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.5981658Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.5987491Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.5997648Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6003362Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6004296Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6004731Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6010618Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6016880Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6018151Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6018570Z | ^~~~~~~~ 2025-08-26T21:49:07.6019204Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6020209Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6021158Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6021988Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6022756Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6023596Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6024695Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6025827Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6027015Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6028159Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6029161Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6030102Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6030951Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6031496Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6032337Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6033155Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6033503Z | ^~~~ 2025-08-26T21:49:07.6034071Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6034934Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6035744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6036527Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6037270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6038128Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6039049Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6039944Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6040826Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6041758Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6042713Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6043544Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6044094Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6045090Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6046649Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6048361Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6050122Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6052062Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6054095Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6059967Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6069866Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6075506Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6076437Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6076827Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6082724Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6088964Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6090248Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6090672Z | ^~~~~~~~ 2025-08-26T21:49:07.6091343Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6092640Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6093541Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6094362Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6095126Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6095958Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6097020Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6098211Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6099365Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6100556Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6101548Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6102485Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6103327Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6103885Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6104734Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6105488Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6105867Z | ^~~~ 2025-08-26T21:49:07.6106447Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6107306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6108122Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6108897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6109638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6110541Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6111463Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6112394Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6113264Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6114195Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6115149Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6115992Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6116544Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6117579Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6119131Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6120861Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6122620Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6124558Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6126580Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6132361Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6142281Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6147945Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6148900Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6149290Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6155162Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6161439Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6162721Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6163145Z | ^~~~~~~~ 2025-08-26T21:49:07.6163768Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6164774Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6165660Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6166459Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6167218Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6168050Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6169101Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6170276Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6171404Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6172570Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6173571Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6174508Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6175346Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6175896Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6176752Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6177492Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6177840Z | ^~~~ 2025-08-26T21:49:07.6178411Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6179263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6180076Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6180971Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6181754Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6182594Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6183497Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6184386Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6185270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6186200Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6187154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6187993Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6188536Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6189529Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6191091Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6193032Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6194791Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6196775Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6198936Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6204787Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6214523Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6220186Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6221205Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6221596Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6227556Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6233804Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6235086Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6235511Z | ^~~~~~~~ 2025-08-26T21:49:07.6236175Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6237164Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6238055Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6238867Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6239623Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6240455Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6241667Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6242803Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6243944Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6245056Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6246057Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6246991Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6247835Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6248372Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6249209Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6249946Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6250295Z | ^~~~ 2025-08-26T21:49:07.6250858Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6251713Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6252525Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6253314Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6254062Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6254937Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6255855Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6256771Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6257658Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6258599Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6259557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6260460Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6261053Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6262054Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6263618Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6265333Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6267096Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6269025Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6271052Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6276853Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6286700Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6292713Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6293655Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6294046Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6299915Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6306211Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6307503Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6307907Z | ^~~~~~~~ 2025-08-26T21:49:07.6308595Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6309601Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6310528Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6311338Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6312102Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6312923Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6313984Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6315155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6316295Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6317410Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6318402Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6319340Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6320181Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6320731Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6321567Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6322295Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6322640Z | ^~~~ 2025-08-26T21:49:07.6323214Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6324065Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6324872Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6325646Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6326429Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6327272Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6328197Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6329085Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6329962Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6330904Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6331860Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6332738Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6333296Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6334284Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6335881Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6337596Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6339364Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6341385Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6343423Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6349279Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6359048Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6364716Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6365641Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6366065Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6371957Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6378132Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6379416Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6379833Z | ^~~~~~~~ 2025-08-26T21:49:07.6380532Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6381532Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6382404Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6383215Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6384023Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6384862Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6385983Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6387124Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6388252Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6389367Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6390370Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6391305Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6392382Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6393010Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6393862Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6394649Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6394996Z | ^~~~ 2025-08-26T21:49:07.6395575Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6396425Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6397240Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6398033Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6398779Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6399655Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6400568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6401513Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6402403Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6403334Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6404289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6405134Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6405674Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6406668Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6408234Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6409998Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6411822Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6413704Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6415737Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6422049Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6432484Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6438414Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6439345Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6439732Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6445923Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6452419Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6453703Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6454152Z | ^~~~~~~~ 2025-08-26T21:49:07.6454777Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6455764Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6456650Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6457461Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6458241Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6459097Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6460152Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6461360Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6462511Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6463639Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6464686Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6465620Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6466641Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6467199Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6468039Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6468781Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6469115Z | ^~~~ 2025-08-26T21:49:07.6469691Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6470548Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6471360Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6472155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6473036Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6473934Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6474861Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6475751Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6476639Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6477613Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6478576Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6479952Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6480509Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6481534Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6483098Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6484921Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6486739Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6488624Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6490652Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6497148Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6507891Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6513931Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6514860Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6515259Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6521682Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6528265Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6529580Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6529997Z | ^~~~~~~~ 2025-08-26T21:49:07.6530615Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6531620Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6532514Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6533332Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6534100Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6534927Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6535981Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6537114Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6538257Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6539415Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6540535Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6541461Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6542303Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6542857Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6543697Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6544440Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6544779Z | ^~~~ 2025-08-26T21:49:07.6545389Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6546251Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6547072Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6547850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6548601Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6549441Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6550393Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6551284Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6552165Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6553091Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6554060Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6554897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6555448Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6556439Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6557988Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6559700Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6561461Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6563340Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6565418Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6571730Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6582362Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6588359Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6589291Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6589666Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6596303Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6602916Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6604208Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6604615Z | ^~~~~~~~ 2025-08-26T21:49:07.6605353Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6606353Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6607249Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6608066Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6608816Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6609658Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6610750Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6611889Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6613035Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6614156Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6615184Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6616130Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6616971Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6617522Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6618356Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6619079Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6619425Z | ^~~~ 2025-08-26T21:49:07.6620000Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6620945Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6621801Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6622575Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6623324Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6624207Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6625154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6626038Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6626911Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6627847Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6628797Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6629643Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6630224Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6631208Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6632768Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6634484Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6636244Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6638172Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6640198Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6646418Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6657109Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6663208Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6664139Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6664512Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6670856Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6677485Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6678769Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6679177Z | ^~~~~~~~ 2025-08-26T21:49:07.6679806Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6680806Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6681689Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6682499Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6683252Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6684174Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6685254Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6686391Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6687528Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6688625Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6689625Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6690595Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6691438Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6692215Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6693040Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6693783Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6694131Z | ^~~~ 2025-08-26T21:49:07.6694705Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6695667Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6696472Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6697261Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6698014Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6698850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6699763Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6700706Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6701598Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6702534Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6703491Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6704331Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6704887Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6705874Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6707440Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6709249Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6711014Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6712941Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6714965Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6721269Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6731880Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6737860Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6738777Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6739167Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6745758Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6752366Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6753637Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6754061Z | ^~~~~~~~ 2025-08-26T21:49:07.6754691Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6755728Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6756646Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6757453Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6758224Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6759064Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6760120Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6761257Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6762407Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6763513Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6764522Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6765460Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6766299Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6766855Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6767673Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6768419Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6768763Z | ^~~~ 2025-08-26T21:49:07.6769374Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6770257Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6771056Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6771839Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6772584Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6773422Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6774346Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6775262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6776165Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6777107Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6778070Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6778906Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6779447Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6780564Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6782141Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6783858Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6785611Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6787491Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6789526Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6796072Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6806694Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6812755Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6813693Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6814085Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6820474Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6827017Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6828305Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6828726Z | ^~~~~~~~ 2025-08-26T21:49:07.6829398Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6830416Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6831297Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6832098Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6832855Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6833687Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6834748Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6835915Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6837043Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6838155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6839156Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6840093Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6840963Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6841512Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6842336Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6843075Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6843418Z | ^~~~ 2025-08-26T21:49:07.6843992Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6844829Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6845645Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6846435Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6847185Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6848023Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6848937Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6849825Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6850711Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6851646Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6852602Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6853481Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6854024Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6855041Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6856599Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6858322Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6860145Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6862157Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6864191Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6870431Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6881139Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6887159Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6888091Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6888481Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6895044Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6901689Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6902982Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6903406Z | ^~~~~~~~ 2025-08-26T21:49:07.6904036Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6905036Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6905911Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6906741Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6907605Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6908445Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6909504Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6910625Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6911766Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6912877Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6913957Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6914900Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6915780Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6916320Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6917150Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6917927Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6918278Z | ^~~~ 2025-08-26T21:49:07.6918839Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6919700Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6920582Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6921376Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6922129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6922965Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6923869Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6924753Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.6925675Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.6926612Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.6927573Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.6928398Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6928951Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6929929Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.6931545Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.6933275Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.6935040Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.6936914Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.6938939Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.6945346Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.6956014Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.6962005Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.6962934Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.6963320Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.6969667Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.6976216Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.6977489Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.6977903Z | ^~~~~~~~ 2025-08-26T21:49:07.6978528Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.6979522Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.6980533Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.6981357Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.6982117Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.6982957Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.6984056Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.6985248Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.6986393Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.6987508Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.6988517Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.6989453Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.6990280Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.6990838Z from rng_extension.cpp:1: 2025-08-26T21:49:07.6991868Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.6992615Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.6992969Z | ^~~~ 2025-08-26T21:49:07.6993534Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.6994394Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.6995212Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.6995999Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.6996745Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.6997571Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.6998596Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.6999494Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.7000423Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.7001364Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.7002314Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.7003182Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7003738Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7004786Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.7006450Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.7008198Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.7009968Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.7011848Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.7013955Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.7020172Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.7030898Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.7036980Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.7037919Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.7038308Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.7044652Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.7051211Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.7052499Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.7052916Z | ^~~~~~~~ 2025-08-26T21:49:07.7053539Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.7054518Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.7055406Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.7056221Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.7056979Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.7057815Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.7058888Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.7060025Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.7061272Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.7062385Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.7063389Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.7064334Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.7065205Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7065765Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7066597Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.7067337Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.7067672Z | ^~~~ 2025-08-26T21:49:07.7068248Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.7069103Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.7069917Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.7070741Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.7071483Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.7072320Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.7073243Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.7074129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.7075016Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.7075954Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.7076897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.7077742Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7078300Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7079287Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.7080844Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.7082549Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.7084354Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.7086209Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.7088330Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.7094438Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.7104392Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.7110031Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.7110964Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.7111339Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.7117231Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.7123443Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.7124732Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.7125167Z | ^~~~~~~~ 2025-08-26T21:49:07.7125793Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.7126794Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.7127683Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.7128496Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.7129242Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.7130112Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.7131171Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.7132312Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.7133456Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.7134560Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.7135570Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.7136512Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.7137359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7137916Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7138748Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.7139475Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.7139819Z | ^~~~ 2025-08-26T21:49:07.7140489Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.7141344Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.7141687Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.7142012Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.7142392Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.7142833Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.7143227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.7143609Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.7144005Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.7144435Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.7144888Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.7145205Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7145324Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7146105Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.7146964Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.7147901Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.7148898Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.7149956Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.7151032Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.7156287Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.7162312Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.7163162Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.7163321Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.7163439Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.7169516Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.7170833Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.7171042Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.7171147Z | ^~~~~~~~ 2025-08-26T21:49:07.7171611Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.7172038Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.7172393Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.7172772Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.7173084Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.7173532Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.7174064Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.7174566Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.7175104Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.7175579Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.7176036Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.7176442Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.7176753Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7176886Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7177483Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.7177635Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.7177789Z | ^~~~ 2025-08-26T21:49:07.7178175Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.7178534Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.7178878Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.7179210Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.7179508Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.7179929Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.7180391Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.7180776Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.7181172Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.7181601Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.7182018Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.7182329Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7182456Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7183216Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.7184087Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.7185061Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.7186015Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.7187050Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.7188142Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.7193697Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.7199711Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.7200530Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.7200691Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.7200822Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.7207002Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.7221541Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.7221881Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.7222091Z | ^~~~~~~~ 2025-08-26T21:49:07.7222573Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.7223014Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.7223382Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.7223731Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.7224038Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.7224466Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.7224980Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.7225504Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.7226031Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.7226516Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.7226927Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.7227345Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.7227661Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7227780Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7228478Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.7228677Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.7228783Z | ^~~~ 2025-08-26T21:49:07.7229211Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.7229572Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.7229915Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.7230233Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.7230549Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.7230967Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.7231403Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.7231786Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.7232177Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.7232608Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.7233008Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.7233364Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7233481Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7234312Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.7235177Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.7236119Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.7237096Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.7238275Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.7239406Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.7244742Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.7250953Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.7251819Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.7252004Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.7252123Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.7258204Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.7259480Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.7259735Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.7259826Z | ^~~~~~~~ 2025-08-26T21:49:07.7260425Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.7260843Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.7261207Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.7261548Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.7261863Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.7262295Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.7262844Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.7263358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.7263879Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.7264358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.7264769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.7265213Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.7265533Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7265652Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7266264Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.7266401Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.7266505Z | ^~~~ 2025-08-26T21:49:07.7266891Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.7267249Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.7267594Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.7267916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.7268225Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.7268641Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.7269035Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.7269415Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.7269805Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.7270237Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.7270674Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.7270999Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7271157Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7271926Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.7272784Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.7273721Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.7274677Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.7275732Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.7276810Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.7281662Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.7287212Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.7288047Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.7288218Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.7288332Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.7294280Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.7295605Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.7295815Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.7295906Z | ^~~~~~~~ 2025-08-26T21:49:07.7296382Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.7296798Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.7297189Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.7297529Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.7297841Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.7298264Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.7298776Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.7299287Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.7299807Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.7300290Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.7300779Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.7301247Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.7301564Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7301740Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7302389Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.7302527Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.7302631Z | ^~~~ 2025-08-26T21:49:07.7303019Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:07.7303373Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:07.7303720Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:07.7304072Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:07.7304386Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:07.7304801Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:07.7305197Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:07.7305578Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:07.7305969Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:07.7306431Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:07.7306839Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:07.7307168Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7307284Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7308055Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:07.7308910Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:07.7309849Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:07.7310768Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:07.7311824Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:07.7312901Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:07.7318248Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:07.7324263Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:07.7325108Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:07.7325266Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:07.7325381Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:07.7331516Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:07.7332789Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:07.7333024Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:07.7333134Z | ^~~~~~~~ 2025-08-26T21:49:07.7333600Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:07.7334030Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:07.7334381Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:07.7334759Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:07.7335069Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:07.7335485Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:07.7336010Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:07.7336509Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:07.7337074Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:07.7337545Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:07.7337968Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:07.7338374Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:07.7338686Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:07.7338815Z from rng_extension.cpp:1: 2025-08-26T21:49:07.7339467Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:07.7339622Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:07.7339713Z | ^~~~ 2025-08-26T21:49:07.7342910Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:08.2802085Z running install_lib 2025-08-26T21:49:08.2938053Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:08.3025031Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:08.3109400Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:08.3202090Z running install_egg_info 2025-08-26T21:49:08.3392094Z running egg_info 2025-08-26T21:49:08.3470491Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-08-26T21:49:08.3474131Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-08-26T21:49:08.3480367Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-08-26T21:49:08.3491627Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-08-26T21:49:08.3583595Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:49:08.3593299Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:49:08.3605206Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2025-08-26T21:49:08.3607053Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2025-08-26T21:49:08.3613787Z running install_scripts 2025-08-26T21:49:11.5956305Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:49:11.5958102Z import pkg_resources 2025-08-26T21:49:11.6262331Z 2025-08-26T21:49:11.6262830Z Running tests... 2025-08-26T21:49:11.6263213Z ---------------------------------------------------------------------- 2025-08-26T21:49:12.6531210Z . 2025-08-26T21:49:12.6531611Z ---------------------------------------------------------------------- 2025-08-26T21:49:12.6532031Z Ran 1 test in 1.027s 2025-08-26T21:49:12.6532190Z 2025-08-26T21:49:12.6532274Z OK 2025-08-26T21:49:12.6532396Z 2025-08-26T21:49:12.6532504Z Generating XML reports... 2025-08-26T21:49:13.6554193Z Running test_autoload_enable 1/1 ... [2025-08-26 21:49:13.655078] 2025-08-26T21:49:16.0769971Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:49:16.0771568Z import pkg_resources 2025-08-26T21:49:16.1424903Z running install 2025-08-26T21:49:16.1427040Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-08-26T21:49:16.1427842Z !! 2025-08-26T21:49:16.1427968Z 2025-08-26T21:49:16.1428101Z ******************************************************************************** 2025-08-26T21:49:16.1428491Z Please avoid running ``setup.py`` directly. 2025-08-26T21:49:16.1428902Z Instead, use pypa/build, pypa/installer or other 2025-08-26T21:49:16.1429267Z standards-based tools. 2025-08-26T21:49:16.1429468Z 2025-08-26T21:49:16.1429703Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-08-26T21:49:16.1430188Z or your builds will no longer be supported. 2025-08-26T21:49:16.1430433Z 2025-08-26T21:49:16.1430747Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-08-26T21:49:16.1431284Z ******************************************************************************** 2025-08-26T21:49:16.1431525Z 2025-08-26T21:49:16.1431603Z !! 2025-08-26T21:49:16.1432045Z self.initialize_options() 2025-08-26T21:49:16.1561851Z running build 2025-08-26T21:49:16.1562155Z running build_py 2025-08-26T21:49:16.1648703Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-08-26T21:49:16.1650956Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-08-26T21:49:16.1655151Z running build_ext 2025-08-26T21:49:16.1670397Z building 'torch_test_cpp_extension.cpp' extension 2025-08-26T21:49:16.1672306Z creating build/temp.linux-x86_64-cpython-313 2025-08-26T21:49:16.1676772Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c extension.cpp -o build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=cpp -std=c++17 2025-08-26T21:49:17.2689179Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/Exceptions.h:12, 2025-08-26T21:49:17.2690196Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/python.h:11, 2025-08-26T21:49:17.2691059Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:9, 2025-08-26T21:49:17.2691635Z from extension.cpp:1: 2025-08-26T21:49:17.2693115Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘class pybind11::class_’: 2025-08-26T21:49:17.2694167Z extension.cpp:45:53: required from here 2025-08-26T21:49:17.2695654Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:2041:7: warning: ‘pybind11::class_’ declared with greater visibility than its base ‘pybind11::detail::generic_type’ [-Wattributes] 2025-08-26T21:49:17.2696894Z 2041 | class class_ : public detail::generic_type { 2025-08-26T21:49:17.2697245Z | ^~~~~~ 2025-08-26T21:49:17.2699025Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h: In instantiation of ‘pybind11::class_< , >::class_(pybind11::handle, const char*, const Extra& ...) [with Extra = {}; type_ = MatrixMultiplier; options = {}]’: 2025-08-26T21:49:17.2700496Z extension.cpp:45:53: required from here 2025-08-26T21:49:17.2702869Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/pybind11/pybind11.h:2139:28: warning: ‘pybind11::class_::class_<>(pybind11::handle, const char*)::’ declared with greater visibility than the type of its field ‘pybind11::class_::class_<>(pybind11::handle, const char*)::::’ [-Wattributes] 2025-08-26T21:49:17.2704829Z 2139 | with_internals([&](internals &internals) { 2025-08-26T21:49:17.2705206Z | ^~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:17.2705737Z 2140 | auto &instances = record.module_local ? get_local_internals().registered_types_cpp 2025-08-26T21:49:17.2706316Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:17.2706778Z 2141 | : internals.registered_types_cpp; 2025-08-26T21:49:17.2707195Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:17.2707633Z 2142 | instances[std::type_index(typeid(type_alias))] 2025-08-26T21:49:17.2708138Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:17.2708557Z 2143 | = instances[std::type_index(typeid(type))]; 2025-08-26T21:49:17.2709020Z | ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:17.2709354Z 2144 | }); 2025-08-26T21:49:17.2709598Z | ~ 2025-08-26T21:49:17.2712947Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:17.9834931Z building 'torch_test_cpp_extension.maia' extension 2025-08-26T21:49:17.9838801Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c maia_extension.cpp -o build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=maia -std=c++17 2025-08-26T21:49:19.1065716Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:19.8081357Z building 'torch_test_cpp_extension.rng' extension 2025-08-26T21:49:19.8085158Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -Iself_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c rng_extension.cpp -o build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=rng -std=c++17 2025-08-26T21:49:21.1114769Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:49:21.1117509Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:49:21.1119041Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:49:21.1120754Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:49:21.1122280Z from rng_extension.cpp:6: 2025-08-26T21:49:21.1124270Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1478: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:21.1125867Z 1478 | #pragma unroll 2025-08-26T21:49:21.1126348Z | 2025-08-26T21:49:21.1127217Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_convert.h:4, 2025-08-26T21:49:21.1128688Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1530, 2025-08-26T21:49:21.1130140Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:49:21.1131617Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:49:21.1133019Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:49:21.1134774Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:49:21.1135983Z from rng_extension.cpp:6: 2025-08-26T21:49:21.1137671Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:59: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:21.1138955Z 59 | #pragma unroll 2025-08-26T21:49:21.1139349Z | 2025-08-26T21:49:21.1140862Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:72: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:21.1141888Z 72 | #pragma unroll 2025-08-26T21:49:21.1142178Z | 2025-08-26T21:49:21.1143237Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_n.h:87: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:21.1144022Z 87 | #pragma unroll 2025-08-26T21:49:21.1144270Z | 2025-08-26T21:49:21.1144820Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_base.h:1531, 2025-08-26T21:49:21.1145738Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec256/vec256.h:8, 2025-08-26T21:49:21.1146565Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec.h:7, 2025-08-26T21:49:21.1147359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/Loops.h:37, 2025-08-26T21:49:21.1148271Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/native/cpu/DistributionTemplates.h:9, 2025-08-26T21:49:21.1148942Z from rng_extension.cpp:6: 2025-08-26T21:49:21.1149888Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/cpu/vec/vec_mask.h:160: warning: ignoring ‘#pragma unroll ’ [-Wunknown-pragmas] 2025-08-26T21:49:21.1150695Z 160 | #pragma unroll 2025-08-26T21:49:21.1150944Z | 2025-08-26T21:49:21.1151653Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1152655Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1153470Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1154258Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1154998Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1155842Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1156837Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1157733Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1158665Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1159599Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1160554Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1161396Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1161941Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1163086Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1164673Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1166401Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1168167Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1170086Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1172141Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1178395Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1189216Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1195587Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1196533Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1196931Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1203243Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1209901Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1211188Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1211614Z | ^~~~~~~~ 2025-08-26T21:49:21.1212226Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1213227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1214109Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1214921Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1215682Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1216508Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1217626Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1218769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1219963Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1221180Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1222181Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1223112Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1224014Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1224576Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1225411Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1226153Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1226487Z | ^~~~ 2025-08-26T21:49:21.1227061Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1227927Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1228744Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1229580Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1230317Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1231154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1232077Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1232964Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1233845Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1234769Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1235732Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1236575Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1237126Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1238114Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1239680Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1241385Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1243217Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1245095Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1247164Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1253038Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1262929Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1268559Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1269494Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1269863Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1275811Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1282032Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1283374Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1283795Z | ^~~~~~~~ 2025-08-26T21:49:21.1284458Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1285459Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1286346Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1287158Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1287923Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1288817Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1289877Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1291007Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1292405Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1293529Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1294540Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1295489Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1296344Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1296888Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1297739Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1298484Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1298838Z | ^~~~ 2025-08-26T21:49:21.1299406Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1300262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1301142Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1302061Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1302815Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1303706Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1304614Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1305506Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1306389Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1307327Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1308337Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1309167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1309720Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1310729Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1312289Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1313998Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1315821Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1317692Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1319707Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1325883Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1336293Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1342304Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1343241Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1343624Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1349824Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1356402Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1357705Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1358120Z | ^~~~~~~~ 2025-08-26T21:49:21.1358742Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1359724Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1360612Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1361433Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1362244Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1363085Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1364129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1365262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1366417Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1367531Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1368538Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1369475Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1370303Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1370856Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1371684Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1372422Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1372765Z | ^~~~ 2025-08-26T21:49:21.1373322Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1374217Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1375033Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1375820Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1376563Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1377383Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1378340Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1379224Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1380105Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1381158Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1382129Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1382968Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1383519Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1384518Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1386080Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1387830Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1389599Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1391460Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1393735Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1399869Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1410383Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1416304Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1417229Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1417618Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1423985Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1430464Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1431750Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1432175Z | ^~~~~~~~ 2025-08-26T21:49:21.1432790Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1433837Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1434731Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1435547Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1436361Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1437186Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1438242Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1439384Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1440582Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1441759Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1442793Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1443720Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1444568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1445126Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1445956Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1446702Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1447035Z | ^~~~ 2025-08-26T21:49:21.1447655Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1448512Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1449325Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1450095Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1450835Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1451671Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1452587Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1453477Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1454368Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1455289Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1456238Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1457084Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1457633Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1458657Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1460206Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1462042Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1463807Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1465677Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1467744Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1473980Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1484356Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1490281Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1491212Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1491587Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1498102Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1504710Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1505997Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1506410Z | ^~~~~~~~ 2025-08-26T21:49:21.1507038Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1508090Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1508984Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1509796Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1510549Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1511387Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1512488Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1513631Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1514770Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1515889Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1516879Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1517821Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1518656Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1519236Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1520064Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1520786Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1521175Z | ^~~~ 2025-08-26T21:49:21.1521748Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1522597Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1523399Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1524182Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1524926Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1525809Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1526730Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1527643Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1528555Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1529491Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1530447Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1531290Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1531875Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1532849Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1534419Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1536136Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1537894Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1539847Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1541978Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1547782Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1557596Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1563263Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1564236Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1564627Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1570487Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1576796Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1578090Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1578513Z | ^~~~~~~~ 2025-08-26T21:49:21.1579147Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1580148Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1581103Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1581924Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1582691Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1583529Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1584586Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1585759Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1586938Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1588055Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1589067Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1590009Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1590858Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1591398Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1592561Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1593308Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1593656Z | ^~~~ 2025-08-26T21:49:21.1594216Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1595071Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1595880Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1596668Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1597453Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1598280Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1599202Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1600093Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1600979Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1601915Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1602873Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1603703Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1604257Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1605250Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1606809Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1608520Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1610270Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1612213Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1614270Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1620069Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1629902Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1635581Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1636513Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1636903Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1642833Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1649045Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1650328Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1650749Z | ^~~~~~~~ 2025-08-26T21:49:21.1651416Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1652415Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1653298Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1654113Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1654867Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1655686Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1656773Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1658113Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1659261Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1660467Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1661473Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1662400Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1663243Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1663819Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1664652Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1665389Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1665720Z | ^~~~ 2025-08-26T21:49:21.1666290Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1667144Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1667999Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1668783Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1669519Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1670411Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1671359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1672251Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1673137Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1674086Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1675051Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1675927Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1676480Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1677467Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1679023Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1680718Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1682519Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1684403Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1686425Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1692510Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1702441Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1708176Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1709093Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1709484Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1715388Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1721628Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1722896Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1723328Z | ^~~~~~~~ 2025-08-26T21:49:21.1723962Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1724961Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1725847Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1726646Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1727405Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1728236Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1729341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1730479Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1731654Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1732754Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1733753Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1734689Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1735540Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1736123Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1736940Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1737680Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1738028Z | ^~~~ 2025-08-26T21:49:21.1738602Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1739447Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1740262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1741154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1741906Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1742751Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1743673Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1744545Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1745432Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1746364Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1747325Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1748166Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1748706Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1749701Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1751254Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1752963Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1754776Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1756662Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1758738Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1764597Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1774365Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1780033Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1781032Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1781421Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1787351Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1793787Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1795077Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1795504Z | ^~~~~~~~ 2025-08-26T21:49:21.1796220Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1797210Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1798097Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1798911Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1799669Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1800547Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1801598Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1802759Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1803901Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1805009Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1806012Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1806951Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1807808Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1808367Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1809197Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1809937Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1810282Z | ^~~~ 2025-08-26T21:49:21.1810843Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1811699Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1812512Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1813306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1814154Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1814983Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1815957Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1816850Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1817737Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1818667Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1819609Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1820573Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1821146Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1822147Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1823711Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1825427Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1827238Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1829112Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1831121Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1836964Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1846822Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1852519Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1853454Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1853833Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1859700Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1866044Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1867337Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1867751Z | ^~~~~~~~ 2025-08-26T21:49:21.1868378Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1869383Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1870267Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1871075Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1871823Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1872658Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1873781Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1874924Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1876100Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1877218Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1878210Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1879152Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1880028Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1880586Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1881417Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1882143Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1882508Z | ^~~~ 2025-08-26T21:49:21.1883084Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1884009Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1884806Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1885630Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1886383Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1887218Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1888135Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1889031Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1889907Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1890842Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1892013Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1892870Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1893429Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1894417Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1895981Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1897695Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1899572Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1901551Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1903632Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1909479Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1919260Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1924882Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1925809Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1926193Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.1932157Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.1938372Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.1939684Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.1940104Z | ^~~~~~~~ 2025-08-26T21:49:21.1940823Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.1941830Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.1942703Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.1943517Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.1944276Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.1945155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.1946246Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.1947375Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.1948517Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.1949633Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.1950639Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.1951584Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.1952429Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1952972Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1953803Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.1954543Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.1954885Z | ^~~~ 2025-08-26T21:49:21.1955445Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.1956301Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.1957117Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.1957942Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.1958686Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.1959560Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.1960464Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.1961359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.1962247Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.1963187Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.1964185Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.1965018Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.1965575Z from rng_extension.cpp:1: 2025-08-26T21:49:21.1966565Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.1968125Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.1969846Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.1971680Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.1973560Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.1975631Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.1981491Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.1991297Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.1997247Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.1998182Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.1998567Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2004401Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2010678Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2011965Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2012380Z | ^~~~~~~~ 2025-08-26T21:49:21.2012993Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2013991Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2014877Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2015684Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2016439Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2017323Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2018380Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2019557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2020786Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2021897Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2037284Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2038429Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2039439Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2040012Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2040953Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2041702Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2042065Z | ^~~~ 2025-08-26T21:49:21.2042635Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2043499Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2044370Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2045167Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2045915Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2046747Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2047667Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2048560Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2049447Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2050390Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2051358Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2052187Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2052750Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2053778Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2055350Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2057049Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2058874Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2060943Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2062973Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2069203Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2079677Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2085574Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2086502Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2086875Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2093486Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_full_64_bits_range_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2100065Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2101428Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2101833Z | ^~~~~~~~ 2025-08-26T21:49:21.2102460Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2103472Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2104360Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2105225Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2105981Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2106824Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2107883Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2109022Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2110169Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2111282Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2112271Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2113213Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2114060Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2114617Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2115444Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2116172Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2116518Z | ^~~~ 2025-08-26T21:49:21.2117097Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2117996Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2118810Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2119615Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2120359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2121194Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2122115Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2123001Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2123919Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2124855Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2125812Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2126646Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2127230Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2128209Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2129815Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2131542Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2133298Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2135169Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2137184Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2143629Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2154273Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2160236Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2161203Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2161574Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2167901Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2174479Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2175760Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2176166Z | ^~~~~~~~ 2025-08-26T21:49:21.2176797Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2177840Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2178735Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2179583Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2180405Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2181254Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2182308Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2183451Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2184635Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2185742Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2186743Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2187683Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2188521Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2189166Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2189981Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2190726Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2191074Z | ^~~~ 2025-08-26T21:49:21.2191646Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2192745Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2193545Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2194335Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2195082Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2195925Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2196847Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2197720Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2198605Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2199537Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2200490Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2201324Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2201875Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2202958Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2204525Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2206287Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2208048Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2209987Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2212011Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2218248Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2228905Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2234953Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2235899Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2236288Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2242592Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2249164Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2250484Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2250905Z | ^~~~~~~~ 2025-08-26T21:49:21.2251527Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2252529Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2253414Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2254222Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2254991Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2255831Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2256893Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2258037Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2259175Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2260275Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2261354Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2262299Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2263180Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2263744Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2264597Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2265336Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2265677Z | ^~~~ 2025-08-26T21:49:21.2266249Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2267101Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2267904Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2268692Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2269463Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2270299Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2271214Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2272084Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2272966Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2273925Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2274882Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2275719Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2276274Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2277271Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2278828Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2280537Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2282301Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2284191Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2286210Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2292761Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2303524Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2309540Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2310474Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2310860Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2317184Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2323725Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2325064Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2325479Z | ^~~~~~~~ 2025-08-26T21:49:21.2326102Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2327083Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2327986Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2328837Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2329596Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2330432Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2331485Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2332607Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2333748Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2334895Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2335910Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2336845Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2337672Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2338224Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2339049Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2339787Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2340142Z | ^~~~ 2025-08-26T21:49:21.2340839Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2341700Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2342515Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2343306Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2344055Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2344890Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2345806Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2346692Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2347623Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2348557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2349528Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2350373Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2350931Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2351931Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2353494Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2355264Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2357034Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2358917Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2360971Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2367188Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2377825Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2383908Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2384839Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2385223Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2391629Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2398418Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2399697Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2400118Z | ^~~~~~~~ 2025-08-26T21:49:21.2400744Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2401732Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2402619Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2403435Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2404194Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2405028Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2406068Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2407320Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2408463Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2409620Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2410628Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2411568Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2412398Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2412954Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2413779Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2414572Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2414909Z | ^~~~ 2025-08-26T21:49:21.2415485Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2416344Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2417153Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2417940Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2418672Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2419557Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2420551Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2421453Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2422343Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2423288Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2424232Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2425087Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2425643Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2426652Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2428212Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2429921Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2431669Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2433586Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2435606Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2441971Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2452568Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2458548Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2459474Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2459856Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2466779Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2473410Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2474738Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2475159Z | ^~~~~~~~ 2025-08-26T21:49:21.2475777Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2476772Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2477659Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2478513Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2479307Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2480136Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2481188Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2482327Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2483464Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2484581Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2485584Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2486512Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2487359Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2487915Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2488742Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2489480Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2489811Z | ^~~~ 2025-08-26T21:49:21.2490387Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2491239Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2492234Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2493155Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2493894Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2494787Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2495704Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2496595Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2497481Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2498411Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2499410Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2500257Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2500892Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2501901Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2503468Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2505168Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2506998Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2508871Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2510892Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2517158Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2527779Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2533754Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2534687Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2535065Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2541490Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2548115Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2549399Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2549826Z | ^~~~~~~~ 2025-08-26T21:49:21.2550455Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2551441Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2552337Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2553200Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2553970Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2554845Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2555889Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2557034Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2558178Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2559333Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2560341Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2561283Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2562109Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2562663Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2563486Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2564221Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2564602Z | ^~~~ 2025-08-26T21:49:21.2565164Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2566024Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2566834Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2567617Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2568356Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2569175Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2570089Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2570980Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2571868Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2572802Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2573743Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2574591Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2575139Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2576120Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2577754Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2579470Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2581342Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2583217Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2585283Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2591517Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2602417Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2608370Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2609303Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2609800Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2616146Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2622816Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2624097Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2624569Z | ^~~~~~~~ 2025-08-26T21:49:21.2625184Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2626187Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2627076Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2627891Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2628650Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2629473Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2630526Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2631663Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2632803Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2633914Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2634916Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2635839Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2636683Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2637242Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2638103Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2638843Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2639207Z | ^~~~ 2025-08-26T21:49:21.2639777Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2640629Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2641436Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2642223Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2642958Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2643828Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2644747Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2645638Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2646522Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2647442Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2648403Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2649285Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2649838Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2650822Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2652377Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2654078Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2655825Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2657711Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2659721Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2666136Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2676745Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2682783Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2683720Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2684108Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2690395Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2697212Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2698509Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2698978Z | ^~~~~~~~ 2025-08-26T21:49:21.2699595Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2700697Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2701587Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2702404Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2703177Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2704069Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2705131Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2706270Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2707414Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2708533Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2709585Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2710515Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2711361Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2711912Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2712745Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2713484Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2713815Z | ^~~~ 2025-08-26T21:49:21.2714388Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2715243Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2716056Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2716841Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2717587Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2718423Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2719343Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2720233Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2721121Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2722094Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2723057Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2723930Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2724480Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2725467Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2727002Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2728740Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2730543Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2732424Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2734443Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2740279Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2750203Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2755865Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2756797Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2757181Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2763045Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2769249Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2770510Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2770928Z | ^~~~~~~~ 2025-08-26T21:49:21.2771549Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2772541Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2773421Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2774222Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2774986Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2775823Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2776876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2778006Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2779134Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2780251Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2781348Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2782335Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2783203Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2783758Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2784569Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2785308Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2785651Z | ^~~~ 2025-08-26T21:49:21.2786226Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2787071Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2787492Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2787812Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2788127Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2788541Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2788936Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2789316Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2789725Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2790172Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2790576Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2790902Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2791020Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2793558Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2794457Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2795508Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2796423Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2797526Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2798602Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2803916Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2809878Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2810731Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2810902Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2811017Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2817162Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2818462Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2818682Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2818782Z | ^~~~~~~~ 2025-08-26T21:49:21.2819247Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2819666Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2820038Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2820477Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2820792Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2821211Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2821739Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2822237Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2822798Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2823285Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2823697Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2824115Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2824431Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2824558Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2825163Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2825303Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2825410Z | ^~~~ 2025-08-26T21:49:21.2825830Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2826192Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2826567Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2826896Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2827195Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2827607Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2828008Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2828418Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2828812Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2829247Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2829704Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2830019Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2830136Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2830909Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2831770Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2832714Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2833619Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2834677Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2835806Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2841149Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2847148Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2847973Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2848136Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2848250Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2854362Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2855670Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2855878Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2855977Z | ^~~~~~~~ 2025-08-26T21:49:21.2856478Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2856928Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2857276Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2857627Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2857918Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2858333Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2858861Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2859390Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2859925Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2860494Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2860917Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2861318Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2861637Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2861764Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2862364Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2862516Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2862612Z | ^~~~ 2025-08-26T21:49:21.2863010Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2863354Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2863698Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2864078Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2864378Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2864803Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2865227Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2865616Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2865993Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2866420Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2866832Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2867236Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2867362Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2868142Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2869010Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2869931Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2870855Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2871926Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2873023Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2878329Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2884296Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2885140Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2885297Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2885425Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2891560Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2893052Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2893278Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2893370Z | ^~~~~~~~ 2025-08-26T21:49:21.2893848Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2894263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2894611Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2894972Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2895272Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2895702Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2896222Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2896733Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2897257Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2897725Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2898263Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2898669Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2899038Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2899154Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2899762Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2899903Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2899996Z | ^~~~ 2025-08-26T21:49:21.2900460Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2900810Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2901216Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2901534Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2901834Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2902262Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2902642Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2903035Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2903451Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2903901Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2904309Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2904633Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2904748Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2905506Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2906371Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2907302Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2908220Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2909256Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2910334Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2915225Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2920763Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2921607Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2921763Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2921889Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2927543Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_kernel(at::TensorIteratorBase&, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2928842Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2929090Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2929179Z | ^~~~~~~~ 2025-08-26T21:49:21.2929654Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2930067Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2930415Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2930772Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2931097Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2931527Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2932041Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2932550Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2933069Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2933567Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2933993Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2934393Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2934720Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2934837Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2935444Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2935586Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2935683Z | ^~~~ 2025-08-26T21:49:21.2936082Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/ArrayRef.h:20, 2025-08-26T21:49:21.2936429Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/core/MemoryFormat.h:3, 2025-08-26T21:49:21.2936792Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/TensorBody.h:13, 2025-08-26T21:49:21.2937111Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/core/Tensor.h:3, 2025-08-26T21:49:21.2937423Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/Tensor.h:3, 2025-08-26T21:49:21.2937839Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/function_hook.h:3, 2025-08-26T21:49:21.2938219Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/cpp_hook.h:2, 2025-08-26T21:49:21.2938609Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/variable.h:6, 2025-08-26T21:49:21.2938989Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/autograd/autograd.h:3, 2025-08-26T21:49:21.2939458Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/autograd.h:3, 2025-08-26T21:49:21.2939887Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:7, 2025-08-26T21:49:21.2940209Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2940426Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2941186Z In member function ‘void c10::SmallVectorTemplateCommon >::grow_pod(size_t, size_t) [with T = char*; = void]’, 2025-08-26T21:49:21.2942050Z inlined from ‘void c10::SmallVectorTemplateBase::grow(size_t) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:579:19, 2025-08-26T21:49:21.2943021Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:704:17, 2025-08-26T21:49:21.2943951Z inlined from ‘void c10::SmallVectorImpl::reserve(c10::SmallVectorImpl::size_type) [with T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:702:8, 2025-08-26T21:49:21.2945001Z inlined from ‘void c10::SmallVectorImpl::append(in_iter, in_iter) [with in_iter = char**; = void; T = char*]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:730:18, 2025-08-26T21:49:21.2946074Z inlined from ‘c10::SmallVector::SmallVector(ItTy, ItTy) [with ItTy = char**; = void; T = char*; unsigned int N = 4]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:1295:17, 2025-08-26T21:49:21.2951412Z inlined from ‘at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21, 2025-08-26T21:49:21.2957393Z inlined from ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’ at /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/FunctionRef.h:43:52: 2025-08-26T21:49:21.2958241Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:139:19: warning: ‘data’ may be used uninitialized [-Wmaybe-uninitialized] 2025-08-26T21:49:21.2958416Z 139 | Base::grow_pod(getFirstEl(), MinSize, TSize); 2025-08-26T21:49:21.2958536Z | ~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 2025-08-26T21:49:21.2964672Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h: In static member function ‘static Ret c10::function_ref::callback_fn(intptr_t, Params ...) [with Callable = at::TensorIteratorBase::loop_2d_from_1d(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&):: >(const at::native::CPU_CAPABILITY::cpu_serial_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*):::::: >(at::TensorIteratorBase&, at::native::templates::cpu::{anonymous}::random_from_to_kernel(at::TensorIteratorBase&, uint64_t, int64_t, TestCPUGenerator*)::::::&&, const at::Range&)::&)::; Ret = void; Params = {char**, const long int*, long int, long int}]’: 2025-08-26T21:49:21.2965975Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/c10/util/SmallVector.h:73:8: note: by argument 2 of type ‘const void*’ to ‘void c10::SmallVectorBase::grow_pod(const void*, size_t, size_t) [with Size_T = unsigned int]’ declared here 2025-08-26T21:49:21.2966182Z 73 | void grow_pod(const void* FirstEl, size_t MinSize, size_t TSize); 2025-08-26T21:49:21.2966271Z | ^~~~~~~~ 2025-08-26T21:49:21.2966749Z In file included from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_meta.h:12, 2025-08-26T21:49:21.2967179Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ops/_addmm_activation_native.h:15, 2025-08-26T21:49:21.2967546Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/NativeFunctions.h:37, 2025-08-26T21:49:21.2967893Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIndexing.h:13, 2025-08-26T21:49:21.2968200Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/ATen.h:18, 2025-08-26T21:49:21.2968620Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/types.h:3, 2025-08-26T21:49:21.2969133Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader_options.h:4, 2025-08-26T21:49:21.2969645Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/base.h:3, 2025-08-26T21:49:21.2970212Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader/stateful.h:4, 2025-08-26T21:49:21.2970699Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data/dataloader.h:3, 2025-08-26T21:49:21.2971141Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/data.h:3, 2025-08-26T21:49:21.2971558Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include/torch/all.h:9, 2025-08-26T21:49:21.2971876Z from /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/extension.h:5, 2025-08-26T21:49:21.2971993Z from rng_extension.cpp:1: 2025-08-26T21:49:21.2972599Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/ATen/TensorIterator.h:413:21: note: ‘data’ declared here 2025-08-26T21:49:21.2972739Z 413 | PtrVector data(base, base + ntensor); 2025-08-26T21:49:21.2972881Z | ^~~~ 2025-08-26T21:49:21.2975977Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:21.8562609Z running install_lib 2025-08-26T21:49:21.8694995Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:21.8778110Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:21.8864236Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:21.8956828Z running install_egg_info 2025-08-26T21:49:21.9146351Z running egg_info 2025-08-26T21:49:21.9224482Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-08-26T21:49:21.9228045Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-08-26T21:49:21.9230479Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-08-26T21:49:21.9232737Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-08-26T21:49:21.9316328Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:49:21.9325323Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:49:21.9327248Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2025-08-26T21:49:21.9329370Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2025-08-26T21:49:21.9335807Z running install_scripts 2025-08-26T21:49:25.1829525Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:49:25.1831339Z import pkg_resources 2025-08-26T21:49:25.2092079Z 2025-08-26T21:49:25.2092572Z Running tests... 2025-08-26T21:49:25.2093042Z ---------------------------------------------------------------------- 2025-08-26T21:49:26.2452465Z . 2025-08-26T21:49:26.2452908Z ---------------------------------------------------------------------- 2025-08-26T21:49:26.2453388Z Ran 1 test in 1.036s 2025-08-26T21:49:26.2453549Z 2025-08-26T21:49:26.2453647Z OK 2025-08-26T21:49:26.2453759Z 2025-08-26T21:49:26.2453867Z Generating XML reports... 2025-08-26T21:49:27.2125763Z Running test_cpp_extensions_aot_ninja 1/1 ... [2025-08-26 21:49:27.212248] 2025-08-26T21:49:29.6600455Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:49:29.6602058Z import pkg_resources 2025-08-26T21:49:29.7254413Z running install 2025-08-26T21:49:29.7256672Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-08-26T21:49:29.7257480Z !! 2025-08-26T21:49:29.7257591Z 2025-08-26T21:49:29.7257714Z ******************************************************************************** 2025-08-26T21:49:29.7258104Z Please avoid running ``setup.py`` directly. 2025-08-26T21:49:29.7258512Z Instead, use pypa/build, pypa/installer or other 2025-08-26T21:49:29.7258888Z standards-based tools. 2025-08-26T21:49:29.7259074Z 2025-08-26T21:49:29.7259301Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-08-26T21:49:29.7259901Z or your builds will no longer be supported. 2025-08-26T21:49:29.7260159Z 2025-08-26T21:49:29.7260560Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-08-26T21:49:29.7261176Z ******************************************************************************** 2025-08-26T21:49:29.7261426Z 2025-08-26T21:49:29.7261527Z !! 2025-08-26T21:49:29.7261737Z self.initialize_options() 2025-08-26T21:49:29.7391468Z running build 2025-08-26T21:49:29.7391866Z running build_py 2025-08-26T21:49:29.7476774Z creating build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-08-26T21:49:29.7479073Z copying torch_test_cpp_extension/__init__.py -> build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension 2025-08-26T21:49:29.7483499Z running build_ext 2025-08-26T21:49:29.7828513Z building 'torch_test_cpp_extension.cpp' extension 2025-08-26T21:49:29.7830384Z creating /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313 2025-08-26T21:49:30.5748456Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o.d -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=cpp -std=c++17 2025-08-26T21:49:30.5856527Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:31.1897122Z building 'torch_test_cpp_extension.maia' extension 2025-08-26T21:49:31.9192142Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o.d -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/maia_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=maia -std=c++17 2025-08-26T21:49:31.9249403Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/maia_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:32.5062690Z building 'torch_test_cpp_extension.rng' extension 2025-08-26T21:49:33.4274707Z [1/1] c++ -MMD -MF /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o.d -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include -I/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/include/torch/csrc/api/include -I/var/lib/jenkins/workspace/test/cpp_extensions/self_compiler_include_dirs_test -I/opt/conda/envs/py_3.13/include/python3.13 -c -c /var/lib/jenkins/workspace/test/cpp_extensions/rng_extension.cpp -o /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -g -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=rng -std=c++17 2025-08-26T21:49:33.4330824Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/build/temp.linux-x86_64-cpython-313/rng_extension.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so 2025-08-26T21:49:34.0354114Z running install_lib 2025-08-26T21:49:34.0485713Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/cpp.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:34.0530953Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/maia.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:34.0577308Z copying build/lib.linux-x86_64-cpython-313/torch_test_cpp_extension/rng.cpython-313-x86_64-linux-gnu.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension 2025-08-26T21:49:34.0628847Z running install_egg_info 2025-08-26T21:49:34.0815187Z running egg_info 2025-08-26T21:49:34.0889795Z writing torch_test_cpp_extension.egg-info/PKG-INFO 2025-08-26T21:49:34.0894599Z writing dependency_links to torch_test_cpp_extension.egg-info/dependency_links.txt 2025-08-26T21:49:34.0896186Z writing entry points to torch_test_cpp_extension.egg-info/entry_points.txt 2025-08-26T21:49:34.0898345Z writing top-level names to torch_test_cpp_extension.egg-info/top_level.txt 2025-08-26T21:49:34.0979616Z reading manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:49:34.0988812Z writing manifest file 'torch_test_cpp_extension.egg-info/SOURCES.txt' 2025-08-26T21:49:34.0990514Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info' (and everything under it) 2025-08-26T21:49:34.0992458Z Copying torch_test_cpp_extension.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch_test_cpp_extension-0.0.0-py3.13.egg-info 2025-08-26T21:49:34.0998954Z running install_scripts 2025-08-26T21:49:36.4073758Z running install 2025-08-26T21:49:36.4075130Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/setuptools/_distutils/cmd.py:90: SetuptoolsDeprecationWarning: setup.py install is deprecated. 2025-08-26T21:49:36.4075925Z !! 2025-08-26T21:49:36.4076064Z 2025-08-26T21:49:36.4076199Z ******************************************************************************** 2025-08-26T21:49:36.4076595Z Please avoid running ``setup.py`` directly. 2025-08-26T21:49:36.4077006Z Instead, use pypa/build, pypa/installer or other 2025-08-26T21:49:36.4077370Z standards-based tools. 2025-08-26T21:49:36.4077568Z 2025-08-26T21:49:36.4077797Z By 2025-Oct-31, you need to update your project and remove deprecated calls 2025-08-26T21:49:36.4078282Z or your builds will no longer be supported. 2025-08-26T21:49:36.4078524Z 2025-08-26T21:49:36.4078847Z See https://blog.ganssle.io/articles/2021/10/setup-py-deprecated.html for details. 2025-08-26T21:49:36.4079383Z ******************************************************************************** 2025-08-26T21:49:36.4079626Z 2025-08-26T21:49:36.4079709Z !! 2025-08-26T21:49:36.4079931Z self.initialize_options() 2025-08-26T21:49:36.4210289Z running build 2025-08-26T21:49:36.4210562Z running build_ext 2025-08-26T21:49:36.4555581Z building 'no_python_abi_suffix_test' extension 2025-08-26T21:49:36.5173560Z ninja: no work to do. 2025-08-26T21:49:36.5224877Z g++ -pthread -B /opt/conda/envs/py_3.13/share/python_compiler_compat -fno-strict-overflow -Wsign-compare -DNDEBUG -O2 -Wall -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -fPIC -O2 -isystem /opt/conda/envs/py_3.13/include -shared -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib -Wl,--allow-shlib-undefined -Wl,-rpath,/opt/conda/envs/py_3.13/lib -Wl,-rpath-link,/opt/conda/envs/py_3.13/lib -L/opt/conda/envs/py_3.13/lib /var/lib/jenkins/workspace/test/cpp_extensions/no_python_abi_suffix_test/build/temp.linux-x86_64-cpython-313/no_python_abi_suffix_test.o -L/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib -lc10 -ltorch -ltorch_cpu -ltorch_python -o build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so 2025-08-26T21:49:36.6376105Z running install_lib 2025-08-26T21:49:36.6466746Z copying build/lib.linux-x86_64-cpython-313/no_python_abi_suffix_test.so -> ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages 2025-08-26T21:49:36.6471599Z running install_egg_info 2025-08-26T21:49:36.6665767Z running egg_info 2025-08-26T21:49:36.6740823Z writing no_python_abi_suffix_test.egg-info/PKG-INFO 2025-08-26T21:49:36.6744524Z writing dependency_links to no_python_abi_suffix_test.egg-info/dependency_links.txt 2025-08-26T21:49:36.6747102Z writing top-level names to no_python_abi_suffix_test.egg-info/top_level.txt 2025-08-26T21:49:36.6828003Z reading manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-08-26T21:49:36.6836148Z writing manifest file 'no_python_abi_suffix_test.egg-info/SOURCES.txt' 2025-08-26T21:49:36.6839021Z removing './install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info' (and everything under it) 2025-08-26T21:49:36.6840430Z Copying no_python_abi_suffix_test.egg-info to ./install/opt/conda/envs/py_3.13/lib/python3.13/site-packages/no_python_abi_suffix_test-0.0.0-py3.13.egg-info 2025-08-26T21:49:36.6845203Z running install_scripts 2025-08-26T21:49:37.1879177Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:49:37.1881894Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cpp_extensions_aot_ninja.py', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:49:37.187935] 2025-08-26T21:49:51.7543983Z 2025-08-26T21:49:51.7545456Z test_cpp_extensions_aot_ninja 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cpp_extensions_aot_ninja_1.1_d552c352dec63b43_.log 2025-08-26T21:49:51.7561451Z Running 21 items in this shard: test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_backward, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cublas_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_dlink_libs, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cuda_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_cusolver_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_function, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_extension_module, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_mps_extension, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_no_python_abi_suffix_sets_the_correct_library_name, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_optional, test/test_cpp_extensions_aot_ninja.py::TestCppExtensionAOT::test_sycl_extension, test/test_cpp_extensions_aot_ninja.py::TestPybindTypeCasters::test_pybind_return_types, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_add, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_autocast_apis_for_maia_device, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_conv_backend_override, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_default_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_matmul_autocast_float16_precision, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_unregistered, test/test_cpp_extensions_aot_ninja.py::TestMAIATensor::test_zeros, test/test_cpp_extensions_aot_ninja.py::TestRNGExtension::test_rng, test/test_cpp_extensions_aot_ninja.py::TestTorchLibrary::test_torch_library 2025-08-26T21:49:51.7569003Z 2025-08-26T21:49:51.7569218Z Running dynamo/test_utils 1/1 ... [2025-08-26 21:49:51.754927] 2025-08-26T21:49:51.7569637Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:49:51.7570729Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:49:51.755354] 2025-08-26T21:49:54.9929181Z 2025-08-26T21:49:54.9930363Z dynamo/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_utils_1.1_1bac72ce05c1aa03_.log 2025-08-26T21:49:54.9931043Z 2025-08-26T21:49:54.9933278Z Running dynamo/test_modes 1/1 ... [2025-08-26 21:49:54.993162] 2025-08-26T21:49:54.9933709Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:49:54.9937081Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modes.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:49:54.993496] 2025-08-26T21:50:01.9836985Z 2025-08-26T21:50:01.9838557Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_90aef927c3473698_.log 2025-08-26T21:50:01.9839778Z 2025-08-26T21:50:01.9842633Z Running dynamo/test_logging 1/1 ... [2025-08-26 21:50:01.984070] 2025-08-26T21:50:01.9843338Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:01.9847258Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_logging.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:01.984474] 2025-08-26T21:50:08.9800198Z 2025-08-26T21:50:08.9801100Z dynamo/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_logging_1.1_52b84afca9214188_.log 2025-08-26T21:50:08.9802042Z 2025-08-26T21:50:08.9804100Z Running dynamo/test_higher_order_ops 1/1 ... [2025-08-26 21:50:08.980242] 2025-08-26T21:50:08.9804572Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:08.9807989Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_higher_order_ops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:08.980573] 2025-08-26T21:50:16.8029294Z 2025-08-26T21:50:16.8030532Z 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_6869fffa7e272a73_.log 2025-08-26T21:50:16.8031284Z 2025-08-26T21:50:16.8033721Z Running dynamo/test_aot_autograd_cache 1/1 ... [2025-08-26 21:50:16.803169] 2025-08-26T21:50:16.8034350Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:16.8037614Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:16.803499] 2025-08-26T21:50:23.8476005Z 2025-08-26T21:50:23.8476992Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_131e46efa5e7a25f_.log 2025-08-26T21:50:23.8477779Z 2025-08-26T21:50:23.8479904Z Running dynamo/test_recompile_ux 1/1 ... [2025-08-26 21:50:23.847811] 2025-08-26T21:50:23.8480420Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:23.8483900Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:23.848142] 2025-08-26T21:50:27.3312259Z 2025-08-26T21:50:27.3313190Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_6ae7b50213e9b69a_.log 2025-08-26T21:50:27.3313924Z 2025-08-26T21:50:27.3317268Z Running dynamo/test_deque_reconstruct 1/1 ... [2025-08-26 21:50:27.331482] 2025-08-26T21:50:27.3317817Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:27.3320610Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deque_reconstruct.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:27.331833] 2025-08-26T21:50:30.5749940Z 2025-08-26T21:50:30.5750963Z dynamo/test_deque_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deque_reconstruct_1.1_ed1322258c27b25d_.log 2025-08-26T21:50:30.5751722Z 2025-08-26T21:50:30.5753892Z Running dynamo/test_base_output 1/1 ... [2025-08-26 21:50:30.575208] 2025-08-26T21:50:30.5754409Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:30.5757949Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_output.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:30.575554] 2025-08-26T21:50:34.0520250Z 2025-08-26T21:50:34.0521129Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_79c261b4d320483c_.log 2025-08-26T21:50:34.0521850Z 2025-08-26T21:50:34.0524463Z Running dynamo/test_recompiles 1/1 ... [2025-08-26 21:50:34.052277] 2025-08-26T21:50:34.0524921Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:34.0528687Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompiles.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:34.052643] 2025-08-26T21:50:37.5328529Z 2025-08-26T21:50:37.5329500Z dynamo/test_recompiles 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompiles_1.1_f4dccb8578ff6346_.log 2025-08-26T21:50:37.5330219Z 2025-08-26T21:50:37.5332782Z Running dynamo/test_interop 1/1 ... [2025-08-26 21:50:37.533099] 2025-08-26T21:50:37.5333216Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:37.5336743Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_interop.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:37.533464] 2025-08-26T21:50:41.0096579Z 2025-08-26T21:50:41.0097906Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_d80100dd4c9daeab_.log 2025-08-26T21:50:41.0099198Z 2025-08-26T21:50:41.0099511Z Running dynamo/test_sdpa 1/1 ... [2025-08-26 21:50:41.009749] 2025-08-26T21:50:41.0100155Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:41.0105115Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sdpa.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:41.010187] 2025-08-26T21:50:44.4834945Z 2025-08-26T21:50:44.4835806Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_e63eab344dcaa6ab_.log 2025-08-26T21:50:44.4836468Z 2025-08-26T21:50:44.4839097Z Running dynamo/test_nops 1/1 ... [2025-08-26 21:50:44.483749] 2025-08-26T21:50:44.4839770Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:44.4843285Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:44.484100] 2025-08-26T21:50:47.9606108Z 2025-08-26T21:50:47.9607267Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_600c06a9f42b621b_.log 2025-08-26T21:50:47.9608147Z 2025-08-26T21:50:47.9610401Z Running dynamo/test_metrics_context 1/1 ... [2025-08-26 21:50:47.960865] 2025-08-26T21:50:47.9610850Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:47.9614803Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_metrics_context.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:47.961237] 2025-08-26T21:50:51.2006379Z 2025-08-26T21:50:51.2007709Z dynamo/test_metrics_context 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_metrics_context_1.1_7711fd11eadebd96_.log 2025-08-26T21:50:51.2008462Z 2025-08-26T21:50:51.2010967Z Running dynamo/test_modules 1/1 ... [2025-08-26 21:50:51.200873] 2025-08-26T21:50:51.2011449Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:51.2015366Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modules.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:51.201269] 2025-08-26T21:50:58.3369638Z 2025-08-26T21:50:58.3371066Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_9750b7e384259620_.log 2025-08-26T21:50:58.3372142Z 2025-08-26T21:50:58.3373974Z Running dynamo/test_resume 1/1 ... [2025-08-26 21:50:58.337197] 2025-08-26T21:50:58.3374921Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:50:58.3379756Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_resume.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:50:58.337643] 2025-08-26T21:51:01.5715768Z 2025-08-26T21:51:01.5716728Z dynamo/test_resume 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_resume_1.1_09ddb7100cf78c43_.log 2025-08-26T21:51:01.5717424Z 2025-08-26T21:51:01.5719562Z Running dynamo/test_unittest 1/1 ... [2025-08-26 21:51:01.571782] 2025-08-26T21:51:01.5720078Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:01.5723317Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_unittest.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:01.572108] 2025-08-26T21:51:05.0448126Z 2025-08-26T21:51:05.0449318Z dynamo/test_unittest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unittest_1.1_d40e14d9db1d9cde_.log 2025-08-26T21:51:05.0450033Z 2025-08-26T21:51:05.0452162Z Running dynamo/test_autograd_function 1/1 ... [2025-08-26 21:51:05.045018] 2025-08-26T21:51:05.0452873Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:05.0456058Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_autograd_function.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:05.045339] 2025-08-26T21:51:12.0440980Z 2025-08-26T21:51:12.0442576Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_36e197f61f2179a3_.log 2025-08-26T21:51:12.0443375Z 2025-08-26T21:51:12.0445074Z Running dynamo/test_list 1/1 ... [2025-08-26 21:51:12.044316] 2025-08-26T21:51:12.0445712Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:12.0448964Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_list.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:12.044648] 2025-08-26T21:51:15.5303976Z 2025-08-26T21:51:15.5305083Z dynamo/test_list 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_list_1.1_221ffb248c22ca7f_.log 2025-08-26T21:51:15.5305718Z 2025-08-26T21:51:15.5308149Z Running dynamo/test_profiler 1/1 ... [2025-08-26 21:51:15.530628] 2025-08-26T21:51:15.5308785Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:15.5312066Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:15.530962] 2025-08-26T21:51:19.0055431Z 2025-08-26T21:51:19.0056855Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_240cce8be5d3ae68_.log 2025-08-26T21:51:19.0057553Z 2025-08-26T21:51:19.0059554Z Running dynamo/test_deviceguard 1/1 ... [2025-08-26 21:51:19.005766] 2025-08-26T21:51:19.0060193Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:19.0063686Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deviceguard.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:19.006111] 2025-08-26T21:51:22.4770378Z 2025-08-26T21:51:22.4772014Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_0f0183a0b80f52b7_.log 2025-08-26T21:51:22.4773572Z 2025-08-26T21:51:22.4776169Z Running dynamo/test_flat_apply 1/1 ... [2025-08-26 21:51:22.477400] 2025-08-26T21:51:22.4776699Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:22.4780467Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_flat_apply.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:22.477765] 2025-08-26T21:51:25.9543941Z 2025-08-26T21:51:25.9545089Z dynamo/test_flat_apply 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_flat_apply_1.1_7d4d91f176c33901_.log 2025-08-26T21:51:25.9545859Z 2025-08-26T21:51:25.9547694Z Running dynamo/test_sets 1/1 ... [2025-08-26 21:51:25.954603] 2025-08-26T21:51:25.9548118Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:25.9551778Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sets.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:25.954917] 2025-08-26T21:51:29.4312589Z 2025-08-26T21:51:29.4313702Z dynamo/test_sets 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sets_1.1_ead2ef4d53aaeb15_.log 2025-08-26T21:51:29.4314347Z 2025-08-26T21:51:29.4316681Z Running dynamo/test_aot_autograd 1/1 ... [2025-08-26 21:51:29.431484] 2025-08-26T21:51:29.4317327Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:29.4321158Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:29.431816] 2025-08-26T21:51:32.9401018Z 2025-08-26T21:51:32.9402489Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_47192626f0d74d1c_.log 2025-08-26T21:51:32.9403700Z 2025-08-26T21:51:32.9405410Z Running dynamo/test_compiler_bisector 1/1 ... [2025-08-26 21:51:32.940324] 2025-08-26T21:51:32.9406427Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:32.9409718Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compiler_bisector.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:32.940668] 2025-08-26T21:51:39.9139037Z 2025-08-26T21:51:39.9140426Z dynamo/test_compiler_bisector 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compiler_bisector_1.1_ae53a0863325d05c_.log 2025-08-26T21:51:39.9141197Z 2025-08-26T21:51:39.9143230Z Running dynamo/test_bytecode_utils 1/1 ... [2025-08-26 21:51:39.914145] 2025-08-26T21:51:39.9143884Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:39.9147573Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:39.914475] 2025-08-26T21:51:43.3715832Z 2025-08-26T21:51:43.3717095Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_3c00fd00790c6e83_.log 2025-08-26T21:51:43.3717840Z 2025-08-26T21:51:43.3719870Z Running dynamo/test_torchrec 1/1 ... [2025-08-26 21:51:43.371805] 2025-08-26T21:51:43.3720527Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:43.3723988Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_torchrec.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:43.372129] 2025-08-26T21:51:46.8397086Z 2025-08-26T21:51:46.8398392Z dynamo/test_torchrec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_torchrec_1.1_09493ededd8b823f_.log 2025-08-26T21:51:46.8399101Z 2025-08-26T21:51:46.8401182Z Running dynamo/test_activation_checkpointing 1/1 ... [2025-08-26 21:51:46.839924] 2025-08-26T21:51:46.8401851Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:46.8405134Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:46.840249] 2025-08-26T21:51:53.8149217Z 2025-08-26T21:51:53.8150891Z 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_7ff54a408d1e428c_.log 2025-08-26T21:51:53.8152406Z 2025-08-26T21:51:53.8154719Z Running dynamo/test_hooks 1/1 ... [2025-08-26 21:51:53.815256] 2025-08-26T21:51:53.8155393Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:53.8159893Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_hooks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:53.815661] 2025-08-26T21:51:57.2947184Z 2025-08-26T21:51:57.2948031Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_f8b96225a123c813_.log 2025-08-26T21:51:57.2948696Z 2025-08-26T21:51:57.2951304Z Running dynamo/test_comptime 1/1 ... [2025-08-26 21:51:57.294926] 2025-08-26T21:51:57.2951754Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:51:57.2954973Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_comptime.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:51:57.295278] 2025-08-26T21:52:00.7723849Z 2025-08-26T21:52:00.7724981Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_e751ec2382dea631_.log 2025-08-26T21:52:00.7725674Z 2025-08-26T21:52:00.7728091Z Running test_matmul_cuda 1/1 ... [2025-08-26 21:52:00.772635] 2025-08-26T21:52:00.7728614Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:00.7732228Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:00.772988] 2025-08-26T21:52:04.1525136Z 2025-08-26T21:52:04.1526010Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_0e9261bc5aaf6e43_.log 2025-08-26T21:52:04.1527389Z Running 0 items in this shard: 2025-08-26T21:52:04.1527591Z 2025-08-26T21:52:04.1529627Z Running test_jiterator 1/1 ... [2025-08-26 21:52:04.152789] 2025-08-26T21:52:04.1530061Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:04.1533720Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jiterator.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:04.153161] 2025-08-26T21:52:07.5234965Z 2025-08-26T21:52:07.5235841Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_a4193ad080477fdb_.log 2025-08-26T21:52:07.5236783Z Running 0 items in this shard: 2025-08-26T21:52:07.5236998Z 2025-08-26T21:52:07.5238722Z Running functorch/test_ac 1/1 ... [2025-08-26 21:52:07.523716] 2025-08-26T21:52:07.5239149Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:07.5242828Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:07.524059] 2025-08-26T21:52:14.5191192Z 2025-08-26T21:52:14.5192567Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_070a908d700c84e9_.log 2025-08-26T21:52:14.5193237Z 2025-08-26T21:52:14.5195270Z Running test_cuda_sanitizer 1/1 ... [2025-08-26 21:52:14.519356] 2025-08-26T21:52:14.5195724Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:14.5199292Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_sanitizer.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:14.519694] 2025-08-26T21:52:17.8632869Z 2025-08-26T21:52:17.8634057Z test_cuda_sanitizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_sanitizer_1.1_664175ddb46c8a07_.log 2025-08-26T21:52:17.8634845Z Running 0 items in this shard: 2025-08-26T21:52:17.8635040Z 2025-08-26T21:52:17.8637176Z Running dynamo/test_nested_graph_breaks 1/1 ... [2025-08-26 21:52:17.863534] 2025-08-26T21:52:17.8637822Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:17.8641369Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nested_graph_breaks.py', '-m', 'serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:17.863857] 2025-08-26T21:52:21.3253616Z 2025-08-26T21:52:21.3254983Z dynamo/test_nested_graph_breaks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nested_graph_breaks_1.1_3f5d74644452e278_.log 2025-08-26T21:52:21.3255774Z 2025-08-26T21:52:21.3257409Z Running test_quantization 9/9 ... [2025-08-26 21:52:21.325547] 2025-08-26T21:52:21.3258075Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:21.3261135Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'serial', '--shard-id=9', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:21.325877] 2025-08-26T21:52:26.5477867Z 2025-08-26T21:52:26.5478898Z test_quantization 9/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_9.9_12cde851bf015ace_.log 2025-08-26T21:52:26.5479677Z Running 0 items in this shard: 2025-08-26T21:52:26.5479879Z 2025-08-26T21:52:29.1312200Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:29.1315168Z import pkg_resources 2025-08-26T21:52:29.1319563Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:29.1322425Z import pkg_resources 2025-08-26T21:52:29.1485677Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:29.1488857Z import pkg_resources 2025-08-26T21:52:29.3832181Z Running dynamo/test_utils 1/1 ... [2025-08-26 21:52:29.382691] 2025-08-26T21:52:29.3832971Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:29.3833645Z Running dynamo/test_modes 1/1 ... [2025-08-26 21:52:29.382757] 2025-08-26T21:52:29.3834345Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:29.3834969Z Running dynamo/test_logging 1/1 ... [2025-08-26 21:52:29.382906] 2025-08-26T21:52:29.3835481Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:29.3837372Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:29.383116] 2025-08-26T21:52:29.3839396Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modes.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:29.383156] 2025-08-26T21:52:29.3841431Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_logging.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:29.383381] 2025-08-26T21:52:32.7714287Z 2025-08-26T21:52:32.7715633Z dynamo/test_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_utils_1.1_39025e30160c5719_.log 2025-08-26T21:52:32.7716831Z 2025-08-26T21:52:36.2780204Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:36.2783246Z import pkg_resources 2025-08-26T21:52:36.4030315Z Running dynamo/test_higher_order_ops 1/1 ... [2025-08-26 21:52:36.402589] 2025-08-26T21:52:36.4031691Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:36.4034318Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_higher_order_ops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:36.402986] 2025-08-26T21:52:36.7104922Z 2025-08-26T21:52:36.7106487Z dynamo/test_modes 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modes_1.1_3c7eb58ae0f4cd7f_.log 2025-08-26T21:52:36.7107513Z 2025-08-26T21:52:38.4455182Z 2025-08-26T21:52:38.4456265Z dynamo/test_logging 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_logging_1.1_682fa6d9d5e16556_.log 2025-08-26T21:52:38.4457257Z 2025-08-26T21:52:40.2494399Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:40.2497446Z import pkg_resources 2025-08-26T21:52:40.3746605Z Running dynamo/test_aot_autograd_cache 1/1 ... [2025-08-26 21:52:40.374263] 2025-08-26T21:52:40.3747158Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:40.3749327Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd_cache.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:40.374641] 2025-08-26T21:52:41.9481299Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:41.9484154Z import pkg_resources 2025-08-26T21:52:42.0720863Z Running dynamo/test_recompile_ux 1/1 ... [2025-08-26 21:52:42.071612] 2025-08-26T21:52:42.0721717Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:42.0723625Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompile_ux.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:42.071971] 2025-08-26T21:52:44.7694012Z 2025-08-26T21:52:44.7695044Z 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_75ec68f6ff70dde8_.log 2025-08-26T21:52:44.7695800Z 2025-08-26T21:52:45.7788202Z 2025-08-26T21:52:45.7789384Z dynamo/test_recompile_ux 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompile_ux_1.1_b5e0358f1864aad1_.log 2025-08-26T21:52:45.7790223Z 2025-08-26T21:52:47.9208526Z 2025-08-26T21:52:47.9209905Z dynamo/test_aot_autograd_cache 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_cache_1.1_344a182b58799c34_.log 2025-08-26T21:52:47.9211173Z 2025-08-26T21:52:48.2158835Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:48.2161161Z import pkg_resources 2025-08-26T21:52:48.3415710Z Running dynamo/test_deque_reconstruct 1/1 ... [2025-08-26 21:52:48.341155] 2025-08-26T21:52:48.3416572Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:48.3418996Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deque_reconstruct.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:48.341561] 2025-08-26T21:52:49.2423106Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:49.2426181Z import pkg_resources 2025-08-26T21:52:49.3656535Z Running dynamo/test_base_output 1/1 ... [2025-08-26 21:52:49.365221] 2025-08-26T21:52:49.3657461Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:49.3660540Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_base_output.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:49.365618] 2025-08-26T21:52:51.3209804Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:51.3212709Z import pkg_resources 2025-08-26T21:52:51.4456181Z Running dynamo/test_recompiles 1/1 ... [2025-08-26 21:52:51.445228] 2025-08-26T21:52:51.4457199Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:51.4459946Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_recompiles.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:51.445639] 2025-08-26T21:52:51.7309964Z 2025-08-26T21:52:51.7311822Z dynamo/test_deque_reconstruct 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deque_reconstruct_1.1_7daf83f69637278c_.log 2025-08-26T21:52:51.7313323Z 2025-08-26T21:52:52.9679559Z 2025-08-26T21:52:52.9680566Z dynamo/test_base_output 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_base_output_1.1_424c19588c5d9623_.log 2025-08-26T21:52:52.9681284Z 2025-08-26T21:52:55.1453299Z 2025-08-26T21:52:55.1454321Z dynamo/test_recompiles 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_recompiles_1.1_addefea9f5ae123d_.log 2025-08-26T21:52:55.1455134Z 2025-08-26T21:52:55.3041207Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:55.3042772Z import pkg_resources 2025-08-26T21:52:55.4288590Z Running dynamo/test_interop 1/1 ... [2025-08-26 21:52:55.428399] 2025-08-26T21:52:55.4289444Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:55.4291590Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_interop.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:55.428768] 2025-08-26T21:52:56.5264932Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:56.5267750Z import pkg_resources 2025-08-26T21:52:56.6502058Z Running dynamo/test_sdpa 1/1 ... [2025-08-26 21:52:56.649828] 2025-08-26T21:52:56.6502861Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:56.6506102Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sdpa.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:56.650255] 2025-08-26T21:52:58.6215070Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:52:58.6217913Z import pkg_resources 2025-08-26T21:52:58.7464506Z Running dynamo/test_nops 1/1 ... [2025-08-26 21:52:58.745957] 2025-08-26T21:52:58.7465142Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:52:58.7466596Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nops.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:52:58.746375] 2025-08-26T21:52:59.0707660Z 2025-08-26T21:52:59.0708998Z dynamo/test_interop 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_interop_1.1_2059cef69b2679a1_.log 2025-08-26T21:52:59.0710174Z 2025-08-26T21:53:00.2595041Z 2025-08-26T21:53:00.2602507Z dynamo/test_sdpa 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sdpa_1.1_c26139a5b0723763_.log 2025-08-26T21:53:00.2609914Z 2025-08-26T21:53:02.4425278Z 2025-08-26T21:53:02.4426289Z dynamo/test_nops 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nops_1.1_33711cc5af31658c_.log 2025-08-26T21:53:02.4426947Z 2025-08-26T21:53:02.5131005Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:02.5133961Z import pkg_resources 2025-08-26T21:53:02.6377427Z Running dynamo/test_metrics_context 1/1 ... [2025-08-26 21:53:02.637325] 2025-08-26T21:53:02.6378164Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:02.6379807Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_metrics_context.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:02.637701] 2025-08-26T21:53:03.8570156Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:03.8573084Z import pkg_resources 2025-08-26T21:53:03.9806576Z Running dynamo/test_modules 1/1 ... [2025-08-26 21:53:03.980238] 2025-08-26T21:53:03.9807406Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:03.9810716Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_modules.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:03.980691] 2025-08-26T21:53:05.9392623Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:05.9394426Z import pkg_resources 2025-08-26T21:53:06.0647141Z Running dynamo/test_resume 1/1 ... [2025-08-26 21:53:06.064337] 2025-08-26T21:53:06.0647777Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:06.0650892Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_resume.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:06.064784] 2025-08-26T21:53:06.1480196Z 2025-08-26T21:53:06.1481278Z dynamo/test_metrics_context 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_metrics_context_1.1_14e818e80043de6c_.log 2025-08-26T21:53:06.1482378Z 2025-08-26T21:53:09.5705177Z 2025-08-26T21:53:09.5706200Z dynamo/test_resume 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_resume_1.1_e9a43bc9c07da844_.log 2025-08-26T21:53:09.5706907Z 2025-08-26T21:53:09.6833213Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:09.6836010Z import pkg_resources 2025-08-26T21:53:09.8075415Z Running dynamo/test_unittest 1/1 ... [2025-08-26 21:53:09.807088] 2025-08-26T21:53:09.8076169Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:09.8077811Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_unittest.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:09.807455] 2025-08-26T21:53:11.7204221Z 2025-08-26T21:53:11.7205433Z dynamo/test_modules 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_modules_1.1_7a894a5402b80149_.log 2025-08-26T21:53:11.7206128Z 2025-08-26T21:53:13.0324957Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:13.0326512Z import pkg_resources 2025-08-26T21:53:13.1566136Z Running dynamo/test_autograd_function 1/1 ... [2025-08-26 21:53:13.156176] 2025-08-26T21:53:13.1566872Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:13.1569020Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_autograd_function.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:13.156565] 2025-08-26T21:53:13.4451332Z 2025-08-26T21:53:13.4452717Z dynamo/test_unittest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_unittest_1.1_32126e321483b124_.log 2025-08-26T21:53:13.4453423Z 2025-08-26T21:53:15.1715840Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:15.1718809Z import pkg_resources 2025-08-26T21:53:15.2947977Z Running dynamo/test_list 1/1 ... [2025-08-26 21:53:15.294385] 2025-08-26T21:53:15.2948752Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:15.2951293Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_list.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:15.294762] 2025-08-26T21:53:16.9222350Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:16.9225356Z import pkg_resources 2025-08-26T21:53:17.0452440Z Running dynamo/test_profiler 1/1 ... [2025-08-26 21:53:17.044817] 2025-08-26T21:53:17.0453340Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:17.0455825Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_profiler.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:17.045170] 2025-08-26T21:53:18.9682565Z 2025-08-26T21:53:18.9683785Z dynamo/test_list 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_list_1.1_8895b58032edf123_.log 2025-08-26T21:53:18.9684570Z 2025-08-26T21:53:20.5773932Z 2025-08-26T21:53:20.5775238Z dynamo/test_autograd_function 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_autograd_function_1.1_6a62cffdb19f9ace_.log 2025-08-26T21:53:20.5776497Z 2025-08-26T21:53:20.6859149Z 2025-08-26T21:53:20.6860490Z dynamo/test_profiler 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_profiler_1.1_81c7cb313c58bb2a_.log 2025-08-26T21:53:20.6861550Z 2025-08-26T21:53:22.5171745Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:22.5174704Z import pkg_resources 2025-08-26T21:53:22.6401390Z Running dynamo/test_deviceguard 1/1 ... [2025-08-26 21:53:22.639683] 2025-08-26T21:53:22.6402097Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:22.6403791Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_deviceguard.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:22.640037] 2025-08-26T21:53:24.0186289Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:24.0187886Z import pkg_resources 2025-08-26T21:53:24.0899031Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:24.0900667Z import pkg_resources 2025-08-26T21:53:24.1418270Z Running dynamo/test_flat_apply 1/1 ... [2025-08-26 21:53:24.141421] 2025-08-26T21:53:24.1418814Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:24.1421552Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_flat_apply.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:24.141814] 2025-08-26T21:53:24.2129196Z Running dynamo/test_sets 1/1 ... [2025-08-26 21:53:24.212545] 2025-08-26T21:53:24.2130043Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:24.2132418Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_sets.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:24.212919] 2025-08-26T21:53:26.2657289Z 2025-08-26T21:53:26.2658770Z dynamo/test_deviceguard 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_deviceguard_1.1_f646cea4e902803a_.log 2025-08-26T21:53:26.2659966Z 2025-08-26T21:53:27.7666806Z 2025-08-26T21:53:27.7668585Z dynamo/test_flat_apply 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_flat_apply_1.1_c9f8fbcfc678cfb8_.log 2025-08-26T21:53:27.7669928Z 2025-08-26T21:53:27.8689296Z 2025-08-26T21:53:27.8690349Z dynamo/test_sets 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_sets_1.1_da35f2f0e0a2deb0_.log 2025-08-26T21:53:27.8691012Z 2025-08-26T21:53:29.7365278Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:29.7368163Z import pkg_resources 2025-08-26T21:53:29.8609728Z Running dynamo/test_aot_autograd 1/1 ... [2025-08-26 21:53:29.860552] 2025-08-26T21:53:29.8610535Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:29.8613509Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_aot_autograd.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:29.861007] 2025-08-26T21:53:31.3160363Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:31.3163204Z import pkg_resources 2025-08-26T21:53:31.3306665Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:31.3308208Z import pkg_resources 2025-08-26T21:53:31.4402985Z Running dynamo/test_compiler_bisector 1/1 ... [2025-08-26 21:53:31.439897] 2025-08-26T21:53:31.4403792Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:31.4406742Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_compiler_bisector.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:31.440336] 2025-08-26T21:53:31.4548162Z Running dynamo/test_bytecode_utils 1/1 ... [2025-08-26 21:53:31.454420] 2025-08-26T21:53:31.4548886Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:31.4551541Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_bytecode_utils.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:31.454781] 2025-08-26T21:53:33.5211244Z 2025-08-26T21:53:33.5217860Z dynamo/test_aot_autograd 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_aot_autograd_1.1_7b31348d7259edef_.log 2025-08-26T21:53:33.5219246Z 2025-08-26T21:53:35.1474600Z 2025-08-26T21:53:35.1476389Z dynamo/test_bytecode_utils 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_bytecode_utils_1.1_c1394e2db8d08936_.log 2025-08-26T21:53:35.1477725Z 2025-08-26T21:53:37.0665118Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:37.0666677Z import pkg_resources 2025-08-26T21:53:37.1894187Z Running dynamo/test_torchrec 1/1 ... [2025-08-26 21:53:37.189006] 2025-08-26T21:53:37.1894912Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:37.1898353Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_torchrec.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:37.189458] 2025-08-26T21:53:38.6628012Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:38.6630886Z import pkg_resources 2025-08-26T21:53:38.7862883Z Running dynamo/test_activation_checkpointing 1/1 ... [2025-08-26 21:53:38.785841] 2025-08-26T21:53:38.7864046Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:38.7866402Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_activation_checkpointing.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:38.786254] 2025-08-26T21:53:38.9858543Z 2025-08-26T21:53:38.9860120Z dynamo/test_compiler_bisector 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_compiler_bisector_1.1_e0f81538d4a32b90_.log 2025-08-26T21:53:38.9863158Z 2025-08-26T21:53:40.9217424Z 2025-08-26T21:53:40.9218632Z dynamo/test_torchrec 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_torchrec_1.1_cbbf315bfb881d02_.log 2025-08-26T21:53:40.9219736Z 2025-08-26T21:53:42.4191314Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:42.4194538Z import pkg_resources 2025-08-26T21:53:42.5425280Z Running dynamo/test_hooks 1/1 ... [2025-08-26 21:53:42.542122] 2025-08-26T21:53:42.5425896Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:42.5428240Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_hooks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:42.542524] 2025-08-26T21:53:44.3842383Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:44.3844100Z import pkg_resources 2025-08-26T21:53:44.5078150Z Running dynamo/test_comptime 1/1 ... [2025-08-26 21:53:44.507377] 2025-08-26T21:53:44.5079041Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:44.5081255Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_comptime.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:44.507727] 2025-08-26T21:53:46.1462140Z 2025-08-26T21:53:46.1463636Z 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_57ae92596de4cd06_.log 2025-08-26T21:53:46.1464554Z 2025-08-26T21:53:46.3146645Z 2025-08-26T21:53:46.3147669Z dynamo/test_hooks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_hooks_1.1_4c0317e33b648b34_.log 2025-08-26T21:53:46.3148624Z 2025-08-26T21:53:48.1158442Z 2025-08-26T21:53:48.1160242Z dynamo/test_comptime 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_comptime_1.1_4d6755643c34e25b_.log 2025-08-26T21:53:48.1161498Z 2025-08-26T21:53:49.6236641Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:49.6239724Z import pkg_resources 2025-08-26T21:53:49.7402683Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:49.7405880Z import pkg_resources 2025-08-26T21:53:49.7470637Z Running test_matmul_cuda 1/1 ... [2025-08-26 21:53:49.746709] 2025-08-26T21:53:49.7471381Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:49.7474991Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_matmul_cuda.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:49.747138] 2025-08-26T21:53:49.8651431Z Running test_jiterator 1/1 ... [2025-08-26 21:53:49.864705] 2025-08-26T21:53:49.8652219Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:49.8653620Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_jiterator.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:49.865068] 2025-08-26T21:53:51.5421392Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:51.5424305Z import pkg_resources 2025-08-26T21:53:51.6649068Z Running functorch/test_ac 1/1 ... [2025-08-26 21:53:51.664493] 2025-08-26T21:53:51.6649818Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:51.6652397Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'functorch/test_ac.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:51.664863] 2025-08-26T21:53:53.3949972Z 2025-08-26T21:53:53.3951305Z test_jiterator 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_jiterator_1.1_5dd8e3c1d1927914_.log 2025-08-26T21:53:53.3952571Z Running 0 items in this shard: 2025-08-26T21:53:53.3952938Z 2025-08-26T21:53:53.3974508Z 2025-08-26T21:53:53.3975859Z test_matmul_cuda 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_matmul_cuda_1.1_dc8015787d1afbbf_.log 2025-08-26T21:53:53.3977381Z Running 0 items in this shard: 2025-08-26T21:53:53.3977577Z 2025-08-26T21:53:56.8576186Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:56.8579398Z import pkg_resources 2025-08-26T21:53:56.8902849Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:53:56.8904648Z import pkg_resources 2025-08-26T21:53:56.9839408Z Running test_cuda_sanitizer 1/1 ... [2025-08-26 21:53:56.983500] 2025-08-26T21:53:56.9840193Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:56.9841597Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_cuda_sanitizer.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:56.983888] 2025-08-26T21:53:57.0141158Z Running dynamo/test_nested_graph_breaks 1/1 ... [2025-08-26 21:53:57.013762] 2025-08-26T21:53:57.0141676Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:53:57.0145904Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'dynamo/test_nested_graph_breaks.py', '-m', 'not serial', '--shard-id=1', '--num-shards=1', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:53:57.014216] 2025-08-26T21:53:59.0171173Z 2025-08-26T21:53:59.0172385Z functorch/test_ac 1/1 was successful, full logs can be found in artifacts with path test/test-reports/functorch.test_ac_1.1_35a883facae0efc5_.log 2025-08-26T21:53:59.0173045Z 2025-08-26T21:54:00.4602355Z 2025-08-26T21:54:00.4604078Z test_cuda_sanitizer 1/1 was successful, full logs can be found in artifacts with path test/test-reports/test_cuda_sanitizer_1.1_62ed6beb9d911e36_.log 2025-08-26T21:54:00.4605333Z Running 0 items in this shard: 2025-08-26T21:54:00.4605630Z 2025-08-26T21:54:00.6460107Z 2025-08-26T21:54:00.6461804Z dynamo/test_nested_graph_breaks 1/1 was successful, full logs can be found in artifacts with path test/test-reports/dynamo.test_nested_graph_breaks_1.1_ef1dff773888b98c_.log 2025-08-26T21:54:00.6463105Z 2025-08-26T21:54:02.4558018Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:54:02.4560997Z import pkg_resources 2025-08-26T21:54:02.5798294Z Running test_quantization 9/9 ... [2025-08-26 21:54:02.579440] 2025-08-26T21:54:02.5799094Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T21:54:02.5801644Z Executing ['/opt/conda/envs/py_3.13/bin/python', '-bb', 'test_quantization.py', '-m', 'not serial', '--shard-id=9', '--num-shards=9', '-v', '-vv', '-rfEX', '-p', 'no:xdist', '--use-pytest', '-x', '--reruns=2', '--import-slow-tests', '--import-disabled-tests'] ... [2025-08-26 21:54:02.579823] 2025-08-26T21:54:03.8956089Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:54:03.8959705Z import pkg_resources 2025-08-26T21:54:04.0717106Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T21:54:04.0718926Z import pkg_resources 2025-08-26T22:00:53.2191973Z 2025-08-26T22:00:53.2193217Z test_quantization 9/9 was successful, full logs can be found in artifacts with path test/test-reports/test_quantization_9.9_aed417460e9f6645_.log 2025-08-26T22:00:53.2239479Z Running 128 items in this shard: test/test_quantization.py::TestQuantizedOps::test_adaptive_avg_pool, test/test_quantization.py::TestQuantizedOps::test_interpolate, test/test_quantization.py::TestQuantizedOps::test_max_pool3d, test/test_quantization.py::TestQuantizedOps::test_max_pool3d_nhwc, test/test_quantization.py::TestQuantizedLinear::test_qlinear, test/test_quantization.py::TestQuantizedLinear::test_qlinear_add_fp8, test/test_quantization.py::TestQuantizedConv::test_benchmark, test/test_quantization.py::TestQuantizedConv::test_qconv1d_relu_cudnn, test/test_quantization.py::TestQuantizedConv::test_qconv2d_sum_relu_float_output_pt2e, test/test_quantization.py::TestDynamicQuantizedOps::test_qrnncell, test/test_quantization.py::TestQuantizedEmbeddingOps::test_embedding_2d_indices, test/test_quantization.py::TestFakeQuantizeOps::test_fq_module_per_tensor, test/test_quantization.py::TestQuantizedTensor::test_choose_qparams, test/test_quantization.py::TestQuantizedTensor::test_decomposed_quantize_per_channel_group, test/test_quantization.py::TestQuantizedTensor::test_qtensor_channel_float_assignment, test/test_quantization.py::TestQuantizedTensor::test_qtensor_fill_per_channel_nhwc, test/test_quantization.py::TestObserver::test_per_channel_observers_load_state_dict, test/test_quantization.py::TestStaticQuantizedModule::test_batch_norm2d, test/test_quantization.py::TestStaticQuantizedModule::test_conv1d_relu_api, test/test_quantization.py::TestStaticQuantizedModule::test_linear, test/test_quantization.py::TestStaticQuantizedModule::test_linear_tanh, test/test_quantization.py::TestStaticQuantizedModule::test_relu, test/test_quantization.py::TestReferenceQuantizedModule::test_linear_decomposed_weight_custom_qmin_qmax, test/test_quantization.py::TestReferenceQuantizedModule::test_rnn_cell, test/test_quantization.py::TestRecordHistogramObserver::test_observer_scriptable, test/test_quantization.py::TestDistributed::test_observers_preserve_buffers, test/test_quantization.py::TestBackendConfig::test_backend_config_set_name, test/test_quantization.py::TestUtils::test_quantize_weight_clamping_per_channel, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_manual, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_mha_batch_first_attr_is_copied_in_prepare, test/test_quantization.py::TestQuantizeEagerPTQStatic::test_nested3, test/test_quantization.py::TestQuantizeEagerQAT::test_dropout, test/test_quantization.py::TestQuantizeEagerQAT::test_embedding_qat_qconfig_equal, test/test_quantization.py::TestQuantizeEagerQAT::test_train_save_load_eval, test/test_quantization.py::TestQuantizeEagerQATNumerics::test_fixed_qparam_ops, test/test_quantization.py::TestFuseEager::test_fuse_function_customization, test/test_quantization.py::TestFuseEager::test_fusion_sequential_model_eval, test/test_quantization.py::TestNumericSuiteEager::test_compare_model_outputs_linear_static, test/test_quantization.py::TestNumericSuiteEager::test_compare_weights_linear_static, test/test_quantization.py::TestEqualizeEager::test_equalize_fused_linearrelu, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_matchallnode, test/test_quantization.py::TestFuseFx::test_fusion_pattern_with_multiple_inputs, test/test_quantization.py::TestFuseFx::test_qconfig_fused_module, test/test_quantization.py::TestQuantizeFx::test__convert_to_reference_decomposed_fx, test/test_quantization.py::TestQuantizeFx::test_conv_linear_not_reference, test/test_quantization.py::TestQuantizeFx::test_convert_custom_config_set_observed_to_quantized_mapping, test/test_quantization.py::TestQuantizeFx::test_linear_qint8_activation, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_input_quantized_indexes, test/test_quantization.py::TestQuantizeFx::test_prepare_custom_config_set_non_traceable_module_names, test/test_quantization.py::TestQuantizeFx::test_preserve_attributes, test/test_quantization.py::TestQuantizeFx::test_qconfig_dict_setup, test/test_quantization.py::TestQuantizeFx::test_qconfig_function, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_repr, test/test_quantization.py::TestQuantizeFx::test_qconfig_mapping_set_module_name_object_type_order, test/test_quantization.py::TestQuantizeFx::test_quant_output_always_observed, test/test_quantization.py::TestQuantizeFx::test_quantized_model_type, test/test_quantization.py::TestQuantizeFx::test_static_lstm_with_custom_fixed_qparams, test/test_quantization.py::TestQuantizeFx::test_view_nontensor_args_not_observed, test/test_quantization.py::TestQuantizeFxOps::test_fixed_qparams_ops_wrong_qconfig, test/test_quantization.py::TestQuantizeFxOps::test_pixel_unshuffle, test/test_quantization.py::TestQuantizeFxOps::test_rnn, test/test_quantization.py::TestSubgraphRewriter::test_subgraph_rewriter_preserves_logic, test/test_quantization.py::TestGraphUtils::test_customized_equivalet_types_dict, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_dq_no_static_q, test/test_quantization.py::TestMetaDataPorting::test_metadata_porting_for_two_dq, test/test_quantization.py::TestMetaDataPorting::test_no_metadata_porting_through_unknown_ops, test/test_quantization.py::TestMetaDataPorting::test_simple_metadata_porting, test/test_quantization.py::TestQuantizePT2E::test_fold_quantize_per_channel, test/test_quantization.py::TestQuantizePT2E::test_move_exported_model_bn_device_cpu, test/test_quantization.py::TestQuantizePT2E::test_preserve_nn_module_stack, test/test_quantization.py::TestQuantizePT2E::test_quantization_dtype_float32_int16, test/test_quantization.py::TestQuantizePT2E::test_reentrant, test/test_quantization.py::TestQuantizePT2E::test_transform_for_annotation, test/test_quantization.py::TestXNNPACKQuantizer::test_cat_same_node, test/test_quantization.py::TestXNNPACKQuantizer::test_conv1d, test/test_quantization.py::TestXNNPACKQuantizer::test_linear_relu, test/test_quantization.py::TestXNNPACKQuantizer::test_mul_float32_max, test/test_quantization.py::TestXNNPACKQuantizer::test_obs_sharing_ops, test/test_quantization.py::TestXNNPACKQuantizer::test_set_module_type_case_2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_flatten_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary2, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_linear_binary_unary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_lowering_to_x86, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_maxpool2d_recipe, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_binary, test/test_quantization.py::TestQuantizePT2EX86Inductor::test_qat_conv2d_unary, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn1d::test_qat_conv_bn_fusion_literal_args, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_fold_bn_erases_add_node, test/test_quantization.py::TestQuantizePT2EQAT_ConvBn2d::test_qat_conv_transpose_bn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_int8_shadows_int8_mod, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_fp16_activations, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_linear_kwargs_shadow, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_match_activations_fqn, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_unsupported_op_copy_skips_shadowing, test/test_quantization.py::TestFXNumericSuiteCoreAPIs::test_user_defined_function, test/test_quantization.py::TestFXNumericSuiteNShadows::test_add_loggers_functions, test/test_quantization.py::TestFXNumericSuiteNShadows::test_extract_weights_linear, test/test_quantization.py::TestFXNumericSuiteNShadows::test_linear_relu_mod, test/test_quantization.py::TestFXNumericSuiteNShadows::test_qconfig_multi_mapping_repr, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_prepare, test/test_quantization.py::TestEqualizeFx::test_input_weight_equalization_weights_bias, test/test_quantization.py::TestSerialization::test_conv2d_graph, test/test_quantization.py::TestQuantizeJitPasses::test_conv_trace, test/test_quantization.py::TestQuantizeJitPasses::test_inplace_option, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_child_qconfig, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_if, test/test_quantization.py::TestQuantizeJitPasses::test_insert_observers_for_reused_weight, test/test_quantization.py::TestQuantizeJitPasses::test_replicate_dequantize, test/test_quantization.py::TestQuantizeJitOps::test_group_norm, test/test_quantization.py::TestQuantizeJitOps::test_quantized_conv, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_convert_dynamic_fp16, test/test_quantization.py::TestQuantizeDynamicJitPasses::test_dynamic_shared_weights, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_quant_type, test/test_quantization.py::TestAOMigrationQuantization::test_function_import_utils, test/test_quantization.py::TestAOMigrationNNQuantized::test_modules_linear, test/test_quantization.py::TestAOMigrationNNIntrinsic::test_modules_import_nn_intrinsic_qat, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_convert, test/test_quantization.py::TestAOMigrationQuantizationFx::test_function_import_fx_fusion_patterns, test/test_quantization.py::TestBitsCPU::test_subclass_cpu, test/test_quantization.py::TestFloat8DtypeCPU::test_cast_round_trip_extremes_cpu_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCPU::test_creation_with_zeros_cpu_float8_e5m2fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_empty_cpu_float8_e4m3fnuz, test/test_quantization.py::TestFloat8DtypeCPU::test_finfo_cpu_float8_e4m3fn, test/test_quantization.py::TestFloat8DtypeCPU::test_float8_e8m0fnu_rne_rounding_cpu, test/test_quantization.py::TestFloat8DtypeCPU::test_save_load_cpu_float8_e5m2, test/test_quantization.py::TestFloat8DtypeCPU::test_to_string_cpu_float8_e8m0fnu, test/test_quantization.py::TestFloat8DtypeCPUOnlyCPU::test_mul_cpu_float8_e4m3fn 2025-08-26T22:00:53.2283374Z 2025-08-26T22:00:54.0834233Z Running test batch 'tests to run' cost 7828.57 seconds 2025-08-26T22:00:55.2733903Z 2025-08-26T22:00:55.2734547Z real 130m34.535s 2025-08-26T22:00:55.2734897Z user 160m1.383s 2025-08-26T22:00:55.2735129Z sys 23m23.788s 2025-08-26T22:00:55.2735367Z + assert_git_not_dirty 2025-08-26T22:00:55.2740454Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-08-26T22:00:55.2741172Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-08-26T22:00:55.2765525Z ++ git status --porcelain 2025-08-26T22:00:55.2766124Z ++ grep -v '?? third_party' 2025-08-26T22:01:37.7963991Z ++ true 2025-08-26T22:01:37.8000961Z + git_status= 2025-08-26T22:01:37.8001266Z + [[ -n '' ]] 2025-08-26T22:01:37.8001527Z + [[ 1 == 1 ]] 2025-08-26T22:01:37.8001743Z + test_aten 2025-08-26T22:01:37.8007671Z + echo 'Running ATen tests with pytorch lib' 2025-08-26T22:01:37.8008120Z Running ATen tests with pytorch lib 2025-08-26T22:01:37.8008426Z + [[ -n '' ]] 2025-08-26T22:01:37.8008672Z + echo 'Running test with the build folder' 2025-08-26T22:01:37.8009252Z Running test with the build folder 2025-08-26T22:01:37.8009567Z + TEST_BASE_DIR=build/bin 2025-08-26T22:01:37.8015498Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libc10.so build/bin 2025-08-26T22:01:37.8053148Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libcaffe2*' build/bin 2025-08-26T22:01:37.8064444Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libmkldnn*' build/bin 2025-08-26T22:01:37.8074662Z + ln -sf '/opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libnccl*' build/bin 2025-08-26T22:01:37.8087153Z + ln -sf /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_cpu.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_global_deps.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorch_python.so /opt/conda/envs/py_3.13/lib/python3.13/site-packages/torch/lib/libtorchbind_test.so build/bin 2025-08-26T22:01:37.8096869Z + ls build/bin 2025-08-26T22:01:37.8165962Z BackoffTest c10_typeid_test 2025-08-26T22:01:37.8166540Z CMakeFiles cmake_install.cmake 2025-08-26T22:01:37.8167114Z CTestTestfile.cmake cpu_allocator_test 2025-08-26T22:01:37.8167741Z CppSignature_test cpu_generator_test 2025-08-26T22:01:37.8168707Z Dict_test cpu_profiling_allocator_test 2025-08-26T22:01:37.8169339Z Dimname_test cpu_rng_test 2025-08-26T22:01:37.8169677Z FileStoreTest dlconvertor_test 2025-08-26T22:01:37.8170012Z HashStoreTest example_allreduce 2025-08-26T22:01:37.8170501Z IListRef_test extension_backend_test 2025-08-26T22:01:37.8170922Z KernelFunction_test half_test 2025-08-26T22:01:37.8171376Z List_test inline_container_test 2025-08-26T22:01:37.8171712Z MaybeOwned_test ivalue_test 2025-08-26T22:01:37.8172076Z NamedTensor_test kernel_function_legacy_test 2025-08-26T22:01:37.8172518Z ProcessGroupGlooTest kernel_function_test 2025-08-26T22:01:37.8172943Z StorageUtils_test kernel_lambda_legacy_test 2025-08-26T22:01:37.8173431Z TCPStoreTest kernel_lambda_test 2025-08-26T22:01:37.8173791Z apply_utils_test kernel_stackbased_test 2025-08-26T22:01:37.8174151Z atest lazy_tensor_test 2025-08-26T22:01:37.8174498Z backend_fallback_test legacy_vmap_test 2025-08-26T22:01:37.8174848Z basic libc10.so 2025-08-26T22:01:37.8175133Z broadcast_test 'libcaffe2*' 2025-08-26T22:01:37.8175480Z c10_AllocatorConfig_test 'libmkldnn*' 2025-08-26T22:01:37.8175813Z c10_ArrayRef_test 'libnccl*' 2025-08-26T22:01:37.8176131Z c10_Bitset_test libtorch.so 2025-08-26T22:01:37.8176513Z c10_CompileTimeFunctionPointer_test libtorch_cpu.so 2025-08-26T22:01:37.8176960Z c10_ConstexprCrc_test libtorch_global_deps.so 2025-08-26T22:01:37.8177378Z c10_DeadlockDetection_test libtorch_python.so 2025-08-26T22:01:37.8177796Z c10_DeviceGuard_test libtorchbind_test.so 2025-08-26T22:01:37.8178240Z c10_Device_test make_boxed_from_unboxed_functor_test 2025-08-26T22:01:37.8178676Z c10_DispatchKeySet_test math_kernel_test 2025-08-26T22:01:37.8179062Z c10_Enumerate_test memory_format_test 2025-08-26T22:01:37.8179437Z c10_Half_test memory_overlapping_test 2025-08-26T22:01:37.8179857Z c10_InlineDeviceGuard_test mobile_memory_cleanup 2025-08-26T22:01:37.8180267Z c10_InlineStreamGuard_test native_test 2025-08-26T22:01:37.8180737Z c10_IntrusiveList_test op_allowlist_test 2025-08-26T22:01:37.8181116Z c10_LeftRight_test op_registration_test 2025-08-26T22:01:37.8181512Z c10_Metaprogramming_test operator_name_test 2025-08-26T22:01:37.8181900Z c10_NetworkFlow_test operators_test 2025-08-26T22:01:37.8182281Z c10_Scalar_test packedtensoraccessor_test 2025-08-26T22:01:37.8182714Z c10_Semaphore_test parallel_benchmark 2025-08-26T22:01:37.8183152Z c10_SizesAndStrides_test pow_test 2025-08-26T22:01:37.8183493Z c10_StreamGuard_test protoc 2025-08-26T22:01:37.8183823Z c10_SymInt_test protoc-3.13.0.0 2025-08-26T22:01:37.8184156Z c10_Synchronized_test quantized_test 2025-08-26T22:01:37.8184519Z c10_ThreadLocal_test reduce_ops_test 2025-08-26T22:01:37.8184905Z c10_TypeIndex_test reportMemoryUsage_test 2025-08-26T22:01:37.8185287Z c10_TypeList_test scalar_tensor_test 2025-08-26T22:01:37.8185685Z c10_TypeTraits_test scalar_test 2025-08-26T22:01:37.8186044Z c10_accumulate_test static_runtime_bench 2025-08-26T22:01:37.8186428Z c10_bfloat16_test static_runtime_test 2025-08-26T22:01:37.8186802Z c10_bit_cast_test stride_properties_test 2025-08-26T22:01:37.8187182Z c10_complex_math_test tensor_iterator_test 2025-08-26T22:01:37.8187544Z c10_complex_test test_api 2025-08-26T22:01:37.8187855Z c10_cow_test test_cpp_rpc 2025-08-26T22:01:37.8188181Z c10_error_test test_dist_autograd 2025-08-26T22:01:37.8188517Z c10_exception_test test_jit 2025-08-26T22:01:37.8188822Z c10_flags_test test_lazy 2025-08-26T22:01:37.8189137Z c10_generic_math_test test_nativert 2025-08-26T22:01:37.8189504Z c10_intrusive_ptr_benchmark test_parallel 2025-08-26T22:01:37.8189965Z c10_intrusive_ptr_test thread_init_test 2025-08-26T22:01:37.8190319Z c10_irange_test torch_shm_manager 2025-08-26T22:01:37.8190654Z c10_lazy_test type_ptr_test 2025-08-26T22:01:37.8190974Z c10_logging_test type_test 2025-08-26T22:01:37.8191318Z c10_optional_test undefined_tensor_test 2025-08-26T22:01:37.8192063Z c10_ordered_preserving_dict_test vec_test_all_types_AVX2 2025-08-26T22:01:37.8192531Z c10_registry_test vec_test_all_types_AVX512 2025-08-26T22:01:37.8192960Z c10_small_vector_test vec_test_all_types_DEFAULT 2025-08-26T22:01:37.8193368Z c10_ssize_test verify_api_visibility 2025-08-26T22:01:37.8193725Z c10_string_util_test weakref_test 2025-08-26T22:01:37.8194073Z c10_string_view_test wrapdim_test 2025-08-26T22:01:37.8194488Z c10_tempfile_test xla_tensor_test 2025-08-26T22:01:37.8194829Z + aten/tools/run_tests.sh build/bin 2025-08-26T22:01:37.8214536Z + set -e 2025-08-26T22:01:37.8217759Z ++ dirname aten/tools/run_tests.sh 2025-08-26T22:01:37.8239846Z + VALGRIND_SUP=/var/lib/jenkins/workspace/aten/tools/valgrind.sup 2025-08-26T22:01:37.8240419Z + export CPP_TESTS_DIR=build/bin 2025-08-26T22:01:37.8240728Z + CPP_TESTS_DIR=build/bin 2025-08-26T22:01:37.8240986Z + VALGRIND=ON 2025-08-26T22:01:37.8242812Z + python test/run_test.py --cpp --verbose -i cpp/basic cpp/atest cpp/scalar_test cpp/broadcast_test cpp/wrapdim_test cpp/apply_utils_test cpp/dlconvertor_test cpp/native_test cpp/scalar_tensor_test cpp/undefined_tensor_test cpp/extension_backend_test cpp/lazy_tensor_test cpp/tensor_iterator_test cpp/Dimname_test cpp/Dict_test cpp/NamedTensor_test cpp/cpu_generator_test cpp/legacy_vmap_test cpp/operators_test 2025-08-26T22:01:40.2348090Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:01:40.2351058Z import pkg_resources 2025-08-26T22:01:41.7732201Z Downloading https://ossci-metrics.s3.amazonaws.com/disabled-tests-condensed.json to /var/lib/jenkins/workspace/test/.pytorch-disabled-tests.json 2025-08-26T22:01:41.7900477Z Found test times from artifacts 2025-08-26T22:01:41.8524813Z Found test times from artifacts 2025-08-26T22:01:41.8541901Z Running all tests 2025-08-26T22:01:41.8546586Z Running parallel tests on 3 processes 2025-08-26T22:01:41.8548149Z Name: tests to run (est. time: 0.0min) 2025-08-26T22:01:41.8548559Z Serial tests (0): 2025-08-26T22:01:41.8548952Z Parallel tests (19): 2025-08-26T22:01:41.8549242Z cpp/Dict_test 1/1 2025-08-26T22:01:41.8549514Z cpp/Dimname_test 1/1 2025-08-26T22:01:41.8549828Z cpp/NamedTensor_test 1/1 2025-08-26T22:01:41.8550125Z cpp/apply_utils_test 1/1 2025-08-26T22:01:41.8550395Z cpp/atest 1/1 2025-08-26T22:01:41.8550634Z cpp/basic 1/1 2025-08-26T22:01:41.8550890Z cpp/broadcast_test 1/1 2025-08-26T22:01:41.8551163Z cpp/cpu_generator_test 1/1 2025-08-26T22:01:41.8551462Z cpp/dlconvertor_test 1/1 2025-08-26T22:01:41.8551837Z cpp/extension_backend_test 1/1 2025-08-26T22:01:41.8552155Z cpp/lazy_tensor_test 1/1 2025-08-26T22:01:41.8552520Z cpp/legacy_vmap_test 1/1 2025-08-26T22:01:41.8552904Z cpp/native_test 1/1 2025-08-26T22:01:41.8553242Z cpp/operators_test 1/1 2025-08-26T22:01:41.8553524Z cpp/scalar_tensor_test 1/1 2025-08-26T22:01:41.8553855Z cpp/scalar_test 1/1 2025-08-26T22:01:41.8554128Z cpp/tensor_iterator_test 1/1 2025-08-26T22:01:41.8554440Z cpp/undefined_tensor_test 1/1 2025-08-26T22:01:41.8554754Z cpp/wrapdim_test 1/1 2025-08-26T22:01:41.8555024Z Name: excluded (est. time: 0.0min) 2025-08-26T22:01:41.8555331Z Serial tests (0): 2025-08-26T22:01:41.8555584Z Parallel tests (0): 2025-08-26T22:01:41.8555923Z Running cpp/Dict_test 1/1 ... [2025-08-26 22:01:41.855199] 2025-08-26T22:01:41.8556310Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:41.8567361Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-3fb8893ec48825db.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:41.856488] 2025-08-26T22:01:43.7246304Z 2025-08-26T22:01:43.7247387Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_16535597d432c5fb_.log 2025-08-26T22:01:43.7248006Z 2025-08-26T22:01:44.4455912Z Uploading artifacts took 0.72 seconds 2025-08-26T22:01:44.4456602Z Running cpp/Dimname_test 1/1 ... [2025-08-26 22:01:44.445330] 2025-08-26T22:01:44.4457031Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:44.4471340Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-226429a96a849609.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:44.445789] 2025-08-26T22:01:45.9630300Z 2025-08-26T22:01:45.9631544Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_313f28152fb87c73_.log 2025-08-26T22:01:45.9632622Z 2025-08-26T22:01:45.9632961Z Running cpp/NamedTensor_test 1/1 ... [2025-08-26 22:01:45.962882] 2025-08-26T22:01:45.9633653Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:45.9635933Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-7dfdd8c397cd5439.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:45.963288] 2025-08-26T22:01:47.4303987Z 2025-08-26T22:01:47.4305359Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_4b1a6d7bea838e33_.log 2025-08-26T22:01:47.4306456Z 2025-08-26T22:01:47.4306840Z Running cpp/apply_utils_test 1/1 ... [2025-08-26 22:01:47.430262] 2025-08-26T22:01:47.4307511Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:47.4309821Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-36dba22fc06cf40d.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:47.430665] 2025-08-26T22:01:48.8979280Z 2025-08-26T22:01:48.8980362Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_0b26dde0ad280638_.log 2025-08-26T22:01:48.8981317Z 2025-08-26T22:01:48.8981496Z Running cpp/atest 1/1 ... [2025-08-26 22:01:48.897818] 2025-08-26T22:01:48.8981881Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:48.8984477Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-ec279909d3556715.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:48.898201] 2025-08-26T22:01:50.3653430Z 2025-08-26T22:01:50.3654256Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_32b1adae1905ed5b_.log 2025-08-26T22:01:50.3654853Z 2025-08-26T22:01:50.3655065Z Running cpp/basic 1/1 ... [2025-08-26 22:01:50.365099] 2025-08-26T22:01:50.3655543Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:50.3656808Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-e35b10e714d38330.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:50.365440] 2025-08-26T22:01:51.8323474Z 2025-08-26T22:01:51.8324726Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_785b4d0a38f6bd9c_.log 2025-08-26T22:01:51.8325318Z 2025-08-26T22:01:51.8325775Z Running cpp/broadcast_test 1/1 ... [2025-08-26 22:01:51.832217] 2025-08-26T22:01:51.8326193Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:51.8328435Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-58560e38cadc798c.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:51.832600] 2025-08-26T22:01:53.2998060Z 2025-08-26T22:01:53.2998959Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_fb59cbd94a80cffd_.log 2025-08-26T22:01:53.2999658Z 2025-08-26T22:01:53.2999892Z Running cpp/cpu_generator_test 1/1 ... [2025-08-26 22:01:53.299583] 2025-08-26T22:01:53.3000607Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:53.3001913Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-bc430774e4dcbfef.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:53.299949] 2025-08-26T22:01:54.7670306Z 2025-08-26T22:01:54.7671682Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_046e5cc0eb5fb2d7_.log 2025-08-26T22:01:54.7672395Z 2025-08-26T22:01:54.7672613Z Running cpp/dlconvertor_test 1/1 ... [2025-08-26 22:01:54.766923] 2025-08-26T22:01:54.7673055Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:54.7675790Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-c345ae6fe6b9d744.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:54.767293] 2025-08-26T22:01:56.2345619Z 2025-08-26T22:01:56.2346641Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_b7b75c5a620f76ea_.log 2025-08-26T22:01:56.2347366Z 2025-08-26T22:01:56.2347602Z Running cpp/extension_backend_test 1/1 ... [2025-08-26 22:01:56.234432] 2025-08-26T22:01:56.2348078Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:56.2350564Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d4ef1acfce50d81b.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:56.234805] 2025-08-26T22:01:57.7017288Z 2025-08-26T22:01:57.7018966Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_755c14b460f740f8_.log 2025-08-26T22:01:57.7019746Z 2025-08-26T22:01:57.7019974Z Running cpp/lazy_tensor_test 1/1 ... [2025-08-26 22:01:57.701599] 2025-08-26T22:01:57.7020538Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:57.7022837Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-292b909cfc414c5b.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:57.702012] 2025-08-26T22:01:59.2196719Z 2025-08-26T22:01:59.2197915Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_e498996b4903ba49_.log 2025-08-26T22:01:59.2198658Z 2025-08-26T22:01:59.2198861Z Running cpp/legacy_vmap_test 1/1 ... [2025-08-26 22:01:59.219520] 2025-08-26T22:01:59.2199320Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:01:59.2201426Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-06265df3c922b064.xml', '-x', '--reruns=2'] ... [2025-08-26 22:01:59.219907] 2025-08-26T22:02:00.6868614Z 2025-08-26T22:02:00.6870035Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_a51fb80519b70b1d_.log 2025-08-26T22:02:00.6870833Z 2025-08-26T22:02:00.6871075Z Running cpp/native_test 1/1 ... [2025-08-26 22:02:00.686744] 2025-08-26T22:02:00.6871498Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:00.6873580Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-cafb6ecea96fea36.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:00.687103] 2025-08-26T22:02:02.1541184Z 2025-08-26T22:02:02.1542457Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_cc58ed355241c94f_.log 2025-08-26T22:02:02.1543766Z 2025-08-26T22:02:02.1544051Z Running cpp/operators_test 1/1 ... [2025-08-26 22:02:02.153979] 2025-08-26T22:02:02.1544533Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:02.1545946Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-6857352bf45b8e08.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:02.154352] 2025-08-26T22:02:03.6215447Z 2025-08-26T22:02:03.6216588Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_a49c6454f0f05353_.log 2025-08-26T22:02:03.6217326Z 2025-08-26T22:02:03.6217556Z Running cpp/scalar_tensor_test 1/1 ... [2025-08-26 22:02:03.621451] 2025-08-26T22:02:03.6218066Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:03.6220949Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-7a6bd1bf4d0d9783.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:03.621837] 2025-08-26T22:02:05.1388565Z 2025-08-26T22:02:05.1389997Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_2e5079ee1d80410d_.log 2025-08-26T22:02:05.1391181Z 2025-08-26T22:02:05.1393767Z Running cpp/scalar_test 1/1 ... [2025-08-26 22:02:05.138751] 2025-08-26T22:02:05.1394423Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:05.1396366Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-fd35b91e692c593e.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:05.139186] 2025-08-26T22:02:06.6067786Z 2025-08-26T22:02:06.6068737Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_c65cd73d7106155e_.log 2025-08-26T22:02:06.6069447Z 2025-08-26T22:02:06.6069677Z Running cpp/tensor_iterator_test 1/1 ... [2025-08-26 22:02:06.606631] 2025-08-26T22:02:06.6070131Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:06.6072441Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1296c0ba43ca682d.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:06.606997] 2025-08-26T22:02:08.1241928Z 2025-08-26T22:02:08.1243400Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_9d0f95e51e09afb7_.log 2025-08-26T22:02:08.1244138Z 2025-08-26T22:02:08.1244393Z Running cpp/undefined_tensor_test 1/1 ... [2025-08-26 22:02:08.124072] 2025-08-26T22:02:08.1244869Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:08.1246822Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9c58b3c30de83e15.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:08.124436] 2025-08-26T22:02:09.5913455Z 2025-08-26T22:02:09.5914951Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_b85df50c47857192_.log 2025-08-26T22:02:09.5915705Z 2025-08-26T22:02:09.5915915Z Running cpp/wrapdim_test 1/1 ... [2025-08-26 22:02:09.591200] 2025-08-26T22:02:09.5916454Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:09.5918075Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4181da87db5a27e1.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:09.591555] 2025-08-26T22:02:11.0586101Z 2025-08-26T22:02:11.0587249Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_0c7186e946e069fb_.log 2025-08-26T22:02:11.0587899Z 2025-08-26T22:02:13.6203097Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:13.6204823Z import pkg_resources 2025-08-26T22:02:13.6264770Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:13.6266295Z import pkg_resources 2025-08-26T22:02:13.6555695Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:13.6557212Z import pkg_resources 2025-08-26T22:02:13.7448329Z Running cpp/Dict_test 1/1 ... [2025-08-26 22:02:13.744498] 2025-08-26T22:02:13.7448887Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:13.7452942Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dict_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d72affa719e62c5b.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:13.744987] 2025-08-26T22:02:13.7523325Z Running cpp/Dimname_test 1/1 ... [2025-08-26 22:02:13.751960] 2025-08-26T22:02:13.7523795Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:13.7527561Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/Dimname_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-2b0a6ddcdac09361.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:13.752435] 2025-08-26T22:02:13.7816229Z Running cpp/NamedTensor_test 1/1 ... [2025-08-26 22:02:13.781284] 2025-08-26T22:02:13.7816991Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:13.7821161Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/NamedTensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-5a3231eba214a56c.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:13.781780] 2025-08-26T22:02:16.9841638Z 2025-08-26T22:02:16.9843061Z cpp/Dimname_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dimname_test_1.1_4b562a2d4dc130b7_.log 2025-08-26T22:02:16.9844302Z 2025-08-26T22:02:17.6349737Z 2025-08-26T22:02:17.6351055Z cpp/NamedTensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.NamedTensor_test_1.1_b52eb9b39844dc5d_.log 2025-08-26T22:02:17.6352096Z 2025-08-26T22:02:21.1523621Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:21.1526950Z import pkg_resources 2025-08-26T22:02:21.1681030Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:21.1684720Z import pkg_resources 2025-08-26T22:02:21.2791615Z Running cpp/apply_utils_test 1/1 ... [2025-08-26 22:02:21.278710] 2025-08-26T22:02:21.2792588Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:21.2796934Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/apply_utils_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-df4d34e3db2bc153.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:21.279281] 2025-08-26T22:02:21.2945007Z Running cpp/atest 1/1 ... [2025-08-26 22:02:21.294040] 2025-08-26T22:02:21.2945637Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:21.2949951Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/atest', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-74d0b1795d1c0305.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:21.294590] 2025-08-26T22:02:21.8817907Z 2025-08-26T22:02:21.8819351Z cpp/Dict_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.Dict_test_1.1_5b009902accb0e35_.log 2025-08-26T22:02:21.8825351Z 2025-08-26T22:02:24.3506161Z 2025-08-26T22:02:24.3507701Z cpp/apply_utils_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.apply_utils_test_1.1_1acc42f1b71054c1_.log 2025-08-26T22:02:24.3508949Z 2025-08-26T22:02:25.3177427Z 2025-08-26T22:02:25.3178280Z cpp/atest 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.atest_1.1_a2be85fc2245ade9_.log 2025-08-26T22:02:25.9660099Z 2025-08-26T22:02:25.9663277Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:25.9666161Z import pkg_resources 2025-08-26T22:02:26.0910601Z Running cpp/basic 1/1 ... [2025-08-26 22:02:26.090674] 2025-08-26T22:02:26.0911283Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:26.0916197Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/basic', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-cfa9c23a63ccc76d.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:26.091229] 2025-08-26T22:02:28.2025613Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:28.2028399Z import pkg_resources 2025-08-26T22:02:28.3826499Z Running cpp/broadcast_test 1/1 ... [2025-08-26 22:02:28.382139] 2025-08-26T22:02:28.3827366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:28.3831743Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/broadcast_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-63014ab788482897.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:28.382703] 2025-08-26T22:02:28.9787455Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:28.9790201Z import pkg_resources 2025-08-26T22:02:29.0617238Z 2025-08-26T22:02:29.0618891Z cpp/basic 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.basic_1.1_7bed21a0ed3c338b_.log 2025-08-26T22:02:29.0619930Z 2025-08-26T22:02:29.1312102Z Running cpp/cpu_generator_test 1/1 ... [2025-08-26 22:02:29.130767] 2025-08-26T22:02:29.1313366Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:29.1317561Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/cpu_generator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-fd1298b742dbca94.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:29.131337] 2025-08-26T22:02:30.6133069Z 2025-08-26T22:02:30.6134695Z cpp/broadcast_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.broadcast_test_1.1_a556b4f0228b30f4_.log 2025-08-26T22:02:30.6142006Z 2025-08-26T22:02:32.7541289Z 2025-08-26T22:02:32.7542477Z cpp/cpu_generator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.cpu_generator_test_1.1_b439fcc54d7e782d_.log 2025-08-26T22:02:32.7543486Z 2025-08-26T22:02:33.0839785Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:33.0842659Z import pkg_resources 2025-08-26T22:02:33.2089954Z Running cpp/dlconvertor_test 1/1 ... [2025-08-26 22:02:33.208557] 2025-08-26T22:02:33.2090730Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:33.2094757Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/dlconvertor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-d7296a46e12b235c.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:33.209067] 2025-08-26T22:02:34.5120010Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:34.5121710Z import pkg_resources 2025-08-26T22:02:34.6379018Z Running cpp/extension_backend_test 1/1 ... [2025-08-26 22:02:34.637389] 2025-08-26T22:02:34.6379898Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:34.6382702Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/extension_backend_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-6d8b7048b3e9275f.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:34.637903] 2025-08-26T22:02:35.8292368Z 2025-08-26T22:02:35.8293790Z cpp/dlconvertor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.dlconvertor_test_1.1_34032a5eb1013211_.log 2025-08-26T22:02:35.8295084Z 2025-08-26T22:02:36.5562369Z 2025-08-26T22:02:36.5563892Z cpp/extension_backend_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.extension_backend_test_1.1_2482d4dc7f05c352_.log 2025-08-26T22:02:36.5564790Z 2025-08-26T22:02:36.9523171Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:36.9524854Z import pkg_resources 2025-08-26T22:02:37.0879418Z Running cpp/lazy_tensor_test 1/1 ... [2025-08-26 22:02:37.087529] 2025-08-26T22:02:37.0880259Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:37.0887427Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/lazy_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-bf34792e805ff713.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:37.088341] 2025-08-26T22:02:39.0201646Z 2025-08-26T22:02:39.0203035Z cpp/lazy_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.lazy_tensor_test_1.1_3f8f8b02a7da9b46_.log 2025-08-26T22:02:39.0205103Z 2025-08-26T22:02:39.5592013Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:39.5594112Z import pkg_resources 2025-08-26T22:02:39.6841390Z Running cpp/legacy_vmap_test 1/1 ... [2025-08-26 22:02:39.683758] 2025-08-26T22:02:39.6842404Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:39.6845881Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/legacy_vmap_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-19213750820fb9bd.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:39.684217] 2025-08-26T22:02:40.2372776Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:40.2375811Z import pkg_resources 2025-08-26T22:02:40.3768528Z Running cpp/native_test 1/1 ... [2025-08-26 22:02:40.376391] 2025-08-26T22:02:40.3769531Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:40.3773009Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/native_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9bfddf2c470c9572.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:40.376911] 2025-08-26T22:02:42.8469177Z 2025-08-26T22:02:42.8470780Z cpp/native_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.native_test_1.1_94e6c2f3f29cb081_.log 2025-08-26T22:02:42.8472217Z 2025-08-26T22:02:43.0598336Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:43.0601584Z import pkg_resources 2025-08-26T22:02:43.1873654Z Running cpp/operators_test 1/1 ... [2025-08-26 22:02:43.186929] 2025-08-26T22:02:43.1874355Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:43.1879111Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/operators_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1406170047ddb257.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:43.187482] 2025-08-26T22:02:44.3581371Z 2025-08-26T22:02:44.3582821Z cpp/legacy_vmap_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.legacy_vmap_test_1.1_c0bec52b1bde99aa_.log 2025-08-26T22:02:44.3584141Z 2025-08-26T22:02:45.9577287Z 2025-08-26T22:02:45.9578805Z cpp/operators_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.operators_test_1.1_1134148b19bd584c_.log 2025-08-26T22:02:45.9579706Z 2025-08-26T22:02:46.7441021Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:46.7442563Z import pkg_resources 2025-08-26T22:02:46.8703154Z Running cpp/scalar_tensor_test 1/1 ... [2025-08-26 22:02:46.869863] 2025-08-26T22:02:46.8704009Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:46.8707091Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-4bc15b256c83dfe7.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:46.870361] 2025-08-26T22:02:48.0674546Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:48.0677020Z import pkg_resources 2025-08-26T22:02:48.2387133Z Running cpp/scalar_test 1/1 ... [2025-08-26 22:02:48.238243] 2025-08-26T22:02:48.2387959Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:48.2391376Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/scalar_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-1884e434722eb9d1.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:48.238728] 2025-08-26T22:02:49.2397844Z 2025-08-26T22:02:49.2399298Z cpp/scalar_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_tensor_test_1.1_ac7193913ef4bd4e_.log 2025-08-26T22:02:49.2401933Z 2025-08-26T22:02:49.6730959Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:49.6733439Z import pkg_resources 2025-08-26T22:02:49.8068317Z Running cpp/tensor_iterator_test 1/1 ... [2025-08-26 22:02:49.806350] 2025-08-26T22:02:49.8069471Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:49.8073102Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/tensor_iterator_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-07035c7baa19b728.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:49.806902] 2025-08-26T22:02:50.7084872Z 2025-08-26T22:02:50.7087003Z cpp/scalar_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.scalar_test_1.1_9c43734a33db8422_.log 2025-08-26T22:02:50.7088762Z 2025-08-26T22:02:53.1400528Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:53.1404071Z import pkg_resources 2025-08-26T22:02:53.2676965Z Running cpp/undefined_tensor_test 1/1 ... [2025-08-26 22:02:53.267204] 2025-08-26T22:02:53.2677776Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:53.2682326Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/undefined_tensor_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-9e68af01dd196be9.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:53.267736] 2025-08-26T22:02:54.6182970Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:54.6185864Z import pkg_resources 2025-08-26T22:02:54.8148539Z Running cpp/wrapdim_test 1/1 ... [2025-08-26 22:02:54.814399] 2025-08-26T22:02:54.8149732Z SCRIBE_GRAPHQL_ACCESS_TOKEN is set 2025-08-26T22:02:54.8159521Z Executing ['pytest', '/var/lib/jenkins/workspace/build/bin/wrapdim_test', '-m', 'not serial', '-v', '-vv', '-rfEX', '-n', '3', '--junit-xml-reruns', 'test-reports/python-pytest/test.run_test/test.run_test-6f219e54922beaf9.xml', '-x', '--reruns=2'] ... [2025-08-26 22:02:54.815514] 2025-08-26T22:02:55.7381620Z 2025-08-26T22:02:55.7383482Z cpp/undefined_tensor_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.undefined_tensor_test_1.1_5626168c0f045427_.log 2025-08-26T22:02:55.7384921Z 2025-08-26T22:02:57.1855836Z 2025-08-26T22:02:57.1857159Z cpp/wrapdim_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.wrapdim_test_1.1_5fc0e148473c14ab_.log 2025-08-26T22:02:57.1858318Z 2025-08-26T22:02:59.7049354Z 2025-08-26T22:02:59.7050829Z cpp/tensor_iterator_test 1/1 was successful, full logs can be found in artifacts with path test/test-reports/cpp.tensor_iterator_test_1.1_478ed4fcfcaadec9_.log 2025-08-26T22:02:59.7052198Z 2025-08-26T22:02:59.8920058Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:02:59.8921634Z import pkg_resources 2025-08-26T22:03:01.0649400Z /opt/conda/envs/py_3.13/lib/python3.13/site-packages/hypothesis/entry_points.py:23: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. 2025-08-26T22:03:01.0652275Z import pkg_resources 2025-08-26T22:03:02.0120059Z Running test batch 'tests to run' cost 80.16 seconds 2025-08-26T22:03:02.9029979Z + run_if_exists tensor_interop_test 2025-08-26T22:03:02.9030641Z + local test_name=tensor_interop_test 2025-08-26T22:03:02.9032275Z + [[ -x build/bin/tensor_interop_test ]] 2025-08-26T22:03:02.9033059Z + echo 'Warning: tensor_interop_test does not exist.' 2025-08-26T22:03:02.9033800Z Warning: tensor_interop_test does not exist. 2025-08-26T22:03:02.9034441Z + run_if_exists cudnn_test 2025-08-26T22:03:02.9034950Z + local test_name=cudnn_test 2025-08-26T22:03:02.9035499Z + [[ -x build/bin/cudnn_test ]] 2025-08-26T22:03:02.9036048Z + echo 'Warning: cudnn_test does not exist.' 2025-08-26T22:03:02.9037066Z Warning: cudnn_test does not exist. 2025-08-26T22:03:02.9037655Z + run_if_exists cuda_generator_test 2025-08-26T22:03:02.9038281Z + local test_name=cuda_generator_test 2025-08-26T22:03:02.9038909Z + [[ -x build/bin/cuda_generator_test ]] 2025-08-26T22:03:02.9039608Z + echo 'Warning: cuda_generator_test does not exist.' 2025-08-26T22:03:02.9040055Z Warning: cuda_generator_test does not exist. 2025-08-26T22:03:02.9040404Z + run_if_exists apply_test 2025-08-26T22:03:02.9040678Z + local test_name=apply_test 2025-08-26T22:03:02.9040976Z + [[ -x build/bin/apply_test ]] 2025-08-26T22:03:02.9041297Z + echo 'Warning: apply_test does not exist.' 2025-08-26T22:03:02.9041648Z Warning: apply_test does not exist. 2025-08-26T22:03:02.9041948Z + run_if_exists stream_test 2025-08-26T22:03:02.9042237Z + local test_name=stream_test 2025-08-26T22:03:02.9042531Z + [[ -x build/bin/stream_test ]] 2025-08-26T22:03:02.9042963Z + echo 'Warning: stream_test does not exist.' 2025-08-26T22:03:02.9043358Z Warning: stream_test does not exist. 2025-08-26T22:03:02.9043670Z + run_if_exists cuda_half_test 2025-08-26T22:03:02.9043969Z + local test_name=cuda_half_test 2025-08-26T22:03:02.9044277Z + [[ -x build/bin/cuda_half_test ]] 2025-08-26T22:03:02.9044618Z + echo 'Warning: cuda_half_test does not exist.' 2025-08-26T22:03:02.9044970Z Warning: cuda_half_test does not exist. 2025-08-26T22:03:02.9045305Z + run_if_exists cuda_vectorized_test 2025-08-26T22:03:02.9045635Z + local test_name=cuda_vectorized_test 2025-08-26T22:03:02.9045975Z + [[ -x build/bin/cuda_vectorized_test ]] 2025-08-26T22:03:02.9046344Z + echo 'Warning: cuda_vectorized_test does not exist.' 2025-08-26T22:03:02.9046828Z Warning: cuda_vectorized_test does not exist. 2025-08-26T22:03:02.9047188Z + run_if_exists cuda_distributions_test 2025-08-26T22:03:02.9047534Z + local test_name=cuda_distributions_test 2025-08-26T22:03:02.9047881Z + [[ -x build/bin/cuda_distributions_test ]] 2025-08-26T22:03:02.9048284Z + echo 'Warning: cuda_distributions_test does not exist.' 2025-08-26T22:03:02.9048737Z Warning: cuda_distributions_test does not exist. 2025-08-26T22:03:02.9049105Z + run_if_exists cuda_optional_test 2025-08-26T22:03:02.9049411Z + local test_name=cuda_optional_test 2025-08-26T22:03:02.9049740Z + [[ -x build/bin/cuda_optional_test ]] 2025-08-26T22:03:02.9050109Z + echo 'Warning: cuda_optional_test does not exist.' 2025-08-26T22:03:02.9050501Z Warning: cuda_optional_test does not exist. 2025-08-26T22:03:02.9050847Z + run_if_exists cuda_tensor_interop_test 2025-08-26T22:03:02.9051203Z + local test_name=cuda_tensor_interop_test 2025-08-26T22:03:02.9051567Z + [[ -x build/bin/cuda_tensor_interop_test ]] 2025-08-26T22:03:02.9051978Z + echo 'Warning: cuda_tensor_interop_test does not exist.' 2025-08-26T22:03:02.9052408Z Warning: cuda_tensor_interop_test does not exist. 2025-08-26T22:03:02.9052763Z + run_if_exists cuda_complex_test 2025-08-26T22:03:02.9053084Z + local test_name=cuda_complex_test 2025-08-26T22:03:02.9053412Z + [[ -x build/bin/cuda_complex_test ]] 2025-08-26T22:03:02.9053883Z + echo 'Warning: cuda_complex_test does not exist.' 2025-08-26T22:03:02.9054256Z Warning: cuda_complex_test does not exist. 2025-08-26T22:03:02.9054610Z + run_if_exists cuda_complex_math_test 2025-08-26T22:03:02.9054946Z + local test_name=cuda_complex_math_test 2025-08-26T22:03:02.9055290Z + [[ -x build/bin/cuda_complex_math_test ]] 2025-08-26T22:03:02.9055670Z + echo 'Warning: cuda_complex_math_test does not exist.' 2025-08-26T22:03:02.9056087Z Warning: cuda_complex_math_test does not exist. 2025-08-26T22:03:02.9056495Z + run_if_exists cuda_cub_test 2025-08-26T22:03:02.9056795Z + local test_name=cuda_cub_test 2025-08-26T22:03:02.9057090Z + [[ -x build/bin/cuda_cub_test ]] 2025-08-26T22:03:02.9057430Z + echo 'Warning: cuda_cub_test does not exist.' 2025-08-26T22:03:02.9057796Z Warning: cuda_cub_test does not exist. 2025-08-26T22:03:02.9058134Z + run_if_exists cuda_atomic_ops_test 2025-08-26T22:03:02.9058455Z + local test_name=cuda_atomic_ops_test 2025-08-26T22:03:02.9058793Z + [[ -x build/bin/cuda_atomic_ops_test ]] 2025-08-26T22:03:02.9059212Z + echo 'Warning: cuda_atomic_ops_test does not exist.' 2025-08-26T22:03:02.9059612Z Warning: cuda_atomic_ops_test does not exist. 2025-08-26T22:03:02.9059931Z + '[' ON == ON ']' 2025-08-26T22:03:02.9060731Z + valgrind --suppressions=/var/lib/jenkins/workspace/aten/tools/valgrind.sup --error-exitcode=1 build/bin/basic '--gtest_filter=-*CUDA' 2025-08-26T22:03:02.9345610Z ==32959== Memcheck, a memory error detector 2025-08-26T22:03:02.9346349Z ==32959== Copyright (C) 2002-2022, and GNU GPL'd, by Julian Seward et al. 2025-08-26T22:03:02.9346931Z ==32959== Using Valgrind-3.20.0 and LibVEX; rerun with -h for copyright info 2025-08-26T22:03:02.9347438Z ==32959== Command: build/bin/basic --gtest_filter=-*CUDA 2025-08-26T22:03:02.9347799Z ==32959== 2025-08-26T22:03:03.5008476Z ==32959== Warning: set address range perms: large range [0x4a4c000, 0x1a629000) (defined) 2025-08-26T22:03:03.5009191Z ==32959== Warning: set address range perms: large range [0x5c2f000, 0x173f8000) (defined) 2025-08-26T22:03:27.2524717Z Running main() from /var/lib/jenkins/workspace/third_party/googletest/googletest/src/gtest_main.cc 2025-08-26T22:03:27.2810376Z Note: Google Test filter = -*CUDA 2025-08-26T22:03:27.2860084Z [==========] Running 6 tests from 1 test suite. 2025-08-26T22:03:27.2887054Z [----------] Global test environment set-up. 2025-08-26T22:03:27.2957962Z [----------] 6 tests from BasicTest 2025-08-26T22:03:27.2981646Z [ RUN ] BasicTest.BasicTestCPU 2025-08-26T22:03:27.8516784Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-08-26T22:03:27.8517317Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-08-26T22:03:27.8518025Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-08-26T22:03:27.9028518Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-08-26T22:03:27.9029399Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-08-26T22:03:27.9029853Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-08-26T22:03:27.9747766Z hwloc x86 backend cannot work under Valgrind, disabling. 2025-08-26T22:03:27.9748447Z May be reenabled by dumping CPUIDs with hwloc-gather-cpuid 2025-08-26T22:03:27.9748910Z and reloading them under Valgrind with HWLOC_CPUID_PATH. 2025-08-26T22:03:28.7534147Z 345 ms 2025-08-26T22:03:28.8406122Z 55 ms 2025-08-26T22:03:28.9164506Z 67 ms 2025-08-26T22:03:29.5203695Z [ OK ] BasicTest.BasicTestCPU (2220 ms) 2025-08-26T22:03:29.5293127Z [ RUN ] BasicTest.BasicTestHalfCPU 2025-08-26T22:03:29.6898882Z 112 ms 2025-08-26T22:03:29.7417430Z 46 ms 2025-08-26T22:03:29.8082082Z 64 ms 2025-08-26T22:03:29.8624943Z [ OK ] BasicTest.BasicTestHalfCPU (330 ms) 2025-08-26T22:03:29.8625596Z [ RUN ] BasicTest.FactoryMethodsTest 2025-08-26T22:03:29.8953453Z [ OK ] BasicTest.FactoryMethodsTest (32 ms) 2025-08-26T22:03:29.8954129Z [ RUN ] BasicTest.BasicStdTestCPU 2025-08-26T22:03:30.0499530Z Simple example: called once 2025-08-26T22:03:30.0595045Z throw: call_once will retry 2025-08-26T22:03:30.0609819Z throw: call_once will retry 2025-08-26T22:03:30.0991308Z Didn't throw, call_once will not attempt again 2025-08-26T22:03:30.1011974Z [ OK ] BasicTest.BasicStdTestCPU (205 ms) 2025-08-26T22:03:30.1012697Z [ RUN ] BasicTest.TestForBlobResizeCPU 2025-08-26T22:03:30.1186328Z [ OK ] BasicTest.TestForBlobResizeCPU (17 ms) 2025-08-26T22:03:30.1187036Z [ RUN ] BasicTest.TestForBlobStridesResizeCPU 2025-08-26T22:03:30.1219456Z [ OK ] BasicTest.TestForBlobStridesResizeCPU (3 ms) 2025-08-26T22:03:30.1241053Z [----------] 6 tests from BasicTest (2824 ms total) 2025-08-26T22:03:30.1241378Z 2025-08-26T22:03:30.1252455Z [----------] Global test environment tear-down 2025-08-26T22:03:30.1280716Z [==========] 6 tests from 1 test suite ran. (2850 ms total) 2025-08-26T22:03:30.1293141Z [ PASSED ] 6 tests. 2025-08-26T22:03:32.0580416Z ==32959== 2025-08-26T22:03:32.0583940Z ==32959== HEAP SUMMARY: 2025-08-26T22:03:32.0584640Z ==32959== in use at exit: 431,636 bytes in 6,391 blocks 2025-08-26T22:03:32.0585412Z ==32959== total heap usage: 649,953 allocs, 643,562 frees, 198,944,883 bytes allocated 2025-08-26T22:03:32.0585854Z ==32959== 2025-08-26T22:03:32.0951217Z ==32959== LEAK SUMMARY: 2025-08-26T22:03:32.0951781Z ==32959== definitely lost: 0 bytes in 0 blocks 2025-08-26T22:03:32.0952171Z ==32959== indirectly lost: 0 bytes in 0 blocks 2025-08-26T22:03:32.0952548Z ==32959== possibly lost: 69,920 bytes in 2 blocks 2025-08-26T22:03:32.0953028Z ==32959== still reachable: 361,716 bytes in 6,389 blocks 2025-08-26T22:03:32.0953429Z ==32959== suppressed: 0 bytes in 0 blocks 2025-08-26T22:03:32.0953892Z ==32959== Rerun with --leak-check=full to see details of leaked memory 2025-08-26T22:03:32.0954308Z ==32959== 2025-08-26T22:03:32.0954623Z ==32959== For lists of detected and suppressed errors, rerun with: -s 2025-08-26T22:03:32.0955364Z ==32959== ERROR SUMMARY: 0 errors from 0 contexts (suppressed: 0 from 0) 2025-08-26T22:03:32.1413458Z + [[ -x build/bin/tensor_interop_test ]] 2025-08-26T22:03:32.1415660Z + [[ -n '' ]] 2025-08-26T22:03:32.1416025Z + assert_git_not_dirty 2025-08-26T22:03:32.1416509Z + [[ linux-jammy-py3.13-clang12 != *rocm* ]] 2025-08-26T22:03:32.1417106Z + [[ linux-jammy-py3.13-clang12 != *xla* ]] 2025-08-26T22:03:32.1423727Z ++ git status --porcelain 2025-08-26T22:03:32.1424201Z ++ grep -v '?? third_party' 2025-08-26T22:03:32.4118403Z ++ true 2025-08-26T22:03:32.4119143Z + git_status= 2025-08-26T22:03:32.4119446Z + [[ -n '' ]] 2025-08-26T22:03:32.4166140Z + sccache_epilogue 2025-08-26T22:03:32.4166493Z + echo '::group::Sccache Compilation Log' 2025-08-26T22:03:32.4167486Z ##[group]Sccache Compilation Log 2025-08-26T22:03:32.4167860Z + echo '=================== sccache compilation log ===================' 2025-08-26T22:03:32.4168290Z =================== sccache compilation log =================== 2025-08-26T22:03:32.4173489Z + python /var/lib/jenkins/workspace/.ci/pytorch/print_sccache_log.py /var/lib/jenkins/sccache_error.log 2025-08-26T22:03:32.4320777Z + echo '=========== If your build fails, please take a look at the log above for possible reasons ===========' 2025-08-26T22:03:32.4321504Z =========== If your build fails, please take a look at the log above for possible reasons =========== 2025-08-26T22:03:32.4322027Z + sccache --show-stats 2025-08-26T22:03:32.4350218Z Compile requests 1746 2025-08-26T22:03:32.4350633Z Compile requests executed 117 2025-08-26T22:03:32.4350959Z Cache hits 79 2025-08-26T22:03:32.4351303Z Cache hits (C/C++) 79 2025-08-26T22:03:32.4351629Z Cache misses 37 2025-08-26T22:03:32.4351967Z Cache misses (C/C++) 37 2025-08-26T22:03:32.4352284Z Cache hits rate 68.10 % 2025-08-26T22:03:32.4352623Z Cache hits rate (C/C++) 68.10 % 2025-08-26T22:03:32.4352965Z Cache timeouts 0 2025-08-26T22:03:32.4353406Z Cache read errors 0 2025-08-26T22:03:32.4354033Z Forced recaches 0 2025-08-26T22:03:32.4354441Z Cache write errors 0 2025-08-26T22:03:32.4354784Z Cache errors 0 2025-08-26T22:03:32.4355111Z Compilations 37 2025-08-26T22:03:32.4355430Z Compilation failures 1 2025-08-26T22:03:32.4355776Z Non-cacheable compilations 0 2025-08-26T22:03:32.4356117Z Non-cacheable calls 2 2025-08-26T22:03:32.4356669Z Non-compilation calls 1627 2025-08-26T22:03:32.4357017Z Unsupported compiler calls 0 2025-08-26T22:03:32.4357353Z Average cache write 0.056 s 2025-08-26T22:03:32.4357697Z Average compiler 12.402 s 2025-08-26T22:03:32.4358036Z Average cache read hit 0.041 s 2025-08-26T22:03:32.4358396Z Failed distributed compilations 0 2025-08-26T22:03:32.4358631Z 2025-08-26T22:03:32.4358730Z Non-cacheable reasons: 2025-08-26T22:03:32.4359069Z -E 2 2025-08-26T22:03:32.4359284Z 2025-08-26T22:03:32.4359536Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-08-26T22:03:32.4360035Z Version (client) 0.10.0 2025-08-26T22:03:32.4360336Z + sccache --stop-server 2025-08-26T22:03:32.4372896Z Stopping sccache server... 2025-08-26T22:03:32.4375751Z Compile requests 1746 2025-08-26T22:03:32.4376117Z Compile requests executed 117 2025-08-26T22:03:32.4376551Z Cache hits 79 2025-08-26T22:03:32.4377010Z Cache hits (C/C++) 79 2025-08-26T22:03:32.4377539Z Cache misses 37 2025-08-26T22:03:32.4378126Z Cache misses (C/C++) 37 2025-08-26T22:03:32.4378548Z Cache hits rate 68.10 % 2025-08-26T22:03:32.4379237Z Cache hits rate (C/C++) 68.10 % 2025-08-26T22:03:32.4379833Z Cache timeouts 0 2025-08-26T22:03:32.4380528Z Cache read errors 0 2025-08-26T22:03:32.4380901Z Forced recaches 0 2025-08-26T22:03:32.4381273Z Cache write errors 0 2025-08-26T22:03:32.4381595Z Cache errors 0 2025-08-26T22:03:32.4381915Z Compilations 37 2025-08-26T22:03:32.4382244Z Compilation failures 1 2025-08-26T22:03:32.4382569Z Non-cacheable compilations 0 2025-08-26T22:03:32.4382906Z Non-cacheable calls 2 2025-08-26T22:03:32.4383245Z Non-compilation calls 1627 2025-08-26T22:03:32.4383673Z Unsupported compiler calls 0 2025-08-26T22:03:32.4384004Z Average cache write 0.056 s 2025-08-26T22:03:32.4384347Z Average compiler 12.402 s 2025-08-26T22:03:32.4384691Z Average cache read hit 0.041 s 2025-08-26T22:03:32.4385044Z Failed distributed compilations 0 2025-08-26T22:03:32.4385272Z 2025-08-26T22:03:32.4385370Z Non-cacheable reasons: 2025-08-26T22:03:32.4385642Z -E 2 2025-08-26T22:03:32.4385866Z 2025-08-26T22:03:32.4386104Z Cache location s3, name: ossci-compiler-cache-circleci-v2, prefix: / 2025-08-26T22:03:32.4386573Z Version (client) 0.10.0 2025-08-26T22:03:32.4386912Z + echo ::endgroup:: 2025-08-26T22:03:32.4387427Z ##[endgroup] 2025-08-26T22:03:32.4387643Z + cleanup_workspace 2025-08-26T22:03:32.4388156Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2025-08-26T22:03:32.4388963Z sudo may print the following warning message that can be ignored. The chown command will still run. 2025-08-26T22:03:32.4389654Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2025-08-26T22:03:32.4390129Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2025-08-26T22:03:32.4390671Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2025-08-26T22:03:32.4391286Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2025-08-26T22:03:32.4391969Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2025-08-26T22:03:35.3526959Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main 2025-08-26T22:03:35.3527468Z with: 2025-08-26T22:03:35.3527717Z benchmark-results-dir: test/test-reports 2025-08-26T22:03:35.3528050Z dry-run: false 2025-08-26T22:03:35.3528294Z schema-version: v3 2025-08-26T22:03:35.3528796Z github-token: *** 2025-08-26T22:03:35.3529046Z env: 2025-08-26T22:03:35.3529255Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:35.3529785Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:35.3530352Z ##[endgroup] 2025-08-26T22:03:35.3557126Z ##[group]Run set -eux 2025-08-26T22:03:35.3557425Z set -eux 2025-08-26T22:03:35.3557803Z python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-08-26T22:03:35.3558246Z  2025-08-26T22:03:35.3558565Z DEVICE_NAME="" 2025-08-26T22:03:35.3558829Z DEVICE_TYPE="" 2025-08-26T22:03:35.3559092Z  2025-08-26T22:03:35.3559398Z if command -v nvidia-smi; then 2025-08-26T22:03:35.3559861Z  # NB: I'm using PyTorch here to get the device name, however, it needs to 2025-08-26T22:03:35.3560465Z  # install the correct version of PyTorch manually for now. Any PyTorch 2025-08-26T22:03:35.3561030Z  # version is fine, I just use 2.7.1 to satify PYPIDEP linter 2025-08-26T22:03:35.3561476Z  python3 -mpip install torch==2.7.1 2025-08-26T22:03:35.3561841Z elif command -v rocminfo; then 2025-08-26T22:03:35.3562289Z  # NB: Installing torch on ROCm runner with pip here causes CI to fail 2025-08-26T22:03:35.3562868Z  # with a memoryview is too large error only on MI300 runners. Is pip 2025-08-26T22:03:35.3563446Z  # version on ROCm runner there too old? As a workaround, let's use the 2025-08-26T22:03:35.3563963Z  # GPU device name coming from rocminfo instead 2025-08-26T22:03:35.3564341Z  DEVICE_NAME=rocm 2025-08-26T22:03:35.3564829Z  DEVICE_TYPE=$(rocminfo | grep "Marketing Name" | tail -n1 | awk -F':' '{print $2}' | xargs) 2025-08-26T22:03:35.3565344Z fi 2025-08-26T22:03:35.3565562Z  2025-08-26T22:03:35.3565848Z echo "DEVICE_NAME=$DEVICE_NAME" >> $GITHUB_ENV 2025-08-26T22:03:35.3566264Z echo "DEVICE_TYPE=$DEVICE_TYPE" >> $GITHUB_ENV 2025-08-26T22:03:35.3780668Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:35.3781195Z env: 2025-08-26T22:03:35.3781430Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:35.3781915Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:35.3782425Z ##[endgroup] 2025-08-26T22:03:35.3815597Z + python3 -mpip install boto3==1.35.33 psutil==7.0.0 pynvml==12.0.0 2025-08-26T22:03:35.9035559Z Defaulting to user installation because normal site-packages is not writeable 2025-08-26T22:03:36.9805314Z Collecting boto3==1.35.33 2025-08-26T22:03:36.9984698Z Downloading boto3-1.35.33-py3-none-any.whl (139 kB) 2025-08-26T22:03:37.3200146Z Collecting psutil==7.0.0 2025-08-26T22:03:37.3242732Z 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) 2025-08-26T22:03:37.3544931Z Collecting pynvml==12.0.0 2025-08-26T22:03:37.3596279Z Downloading pynvml-12.0.0-py3-none-any.whl (26 kB) 2025-08-26T22:03:37.4078261Z Collecting s3transfer<0.11.0,>=0.10.0 2025-08-26T22:03:37.4118380Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-08-26T22:03:37.4178738Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.33) (0.10.0) 2025-08-26T22:03:38.5557178Z Collecting botocore<1.36.0,>=1.35.33 2025-08-26T22:03:38.5593275Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-08-26T22:03:38.7482957Z Collecting nvidia-ml-py<13.0.0a0,>=12.0.0 2025-08-26T22:03:38.7581568Z Downloading nvidia_ml_py-12.575.51-py3-none-any.whl (47 kB) 2025-08-26T22:03:38.7718507Z Requirement already satisfied: python-dateutil<3.0.0,>=2.1 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (2.8.1) 2025-08-26T22:03:38.7728679Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.25.10) 2025-08-26T22:03:38.9213104Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil<3.0.0,>=2.1->botocore<1.36.0,>=1.35.33->boto3==1.35.33) (1.15.0) 2025-08-26T22:03:39.0752708Z Installing collected packages: botocore, s3transfer, nvidia-ml-py, pynvml, psutil, boto3 2025-08-26T22:03:39.5886864Z Attempting uninstall: nvidia-ml-py 2025-08-26T22:03:39.5888355Z Found existing installation: nvidia-ml-py 11.525.84 2025-08-26T22:03:39.5908877Z Uninstalling nvidia-ml-py-11.525.84: 2025-08-26T22:03:39.6248114Z Successfully uninstalled nvidia-ml-py-11.525.84 2025-08-26T22:03:39.6920563Z Attempting uninstall: psutil 2025-08-26T22:03:39.6922083Z Found existing installation: psutil 5.9.8 2025-08-26T22:03:39.7000319Z Uninstalling psutil-5.9.8: 2025-08-26T22:03:39.7007377Z Successfully uninstalled psutil-5.9.8 2025-08-26T22:03:39.8754995Z Successfully installed boto3-1.35.33 botocore-1.35.99 nvidia-ml-py-12.575.51 psutil-7.0.0 pynvml-12.0.0 s3transfer-0.10.4 2025-08-26T22:03:40.0264774Z + DEVICE_NAME= 2025-08-26T22:03:40.0265134Z + DEVICE_TYPE= 2025-08-26T22:03:40.0265400Z + command -v nvidia-smi 2025-08-26T22:03:40.0265682Z + command -v rocminfo 2025-08-26T22:03:40.0265984Z + echo DEVICE_NAME= 2025-08-26T22:03:40.0266370Z + echo DEVICE_TYPE= 2025-08-26T22:03:40.0297571Z ##[group]Run set -eux 2025-08-26T22:03:40.0297844Z set -eux 2025-08-26T22:03:40.0298082Z  2025-08-26T22:03:40.0312206Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-08-26T22:03:40.0312674Z  echo "Missing github-token input" 2025-08-26T22:03:40.0313015Z  exit 1 2025-08-26T22:03:40.0313268Z fi 2025-08-26T22:03:40.0318883Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:40.0319276Z env: 2025-08-26T22:03:40.0319513Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:40.0319992Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:40.0320498Z DEVICE_NAME: 2025-08-26T22:03:40.0320734Z DEVICE_TYPE: 2025-08-26T22:03:40.0321228Z GITHUB_TOKEN: *** 2025-08-26T22:03:40.0321469Z ##[endgroup] 2025-08-26T22:03:40.0346666Z + [[ -z *** ]] 2025-08-26T22:03:40.0404818Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-08-26T22:03:40.0405278Z with: 2025-08-26T22:03:40.0405681Z github-token: *** 2025-08-26T22:03:40.0405922Z env: 2025-08-26T22:03:40.0406132Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:40.0406642Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:40.0407169Z DEVICE_NAME: 2025-08-26T22:03:40.0407404Z DEVICE_TYPE: 2025-08-26T22:03:40.0407632Z ##[endgroup] 2025-08-26T22:03:40.0432372Z ##[group]Run set -eux 2025-08-26T22:03:40.0432658Z set -eux 2025-08-26T22:03:40.0432895Z  2025-08-26T22:03:40.0433398Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-08-26T22:03:40.0439332Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:40.0439725Z env: 2025-08-26T22:03:40.0439956Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:40.0440471Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:40.0440981Z DEVICE_NAME: 2025-08-26T22:03:40.0441228Z DEVICE_TYPE: 2025-08-26T22:03:40.0441645Z GITHUB_TOKEN: *** 2025-08-26T22:03:40.0441944Z ##[endgroup] 2025-08-26T22:03:40.0467197Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 17248463620 i-0d10cabc7fe6d3867 2025-08-26T22:03:42.3317031Z setting job-id=48944862621 2025-08-26T22:03:42.3317634Z setting job-name=linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T22:03:42.3440054Z ##[group]Run set -eux 2025-08-26T22:03:42.3440344Z set -eux 2025-08-26T22:03:42.3440563Z  2025-08-26T22:03:42.3440962Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-08-26T22:03:42.3441502Z  --schema-version "${SCHEMA_VERSION}" \ 2025-08-26T22:03:42.3441875Z  --repo "${REPO}" \ 2025-08-26T22:03:42.3442193Z  --head-branch "${HEAD_BRANCH}" \ 2025-08-26T22:03:42.3442529Z  --head-sha "${HEAD_SHA}" \ 2025-08-26T22:03:42.3442878Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-08-26T22:03:42.3443246Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-08-26T22:03:42.3443699Z  --job-id "${JOB_ID}" \ 2025-08-26T22:03:42.3444005Z  --job-name "${JOB_NAME}" 2025-08-26T22:03:42.3449628Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:42.3450025Z env: 2025-08-26T22:03:42.3450249Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:42.3450727Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:42.3451236Z DEVICE_NAME: 2025-08-26T22:03:42.3451469Z DEVICE_TYPE: 2025-08-26T22:03:42.3451706Z SCHEMA_VERSION: v3 2025-08-26T22:03:42.3451966Z REPO: pytorch/pytorch 2025-08-26T22:03:42.3452234Z HEAD_BRANCH: refs/heads/main 2025-08-26T22:03:42.3452564Z HEAD_SHA: 262640fd220236042fbf4443cc163c8838c84c3d 2025-08-26T22:03:42.3452914Z WORKFLOW_RUN_ID: 17248463620 2025-08-26T22:03:42.3453191Z RUN_ATTEMPT: 1 2025-08-26T22:03:42.3453413Z JOB_ID: 48944862621 2025-08-26T22:03:42.3453839Z JOB_NAME: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T22:03:42.3454337Z ##[endgroup] 2025-08-26T22:03:42.3483235Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/heads/main --head-sha 262640fd220236042fbf4443cc163c8838c84c3d --workflow-id 17248463620 --run-attempt 1 --job-id 48944862621 --job-name 'linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)' 2025-08-26T22:03:42.3821523Z ##[group]Run set -eux 2025-08-26T22:03:42.3821843Z set -eux 2025-08-26T22:03:42.3822180Z  2025-08-26T22:03:42.3822577Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_runners_info.py" 2025-08-26T22:03:42.3828349Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:42.3828739Z env: 2025-08-26T22:03:42.3828966Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:42.3829442Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:42.3829957Z DEVICE_NAME: 2025-08-26T22:03:42.3830189Z DEVICE_TYPE: 2025-08-26T22:03:42.3830428Z ##[endgroup] 2025-08-26T22:03:42.3855065Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_runners_info.py 2025-08-26T22:03:42.4271602Z INFO:root:Fail to import torch to get the device name 2025-08-26T22:03:42.4389286Z ##[group]Run set -eux 2025-08-26T22:03:42.4389565Z set -eux 2025-08-26T22:03:42.4389808Z  2025-08-26T22:03:42.4390070Z # TODO (huydhn): Implement this part 2025-08-26T22:03:42.4390487Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-08-26T22:03:42.4396513Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:42.4396914Z env: 2025-08-26T22:03:42.4397155Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:42.4397638Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:42.4398171Z DEVICE_NAME: 2025-08-26T22:03:42.4398414Z DEVICE_TYPE: 2025-08-26T22:03:42.4398804Z ##[endgroup] 2025-08-26T22:03:42.4421313Z + echo 'dependencies={}' 2025-08-26T22:03:42.4450365Z ##[group]Run set -eux 2025-08-26T22:03:42.4450664Z set -eux 2025-08-26T22:03:42.4450904Z  2025-08-26T22:03:42.4451177Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-08-26T22:03:42.4451628Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-08-26T22:03:42.4452120Z  # We don't want the job to fail if the directory doesn't exist 2025-08-26T22:03:42.4452539Z  exit 0 2025-08-26T22:03:42.4452771Z fi 2025-08-26T22:03:42.4452988Z  2025-08-26T22:03:42.4453222Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-08-26T22:03:42.4453774Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-08-26T22:03:42.4454352Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-08-26T22:03:42.4454890Z  --metadata "${BENCHMARK_METADATA}" \ 2025-08-26T22:03:42.4455278Z  --runners "${RUNNER_INFO}" \ 2025-08-26T22:03:42.4455632Z  --dependencies "${DEPENDENCIES}" \ 2025-08-26T22:03:42.4455977Z  --dry-run 2025-08-26T22:03:42.4456238Z else 2025-08-26T22:03:42.4456639Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-08-26T22:03:42.4457201Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-08-26T22:03:42.4457640Z  --metadata "${BENCHMARK_METADATA}" \ 2025-08-26T22:03:42.4458012Z  --runners "${RUNNER_INFO}" \ 2025-08-26T22:03:42.4458367Z  --dependencies "${DEPENDENCIES}" 2025-08-26T22:03:42.4458696Z fi 2025-08-26T22:03:42.4464281Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:42.4464673Z env: 2025-08-26T22:03:42.4464913Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:42.4465391Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:42.4465904Z DEVICE_NAME: 2025-08-26T22:03:42.4466146Z DEVICE_TYPE: 2025-08-26T22:03:42.4466411Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-08-26T22:03:42.4466749Z DRY_RUN: false 2025-08-26T22:03:42.4468091Z BENCHMARK_METADATA: {"timestamp": 1756245822, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "262640fd220236042fbf4443cc163c8838c84c3d", "workflow_id": 17248463620, "run_attempt": 1, "job_id": 48944862621} 2025-08-26T22:03:42.4469937Z RUNNER_INFO: [{"cpu_info": "x86_64", "cpu_count": 8, "avail_mem_in_gb": 15, "extra_info": {"hostname": "ip-10-1-64-236.ec2.internal"}, "name": "", "type": ""}] 2025-08-26T22:03:42.4470599Z DEPENDENCIES: {} 2025-08-26T22:03:42.4470846Z ##[endgroup] 2025-08-26T22:03:42.4493478Z + [[ ! -d test/test-reports ]] 2025-08-26T22:03:42.4493793Z + [[ false == \t\r\u\e ]] 2025-08-26T22:03:42.4496639Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py --benchmark-results-dir test/test-reports --metadata '{"timestamp": 1756245822, "schema_version": "v3", "name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)", "repo": "pytorch/pytorch", "head_branch": "refs/heads/main", "head_sha": "262640fd220236042fbf4443cc163c8838c84c3d", "workflow_id": 17248463620, "run_attempt": 1, "job_id": 48944862621}' --runners '[{"cpu_info": "x86_64", "cpu_count": 8, "avail_mem_in_gb": 15, "extra_info": {"hostname": "ip-10-1-64-236.ec2.internal"}, "name": "", "type": ""}]' --dependencies '{}' 2025-08-26T22:03:42.6712686Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'dynamo/cpython/3_13/test_sort'}, {'test_file': 'dynamo/test_utils'}, {'test_file': 'dynamo/test_dynamic_shapes'}, {'test_file': 'dynamo/test_dicts'}, {'test_file': 'dynamo/test_modes'}, {'test_file': 'dynamo/test_package'}, {'test_file': 'dynamo/test_functions'}, {'test_file': 'dynamo/test_logging'}, {'test_file': 'dynamo/test_repros'}, {'test_file': 'dynamo/test_structured_trace'}, {'test_file': 'dynamo/test_higher_order_ops'}, {'test_file': 'dynamo/test_error_messages'}, {'test_file': 'dynamo/test_exceptions'}, {'test_file': 'dynamo/test_aot_autograd_cache'}, {'test_file': 'dynamo/test_trace_rules'}, {'test_file': 'dynamo/test_misc'}, {'test_file': 'dynamo/test_recompile_ux'}, {'test_file': 'dynamo/test_subclasses'}, {'test_file': 'test_autograd'}, {'test_file': 'test_nestedtensor'}, {'test_file': 'test_ci_sanity_check_fail'}, {'test_file': 'test_type_hints'}, {'test_file': 'nn/test_parametrization'}, {'test_file': 'test_jit_fuser_te'}, {'test_file': 'test_jit'}, {'test_file': 'test_binary_ufuncs'}, {'test_file': 'higher_order_ops/test_with_effects'}, {'test_file': 'test_overrides'}, {'test_file': 'test_content_store'}, {'test_file': 'test_numpy_interop'}, {'test_file': 'functorch/test_control_flow'}, {'test_file': 'test_reductions'}, {'test_file': 'test_tensor_creation_ops'}, {'test_file': 'test_cpp_extensions_mtia_backend'}, {'test_file': 'test_nn'}, {'test_file': 'test_cpp_extensions_stream_and_event'}, {'test_file': 'test_indexing'}, {'test_file': 'test_type_promotion'}, {'test_file': 'functorch/test_eager_transforms'}, {'test_file': 'doctests'}, {'test_file': 'dynamo/cpython/3_13/test_generator_stop'}, {'test_file': 'dynamo/cpython/3_13/test_exception_variations'}, {'test_file': 'dynamo/test_fx_graph_runnable'}, {'test_file': 'dynamo/cpython/3_13/test_int_literal'}, {'test_file': 'dynamo/cpython/3_13/test_itertools'}, {'test_file': 'dynamo/cpython/3_13/test_contextlib'}, {'test_file': 'dynamo/cpython/3_13/test_numeric_tower'}, {'test_file': 'dynamo/cpython/3_13/test_ordered_dict'}, {'test_file': 'dynamo/cpython/3_13/test_with'}, {'test_file': 'dynamo/cpython/3_13/test_baseexception'}, {'test_file': 'dynamo/cpython/3_13/test_exceptions'}, {'test_file': 'dynamo/cpython/3_13/test_raise'}, {'test_file': 'dynamo/cpython/3_13/test_userlist'}, {'test_file': 'dynamo/cpython/3_13/test_generators'}, {'test_file': 'dynamo/cpython/3_13/test_int'}, {'test_file': 'dynamo/cpython/3_13/test_userdict'}, {'test_file': 'dynamo/cpython/3_13/test_defaultdict'}, {'test_file': 'dynamo/cpython/3_13/test_collections'}, {'test_file': 'dynamo/cpython/3_13/test_heapq'}, {'test_file': 'dynamo/cpython/3_13/test_cmath'}, {'test_file': 'dynamo/cpython/3_13/test_operator'}, {'test_file': 'dynamo/cpython/3_13/test_set'}, {'test_file': 'dynamo/cpython/3_13/test_math'}, {'test_file': 'dynamo/cpython/3_13/test_iter'}, {'test_file': 'dynamo/cpython/3_13/test_sys'}, {'test_file': 'dynamo/cpython/3_13/test_bool'}, {'test_file': 'dynamo/cpython/3_13/test_list'}, {'test_file': 'dynamo/cpython/3_13/test_float'}, {'test_file': 'dynamo/cpython/3_13/test_tuple'}, {'test_file': 'dynamo/cpython/3_13/test_dict'}, {'test_file': 'dynamo/cpython/3_13/test_complex'}, 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'test_weak'}, {'test_file': 'test_compile_benchmark_util'}, {'test_file': 'test_cuda_multigpu'}, {'test_file': 'test_per_overload_api'}, {'test_file': 'test_legacy_vmap'}, {'test_file': 'test_hub'}, {'test_file': 'lazy/test_functionalization'}, {'test_file': 'torch_np/numpy_tests/core/test_einsum'}, {'test_file': 'test_set_default_mobile_cpu_allocator'}, {'test_file': 'test_meta'}, {'test_file': 'test_stateless'}, {'test_file': 'functorch/test_ac'}, {'test_file': 'test_namedtuple_return_api'}, {'test_file': 'distributions/test_constraints'}, {'test_file': 'cpp_extensions/python_agnostic_extension/test/test_python_agnostic'}, {'test_file': 'torch_np/test_ufuncs_basic'}, {'test_file': 'test_out_dtype_op'}, {'test_file': 'test_ops_gradients'}, {'test_file': 'test_ops_fwd_gradients'}, {'test_file': 'lazy/test_step_closures'}, {'test_file': 'test_multiprocessing'}, {'test_file': 'test_sparse_semi_structured'}, {'test_file': 'nn/test_packed_sequence'}, {'test_file': 'test_autograd_fallback'}, {'test_file': 'profiler/test_record_function'}, {'test_file': 'nn/test_lazy_modules'}, {'test_file': 'test_fx_reinplace_pass'}, {'test_file': 'profiler/test_cpp_thread'}, {'test_file': 'test_cuda_expandable_segments'}, {'test_file': 'test_numba_integration'}, {'test_file': 'test_segment_reductions'}, {'test_file': 'test_fake_tensor'}, {'test_file': 'test_logging'}, {'test_file': 'test_functionalization_of_rng_ops'}, {'test_file': 'functorch/test_aot_joint_with_descriptors'}, {'test_file': 'test_python_dispatch'}, {'test_file': 'test_subclass'}, {'test_file': 'test_itt'}, {'test_file': 'profiler/test_torch_tidy'}, {'test_file': 'torch_np/numpy_tests/core/test_numeric'}, {'test_file': 'test_bundled_inputs'}, {'test_file': 'test_masked'}, {'test_file': 'test_monitor'}, {'test_file': 'test_sympy_utils'}, {'test_file': 'torch_np/numpy_tests/lib/test_function_base'}, {'test_file': 'test_autocast'}, {'test_file': 'functorch/test_vmap_registrations'}, {'test_file': 'torch_np/test_indexing'}, {'test_file': 'test_cuda_sanitizer'}, {'test_file': 'optim/test_swa_utils'}, {'test_file': 'optim/test_lrscheduler'}, {'test_file': 'test_jit_disabled'}, {'test_file': 'cpp_extensions/libtorch_agnostic_extension/test/test_libtorch_agnostic'}, {'test_file': 'dynamo/test_nested_graph_breaks'}, {'test_file': 'test_mkldnn_fusion'}, {'test_file': 'test_import_stats'}, {'test_file': 'test_tensorboard'}, {'test_file': 'nn/test_embedding'}, {'test_file': 'nn/test_dropout'}, {'test_file': 'torch_np/numpy_tests/lib/test_type_check'}, {'test_file': 'test_public_bindings'}, {'test_file': 'torch_np/numpy_tests/core/test_indexing'}, {'test_file': 'test_maskedtensor'}, {'test_file': 'torch_np/numpy_tests/lib/test_histograms'}, {'test_file': 'lazy/test_ts_opinfo'}, {'test_file': 'test_schema_check'}, {'test_file': 'test_functional_optim'}, {'test_file': 'profiler/test_execution_trace'}, {'test_file': 'test_futures'}, {'test_file': 'test_dispatch'}, {'test_file': 'test_linalg'}, {'test_file': 'benchmark_utils/test_benchmark_utils'}, {'test_file': 'optim/test_optim'}, {'test_file': 'test_native_mha'}, {'test_file': 'torch_np/numpy_tests/core/test_scalarmath'}, {'test_file': 'test_vulkan'}, {'test_file': 'nn/test_load_state_dict'}, {'test_file': 'torch_np/numpy_tests/core/test_shape_base'}, {'test_file': 'test_shape_ops'}, {'test_file': 'torch_np/numpy_tests/core/test_dtype'}, {'test_file': 'nn/test_module_hooks'}, {'test_file': 'test_sort_and_select'}, {'test_file': 'test_dynamic_shapes'}, {'test_file': 'torch_np/numpy_tests/lib/test_twodim_base'}, {'test_file': 'test_jit_llga_fuser'}, {'test_file': 'test_type_info'}, {'test_file': 'torch_np/numpy_tests/linalg/test_linalg'}, {'test_file': 'test_serialization'}, {'test_file': 'test_sparse_csr'}, {'test_file': 'lazy/test_generator'}, {'test_file': 'lazy/test_debug_util'}, {'test_file': 'test_numa_binding'}, {'test_file': 'test_view_ops'}, {'test_file': 'torch_np/numpy_tests/fft/test_pocketfft'}, {'test_file': 'torch_np/test_ndarray_methods'}, {'test_file': 'torch_np/numpy_tests/lib/test_shape_base_'}, {'test_file': 'torch_np/numpy_tests/core/test_getlimits'}, {'test_file': 'test_scatter_gather_ops'}, {'test_file': 'functorch/test_vmap'}, {'test_file': 'test_mkldnn'}, {'test_file': 'torch_np/numpy_tests/core/test_dlpack'}, {'test_file': 'test_unary_ufuncs'}, {'test_file': 'test_cpp_extensions_jit'}, {'test_file': 'nn/test_pooling'}, {'test_file': 'test_xnnpack_integration'}, {'test_file': 'torch_np/numpy_tests/lib/test_index_tricks'}, {'test_file': 'nn/test_init'}, {'test_file': 'nn/test_convolution'}, {'test_file': 'test_multiprocessing_spawn'}, {'test_file': 'test_cuda_primary_ctx'}, {'test_file': 'test_accelerator'}, {'test_file': 'torch_np/numpy_tests/core/test_numerictypes'}, {'test_file': 'torch_np/numpy_tests/fft/test_helper'}, {'test_file': 'test_function_schema'}, {'test_file': 'torch_np/test_function_base'}, {'test_file': 'test_mobile_optimizer'}, {'test_file': 'test_cuda_trace'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_methods'}, {'test_file': 'test_cuda_nvml_based_avail'}, {'test_file': 'test_sparse'}, {'test_file': 'torch_np/test_scalars_0D_arrays'}, {'test_file': 'test_jit_autocast'}, {'test_file': 'torch_np/numpy_tests/lib/test_arraysetops'}, {'test_file': 'test_dlpack'}, {'test_file': 'profiler/test_profiler_tree'}, {'test_file': 'torch_np/numpy_tests/core/test_scalar_ctors'}, {'test_file': 'torch_np/test_reductions'}, {'test_file': 'lazy/test_reuse_ir'}, {'test_file': 'torch_np/numpy_tests/lib/test_arraypad'}, {'test_file': 'test_quantization'}, {'test_file': 'test_spectral_ops'}, {'test_file': 'test_functional_autograd_benchmark'}, {'test_file': 'test_prims'}, {'test_file': 'profiler/test_python_tracer'}, {'test_file': 'distributions/test_distributions'}, {'test_file': 'test_cpp_extensions_aot_no_ninja'}, {'test_file': 'test_autoload_disable'}, {'test_file': 'test_autoload_enable'}, {'test_file': 'test_cpp_extensions_aot_ninja'}], 'excluded': []} from test/test-reports/td_exclusions-a72715bf5af1d2c09259.json is not a benchmark record, skipping 2025-08-26T22:03:42.6764678Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-08-26T22:03:42.6768545Z /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/upload_benchmark_results.py:236: UserWarning: {'included': [{'test_file': 'cpp/Dict_test'}, {'test_file': 'cpp/Dimname_test'}, {'test_file': 'cpp/NamedTensor_test'}, {'test_file': 'cpp/apply_utils_test'}, {'test_file': 'cpp/atest'}, {'test_file': 'cpp/basic'}, {'test_file': 'cpp/broadcast_test'}, {'test_file': 'cpp/cpu_generator_test'}, {'test_file': 'cpp/dlconvertor_test'}, {'test_file': 'cpp/extension_backend_test'}, {'test_file': 'cpp/lazy_tensor_test'}, {'test_file': 'cpp/legacy_vmap_test'}, {'test_file': 'cpp/native_test'}, {'test_file': 'cpp/operators_test'}, {'test_file': 'cpp/scalar_tensor_test'}, {'test_file': 'cpp/scalar_test'}, {'test_file': 'cpp/tensor_iterator_test'}, {'test_file': 'cpp/undefined_tensor_test'}, {'test_file': 'cpp/wrapdim_test'}], 'excluded': []} from test/test-reports/td_exclusions-935b0043dc0843d72ed7.json is not a benchmark record, skipping 2025-08-26T22:03:42.6923195Z warn(f"{result} from {filepath} is not a benchmark record, skipping") 2025-08-26T22:03:42.6963337Z ##[group]Run cat test/**/*_toprint.log || true 2025-08-26T22:03:42.6963758Z cat test/**/*_toprint.log || true 2025-08-26T22:03:42.6969434Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:42.6969891Z env: 2025-08-26T22:03:42.6970131Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:42.6970610Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:42.6971130Z DEVICE_NAME: 2025-08-26T22:03:42.6971353Z DEVICE_TYPE: 2025-08-26T22:03:42.6971599Z ##[endgroup] 2025-08-26T22:03:42.7055048Z cat: 'test/**/*_toprint.log': No such file or directory 2025-08-26T22:03:42.7092250Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-08-26T22:03:42.7092647Z kill "$MONITOR_SCRIPT_PID" 2025-08-26T22:03:42.7098137Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:42.7098529Z env: 2025-08-26T22:03:42.7098846Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:42.7099330Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:42.7099830Z DEVICE_NAME: 2025-08-26T22:03:42.7100063Z DEVICE_TYPE: 2025-08-26T22:03:42.7100390Z MONITOR_SCRIPT_PID: 41524 2025-08-26T22:03:42.7100668Z ##[endgroup] 2025-08-26T22:03:42.7247052Z Prepare all required actions 2025-08-26T22:03:42.7247624Z Getting action download info 2025-08-26T22:03:42.9127788Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-08-26T22:03:43.1654140Z Download action repository 'actions/upload-artifact@v4' (SHA:ea165f8d65b6e75b540449e92b4886f43607fa02) 2025-08-26T22:03:43.5872938Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-08-26T22:03:43.5873316Z with: 2025-08-26T22:03:43.5873661Z file-suffix: test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621 2025-08-26T22:03:43.5874104Z s3-bucket: gha-artifacts 2025-08-26T22:03:43.5874422Z env: 2025-08-26T22:03:43.5874642Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:43.5875118Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:43.5875633Z DEVICE_NAME: 2025-08-26T22:03:43.5875850Z DEVICE_TYPE: 2025-08-26T22:03:43.5876078Z ##[endgroup] 2025-08-26T22:03:43.5904154Z ##[group]Run # Remove any previous test jsons if they exist 2025-08-26T22:03:43.5904639Z # Remove any previous test jsons if they exist 2025-08-26T22:03:43.5905012Z rm -f test-jsons-*.zip 2025-08-26T22:03:43.5905451Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-08-26T22:03:43.5911216Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:43.5911603Z env: 2025-08-26T22:03:43.5911816Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:43.5912292Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:43.5912804Z DEVICE_NAME: 2025-08-26T22:03:43.5913041Z DEVICE_TYPE: 2025-08-26T22:03:43.5913390Z FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621 2025-08-26T22:03:43.5913793Z ##[endgroup] 2025-08-26T22:03:43.6053162Z adding: test/test-reports/td_exclusions-a72715bf5af1d2c09259.json (deflated 82%) 2025-08-26T22:03:43.6054123Z adding: test/test-reports/td_exclusions-935b0043dc0843d72ed7.json (deflated 73%) 2025-08-26T22:03:43.6080431Z ##[group]Run # Remove any previous test reports if they exist 2025-08-26T22:03:43.6080936Z # Remove any previous test reports if they exist 2025-08-26T22:03:43.6081344Z rm -f test-reports-*.zip 2025-08-26T22:03:43.6081846Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2025-08-26T22:03:43.6087512Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:43.6087904Z env: 2025-08-26T22:03:43.6088135Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:43.6088619Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:43.6089152Z DEVICE_NAME: 2025-08-26T22:03:43.6089371Z DEVICE_TYPE: 2025-08-26T22:03:43.6089725Z FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621 2025-08-26T22:03:43.6090140Z ##[endgroup] 2025-08-26T22:03:43.6174133Z adding: test/test-reports/python-pytest/test_overrides/test_overrides-28b3adca641599be.xml (deflated 96%) 2025-08-26T22:03:43.6224448Z adding: test/test-reports/python-pytest/test_reductions/test_reductions-d92f7e7350dc9d47.xml (deflated 97%) 2025-08-26T22:03:43.6225563Z adding: test/test-reports/python-pytest/test_cpp_extensions_mtia_backend/test_cpp_extensions_mtia_backend-eb8935b694a53858.xml (deflated 81%) 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adding: test/test-reports/python-pytest/test_multiprocessing/test_multiprocessing-0183eda52b446f3c.xml (deflated 90%) 2025-08-26T22:03:43.6467243Z adding: test/test-reports/python-pytest/test_autograd_fallback/test_autograd_fallback-ec8b84787c02a07a.xml (deflated 86%) 2025-08-26T22:03:43.6475639Z adding: test/test-reports/python-pytest/test_fake_tensor/test_fake_tensor-0a659559f9676379.xml (deflated 93%) 2025-08-26T22:03:43.6480340Z adding: test/test-reports/python-pytest/test_python_dispatch/test_python_dispatch-d01975a34b2d3a36.xml (deflated 91%) 2025-08-26T22:03:43.6482081Z adding: test/test-reports/python-pytest/test_autocast/test_autocast-67d6ee335ad35613.xml (deflated 86%) 2025-08-26T22:03:43.6483503Z adding: test/test-reports/python-pytest/test_jit_disabled/test_jit_disabled-51f09febfbe7046c.xml (deflated 57%) 2025-08-26T22:03:43.6484425Z adding: test/test-reports/python-pytest/test_dispatch/test_dispatch-8d9234c71f092b0a.xml (deflated 76%) 2025-08-26T22:03:43.6485681Z adding: 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logs if they exist 2025-08-26T22:03:43.6621408Z # Remove any previous usage logs if they exist 2025-08-26T22:03:43.6621802Z rm -f logs-*.zip 2025-08-26T22:03:43.6622170Z zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' || true 2025-08-26T22:03:43.6622683Z zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log' || true 2025-08-26T22:03:43.6628548Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:43.6628948Z env: 2025-08-26T22:03:43.6629180Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:43.6629658Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:43.6630163Z DEVICE_NAME: 2025-08-26T22:03:43.6630396Z DEVICE_TYPE: 2025-08-26T22:03:43.6630748Z FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621 2025-08-26T22:03:43.6631270Z ##[endgroup] 2025-08-26T22:03:43.6825574Z adding: usage_log.txt (deflated 95%) 2025-08-26T22:03:43.6884238Z adding: test/test-reports/test_overrides_1.1_b528260c2a0ae71c_.log 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(deflated 88%) 2025-08-26T22:03:43.7438630Z ##[group]Run # Remove any previous debugging artifacts if they exist 2025-08-26T22:03:43.7439177Z # Remove any previous debugging artifacts if they exist 2025-08-26T22:03:43.7439600Z rm -f debug-*.zip 2025-08-26T22:03:43.7439895Z if [ -d 'test/debug' ]; then 2025-08-26T22:03:43.7440263Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-08-26T22:03:43.7440616Z fi 2025-08-26T22:03:43.7446043Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:43.7446429Z env: 2025-08-26T22:03:43.7446659Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:43.7447139Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:43.7447658Z DEVICE_NAME: 2025-08-26T22:03:43.7447893Z DEVICE_TYPE: 2025-08-26T22:03:43.7448246Z FILE_SUFFIX: test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621 2025-08-26T22:03:43.7448648Z ##[endgroup] 2025-08-26T22:03:43.7554927Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-08-26T22:03:43.7555290Z with: 2025-08-26T22:03:43.7555504Z s3-bucket: gha-artifacts 2025-08-26T22:03:43.7555837Z s3-prefix: pytorch/pytorch/17248463620/1/artifact 2025-08-26T22:03:43.7556194Z retention-days: 14 2025-08-26T22:03:43.7556459Z if-no-files-found: warn 2025-08-26T22:03:43.7556728Z path: test-jsons-*.zip 2025-08-26T22:03:43.7557001Z name: artifact 2025-08-26T22:03:43.7557236Z region: us-east-1 2025-08-26T22:03:43.7557474Z env: 2025-08-26T22:03:43.7557676Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:43.7558151Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:43.7558659Z DEVICE_NAME: 2025-08-26T22:03:43.7558908Z DEVICE_TYPE: 2025-08-26T22:03:43.7559123Z ##[endgroup] 2025-08-26T22:03:44.1524026Z NOTE: s3-prefix specified, ignoring name parameter 2025-08-26T22:03:44.1524524Z With the provided path, there will be 1 file uploaded 2025-08-26T22:03:44.1525001Z Uploading to s3 prefix: pytorch/pytorch/17248463620/1/artifact 2025-08-26T22:03:44.1691570Z Starting upload of test-jsons-test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621.zip 2025-08-26T22:03:44.3279361Z Finished upload of test-jsons-test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621.zip 2025-08-26T22:03:44.3475406Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-08-26T22:03:44.3475801Z with: 2025-08-26T22:03:44.3476143Z s3-bucket: gha-artifacts 2025-08-26T22:03:44.3476484Z s3-prefix: pytorch/pytorch/17248463620/1/artifact 2025-08-26T22:03:44.3476848Z retention-days: 14 2025-08-26T22:03:44.3477109Z if-no-files-found: error 2025-08-26T22:03:44.3477383Z path: test-reports-*.zip 2025-08-26T22:03:44.3477655Z name: artifact 2025-08-26T22:03:44.3478006Z region: us-east-1 2025-08-26T22:03:44.3478233Z env: 2025-08-26T22:03:44.3478457Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:44.3478933Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:44.3479474Z DEVICE_NAME: 2025-08-26T22:03:44.3479771Z DEVICE_TYPE: 2025-08-26T22:03:44.3479982Z ##[endgroup] 2025-08-26T22:03:44.6831827Z NOTE: s3-prefix specified, ignoring name parameter 2025-08-26T22:03:44.6832321Z With the provided path, there will be 1 file uploaded 2025-08-26T22:03:44.6832787Z Uploading to s3 prefix: pytorch/pytorch/17248463620/1/artifact 2025-08-26T22:03:44.6872682Z Starting upload of test-reports-test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621.zip 2025-08-26T22:03:44.8301995Z Finished upload of test-reports-test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621.zip 2025-08-26T22:03:44.8498606Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-08-26T22:03:44.8498962Z with: 2025-08-26T22:03:44.8499184Z s3-bucket: gha-artifacts 2025-08-26T22:03:44.8499548Z s3-prefix: pytorch/pytorch/17248463620/1/artifact 2025-08-26T22:03:44.8499913Z retention-days: 14 2025-08-26T22:03:44.8500183Z if-no-files-found: ignore 2025-08-26T22:03:44.8500572Z path: logs-*.zip 2025-08-26T22:03:44.8500818Z name: artifact 2025-08-26T22:03:44.8501161Z region: us-east-1 2025-08-26T22:03:44.8501384Z env: 2025-08-26T22:03:44.8501643Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:44.8502125Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:44.8502641Z DEVICE_NAME: 2025-08-26T22:03:44.8502872Z DEVICE_TYPE: 2025-08-26T22:03:44.8503088Z ##[endgroup] 2025-08-26T22:03:45.2482897Z NOTE: s3-prefix specified, ignoring name parameter 2025-08-26T22:03:45.2483454Z With the provided path, there will be 1 file uploaded 2025-08-26T22:03:45.2483941Z Uploading to s3 prefix: pytorch/pytorch/17248463620/1/artifact 2025-08-26T22:03:45.2524429Z Starting upload of logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621.zip 2025-08-26T22:03:45.4918607Z Finished upload of logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621.zip 2025-08-26T22:03:45.5116805Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-08-26T22:03:45.5117166Z with: 2025-08-26T22:03:45.5117383Z s3-bucket: gha-artifacts 2025-08-26T22:03:45.5117751Z s3-prefix: pytorch/pytorch/17248463620/1/artifact 2025-08-26T22:03:45.5118115Z retention-days: 14 2025-08-26T22:03:45.5118379Z if-no-files-found: ignore 2025-08-26T22:03:45.5118652Z path: debug-*.zip 2025-08-26T22:03:45.5118899Z name: artifact 2025-08-26T22:03:45.5119143Z region: us-east-1 2025-08-26T22:03:45.5119367Z env: 2025-08-26T22:03:45.5119600Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:45.5120085Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:45.5120598Z DEVICE_NAME: 2025-08-26T22:03:45.5120829Z DEVICE_TYPE: 2025-08-26T22:03:45.5121047Z ##[endgroup] 2025-08-26T22:03:45.8401867Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-08-26T22:03:45.8602227Z ##[group]Run # shellcheck disable=SC2156 2025-08-26T22:03:45.8602627Z # shellcheck disable=SC2156 2025-08-26T22:03:45.8603216Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-08-26T22:03:45.8609653Z shell: /usr/bin/bash -e {0} 2025-08-26T22:03:45.8609943Z env: 2025-08-26T22:03:45.8610175Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:45.8610656Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:45.8611242Z DEVICE_NAME: 2025-08-26T22:03:45.8611583Z DEVICE_TYPE: 2025-08-26T22:03:45.8611819Z ##[endgroup] 2025-08-26T22:03:46.1466822Z Prepare all required actions 2025-08-26T22:03:46.1467257Z Getting action download info 2025-08-26T22:03:46.2701447Z ##[group]Run ./.github/actions/upload-utilization-stats 2025-08-26T22:03:46.2701821Z with: 2025-08-26T22:03:46.2702046Z job_id: 48944862621 2025-08-26T22:03:46.2702478Z job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T22:03:46.2702976Z workflow_name: pull 2025-08-26T22:03:46.2703226Z workflow_run_id: 17248463620 2025-08-26T22:03:46.2703512Z workflow_attempt: 1 2025-08-26T22:03:46.2703772Z env: 2025-08-26T22:03:46.2703994Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:46.2704453Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:46.2704963Z DEVICE_NAME: 2025-08-26T22:03:46.2705197Z DEVICE_TYPE: 2025-08-26T22:03:46.2705421Z ##[endgroup] 2025-08-26T22:03:46.2748200Z ##[group]Run echo "workflow_id: 17248463620" 2025-08-26T22:03:46.2748642Z echo "workflow_id: 17248463620" 2025-08-26T22:03:46.2748965Z echo "workflow_attempt: 1" 2025-08-26T22:03:46.2749288Z echo "workflow_Name: pull" 2025-08-26T22:03:46.2749598Z echo "job_id: 48944862621" 2025-08-26T22:03:46.2750138Z echo "job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)" 2025-08-26T22:03:46.2750688Z echo "artifact_prefix: " 2025-08-26T22:03:46.2751007Z python3 --version 2025-08-26T22:03:46.2756570Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:46.2757077Z env: 2025-08-26T22:03:46.2757292Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:46.2757768Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:46.2758284Z DEVICE_NAME: 2025-08-26T22:03:46.2758515Z DEVICE_TYPE: 2025-08-26T22:03:46.2758730Z ##[endgroup] 2025-08-26T22:03:46.2782751Z workflow_id: 17248463620 2025-08-26T22:03:46.2783285Z workflow_attempt: 1 2025-08-26T22:03:46.2783699Z workflow_Name: pull 2025-08-26T22:03:46.2784089Z job_id: 48944862621 2025-08-26T22:03:46.2784783Z job_name: linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge) 2025-08-26T22:03:46.2785296Z artifact_prefix: 2025-08-26T22:03:46.2795171Z Python 3.9.23 2025-08-26T22:03:46.2838882Z ##[group]Run nick-fields/retry@v3.0.0 2025-08-26T22:03:46.2839194Z with: 2025-08-26T22:03:46.2839413Z shell: bash 2025-08-26T22:03:46.2839679Z timeout_minutes: 5 2025-08-26T22:03:46.2839936Z max_attempts: 5 2025-08-26T22:03:46.2840168Z retry_wait_seconds: 30 2025-08-26T22:03:46.2840750Z command: set -eu python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3 dataclasses_json==0.6.7 2025-08-26T22:03:46.2841361Z polling_interval_seconds: 1 2025-08-26T22:03:46.2841656Z warning_on_retry: true 2025-08-26T22:03:46.2841921Z continue_on_error: false 2025-08-26T22:03:46.2842187Z env: 2025-08-26T22:03:46.2842415Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:46.2842905Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:46.2843424Z DEVICE_NAME: 2025-08-26T22:03:46.2843659Z DEVICE_TYPE: 2025-08-26T22:03:46.2843889Z ##[endgroup] 2025-08-26T22:03:46.6277619Z Defaulting to user installation because normal site-packages is not writeable 2025-08-26T22:03:46.7063884Z Collecting python-dateutil==2.8.2 2025-08-26T22:03:46.7244304Z Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) 2025-08-26T22:03:47.7202717Z Collecting boto3==1.35.42 2025-08-26T22:03:47.7244879Z Downloading boto3-1.35.42-py3-none-any.whl (139 kB) 2025-08-26T22:03:48.2548982Z Collecting pandas==2.1.3 2025-08-26T22:03:48.2589990Z Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) 2025-08-26T22:03:48.4068672Z Requirement already satisfied: dataclasses_json==0.6.7 in /home/ec2-user/.local/lib/python3.9/site-packages (0.6.7) 2025-08-26T22:03:48.4086831Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0) 2025-08-26T22:03:48.4133670Z Requirement already satisfied: botocore<1.36.0,>=1.35.42 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (1.35.99) 2025-08-26T22:03:48.4139475Z Requirement already satisfied: s3transfer<0.11.0,>=0.10.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.4) 2025-08-26T22:03:48.4143722Z Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /usr/lib/python3.9/site-packages (from boto3==1.35.42) (0.10.0) 2025-08-26T22:03:48.4777243Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1) 2025-08-26T22:03:49.3279487Z Collecting numpy<2,>=1.22.4 2025-08-26T22:03:49.3321822Z Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB) 2025-08-26T22:03:49.5614953Z Collecting tzdata>=2022.1 2025-08-26T22:03:49.5674338Z Downloading tzdata-2025.2-py2.py3-none-any.whl (347 kB) 2025-08-26T22:03:49.5836373Z Requirement already satisfied: typing-inspect<1,>=0.4.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (0.9.0) 2025-08-26T22:03:49.5840304Z Requirement already satisfied: marshmallow<4.0.0,>=3.18.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from dataclasses_json==0.6.7) (3.26.1) 2025-08-26T22:03:49.5922433Z Requirement already satisfied: urllib3<1.27,>=1.25.4 in /usr/lib/python3.9/site-packages (from botocore<1.36.0,>=1.35.42->boto3==1.35.42) (1.25.10) 2025-08-26T22:03:49.6016830Z Requirement already satisfied: packaging>=17.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from marshmallow<4.0.0,>=3.18.0->dataclasses_json==0.6.7) (25.0) 2025-08-26T22:03:49.6121943Z Requirement already satisfied: typing-extensions>=3.7.4 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (4.15.0) 2025-08-26T22:03:49.6127839Z Requirement already satisfied: mypy-extensions>=0.3.0 in /home/ec2-user/.local/lib/python3.9/site-packages (from typing-inspect<1,>=0.4.0->dataclasses_json==0.6.7) (1.1.0) 2025-08-26T22:03:49.7785456Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3 2025-08-26T22:03:54.9885925Z Attempting uninstall: boto3 2025-08-26T22:03:54.9886746Z Found existing installation: boto3 1.35.33 2025-08-26T22:03:54.9984852Z Uninstalling boto3-1.35.33: 2025-08-26T22:03:54.9998399Z Successfully uninstalled boto3-1.35.33 2025-08-26T22:03:55.0575658Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.2 2025-08-26T22:03:55.3689864Z Command completed after 1 attempt(s). 2025-08-26T22:03:55.3750026Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-08-26T22:03:55.3750766Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-08-26T22:03:55.3751287Z  --workflow-run-id "17248463620" \ 2025-08-26T22:03:55.3751668Z  --workflow-name "pull" \ 2025-08-26T22:03:55.3752042Z  --workflow-run-attempt "1" \ 2025-08-26T22:03:55.3752390Z  --job-id "48944862621" \ 2025-08-26T22:03:55.3752902Z  --job-name "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)" \ 2025-08-26T22:03:55.3753453Z  --local-path "" \ 2025-08-26T22:03:55.3753753Z  --artifact-prefix "" 2025-08-26T22:03:55.3759254Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:55.3759628Z env: 2025-08-26T22:03:55.3759907Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:55.3760385Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:55.3760898Z DEVICE_NAME: 2025-08-26T22:03:55.3761130Z DEVICE_TYPE: 2025-08-26T22:03:55.3761345Z ##[endgroup] 2025-08-26T22:03:57.7022728Z repo: pytorch/pytorch 2025-08-26T22:03:57.7023154Z Search for test log in s3 bucket: ossci-utilization 2025-08-26T22:03:57.7024129Z Downloading logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621.zip 2025-08-26T22:03:57.7024841Z extracting usage_log.txt from zip file logs-test-dynamo_wrapped-1-3-lf.linux.2xlarge_48944862621.zip 2025-08-26T22:03:57.7025439Z Converted Log Model: UtilizationMetadata: 2025-08-26T22:03:57.7026841Z UtilizationMetadata(level='metadata', workflow_id='17248463620', job_id='48944862621', workflow_name='pull', job_name='linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)', usage_collect_interval=1.0, data_model_version=1.5, start_at=1756237671, gpu_count=0, cpu_count=8, gpu_type=None, error=None) 2025-08-26T22:03:57.7028317Z [Db Segments] detected pytest cmd: 11, generated segments: 11 2025-08-26T22:03:57.7028742Z [db model] Peek db timeseries 2025-08-26T22:03:57.7029009Z :{ 2025-08-26T22:03:57.7029221Z "created_at": 1756245837, 2025-08-26T22:03:57.7029513Z "type": "utilization", 2025-08-26T22:03:57.7029781Z "tags": [ 2025-08-26T22:03:57.7029993Z "record" 2025-08-26T22:03:57.7030223Z ], 2025-08-26T22:03:57.7030439Z "time_stamp": 1756237671, 2025-08-26T22:03:57.7030748Z "repo": "pytorch/pytorch", 2025-08-26T22:03:57.7031077Z "workflow_id": 17248463620, 2025-08-26T22:03:57.7031360Z "run_attempt": 1, 2025-08-26T22:03:57.7031609Z "job_id": 48944862621, 2025-08-26T22:03:57.7031880Z "workflow_name": "pull", 2025-08-26T22:03:57.7032341Z "job_name": "linux-jammy-py3.13-clang12 / test (dynamo_wrapped, 1, 3, lf.linux.2xlarge)", 2025-08-26T22:03:57.7032840Z "json_data": "{}" 2025-08-26T22:03:57.7033080Z } 2025-08-26T22:03:57.7033656Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.5/pytorch/pytorch/17248463620/1/48944862621/metadata 2025-08-26T22:03:57.7034590Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.5/pytorch/pytorch/17248463620/1/48944862621/metadata 2025-08-26T22:03:57.7035571Z Writing 1620 documents to S3 ossci-utilization/util_timeseries/v_1.5/pytorch/pytorch/17248463620/1/48944862621/time_series 2025-08-26T22:03:57.7036588Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.5/pytorch/pytorch/17248463620/1/48944862621/time_series 2025-08-26T22:03:57.8436317Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-08-26T22:03:57.8436764Z with: 2025-08-26T22:03:57.8436980Z env: 2025-08-26T22:03:57.8437201Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:57.8437673Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:57.8438190Z DEVICE_NAME: 2025-08-26T22:03:57.8438423Z DEVICE_TYPE: 2025-08-26T22:03:57.8438660Z ##[endgroup] 2025-08-26T22:03:57.8465247Z ##[group]Run set -eou pipefail 2025-08-26T22:03:57.8465595Z set -eou pipefail 2025-08-26T22:03:57.8465880Z  2025-08-26T22:03:57.8466262Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-08-26T22:03:57.8466732Z for _ in $(seq 1440); do 2025-08-26T22:03:57.8467091Z  # Break if no ssh session exists anymore 2025-08-26T22:03:57.8467456Z  if [ "$(who)" = "" ]; then 2025-08-26T22:03:57.8467765Z  break 2025-08-26T22:03:57.8468049Z  fi 2025-08-26T22:03:57.8468284Z  echo "." 2025-08-26T22:03:57.8468520Z  sleep 5 2025-08-26T22:03:57.8468763Z done 2025-08-26T22:03:57.8474419Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:57.8474809Z env: 2025-08-26T22:03:57.8475021Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:57.8475497Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:57.8476017Z DEVICE_NAME: 2025-08-26T22:03:57.8476249Z DEVICE_TYPE: 2025-08-26T22:03:57.8476463Z ##[endgroup] 2025-08-26T22:03:57.8500170Z Holding runner for 2 hours until all ssh sessions have logged out 2025-08-26T22:03:57.8588476Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-08-26T22:03:57.8589284Z # ignore expansion of "docker ps -q" since it could be empty 2025-08-26T22:03:57.8589730Z # shellcheck disable=SC2046 2025-08-26T22:03:57.8590092Z docker stop $(docker ps -q) || true 2025-08-26T22:03:57.8590437Z # Prune all of the docker images 2025-08-26T22:03:57.8590773Z docker system prune -af 2025-08-26T22:03:57.8596631Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:03:57.8597033Z env: 2025-08-26T22:03:57.8597277Z GIT_DEFAULT_BRANCH: main 2025-08-26T22:03:57.8597758Z DOCKER_CONTAINER_ID: 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:03:57.8598283Z DEVICE_NAME: 2025-08-26T22:03:57.8598514Z DEVICE_TYPE: 2025-08-26T22:03:57.8598744Z ##[endgroup] 2025-08-26T22:04:08.9210582Z 77c509ac59ad 2025-08-26T22:04:09.8858282Z Deleted Containers: 2025-08-26T22:04:09.8858787Z 77c509ac59ad19a8f11cf777a7123dae13e8994af552230ebf9da50a353fd8db 2025-08-26T22:04:09.8859160Z 2025-08-26T22:04:16.7404627Z Deleted Images: 2025-08-26T22:04:16.7406107Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/ci-image:pytorch-linux-jammy-py3.13-clang12-16b1c8d10f4f7ec1a604612d52021e8c98b48fe6 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'android/libs/fbjni' 2025-08-26T22:04:16.9594689Z Entering 'third_party/FP16' 2025-08-26T22:04:16.9647954Z Entering 'third_party/FXdiv' 2025-08-26T22:04:16.9700248Z Entering 'third_party/NNPACK' 2025-08-26T22:04:16.9752158Z Entering 'third_party/NVTX' 2025-08-26T22:04:16.9806131Z Entering 'third_party/VulkanMemoryAllocator' 2025-08-26T22:04:16.9859025Z Entering 'third_party/XNNPACK' 2025-08-26T22:04:16.9928349Z Entering 'third_party/aiter' 2025-08-26T22:04:16.9982430Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-08-26T22:04:17.0044039Z Entering 'third_party/benchmark' 2025-08-26T22:04:17.0099512Z Entering 'third_party/composable_kernel' 2025-08-26T22:04:17.0161530Z Entering 'third_party/cpp-httplib' 2025-08-26T22:04:17.0214552Z Entering 'third_party/cpuinfo' 2025-08-26T22:04:17.0267860Z Entering 'third_party/cudnn_frontend' 2025-08-26T22:04:17.0322690Z Entering 'third_party/cutlass' 2025-08-26T22:04:17.0386050Z Entering 'third_party/fbgemm' 2025-08-26T22:04:17.0442803Z Entering 'third_party/fbgemm/external/asmjit' 2025-08-26T22:04:17.0495579Z Entering 'third_party/fbgemm/external/composable_kernel' 2025-08-26T22:04:17.0555780Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-08-26T22:04:17.0606936Z Entering 'third_party/fbgemm/external/cutlass' 2025-08-26T22:04:17.0668856Z Entering 'third_party/fbgemm/external/googletest' 2025-08-26T22:04:17.0720757Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-08-26T22:04:17.0771860Z Entering 'third_party/fbgemm/external/json' 2025-08-26T22:04:17.0828081Z Entering 'third_party/flash-attention' 2025-08-26T22:04:17.0882721Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-08-26T22:04:17.0941619Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-08-26T22:04:17.1004523Z Entering 'third_party/flatbuffers' 2025-08-26T22:04:17.1062843Z Entering 'third_party/fmt' 2025-08-26T22:04:17.1115964Z Entering 'third_party/gemmlowp/gemmlowp' 2025-08-26T22:04:17.1168250Z Entering 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'third_party/pocketfft' 2025-08-26T22:04:17.3074685Z Entering 'third_party/protobuf' 2025-08-26T22:04:17.3132211Z Entering 'third_party/protobuf/third_party/benchmark' 2025-08-26T22:04:17.3184888Z Entering 'third_party/protobuf/third_party/googletest' 2025-08-26T22:04:17.3238152Z Entering 'third_party/psimd' 2025-08-26T22:04:17.3291254Z Entering 'third_party/pthreadpool' 2025-08-26T22:04:17.3344218Z Entering 'third_party/pybind11' 2025-08-26T22:04:17.3396971Z Entering 'third_party/python-peachpy' 2025-08-26T22:04:17.3449675Z Entering 'third_party/sleef' 2025-08-26T22:04:17.3502754Z Entering 'third_party/tensorpipe' 2025-08-26T22:04:17.3555355Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-08-26T22:04:17.3607911Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-08-26T22:04:17.3658923Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-08-26T22:04:17.3712334Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-08-26T22:04:17.3762486Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-08-26T22:04:17.3834389Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-08-26T22:04:17.3854032Z http.https://github.com/.extraheader 2025-08-26T22:04:17.3864012Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-08-26T22:04:17.3894990Z [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' || :" 2025-08-26T22:04:17.4181276Z Entering 'android/libs/fbjni' 2025-08-26T22:04:17.4217867Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4250624Z Entering 'third_party/FP16' 2025-08-26T22:04:17.4285795Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4318802Z Entering 'third_party/FXdiv' 2025-08-26T22:04:17.4353789Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4385783Z Entering 'third_party/NNPACK' 2025-08-26T22:04:17.4422203Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4455488Z Entering 'third_party/NVTX' 2025-08-26T22:04:17.4490832Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4524285Z Entering 'third_party/VulkanMemoryAllocator' 2025-08-26T22:04:17.4560279Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4594401Z Entering 'third_party/XNNPACK' 2025-08-26T22:04:17.4630001Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4679096Z Entering 'third_party/aiter' 2025-08-26T22:04:17.4716824Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4750530Z Entering 'third_party/aiter/3rdparty/composable_kernel' 2025-08-26T22:04:17.4785067Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4827640Z Entering 'third_party/benchmark' 2025-08-26T22:04:17.4864251Z http.https://github.com/.extraheader 2025-08-26T22:04:17.4897805Z Entering 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http.https://github.com/.extraheader 2025-08-26T22:04:17.5475985Z Entering 'third_party/fbgemm/external/cpuinfo' 2025-08-26T22:04:17.5510711Z http.https://github.com/.extraheader 2025-08-26T22:04:17.5542855Z Entering 'third_party/fbgemm/external/cutlass' 2025-08-26T22:04:17.5577773Z http.https://github.com/.extraheader 2025-08-26T22:04:17.5620982Z Entering 'third_party/fbgemm/external/googletest' 2025-08-26T22:04:17.5656431Z http.https://github.com/.extraheader 2025-08-26T22:04:17.5688703Z Entering 'third_party/fbgemm/external/hipify_torch' 2025-08-26T22:04:17.5723907Z http.https://github.com/.extraheader 2025-08-26T22:04:17.5755337Z Entering 'third_party/fbgemm/external/json' 2025-08-26T22:04:17.5788890Z http.https://github.com/.extraheader 2025-08-26T22:04:17.5824299Z Entering 'third_party/flash-attention' 2025-08-26T22:04:17.5859355Z http.https://github.com/.extraheader 2025-08-26T22:04:17.5894186Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-08-26T22:04:17.5927178Z http.https://github.com/.extraheader 2025-08-26T22:04:17.5965977Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-08-26T22:04:17.6000458Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6043597Z Entering 'third_party/flatbuffers' 2025-08-26T22:04:17.6081109Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6116746Z Entering 'third_party/fmt' 2025-08-26T22:04:17.6153274Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6187359Z Entering 'third_party/gemmlowp/gemmlowp' 2025-08-26T22:04:17.6225271Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6257701Z Entering 'third_party/gloo' 2025-08-26T22:04:17.6295361Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6327805Z Entering 'third_party/googletest' 2025-08-26T22:04:17.6362984Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6395046Z Entering 'third_party/ideep' 2025-08-26T22:04:17.6431114Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6462525Z Entering 'third_party/ideep/mkl-dnn' 2025-08-26T22:04:17.6497521Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6538197Z Entering 'third_party/ittapi' 2025-08-26T22:04:17.6575462Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6609715Z Entering 'third_party/kineto' 2025-08-26T22:04:17.6644865Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6678053Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-08-26T22:04:17.6714376Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6747253Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-08-26T22:04:17.6783888Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6817986Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-08-26T22:04:17.6853870Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6886459Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-08-26T22:04:17.6924475Z http.https://github.com/.extraheader 2025-08-26T22:04:17.6957270Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-08-26T22:04:17.6993091Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7026163Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-08-26T22:04:17.7061173Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7096838Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-08-26T22:04:17.7131048Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7164007Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-08-26T22:04:17.7199016Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7231452Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-08-26T22:04:17.7266634Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7303296Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-08-26T22:04:17.7335722Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7370266Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-08-26T22:04:17.7407075Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7439729Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-08-26T22:04:17.7475565Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7512855Z Entering 'third_party/kleidiai' 2025-08-26T22:04:17.7549507Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7582190Z Entering 'third_party/mimalloc' 2025-08-26T22:04:17.7617440Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7648741Z Entering 'third_party/nlohmann' 2025-08-26T22:04:17.7684619Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7719616Z Entering 'third_party/onnx' 2025-08-26T22:04:17.7755925Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7808519Z Entering 'third_party/onnx/third_party/pybind11' 2025-08-26T22:04:17.7844588Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7879316Z Entering 'third_party/opentelemetry-cpp' 2025-08-26T22:04:17.7915365Z http.https://github.com/.extraheader 2025-08-26T22:04:17.7950988Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-08-26T22:04:17.7985333Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8017064Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-08-26T22:04:17.8050630Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8081803Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-08-26T22:04:17.8116065Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8147308Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-08-26T22:04:17.8180853Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8215060Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-08-26T22:04:17.8249187Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8281105Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-08-26T22:04:17.8316283Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8348683Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-08-26T22:04:17.8383467Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8416411Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-08-26T22:04:17.8450987Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8485472Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-08-26T22:04:17.8519584Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8553605Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-08-26T22:04:17.8588443Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8643875Z Entering 'third_party/pocketfft' 2025-08-26T22:04:17.8680057Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8712363Z Entering 'third_party/protobuf' 2025-08-26T22:04:17.8747093Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8782534Z Entering 'third_party/protobuf/third_party/benchmark' 2025-08-26T22:04:17.8817183Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8849297Z Entering 'third_party/protobuf/third_party/googletest' 2025-08-26T22:04:17.8883837Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8919152Z Entering 'third_party/psimd' 2025-08-26T22:04:17.8956937Z http.https://github.com/.extraheader 2025-08-26T22:04:17.8989740Z Entering 'third_party/pthreadpool' 2025-08-26T22:04:17.9026467Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9058784Z Entering 'third_party/pybind11' 2025-08-26T22:04:17.9094988Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9128085Z Entering 'third_party/python-peachpy' 2025-08-26T22:04:17.9163117Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9195508Z Entering 'third_party/sleef' 2025-08-26T22:04:17.9230941Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9263153Z Entering 'third_party/tensorpipe' 2025-08-26T22:04:17.9298925Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9330904Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-08-26T22:04:17.9365428Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9397433Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-08-26T22:04:17.9433068Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9464403Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-08-26T22:04:17.9499647Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9532421Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-08-26T22:04:17.9568536Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9602114Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-08-26T22:04:17.9636725Z http.https://github.com/.extraheader 2025-08-26T22:04:17.9757567Z A job completed hook has been configured by the self-hosted runner administrator 2025-08-26T22:04:17.9791942Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-08-26T22:04:17.9797343Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-08-26T22:04:17.9797750Z ##[endgroup] 2025-08-26T22:04:27.8184368Z Cleaning up orphan processes