2025-03-04T21:01:22.9845286Z Current runner version: '2.322.0' 2025-03-04T21:01:22.9851898Z Runner name: 'i-01112049b7e156298' 2025-03-04T21:01:22.9852820Z Runner group name: 'Default' 2025-03-04T21:01:22.9853822Z Machine name: 'ip-10-0-51-51' 2025-03-04T21:01:22.9858799Z ##[group]GITHUB_TOKEN Permissions 2025-03-04T21:01:22.9861915Z Actions: read 2025-03-04T21:01:22.9862601Z Attestations: read 2025-03-04T21:01:22.9863273Z Checks: read 2025-03-04T21:01:22.9863911Z Contents: read 2025-03-04T21:01:22.9864569Z Deployments: read 2025-03-04T21:01:22.9865241Z Discussions: read 2025-03-04T21:01:22.9865902Z Issues: read 2025-03-04T21:01:22.9866538Z Metadata: read 2025-03-04T21:01:22.9867187Z Packages: read 2025-03-04T21:01:22.9867840Z Pages: read 2025-03-04T21:01:22.9868483Z PullRequests: read 2025-03-04T21:01:22.9869175Z RepositoryProjects: read 2025-03-04T21:01:22.9869921Z SecurityEvents: read 2025-03-04T21:01:22.9870608Z Statuses: read 2025-03-04T21:01:22.9871250Z ##[endgroup] 2025-03-04T21:01:22.9874488Z Secret source: Actions 2025-03-04T21:01:22.9875353Z Prepare workflow directory 2025-03-04T21:01:23.0350557Z Prepare all required actions 2025-03-04T21:01:23.0387454Z Getting action download info 2025-03-04T21:01:23.2294786Z Download action repository 'pytorch/test-infra@main' (SHA:79438512a0632583899938d3b0277da78f5569e0) 2025-03-04T21:01:24.7691986Z Download action repository 'pytorch/pytorch@main' (SHA:f30776c37a52cc70cc0494eb8eefd0e9544cd748) 2025-03-04T21:01:38.6594057Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0) 2025-03-04T21:01:38.8623868Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a) 2025-03-04T21:01:39.1595148Z Getting action download info 2025-03-04T21:01:39.2747321Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683) 2025-03-04T21:01:39.5405982Z Getting action download info 2025-03-04T21:01:39.6465038Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e) 2025-03-04T21:01:39.8214199Z Getting action download info 2025-03-04T21:01:39.9625163Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482) 2025-03-04T21:01:40.1367123Z Getting action download info 2025-03-04T21:01:40.2890978Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/tags/ciflow/inductor/148205 (1b7498080987913ecb3aff6253c5e88f3540d911) 2025-03-04T21:01:40.2893113Z ##[group] Inputs 2025-03-04T21:01:40.2893494Z build-environment: linux-focal-cuda12.4-py3.10-gcc9-sm86 2025-03-04T21:01:40.2895341Z test-matrix: {"include": [{"config": "inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}]} 2025-03-04T21:01:40.2897595Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:01:40.2898451Z sync-tag: 2025-03-04T21:01:40.2899193Z timeout-minutes: 240 2025-03-04T21:01:40.2899460Z use-gha: 2025-03-04T21:01:40.2899691Z dashboard-tag: 2025-03-04T21:01:40.2899947Z s3-bucket: gha-artifacts 2025-03-04T21:01:40.2900227Z aws-role-to-assume: 2025-03-04T21:01:40.2900755Z disable-monitor: false 2025-03-04T21:01:40.2901042Z ##[endgroup] 2025-03-04T21:01:40.2901535Z Complete job name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:01:40.3464827Z A job started hook has been configured by the self-hosted runner administrator 2025-03-04T21:01:40.3564631Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh' 2025-03-04T21:01:40.3576141Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:01:40.3576747Z ##[endgroup] 2025-03-04T21:01:41.5745766Z Runner Type: linux.g5.4xlarge.nvidia.gpu 2025-03-04T21:01:41.5746288Z Instance Type: g5.4xlarge 2025-03-04T21:01:41.5746600Z AMI Name: unknown 2025-03-04T21:01:41.5786879Z AMI ID: ami-05b10e08d247fb927 2025-03-04T21:01:46.9572234Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main 2025-03-04T21:01:46.9572655Z with: 2025-03-04T21:01:46.9573311Z github-secret: *** 2025-03-04T21:01:46.9573996Z instructions: All testing is done inside the container, to start an interactive session run: docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-03-04T21:01:46.9574719Z activate-with-label: false 2025-03-04T21:01:46.9574999Z label: with-ssh 2025-03-04T21:01:46.9575264Z remove-existing-keys: true 2025-03-04T21:01:46.9575555Z fail-silently: true 2025-03-04T21:01:46.9575857Z env: 2025-03-04T21:01:46.9576085Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:01:46.9576364Z ##[endgroup] 2025-03-04T21:01:47.0779354Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info. 2025-03-04T21:01:47.0780961Z ciflow reference detected, attempting to extract PR number 2025-03-04T21:01:47.4360020Z Grabbing public ssh keys from https://github.com/pytorch-bot[bot].keys 2025-03-04T21:01:47.4808394Z No SSH keys found for user pytorch-bot[bot] 2025-03-04T21:01:47.4808907Z Grabbing public ssh keys from https://github.com/williamwen42.keys 2025-03-04T21:01:47.5442764Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh 2025-03-04T21:01:47.5458388Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys 2025-03-04T21:01:47.5493170Z Login using: ssh ec2-user@ec2-54-145-36-250.compute-1.amazonaws.com 2025-03-04T21:01:47.5493741Z All testing is done inside the container, to start an interactive session run: 2025-03-04T21:01:47.5494323Z docker exec -it $(docker container ps --format '{{.ID}}') bash 2025-03-04T21:01:47.5644779Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main 2025-03-04T21:01:47.5645226Z with: 2025-03-04T21:01:47.5645456Z no-sudo: true 2025-03-04T21:01:47.5645705Z submodules: recursive 2025-03-04T21:01:47.5645972Z fetch-depth: 0 2025-03-04T21:01:47.5646201Z env: 2025-03-04T21:01:47.5646428Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:01:47.5646698Z ##[endgroup] 2025-03-04T21:01:47.5725327Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T21:01:47.5726294Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T21:01:47.5739256Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:01:47.5739643Z env: 2025-03-04T21:01:47.5739875Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:01:47.5740168Z ##[endgroup] 2025-03-04T21:01:47.5849528Z ##[group]Run retry () { 2025-03-04T21:01:47.5849890Z retry () { 2025-03-04T21:01:47.5850251Z  $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*) 2025-03-04T21:01:47.5850649Z } 2025-03-04T21:01:47.5850914Z echo "${GITHUB_WORKSPACE}" 2025-03-04T21:01:47.5851251Z if [ -z "${NO_SUDO}" ]; then 2025-03-04T21:01:47.5851617Z  retry sudo rm -rf "${GITHUB_WORKSPACE}" 2025-03-04T21:01:47.5851959Z else 2025-03-04T21:01:47.5852240Z  retry rm -rf "${GITHUB_WORKSPACE}" 2025-03-04T21:01:47.5852568Z fi 2025-03-04T21:01:47.5852826Z mkdir "${GITHUB_WORKSPACE}" 2025-03-04T21:01:47.5853202Z  2025-03-04T21:01:47.5853489Z # Use all available CPUs for fetching 2025-03-04T21:01:47.5853841Z cd "${GITHUB_WORKSPACE}" 2025-03-04T21:01:47.5854195Z git config --global fetch.parallel 0 2025-03-04T21:01:47.5854840Z git config --global submodule.fetchJobs 0 2025-03-04T21:01:47.5864003Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:01:47.5864391Z env: 2025-03-04T21:01:47.5864633Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:01:47.5864915Z NO_SUDO: true 2025-03-04T21:01:47.5865165Z ##[endgroup] 2025-03-04T21:01:47.5894518Z /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T21:01:47.6030391Z ##[group]Run actions/checkout@v4 2025-03-04T21:01:47.6030716Z with: 2025-03-04T21:01:47.6030999Z ref: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:01:47.6031345Z fetch-depth: 0 2025-03-04T21:01:47.6031623Z submodules: recursive 2025-03-04T21:01:47.6031910Z show-progress: false 2025-03-04T21:01:47.6032203Z repository: pytorch/pytorch 2025-03-04T21:01:47.6032610Z token: *** 2025-03-04T21:01:47.6032857Z ssh-strict: true 2025-03-04T21:01:47.6033109Z ssh-user: git 2025-03-04T21:01:47.6033383Z persist-credentials: true 2025-03-04T21:01:47.6033687Z clean: true 2025-03-04T21:01:47.6033958Z sparse-checkout-cone-mode: true 2025-03-04T21:01:47.6034286Z fetch-tags: false 2025-03-04T21:01:47.6034543Z lfs: false 2025-03-04T21:01:47.6034799Z set-safe-directory: true 2025-03-04T21:01:47.6035077Z env: 2025-03-04T21:01:47.6035317Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:01:47.6035596Z ##[endgroup] 2025-03-04T21:01:47.7194609Z Syncing repository: pytorch/pytorch 2025-03-04T21:01:47.7196575Z ##[group]Getting Git version info 2025-03-04T21:01:47.7197318Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-03-04T21:01:47.7198428Z [command]/usr/bin/git version 2025-03-04T21:01:47.7198929Z git version 2.47.1 2025-03-04T21:01:47.7224134Z ##[endgroup] 2025-03-04T21:01:47.7236947Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/988a45d6-5e7e-4ff6-a1c8-3612342f6f03/.gitconfig' 2025-03-04T21:01:47.7258265Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/988a45d6-5e7e-4ff6-a1c8-3612342f6f03' before making global git config changes 2025-03-04T21:01:47.7260340Z Adding repository directory to the temporary git global config as a safe directory 2025-03-04T21:01:47.7265015Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T21:01:47.7302306Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch' 2025-03-04T21:01:47.7305808Z ##[group]Initializing the repository 2025-03-04T21:01:47.7320656Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T21:01:47.7362509Z hint: Using 'master' as the name for the initial branch. This default branch name 2025-03-04T21:01:47.7363141Z hint: is subject to change. To configure the initial branch name to use in all 2025-03-04T21:01:47.7363720Z hint: of your new repositories, which will suppress this warning, call: 2025-03-04T21:01:47.7364137Z hint: 2025-03-04T21:01:47.7364461Z hint: git config --global init.defaultBranch 2025-03-04T21:01:47.7364845Z hint: 2025-03-04T21:01:47.7365207Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and 2025-03-04T21:01:47.7365771Z hint: 'development'. The just-created branch can be renamed via this command: 2025-03-04T21:01:47.7366215Z hint: 2025-03-04T21:01:47.7366469Z hint: git branch -m 2025-03-04T21:01:47.7366976Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/ 2025-03-04T21:01:47.7374993Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch 2025-03-04T21:01:47.7410273Z ##[endgroup] 2025-03-04T21:01:47.7410762Z ##[group]Disabling automatic garbage collection 2025-03-04T21:01:47.7413892Z [command]/usr/bin/git config --local gc.auto 0 2025-03-04T21:01:47.7444836Z ##[endgroup] 2025-03-04T21:01:47.7445267Z ##[group]Setting up auth 2025-03-04T21:01:47.7451390Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-03-04T21:01:47.7483091Z [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' || :" 2025-03-04T21:01:47.7842195Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-03-04T21:01:47.7873358Z [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' || :" 2025-03-04T21:01:47.8222262Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-03-04T21:01:47.8271305Z ##[endgroup] 2025-03-04T21:01:47.8271773Z ##[group]Fetching the repository 2025-03-04T21:01:47.8278663Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/* 2025-03-04T21:02:36.5782553Z From https://github.com/pytorch/pytorch 2025-03-04T21:02:36.5784709Z * [new branch] 2.1-dynamic-doc -> origin/2.1-dynamic-doc 2025-03-04T21:02:36.5787129Z * [new branch] 2.6.0.dev20241004+ -> origin/2.6.0.dev20241004+ 2025-03-04T21:02:36.5787809Z * [new branch] 20250219_e8m0_intermediate -> origin/20250219_e8m0_intermediate 2025-03-04T21:02:36.5788344Z * [new branch] 20250219_test -> origin/20250219_test 2025-03-04T21:02:36.5789222Z * [new branch] Adjust-Description-for-linux-binary-test-Workflow -> origin/Adjust-Description-for-linux-binary-test-Workflow 2025-03-04T21:02:36.5790037Z * [new branch] Chillee-patch-5 -> origin/Chillee-patch-5 2025-03-04T21:02:36.5791161Z * [new branch] Flamefire-patch-1 -> origin/Flamefire-patch-1 2025-03-04T21:02:36.5793266Z * [new branch] HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes 2025-03-04T21:02:36.5795720Z * [new branch] JackCaoG/add_new_lazy_counter_macro -> origin/JackCaoG/add_new_lazy_counter_macro 2025-03-04T21:02:36.5797637Z * [new branch] JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops 2025-03-04T21:02:36.5798952Z * [new branch] JackCaoG/fix_xla_torchbench -> origin/JackCaoG/fix_xla_torchbench 2025-03-04T21:02:36.5800811Z * [new branch] JackCaoG/update_dynamo_doc -> origin/JackCaoG/update_dynamo_doc 2025-03-04T21:02:36.5802865Z * [new branch] JackCaoG/update_xla_pin_to_skip_test -> origin/JackCaoG/update_xla_pin_to_skip_test 2025-03-04T21:02:36.5804950Z * [new branch] JackCaoG/update_xla_pin_to_skip_test2 -> origin/JackCaoG/update_xla_pin_to_skip_test2 2025-03-04T21:02:36.5806660Z * [new branch] NicolasHug-patch-2 -> origin/NicolasHug-patch-2 2025-03-04T21:02:36.5809265Z * [new branch] PR-AOTInductorNoneBug -> origin/PR-AOTInductorNoneBug 2025-03-04T21:02:36.5811054Z * [new branch] PR-AOTInductorNoneBugFix -> origin/PR-AOTInductorNoneBugFix 2025-03-04T21:02:36.5812499Z * [new branch] PR-FixConfigsIssue -> origin/PR-FixConfigsIssue 2025-03-04T21:02:36.5814343Z * [new branch] PR-NoneBugFix-viable -> origin/PR-NoneBugFix-viable 2025-03-04T21:02:36.5816055Z * [new branch] PR-ResetToZero -> origin/PR-ResetToZero 2025-03-04T21:02:36.5817873Z * [new branch] Remove-linux_t4g_2xlarge-Usage -> origin/Remove-linux_t4g_2xlarge-Usage 2025-03-04T21:02:36.5819525Z * [new branch] Revert-PR-110949 -> origin/Revert-PR-110949 2025-03-04T21:02:36.5821331Z * [new branch] Update-Flash-Packaging -> origin/Update-Flash-Packaging 2025-03-04T21:02:36.5823760Z * [new branch] Valentine/flash_attention_bf16 -> origin/Valentine/flash_attention_bf16 2025-03-04T21:02:36.5826645Z * [new branch] _tmp-orig/release/2.6 -> origin/_tmp-orig/release/2.6 2025-03-04T21:02:36.5828934Z * [new branch] _tmp-release/2.6 -> origin/_tmp-release/2.6 2025-03-04T21:02:36.5831300Z * [new branch] abock/onnx-1.15.0-validation -> origin/abock/onnx-1.15.0-validation 2025-03-04T21:02:36.5833078Z * [new branch] abock/ort-nightly==1.16.0.dev20230908001 -> origin/abock/ort-nightly==1.16.0.dev20230908001 2025-03-04T21:02:36.5834987Z * [new branch] add-android-build-workflow -> origin/add-android-build-workflow 2025-03-04T21:02:36.5836232Z * [new branch] add-assign -> origin/add-assign 2025-03-04T21:02:36.5838324Z * [new branch] add_broadcast_functional_collective -> origin/add_broadcast_functional_collective 2025-03-04T21:02:36.5840035Z * [new branch] add_from_group_doc_and_test -> origin/add_from_group_doc_and_test 2025-03-04T21:02:36.5841725Z * [new branch] add_mha_to_autocast_policy -> origin/add_mha_to_autocast_policy 2025-03-04T21:02:36.5843486Z * [new branch] add_non_parallel_model_comparison -> origin/add_non_parallel_model_comparison 2025-03-04T21:02:36.5845130Z * [new branch] add_test_to_show_view_gap -> origin/add_test_to_show_view_gap 2025-03-04T21:02:36.5846895Z * [new branch] add_windows_testing_back -> origin/add_windows_testing_back 2025-03-04T21:02:36.5848830Z * [new branch] addmm-heuristic -> origin/addmm-heuristic 2025-03-04T21:02:36.5850577Z * [new branch] addsimde -> origin/addsimde 2025-03-04T21:02:36.5853209Z * [new branch] adi/gemm_bf16f32 -> origin/adi/gemm_bf16f32 2025-03-04T21:02:36.5855034Z * [new branch] ah-globalfeedback-hook -> origin/ah-globalfeedback-hook 2025-03-04T21:02:36.5857273Z * [new branch] alanwaketan/pin2 -> origin/alanwaketan/pin2 2025-03-04T21:02:36.5859066Z * [new branch] albanD-patch-1 -> origin/albanD-patch-1 2025-03-04T21:02:36.5860935Z * [new branch] albanD-patch-2 -> origin/albanD-patch-2 2025-03-04T21:02:36.5862924Z * [new branch] alt-disable -> origin/alt-disable 2025-03-04T21:02:36.5865302Z * [new branch] angelayi/144772 -> origin/angelayi/144772 2025-03-04T21:02:36.5867025Z * [new branch] angelayi/aot_inductor_bench_comp_time -> origin/angelayi/aot_inductor_bench_comp_time 2025-03-04T21:02:36.5868391Z * [new branch] angelayi/aot_inductor_benchmark -> origin/angelayi/aot_inductor_benchmark 2025-03-04T21:02:36.5943366Z * [new branch] angelayi/aot_inductor_torch -> origin/angelayi/aot_inductor_torch 2025-03-04T21:02:36.5944165Z * [new branch] angelayi/aoti_additional_files -> origin/angelayi/aoti_additional_files 2025-03-04T21:02:36.5944850Z * [new 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tag] ciflow/binaries/143082 -> ciflow/binaries/143082 2025-03-04T21:02:37.4404844Z * [new tag] ciflow/binaries/143416 -> ciflow/binaries/143416 2025-03-04T21:02:37.4406614Z * [new tag] ciflow/binaries/143794 -> ciflow/binaries/143794 2025-03-04T21:02:37.4407877Z * [new tag] ciflow/binaries/144127 -> ciflow/binaries/144127 2025-03-04T21:02:37.4408981Z * [new tag] ciflow/binaries/145119 -> ciflow/binaries/145119 2025-03-04T21:02:37.4410562Z * [new tag] ciflow/binaries/145224 -> ciflow/binaries/145224 2025-03-04T21:02:37.4412186Z * [new tag] ciflow/binaries/146717 -> ciflow/binaries/146717 2025-03-04T21:02:37.4413855Z * [new tag] ciflow/binaries/147448 -> ciflow/binaries/147448 2025-03-04T21:02:37.4415217Z * [new tag] ciflow/binaries/147498 -> ciflow/binaries/147498 2025-03-04T21:02:37.4418497Z * [new tag] ciflow/binaries/147607 -> ciflow/binaries/147607 2025-03-04T21:02:37.4419556Z * [new tag] ciflow/binaries/147664 -> ciflow/binaries/147664 2025-03-04T21:02:37.4420983Z * [new tag] 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ciflow/inductor-cu126/140793 -> ciflow/inductor-cu126/140793 2025-03-04T21:02:37.4448789Z * [new tag] ciflow/inductor-micro-benchmark/141910 -> ciflow/inductor-micro-benchmark/141910 2025-03-04T21:02:37.4450142Z * [new tag] ciflow/inductor-perf-compare/140195 -> ciflow/inductor-perf-compare/140195 2025-03-04T21:02:37.4451578Z * [new tag] ciflow/inductor-perf-test-nightly/140195 -> ciflow/inductor-perf-test-nightly/140195 2025-03-04T21:02:37.4453008Z * [new tag] ciflow/inductor-periodic/140793 -> ciflow/inductor-periodic/140793 2025-03-04T21:02:37.4454043Z * [new tag] ciflow/inductor-periodic/145612 -> ciflow/inductor-periodic/145612 2025-03-04T21:02:37.4455352Z * [new tag] ciflow/inductor-periodic/147315 -> ciflow/inductor-periodic/147315 2025-03-04T21:02:37.4456923Z * [new tag] ciflow/inductor-rocm/140989 -> ciflow/inductor-rocm/140989 2025-03-04T21:02:37.4458375Z * [new tag] ciflow/inductor-rocm/141309 -> ciflow/inductor-rocm/141309 2025-03-04T21:02:37.4459531Z * [new tag] ciflow/inductor-rocm/141355 -> ciflow/inductor-rocm/141355 2025-03-04T21:02:37.4460666Z * [new tag] ciflow/inductor-rocm/146264 -> ciflow/inductor-rocm/146264 2025-03-04T21:02:37.4462215Z * [new tag] ciflow/inductor-rocm/146903 -> ciflow/inductor-rocm/146903 2025-03-04T21:02:37.4463180Z * [new tag] ciflow/inductor-rocm/147315 -> ciflow/inductor-rocm/147315 2025-03-04T21:02:37.4464500Z * [new tag] ciflow/inductor-rocm/147320 -> ciflow/inductor-rocm/147320 2025-03-04T21:02:37.4465834Z * [new tag] ciflow/inductor-rocm/147452 -> ciflow/inductor-rocm/147452 2025-03-04T21:02:37.4467200Z * [new tag] ciflow/inductor-rocm/147583 -> ciflow/inductor-rocm/147583 2025-03-04T21:02:37.4468499Z * [new tag] ciflow/inductor-rocm/147619 -> ciflow/inductor-rocm/147619 2025-03-04T21:02:37.4469854Z * [new tag] ciflow/inductor-rocm/148305 -> ciflow/inductor-rocm/148305 2025-03-04T21:02:37.4471434Z * [new tag] ciflow/inductor-rocm/148437 -> ciflow/inductor-rocm/148437 2025-03-04T21:02:37.4472793Z * [new tag] ciflow/inductor/110155 -> ciflow/inductor/110155 2025-03-04T21:02:37.4473906Z * [new tag] ciflow/inductor/113257 -> ciflow/inductor/113257 2025-03-04T21:02:37.4475034Z * [new tag] ciflow/inductor/119496 -> ciflow/inductor/119496 2025-03-04T21:02:37.4476158Z * [new tag] ciflow/inductor/119977 -> ciflow/inductor/119977 2025-03-04T21:02:37.4477240Z * [new tag] ciflow/inductor/120076 -> ciflow/inductor/120076 2025-03-04T21:02:37.4478337Z * [new tag] ciflow/inductor/121445 -> ciflow/inductor/121445 2025-03-04T21:02:37.4479515Z * [new tag] ciflow/inductor/124490 -> ciflow/inductor/124490 2025-03-04T21:02:37.4480397Z * [new tag] ciflow/inductor/125270 -> ciflow/inductor/125270 2025-03-04T21:02:37.4481650Z * [new tag] ciflow/inductor/125326 -> ciflow/inductor/125326 2025-03-04T21:02:37.4482784Z * [new tag] ciflow/inductor/125428 -> ciflow/inductor/125428 2025-03-04T21:02:37.4483863Z * [new tag] ciflow/inductor/125469 -> ciflow/inductor/125469 2025-03-04T21:02:37.4485277Z * [new tag] ciflow/inductor/125806 -> ciflow/inductor/125806 2025-03-04T21:02:37.4486959Z * [new tag] ciflow/inductor/125888 -> ciflow/inductor/125888 2025-03-04T21:02:37.4488717Z * [new tag] ciflow/inductor/125995 -> ciflow/inductor/125995 2025-03-04T21:02:37.4489835Z * [new tag] ciflow/inductor/126348 -> ciflow/inductor/126348 2025-03-04T21:02:37.4490801Z * [new tag] ciflow/inductor/127011 -> ciflow/inductor/127011 2025-03-04T21:02:37.4492210Z * [new tag] ciflow/inductor/127171 -> ciflow/inductor/127171 2025-03-04T21:02:37.4493377Z * [new tag] ciflow/inductor/127293 -> ciflow/inductor/127293 2025-03-04T21:02:37.4494611Z * [new tag] ciflow/inductor/127294 -> ciflow/inductor/127294 2025-03-04T21:02:37.4495876Z * [new tag] ciflow/inductor/129352 -> ciflow/inductor/129352 2025-03-04T21:02:37.4497085Z * [new tag] ciflow/inductor/129420 -> ciflow/inductor/129420 2025-03-04T21:02:37.4498270Z * [new tag] ciflow/inductor/130141 -> ciflow/inductor/130141 2025-03-04T21:02:37.4499560Z * [new tag] ciflow/inductor/130499 -> ciflow/inductor/130499 2025-03-04T21:02:37.4500731Z * [new tag] ciflow/inductor/130887 -> ciflow/inductor/130887 2025-03-04T21:02:37.4501882Z * [new tag] ciflow/inductor/131354 -> ciflow/inductor/131354 2025-03-04T21:02:37.4503115Z * [new tag] ciflow/inductor/132021 -> ciflow/inductor/132021 2025-03-04T21:02:37.4504294Z * [new tag] ciflow/inductor/132414 -> ciflow/inductor/132414 2025-03-04T21:02:37.4505435Z * [new tag] ciflow/inductor/133044 -> ciflow/inductor/133044 2025-03-04T21:02:37.4506658Z * [new tag] ciflow/inductor/133121 -> ciflow/inductor/133121 2025-03-04T21:02:37.4507895Z * [new tag] ciflow/inductor/133287 -> ciflow/inductor/133287 2025-03-04T21:02:37.4508868Z * [new tag] ciflow/inductor/133289 -> ciflow/inductor/133289 2025-03-04T21:02:37.4510189Z * [new tag] ciflow/inductor/133296 -> ciflow/inductor/133296 2025-03-04T21:02:37.4511367Z * [new tag] ciflow/inductor/133297 -> ciflow/inductor/133297 2025-03-04T21:02:37.4512487Z * [new tag] ciflow/inductor/133315 -> ciflow/inductor/133315 2025-03-04T21:02:37.4514152Z * [new tag] ciflow/inductor/133392 -> ciflow/inductor/133392 2025-03-04T21:02:37.4515330Z * [new tag] ciflow/inductor/133419 -> ciflow/inductor/133419 2025-03-04T21:02:37.4516555Z * [new tag] ciflow/inductor/133423 -> ciflow/inductor/133423 2025-03-04T21:02:37.4517707Z * [new tag] ciflow/inductor/133667 -> ciflow/inductor/133667 2025-03-04T21:02:37.4518943Z * [new tag] ciflow/inductor/133753 -> ciflow/inductor/133753 2025-03-04T21:02:37.4520234Z * [new tag] ciflow/inductor/134592 -> ciflow/inductor/134592 2025-03-04T21:02:37.4521385Z * [new tag] ciflow/inductor/134681 -> ciflow/inductor/134681 2025-03-04T21:02:37.4522605Z * [new tag] ciflow/inductor/135708 -> ciflow/inductor/135708 2025-03-04T21:02:37.4523797Z * [new tag] ciflow/inductor/135792 -> ciflow/inductor/135792 2025-03-04T21:02:37.4524943Z * [new tag] ciflow/inductor/136355 -> ciflow/inductor/136355 2025-03-04T21:02:37.4526093Z * [new tag] 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ciflow/inductor/141393 -> ciflow/inductor/141393 2025-03-04T21:02:37.4567732Z * [new tag] ciflow/inductor/141641 -> ciflow/inductor/141641 2025-03-04T21:02:37.4569225Z * [new tag] ciflow/inductor/141684 -> ciflow/inductor/141684 2025-03-04T21:02:37.4570405Z * [new tag] ciflow/inductor/141700 -> ciflow/inductor/141700 2025-03-04T21:02:37.4571703Z * [new tag] ciflow/inductor/141730 -> ciflow/inductor/141730 2025-03-04T21:02:37.4572902Z * [new tag] ciflow/inductor/141842 -> ciflow/inductor/141842 2025-03-04T21:02:37.4574028Z * [new tag] ciflow/inductor/141940 -> ciflow/inductor/141940 2025-03-04T21:02:37.4575217Z * [new tag] ciflow/inductor/141944 -> ciflow/inductor/141944 2025-03-04T21:02:37.4576578Z * [new tag] ciflow/inductor/141961 -> ciflow/inductor/141961 2025-03-04T21:02:37.4577449Z * [new tag] ciflow/inductor/142091 -> ciflow/inductor/142091 2025-03-04T21:02:37.4578855Z * [new tag] ciflow/inductor/142092 -> ciflow/inductor/142092 2025-03-04T21:02:37.4580188Z * [new tag] ciflow/inductor/142163 -> ciflow/inductor/142163 2025-03-04T21:02:37.4581589Z * [new tag] ciflow/inductor/142272 -> ciflow/inductor/142272 2025-03-04T21:02:37.4583056Z * [new tag] ciflow/inductor/142273 -> ciflow/inductor/142273 2025-03-04T21:02:37.4584242Z * [new tag] ciflow/inductor/142295 -> ciflow/inductor/142295 2025-03-04T21:02:37.4585426Z * [new tag] ciflow/inductor/142296 -> ciflow/inductor/142296 2025-03-04T21:02:37.4586710Z * [new tag] ciflow/inductor/142309 -> ciflow/inductor/142309 2025-03-04T21:02:37.4587904Z * [new tag] ciflow/inductor/142350 -> ciflow/inductor/142350 2025-03-04T21:02:37.4589095Z * [new tag] ciflow/inductor/142372 -> ciflow/inductor/142372 2025-03-04T21:02:37.4590281Z * [new tag] ciflow/inductor/142483 -> ciflow/inductor/142483 2025-03-04T21:02:37.4591452Z * [new tag] ciflow/inductor/142851 -> ciflow/inductor/142851 2025-03-04T21:02:37.4593121Z * [new tag] ciflow/inductor/143044 -> ciflow/inductor/143044 2025-03-04T21:02:37.4594236Z * [new tag] ciflow/inductor/143103 -> ciflow/inductor/143103 2025-03-04T21:02:37.4595397Z * [new tag] ciflow/inductor/143220 -> ciflow/inductor/143220 2025-03-04T21:02:37.4596724Z * [new tag] ciflow/inductor/143256 -> ciflow/inductor/143256 2025-03-04T21:02:37.4597912Z * [new tag] ciflow/inductor/143275 -> ciflow/inductor/143275 2025-03-04T21:02:37.4598966Z * [new tag] ciflow/inductor/143313 -> ciflow/inductor/143313 2025-03-04T21:02:37.4600275Z * [new tag] ciflow/inductor/143411 -> ciflow/inductor/143411 2025-03-04T21:02:37.4601515Z * [new tag] ciflow/inductor/143457 -> ciflow/inductor/143457 2025-03-04T21:02:37.4602955Z * [new tag] ciflow/inductor/143464 -> ciflow/inductor/143464 2025-03-04T21:02:37.4604239Z * [new tag] ciflow/inductor/143475 -> ciflow/inductor/143475 2025-03-04T21:02:37.4605397Z * [new tag] ciflow/inductor/143525 -> ciflow/inductor/143525 2025-03-04T21:02:37.4606942Z * [new tag] ciflow/inductor/143527 -> ciflow/inductor/143527 2025-03-04T21:02:37.4608190Z * [new tag] ciflow/inductor/143533 -> ciflow/inductor/143533 2025-03-04T21:02:37.4609472Z * [new tag] ciflow/inductor/143534 -> ciflow/inductor/143534 2025-03-04T21:02:37.4610799Z * [new tag] ciflow/inductor/143544 -> ciflow/inductor/143544 2025-03-04T21:02:37.4611992Z * [new tag] ciflow/inductor/143666 -> ciflow/inductor/143666 2025-03-04T21:02:37.4613165Z * [new tag] ciflow/inductor/143671 -> ciflow/inductor/143671 2025-03-04T21:02:37.4614331Z * [new tag] ciflow/inductor/143712 -> ciflow/inductor/143712 2025-03-04T21:02:37.4615553Z * [new tag] ciflow/inductor/143812 -> ciflow/inductor/143812 2025-03-04T21:02:37.4616854Z * [new tag] ciflow/inductor/143833 -> ciflow/inductor/143833 2025-03-04T21:02:37.4618145Z * [new tag] ciflow/inductor/143961 -> ciflow/inductor/143961 2025-03-04T21:02:37.4619370Z * [new tag] ciflow/inductor/143987 -> ciflow/inductor/143987 2025-03-04T21:02:37.4620868Z * [new tag] ciflow/inductor/144008 -> ciflow/inductor/144008 2025-03-04T21:02:37.4622332Z * [new tag] ciflow/inductor/144017 -> ciflow/inductor/144017 2025-03-04T21:02:37.4623219Z * [new tag] ciflow/inductor/144073 -> ciflow/inductor/144073 2025-03-04T21:02:37.4624545Z * [new tag] ciflow/inductor/144097 -> ciflow/inductor/144097 2025-03-04T21:02:37.4625738Z * [new tag] ciflow/inductor/144120 -> ciflow/inductor/144120 2025-03-04T21:02:37.4627108Z * [new tag] ciflow/inductor/144172 -> ciflow/inductor/144172 2025-03-04T21:02:37.4628399Z * [new tag] ciflow/inductor/144234 -> ciflow/inductor/144234 2025-03-04T21:02:37.4629745Z * [new tag] ciflow/inductor/144272 -> ciflow/inductor/144272 2025-03-04T21:02:37.4630939Z * [new tag] ciflow/inductor/144288 -> ciflow/inductor/144288 2025-03-04T21:02:37.4632120Z * [new tag] ciflow/inductor/144293 -> ciflow/inductor/144293 2025-03-04T21:02:37.4633759Z * [new tag] ciflow/inductor/144294 -> ciflow/inductor/144294 2025-03-04T21:02:37.4634958Z * [new tag] ciflow/inductor/144332 -> ciflow/inductor/144332 2025-03-04T21:02:37.4636114Z * [new tag] ciflow/inductor/144333 -> ciflow/inductor/144333 2025-03-04T21:02:37.4637291Z * [new tag] ciflow/inductor/144349 -> ciflow/inductor/144349 2025-03-04T21:02:37.4638487Z * [new tag] ciflow/inductor/144353 -> ciflow/inductor/144353 2025-03-04T21:02:37.4639653Z * [new tag] ciflow/inductor/144365 -> ciflow/inductor/144365 2025-03-04T21:02:37.4640831Z * [new tag] ciflow/inductor/144366 -> ciflow/inductor/144366 2025-03-04T21:02:37.4642025Z * [new tag] ciflow/inductor/144405 -> ciflow/inductor/144405 2025-03-04T21:02:37.4643235Z * [new tag] ciflow/inductor/144413 -> ciflow/inductor/144413 2025-03-04T21:02:37.4644242Z * [new tag] ciflow/inductor/144414 -> ciflow/inductor/144414 2025-03-04T21:02:37.4645597Z * [new tag] ciflow/inductor/144438 -> ciflow/inductor/144438 2025-03-04T21:02:37.4646898Z * [new tag] ciflow/inductor/144452 -> ciflow/inductor/144452 2025-03-04T21:02:37.4648137Z * [new tag] ciflow/inductor/144458 -> ciflow/inductor/144458 2025-03-04T21:02:37.4649351Z * [new tag] ciflow/inductor/144501 -> ciflow/inductor/144501 2025-03-04T21:02:37.4650543Z * [new tag] ciflow/inductor/144505 -> ciflow/inductor/144505 2025-03-04T21:02:37.4651785Z * [new tag] ciflow/inductor/144507 -> ciflow/inductor/144507 2025-03-04T21:02:37.4653023Z * [new tag] ciflow/inductor/144516 -> ciflow/inductor/144516 2025-03-04T21:02:37.4654142Z * [new tag] ciflow/inductor/144542 -> ciflow/inductor/144542 2025-03-04T21:02:37.4655337Z * [new tag] ciflow/inductor/144548 -> ciflow/inductor/144548 2025-03-04T21:02:37.4656558Z * [new tag] ciflow/inductor/144551 -> ciflow/inductor/144551 2025-03-04T21:02:37.4657743Z * [new tag] ciflow/inductor/144553 -> ciflow/inductor/144553 2025-03-04T21:02:37.4658917Z * [new tag] ciflow/inductor/144555 -> ciflow/inductor/144555 2025-03-04T21:02:37.4660091Z * [new tag] ciflow/inductor/144556 -> ciflow/inductor/144556 2025-03-04T21:02:37.4661645Z * [new tag] ciflow/inductor/144579 -> ciflow/inductor/144579 2025-03-04T21:02:37.4664944Z * [new tag] ciflow/inductor/144598 -> ciflow/inductor/144598 2025-03-04T21:02:37.4666047Z * [new tag] ciflow/inductor/144712 -> ciflow/inductor/144712 2025-03-04T21:02:37.4667191Z * [new tag] ciflow/inductor/144721 -> ciflow/inductor/144721 2025-03-04T21:02:37.4668584Z * [new tag] ciflow/inductor/144724 -> ciflow/inductor/144724 2025-03-04T21:02:37.4669940Z * [new tag] ciflow/inductor/144733 -> ciflow/inductor/144733 2025-03-04T21:02:37.4670926Z * [new tag] ciflow/inductor/144741 -> ciflow/inductor/144741 2025-03-04T21:02:37.4672183Z * [new tag] ciflow/inductor/144765 -> ciflow/inductor/144765 2025-03-04T21:02:37.4673490Z * [new tag] ciflow/inductor/144771 -> ciflow/inductor/144771 2025-03-04T21:02:37.4675319Z * [new tag] ciflow/inductor/144880 -> ciflow/inductor/144880 2025-03-04T21:02:37.4676513Z * [new tag] ciflow/inductor/144905 -> ciflow/inductor/144905 2025-03-04T21:02:37.4677689Z * [new tag] ciflow/inductor/144925 -> ciflow/inductor/144925 2025-03-04T21:02:37.4678857Z * [new tag] ciflow/inductor/144943 -> ciflow/inductor/144943 2025-03-04T21:02:37.4680188Z * [new tag] ciflow/inductor/144953 -> ciflow/inductor/144953 2025-03-04T21:02:37.4681377Z * [new tag] ciflow/inductor/144975 -> ciflow/inductor/144975 2025-03-04T21:02:37.4682559Z * [new tag] ciflow/inductor/144979 -> ciflow/inductor/144979 2025-03-04T21:02:37.4683748Z * [new tag] ciflow/inductor/144986 -> ciflow/inductor/144986 2025-03-04T21:02:37.4685071Z * [new tag] ciflow/inductor/144992 -> ciflow/inductor/144992 2025-03-04T21:02:37.4686255Z * [new tag] ciflow/inductor/145024 -> ciflow/inductor/145024 2025-03-04T21:02:37.4687416Z * [new tag] ciflow/inductor/145061 -> ciflow/inductor/145061 2025-03-04T21:02:37.4688901Z * [new tag] ciflow/inductor/145117 -> ciflow/inductor/145117 2025-03-04T21:02:37.4690108Z * [new tag] ciflow/inductor/145119 -> ciflow/inductor/145119 2025-03-04T21:02:37.4691242Z * [new tag] ciflow/inductor/145150 -> ciflow/inductor/145150 2025-03-04T21:02:37.4692593Z * [new tag] ciflow/inductor/145153 -> ciflow/inductor/145153 2025-03-04T21:02:37.4693799Z * [new tag] ciflow/inductor/145254 -> ciflow/inductor/145254 2025-03-04T21:02:37.4694961Z * [new tag] ciflow/inductor/145331 -> ciflow/inductor/145331 2025-03-04T21:02:37.4696123Z * [new tag] ciflow/inductor/145353 -> ciflow/inductor/145353 2025-03-04T21:02:37.4697297Z * [new tag] ciflow/inductor/145475 -> ciflow/inductor/145475 2025-03-04T21:02:37.4698481Z * [new tag] ciflow/inductor/145523 -> ciflow/inductor/145523 2025-03-04T21:02:37.4699662Z * [new tag] ciflow/inductor/145540 -> ciflow/inductor/145540 2025-03-04T21:02:37.4700815Z * [new tag] ciflow/inductor/145559 -> ciflow/inductor/145559 2025-03-04T21:02:37.4702018Z * [new tag] ciflow/inductor/145562 -> ciflow/inductor/145562 2025-03-04T21:02:37.4703214Z * [new tag] ciflow/inductor/145594 -> ciflow/inductor/145594 2025-03-04T21:02:37.4704353Z * [new tag] ciflow/inductor/145595 -> ciflow/inductor/145595 2025-03-04T21:02:37.4705544Z * [new tag] ciflow/inductor/145605 -> ciflow/inductor/145605 2025-03-04T21:02:37.4706711Z * [new tag] ciflow/inductor/145612 -> ciflow/inductor/145612 2025-03-04T21:02:37.4707976Z * [new tag] ciflow/inductor/145636 -> ciflow/inductor/145636 2025-03-04T21:02:37.4709114Z * [new tag] ciflow/inductor/145647 -> ciflow/inductor/145647 2025-03-04T21:02:37.4710358Z * [new tag] ciflow/inductor/145681 -> ciflow/inductor/145681 2025-03-04T21:02:37.4711539Z * [new tag] ciflow/inductor/145865 -> ciflow/inductor/145865 2025-03-04T21:02:37.4712747Z * [new tag] ciflow/inductor/145885 -> ciflow/inductor/145885 2025-03-04T21:02:37.4714013Z * [new tag] ciflow/inductor/145911 -> ciflow/inductor/145911 2025-03-04T21:02:37.4715118Z * [new tag] ciflow/inductor/145922 -> ciflow/inductor/145922 2025-03-04T21:02:37.4716320Z * [new tag] ciflow/inductor/145936 -> ciflow/inductor/145936 2025-03-04T21:02:37.4717474Z * [new tag] ciflow/inductor/145969 -> ciflow/inductor/145969 2025-03-04T21:02:37.4718632Z * [new tag] ciflow/inductor/145979 -> ciflow/inductor/145979 2025-03-04T21:02:37.4719851Z * [new tag] ciflow/inductor/145992 -> ciflow/inductor/145992 2025-03-04T21:02:37.4721172Z * [new tag] ciflow/inductor/146051 -> ciflow/inductor/146051 2025-03-04T21:02:37.4722439Z * [new tag] ciflow/inductor/146063 -> ciflow/inductor/146063 2025-03-04T21:02:37.4723586Z * [new tag] ciflow/inductor/146101 -> ciflow/inductor/146101 2025-03-04T21:02:37.4725072Z * [new tag] ciflow/inductor/146115 -> ciflow/inductor/146115 2025-03-04T21:02:37.4726327Z * [new tag] ciflow/inductor/146135 -> ciflow/inductor/146135 2025-03-04T21:02:37.4727654Z * [new tag] ciflow/inductor/146171 -> ciflow/inductor/146171 2025-03-04T21:02:37.4728936Z * [new tag] ciflow/inductor/146172 -> ciflow/inductor/146172 2025-03-04T21:02:37.4730144Z * [new tag] ciflow/inductor/146176 -> ciflow/inductor/146176 2025-03-04T21:02:37.4731313Z * [new tag] ciflow/inductor/146180 -> ciflow/inductor/146180 2025-03-04T21:02:37.4732495Z * [new tag] ciflow/inductor/146218 -> ciflow/inductor/146218 2025-03-04T21:02:37.4734004Z * [new tag] ciflow/inductor/146228 -> ciflow/inductor/146228 2025-03-04T21:02:37.4735184Z * [new tag] ciflow/inductor/146264 -> ciflow/inductor/146264 2025-03-04T21:02:37.4736490Z * [new tag] ciflow/inductor/146267 -> ciflow/inductor/146267 2025-03-04T21:02:37.4737726Z * [new tag] ciflow/inductor/146275 -> ciflow/inductor/146275 2025-03-04T21:02:37.4738947Z * [new tag] ciflow/inductor/146280 -> ciflow/inductor/146280 2025-03-04T21:02:37.4740092Z * [new tag] ciflow/inductor/146288 -> ciflow/inductor/146288 2025-03-04T21:02:37.4741276Z * [new tag] ciflow/inductor/146319 -> ciflow/inductor/146319 2025-03-04T21:02:37.4742495Z * [new tag] ciflow/inductor/146335 -> ciflow/inductor/146335 2025-03-04T21:02:37.4743681Z * [new tag] ciflow/inductor/146341 -> ciflow/inductor/146341 2025-03-04T21:02:37.4744865Z * [new tag] ciflow/inductor/146393 -> ciflow/inductor/146393 2025-03-04T21:02:37.4746043Z * [new tag] ciflow/inductor/146395 -> ciflow/inductor/146395 2025-03-04T21:02:37.4747270Z * [new tag] ciflow/inductor/146415 -> ciflow/inductor/146415 2025-03-04T21:02:37.4748470Z * [new tag] ciflow/inductor/146421 -> ciflow/inductor/146421 2025-03-04T21:02:37.4749641Z * [new tag] ciflow/inductor/146436 -> ciflow/inductor/146436 2025-03-04T21:02:37.4750821Z * [new tag] ciflow/inductor/146455 -> ciflow/inductor/146455 2025-03-04T21:02:37.4752006Z * [new tag] ciflow/inductor/146499 -> ciflow/inductor/146499 2025-03-04T21:02:37.4753668Z * [new tag] ciflow/inductor/146500 -> ciflow/inductor/146500 2025-03-04T21:02:37.4754868Z * [new tag] ciflow/inductor/146501 -> ciflow/inductor/146501 2025-03-04T21:02:37.4756072Z * [new tag] ciflow/inductor/146502 -> ciflow/inductor/146502 2025-03-04T21:02:37.4757284Z * [new tag] ciflow/inductor/146504 -> ciflow/inductor/146504 2025-03-04T21:02:37.4758446Z * [new tag] ciflow/inductor/146505 -> ciflow/inductor/146505 2025-03-04T21:02:37.4759803Z * [new tag] ciflow/inductor/146506 -> ciflow/inductor/146506 2025-03-04T21:02:37.4760886Z * [new tag] ciflow/inductor/146526 -> ciflow/inductor/146526 2025-03-04T21:02:37.4762665Z * [new tag] ciflow/inductor/146530 -> ciflow/inductor/146530 2025-03-04T21:02:37.4763813Z * [new tag] ciflow/inductor/146535 -> ciflow/inductor/146535 2025-03-04T21:02:37.4764975Z * [new tag] ciflow/inductor/146558 -> ciflow/inductor/146558 2025-03-04T21:02:37.4766161Z * [new tag] ciflow/inductor/146561 -> ciflow/inductor/146561 2025-03-04T21:02:37.4767462Z * [new tag] ciflow/inductor/146562 -> ciflow/inductor/146562 2025-03-04T21:02:37.4768804Z * [new tag] ciflow/inductor/146636 -> ciflow/inductor/146636 2025-03-04T21:02:37.4770006Z * [new tag] ciflow/inductor/146661 -> ciflow/inductor/146661 2025-03-04T21:02:37.4771251Z * [new tag] ciflow/inductor/146678 -> ciflow/inductor/146678 2025-03-04T21:02:37.4772419Z * [new tag] ciflow/inductor/146706 -> ciflow/inductor/146706 2025-03-04T21:02:37.4773523Z * [new tag] ciflow/inductor/146718 -> ciflow/inductor/146718 2025-03-04T21:02:37.4774726Z * [new tag] ciflow/inductor/146779 -> ciflow/inductor/146779 2025-03-04T21:02:37.4776145Z * [new tag] ciflow/inductor/146781 -> ciflow/inductor/146781 2025-03-04T21:02:37.4777458Z * [new tag] ciflow/inductor/146823 -> ciflow/inductor/146823 2025-03-04T21:02:37.4778652Z * [new tag] ciflow/inductor/146826 -> ciflow/inductor/146826 2025-03-04T21:02:37.4779828Z * [new tag] ciflow/inductor/146827 -> ciflow/inductor/146827 2025-03-04T21:02:37.4781242Z * [new tag] ciflow/inductor/146844 -> ciflow/inductor/146844 2025-03-04T21:02:37.4782253Z * [new tag] ciflow/inductor/146845 -> ciflow/inductor/146845 2025-03-04T21:02:37.4783590Z * [new tag] ciflow/inductor/146850 -> ciflow/inductor/146850 2025-03-04T21:02:37.4784812Z * [new tag] ciflow/inductor/146864 -> ciflow/inductor/146864 2025-03-04T21:02:37.4786000Z * [new tag] ciflow/inductor/146870 -> ciflow/inductor/146870 2025-03-04T21:02:37.4787155Z * [new tag] ciflow/inductor/146873 -> ciflow/inductor/146873 2025-03-04T21:02:37.4788659Z * [new tag] ciflow/inductor/146874 -> ciflow/inductor/146874 2025-03-04T21:02:37.4789858Z * [new tag] ciflow/inductor/146894 -> ciflow/inductor/146894 2025-03-04T21:02:37.4791131Z * [new tag] ciflow/inductor/146895 -> ciflow/inductor/146895 2025-03-04T21:02:37.4792473Z * [new tag] ciflow/inductor/146919 -> ciflow/inductor/146919 2025-03-04T21:02:37.4793663Z * [new tag] ciflow/inductor/146921 -> ciflow/inductor/146921 2025-03-04T21:02:37.4794887Z * [new tag] ciflow/inductor/146928 -> ciflow/inductor/146928 2025-03-04T21:02:37.4796105Z * [new tag] ciflow/inductor/146935 -> ciflow/inductor/146935 2025-03-04T21:02:37.4797291Z * [new tag] ciflow/inductor/146942 -> ciflow/inductor/146942 2025-03-04T21:02:37.4798603Z * [new tag] ciflow/inductor/146962 -> ciflow/inductor/146962 2025-03-04T21:02:37.4799906Z * [new tag] ciflow/inductor/146983 -> ciflow/inductor/146983 2025-03-04T21:02:37.4801275Z * [new tag] ciflow/inductor/146989 -> ciflow/inductor/146989 2025-03-04T21:02:37.4802669Z * [new tag] ciflow/inductor/147007 -> ciflow/inductor/147007 2025-03-04T21:02:37.4803849Z * [new tag] ciflow/inductor/147014 -> ciflow/inductor/147014 2025-03-04T21:02:37.4805111Z * [new tag] ciflow/inductor/147021 -> ciflow/inductor/147021 2025-03-04T21:02:37.4806476Z * [new tag] ciflow/inductor/147036 -> ciflow/inductor/147036 2025-03-04T21:02:37.4807633Z * [new tag] ciflow/inductor/147049 -> ciflow/inductor/147049 2025-03-04T21:02:37.4808937Z * [new tag] ciflow/inductor/147105 -> ciflow/inductor/147105 2025-03-04T21:02:37.4810079Z * [new tag] ciflow/inductor/147146 -> ciflow/inductor/147146 2025-03-04T21:02:37.4811264Z * [new tag] ciflow/inductor/147149 -> ciflow/inductor/147149 2025-03-04T21:02:37.4812539Z * [new tag] ciflow/inductor/147155 -> ciflow/inductor/147155 2025-03-04T21:02:37.4813711Z * [new tag] ciflow/inductor/147178 -> ciflow/inductor/147178 2025-03-04T21:02:37.4814970Z * [new tag] ciflow/inductor/147205 -> ciflow/inductor/147205 2025-03-04T21:02:37.4816091Z * [new tag] ciflow/inductor/147225 -> ciflow/inductor/147225 2025-03-04T21:02:37.4817317Z * [new tag] ciflow/inductor/147229 -> ciflow/inductor/147229 2025-03-04T21:02:37.4818498Z * [new tag] ciflow/inductor/147269 -> ciflow/inductor/147269 2025-03-04T21:02:37.4819687Z * [new tag] ciflow/inductor/147272 -> ciflow/inductor/147272 2025-03-04T21:02:37.4821067Z * [new tag] ciflow/inductor/147314 -> ciflow/inductor/147314 2025-03-04T21:02:37.4822234Z * [new tag] ciflow/inductor/147315 -> ciflow/inductor/147315 2025-03-04T21:02:37.4823485Z * [new tag] ciflow/inductor/147320 -> ciflow/inductor/147320 2025-03-04T21:02:37.4824686Z * [new tag] ciflow/inductor/147341 -> ciflow/inductor/147341 2025-03-04T21:02:37.4825927Z * [new tag] ciflow/inductor/147360 -> ciflow/inductor/147360 2025-03-04T21:02:37.4827122Z * [new tag] ciflow/inductor/147368 -> ciflow/inductor/147368 2025-03-04T21:02:37.4828352Z * [new tag] ciflow/inductor/147403 -> ciflow/inductor/147403 2025-03-04T21:02:37.4829573Z * [new tag] ciflow/inductor/147410 -> ciflow/inductor/147410 2025-03-04T21:02:37.4830732Z * [new tag] ciflow/inductor/147414 -> ciflow/inductor/147414 2025-03-04T21:02:37.4831937Z * [new tag] ciflow/inductor/147415 -> ciflow/inductor/147415 2025-03-04T21:02:37.4833671Z * [new tag] ciflow/inductor/147422 -> ciflow/inductor/147422 2025-03-04T21:02:37.4835103Z * [new tag] ciflow/inductor/147445 -> ciflow/inductor/147445 2025-03-04T21:02:37.4836484Z * [new tag] ciflow/inductor/147452 -> ciflow/inductor/147452 2025-03-04T21:02:37.4837772Z * [new tag] ciflow/inductor/147481 -> ciflow/inductor/147481 2025-03-04T21:02:37.4838964Z * [new tag] ciflow/inductor/147485 -> ciflow/inductor/147485 2025-03-04T21:02:37.4840225Z * [new tag] ciflow/inductor/147498 -> ciflow/inductor/147498 2025-03-04T21:02:37.4841427Z * [new tag] ciflow/inductor/147514 -> ciflow/inductor/147514 2025-03-04T21:02:37.4842635Z * [new tag] ciflow/inductor/147528 -> ciflow/inductor/147528 2025-03-04T21:02:37.4843845Z * [new tag] ciflow/inductor/147552 -> ciflow/inductor/147552 2025-03-04T21:02:37.4845120Z * [new tag] ciflow/inductor/147557 -> ciflow/inductor/147557 2025-03-04T21:02:37.4846316Z * [new tag] ciflow/inductor/147561 -> ciflow/inductor/147561 2025-03-04T21:02:37.4847664Z * [new tag] ciflow/inductor/147562 -> ciflow/inductor/147562 2025-03-04T21:02:37.4849052Z * [new tag] ciflow/inductor/147574 -> ciflow/inductor/147574 2025-03-04T21:02:37.4850317Z * [new tag] ciflow/inductor/147583 -> ciflow/inductor/147583 2025-03-04T21:02:37.4851654Z * [new tag] ciflow/inductor/147592 -> ciflow/inductor/147592 2025-03-04T21:02:37.4852796Z * [new tag] ciflow/inductor/147603 -> ciflow/inductor/147603 2025-03-04T21:02:37.4854036Z * [new tag] ciflow/inductor/147619 -> ciflow/inductor/147619 2025-03-04T21:02:37.4855256Z * [new tag] ciflow/inductor/147648 -> ciflow/inductor/147648 2025-03-04T21:02:37.4856448Z * [new tag] ciflow/inductor/147660 -> ciflow/inductor/147660 2025-03-04T21:02:37.4857659Z * [new tag] ciflow/inductor/147727 -> ciflow/inductor/147727 2025-03-04T21:02:37.4858885Z * [new tag] ciflow/inductor/147741 -> ciflow/inductor/147741 2025-03-04T21:02:37.4860194Z * [new tag] ciflow/inductor/147745 -> ciflow/inductor/147745 2025-03-04T21:02:37.4861769Z * [new tag] ciflow/inductor/147768 -> ciflow/inductor/147768 2025-03-04T21:02:37.4863015Z * [new tag] ciflow/inductor/147790 -> ciflow/inductor/147790 2025-03-04T21:02:37.4864157Z * [new tag] ciflow/inductor/147797 -> ciflow/inductor/147797 2025-03-04T21:02:37.4865393Z * [new tag] ciflow/inductor/147798 -> ciflow/inductor/147798 2025-03-04T21:02:37.4866655Z * [new tag] ciflow/inductor/147800 -> ciflow/inductor/147800 2025-03-04T21:02:37.4867901Z * [new tag] ciflow/inductor/147817 -> ciflow/inductor/147817 2025-03-04T21:02:37.4869242Z * [new tag] ciflow/inductor/147821 -> ciflow/inductor/147821 2025-03-04T21:02:37.4870472Z * [new tag] ciflow/inductor/147836 -> ciflow/inductor/147836 2025-03-04T21:02:37.4871706Z * [new tag] ciflow/inductor/147863 -> ciflow/inductor/147863 2025-03-04T21:02:37.4873062Z * [new tag] ciflow/inductor/147870 -> ciflow/inductor/147870 2025-03-04T21:02:37.4874206Z * [new tag] ciflow/inductor/147881 -> ciflow/inductor/147881 2025-03-04T21:02:37.4875670Z * [new tag] ciflow/inductor/147899 -> ciflow/inductor/147899 2025-03-04T21:02:37.4876977Z * [new tag] ciflow/inductor/147902 -> ciflow/inductor/147902 2025-03-04T21:02:37.4878171Z * [new tag] ciflow/inductor/147903 -> ciflow/inductor/147903 2025-03-04T21:02:37.4879648Z * [new tag] ciflow/inductor/147908 -> ciflow/inductor/147908 2025-03-04T21:02:37.4880871Z * [new tag] ciflow/inductor/147910 -> ciflow/inductor/147910 2025-03-04T21:02:37.4882114Z * [new tag] ciflow/inductor/147915 -> ciflow/inductor/147915 2025-03-04T21:02:37.4883356Z * [new tag] ciflow/inductor/147917 -> ciflow/inductor/147917 2025-03-04T21:02:37.4884690Z * [new tag] ciflow/inductor/147927 -> ciflow/inductor/147927 2025-03-04T21:02:37.4885966Z * [new tag] ciflow/inductor/147945 -> ciflow/inductor/147945 2025-03-04T21:02:37.4887379Z * [new tag] ciflow/inductor/147955 -> ciflow/inductor/147955 2025-03-04T21:02:37.4889092Z * [new tag] ciflow/inductor/147956 -> ciflow/inductor/147956 2025-03-04T21:02:37.4890433Z * [new tag] ciflow/inductor/147957 -> ciflow/inductor/147957 2025-03-04T21:02:37.4891758Z * [new tag] ciflow/inductor/147958 -> ciflow/inductor/147958 2025-03-04T21:02:37.4893126Z * [new tag] ciflow/inductor/147959 -> ciflow/inductor/147959 2025-03-04T21:02:37.4894519Z * [new tag] ciflow/inductor/147960 -> ciflow/inductor/147960 2025-03-04T21:02:37.4895740Z * [new tag] ciflow/inductor/147962 -> ciflow/inductor/147962 2025-03-04T21:02:37.4897002Z * [new tag] ciflow/inductor/147990 -> ciflow/inductor/147990 2025-03-04T21:02:37.4898251Z * [new tag] ciflow/inductor/148002 -> ciflow/inductor/148002 2025-03-04T21:02:37.4899744Z * [new tag] ciflow/inductor/148007 -> ciflow/inductor/148007 2025-03-04T21:02:37.4901081Z * [new tag] ciflow/inductor/148008 -> ciflow/inductor/148008 2025-03-04T21:02:37.4902447Z * [new tag] ciflow/inductor/148010 -> ciflow/inductor/148010 2025-03-04T21:02:37.4903784Z * [new tag] ciflow/inductor/148042 -> ciflow/inductor/148042 2025-03-04T21:02:37.4905136Z * [new tag] ciflow/inductor/148046 -> ciflow/inductor/148046 2025-03-04T21:02:37.4906500Z * [new tag] ciflow/inductor/148063 -> ciflow/inductor/148063 2025-03-04T21:02:37.4907846Z * [new tag] ciflow/inductor/148083 -> ciflow/inductor/148083 2025-03-04T21:02:37.4909212Z * [new tag] ciflow/inductor/148091 -> ciflow/inductor/148091 2025-03-04T21:02:37.4910548Z * [new tag] ciflow/inductor/148092 -> ciflow/inductor/148092 2025-03-04T21:02:37.4919578Z * [new tag] ciflow/inductor/148104 -> ciflow/inductor/148104 2025-03-04T21:02:37.4919977Z * [new tag] ciflow/inductor/148130 -> ciflow/inductor/148130 2025-03-04T21:02:37.4920280Z * [new tag] ciflow/inductor/148131 -> ciflow/inductor/148131 2025-03-04T21:02:37.4920558Z * [new tag] ciflow/inductor/148132 -> ciflow/inductor/148132 2025-03-04T21:02:37.4920748Z * [new tag] ciflow/inductor/148138 -> ciflow/inductor/148138 2025-03-04T21:02:37.4920944Z * [new tag] ciflow/inductor/148139 -> ciflow/inductor/148139 2025-03-04T21:02:37.4921256Z * [new tag] ciflow/inductor/148160 -> ciflow/inductor/148160 2025-03-04T21:02:37.4922593Z * [new tag] ciflow/inductor/148163 -> ciflow/inductor/148163 2025-03-04T21:02:37.4923898Z * [new tag] ciflow/inductor/148173 -> ciflow/inductor/148173 2025-03-04T21:02:37.4925261Z * [new tag] ciflow/inductor/148174 -> ciflow/inductor/148174 2025-03-04T21:02:37.4926627Z * [new tag] ciflow/inductor/148176 -> ciflow/inductor/148176 2025-03-04T21:02:37.4928110Z * [new tag] ciflow/inductor/148186 -> ciflow/inductor/148186 2025-03-04T21:02:37.4929630Z * [new tag] ciflow/inductor/148190 -> ciflow/inductor/148190 2025-03-04T21:02:37.4930941Z * [new tag] ciflow/inductor/148202 -> ciflow/inductor/148202 2025-03-04T21:02:37.4932259Z * [new tag] ciflow/inductor/148205 -> ciflow/inductor/148205 2025-03-04T21:02:37.4933740Z * [new tag] ciflow/inductor/148206 -> ciflow/inductor/148206 2025-03-04T21:02:37.4935097Z * [new tag] ciflow/inductor/148209 -> ciflow/inductor/148209 2025-03-04T21:02:37.4936431Z * [new tag] ciflow/inductor/148210 -> ciflow/inductor/148210 2025-03-04T21:02:37.4937782Z * [new tag] ciflow/inductor/148212 -> ciflow/inductor/148212 2025-03-04T21:02:37.4939138Z * [new tag] ciflow/inductor/148220 -> ciflow/inductor/148220 2025-03-04T21:02:37.4940721Z * [new tag] ciflow/inductor/148223 -> ciflow/inductor/148223 2025-03-04T21:02:37.4942045Z * [new tag] ciflow/inductor/148233 -> ciflow/inductor/148233 2025-03-04T21:02:37.4943439Z * [new tag] ciflow/inductor/148234 -> ciflow/inductor/148234 2025-03-04T21:02:37.4944768Z * [new tag] ciflow/inductor/148235 -> ciflow/inductor/148235 2025-03-04T21:02:37.4946178Z * [new tag] ciflow/inductor/148236 -> ciflow/inductor/148236 2025-03-04T21:02:37.4947503Z * [new tag] ciflow/inductor/148243 -> ciflow/inductor/148243 2025-03-04T21:02:37.4948838Z * [new tag] ciflow/inductor/148260 -> ciflow/inductor/148260 2025-03-04T21:02:37.4950300Z * [new tag] ciflow/inductor/148261 -> ciflow/inductor/148261 2025-03-04T21:02:37.4951595Z * [new tag] ciflow/inductor/148279 -> ciflow/inductor/148279 2025-03-04T21:02:37.4952974Z * [new tag] ciflow/inductor/148288 -> ciflow/inductor/148288 2025-03-04T21:02:37.4954446Z * [new tag] ciflow/inductor/148290 -> ciflow/inductor/148290 2025-03-04T21:02:37.4955794Z * [new tag] ciflow/inductor/148292 -> ciflow/inductor/148292 2025-03-04T21:02:37.4957399Z * [new tag] ciflow/inductor/148294 -> ciflow/inductor/148294 2025-03-04T21:02:37.4958771Z * [new tag] ciflow/inductor/148303 -> ciflow/inductor/148303 2025-03-04T21:02:37.4960094Z * [new tag] ciflow/inductor/148305 -> ciflow/inductor/148305 2025-03-04T21:02:37.4961622Z * [new tag] ciflow/inductor/148323 -> ciflow/inductor/148323 2025-03-04T21:02:37.4965656Z * [new tag] ciflow/inductor/148328 -> ciflow/inductor/148328 2025-03-04T21:02:37.4966952Z * [new tag] ciflow/inductor/148357 -> ciflow/inductor/148357 2025-03-04T21:02:37.4968325Z * [new tag] ciflow/inductor/148358 -> ciflow/inductor/148358 2025-03-04T21:02:37.4969778Z * [new tag] ciflow/inductor/148359 -> ciflow/inductor/148359 2025-03-04T21:02:37.4971362Z * [new tag] ciflow/inductor/148363 -> ciflow/inductor/148363 2025-03-04T21:02:37.4972702Z * [new tag] ciflow/inductor/148364 -> ciflow/inductor/148364 2025-03-04T21:02:37.4974001Z * [new tag] ciflow/inductor/148366 -> ciflow/inductor/148366 2025-03-04T21:02:37.4975351Z * [new tag] ciflow/inductor/148367 -> ciflow/inductor/148367 2025-03-04T21:02:37.4976693Z * [new tag] ciflow/inductor/148376 -> ciflow/inductor/148376 2025-03-04T21:02:37.4978050Z * [new tag] ciflow/inductor/148377 -> ciflow/inductor/148377 2025-03-04T21:02:37.4979655Z * [new tag] ciflow/inductor/148380 -> ciflow/inductor/148380 2025-03-04T21:02:37.4981088Z * [new tag] ciflow/inductor/148381 -> ciflow/inductor/148381 2025-03-04T21:02:37.4982451Z * [new tag] ciflow/inductor/148385 -> ciflow/inductor/148385 2025-03-04T21:02:37.4983782Z * [new tag] ciflow/inductor/148386 -> ciflow/inductor/148386 2025-03-04T21:02:37.4985145Z * [new tag] ciflow/inductor/148401 -> ciflow/inductor/148401 2025-03-04T21:02:37.4986636Z * [new tag] ciflow/inductor/148407 -> ciflow/inductor/148407 2025-03-04T21:02:37.4987999Z * [new tag] ciflow/inductor/148413 -> ciflow/inductor/148413 2025-03-04T21:02:37.4989335Z * [new tag] ciflow/inductor/148414 -> ciflow/inductor/148414 2025-03-04T21:02:37.4990685Z * [new tag] ciflow/inductor/148415 -> ciflow/inductor/148415 2025-03-04T21:02:37.4992167Z * [new tag] ciflow/inductor/148418 -> ciflow/inductor/148418 2025-03-04T21:02:37.4993550Z * [new tag] ciflow/inductor/148423 -> ciflow/inductor/148423 2025-03-04T21:02:37.4994966Z * [new tag] ciflow/inductor/148424 -> ciflow/inductor/148424 2025-03-04T21:02:37.4996300Z * [new tag] ciflow/inductor/148430 -> ciflow/inductor/148430 2025-03-04T21:02:37.4997837Z * [new tag] ciflow/inductor/148432 -> ciflow/inductor/148432 2025-03-04T21:02:37.4999354Z * [new tag] ciflow/inductor/148445 -> ciflow/inductor/148445 2025-03-04T21:02:37.5000711Z * [new tag] ciflow/inductor/148450 -> ciflow/inductor/148450 2025-03-04T21:02:37.5002055Z * [new tag] ciflow/inductor/148454 -> ciflow/inductor/148454 2025-03-04T21:02:37.5003391Z * [new tag] ciflow/inductor/148459 -> ciflow/inductor/148459 2025-03-04T21:02:37.5004950Z * [new tag] ciflow/inductor/148470 -> ciflow/inductor/148470 2025-03-04T21:02:37.5006245Z * [new tag] ciflow/inductor/148480 -> ciflow/inductor/148480 2025-03-04T21:02:37.5007566Z * [new tag] ciflow/inductor/148484 -> ciflow/inductor/148484 2025-03-04T21:02:37.5009179Z * [new tag] ciflow/inductor/148485 -> ciflow/inductor/148485 2025-03-04T21:02:37.5011258Z * [new tag] ciflow/inductor/3b9a386 -> ciflow/inductor/3b9a386 2025-03-04T21:02:37.5012739Z * [new tag] ciflow/inductor/3d4b92b -> ciflow/inductor/3d4b92b 2025-03-04T21:02:37.5014337Z * [new tag] ciflow/inductor/88106 -> ciflow/inductor/88106 2025-03-04T21:02:37.5015905Z * [new tag] ciflow/inductor/88196 -> ciflow/inductor/88196 2025-03-04T21:02:37.5017756Z * [new tag] ciflow/inductor/88998 -> ciflow/inductor/88998 2025-03-04T21:02:37.5019316Z * [new tag] ciflow/inductor/d224ac7 -> ciflow/inductor/d224ac7 2025-03-04T21:02:37.5020694Z * [new tag] ciflow/linux-aarch64/125888 -> ciflow/linux-aarch64/125888 2025-03-04T21:02:37.5021806Z * [new tag] ciflow/linux-aarch64/126050 -> ciflow/linux-aarch64/126050 2025-03-04T21:02:37.5022712Z * [new tag] ciflow/linux-aarch64/126054 -> ciflow/linux-aarch64/126054 2025-03-04T21:02:37.5023956Z * [new tag] ciflow/linux-aarch64/133297 -> ciflow/linux-aarch64/133297 2025-03-04T21:02:37.5025178Z * [new tag] ciflow/linux-aarch64/133315 -> ciflow/linux-aarch64/133315 2025-03-04T21:02:37.5026341Z * [new tag] ciflow/linux-aarch64/133392 -> ciflow/linux-aarch64/133392 2025-03-04T21:02:37.5027415Z * [new tag] ciflow/linux-aarch64/133419 -> ciflow/linux-aarch64/133419 2025-03-04T21:02:37.5028523Z * [new tag] ciflow/linux-aarch64/133423 -> ciflow/linux-aarch64/133423 2025-03-04T21:02:37.5029563Z * [new tag] ciflow/linux-aarch64/133667 -> ciflow/linux-aarch64/133667 2025-03-04T21:02:37.5030720Z * [new tag] ciflow/linux-aarch64/133753 -> ciflow/linux-aarch64/133753 2025-03-04T21:02:37.5031984Z * [new tag] ciflow/linux-aarch64/135058 -> ciflow/linux-aarch64/135058 2025-03-04T21:02:37.5033507Z * [new tag] ciflow/linux-aarch64/135333 -> ciflow/linux-aarch64/135333 2025-03-04T21:02:37.5034962Z * [new tag] ciflow/linux-aarch64/135792 -> ciflow/linux-aarch64/135792 2025-03-04T21:02:37.5036383Z * [new tag] ciflow/linux-aarch64/136355 -> ciflow/linux-aarch64/136355 2025-03-04T21:02:37.5037561Z * [new tag] ciflow/linux-aarch64/137568 -> ciflow/linux-aarch64/137568 2025-03-04T21:02:37.5038669Z * [new tag] ciflow/linux-aarch64/138388 -> ciflow/linux-aarch64/138388 2025-03-04T21:02:37.5039851Z * [new tag] ciflow/linux-aarch64/138889 -> ciflow/linux-aarch64/138889 2025-03-04T21:02:37.5041105Z * [new tag] ciflow/linux-aarch64/140159 -> ciflow/linux-aarch64/140159 2025-03-04T21:02:37.5042437Z * [new tag] ciflow/linux-aarch64/143741 -> ciflow/linux-aarch64/143741 2025-03-04T21:02:37.5043929Z * [new tag] ciflow/linux-aarch64/145942 -> ciflow/linux-aarch64/145942 2025-03-04T21:02:37.5045062Z * [new tag] ciflow/linux-aarch64/146823 -> ciflow/linux-aarch64/146823 2025-03-04T21:02:37.5046211Z * [new tag] ciflow/linux-aarch64/146826 -> ciflow/linux-aarch64/146826 2025-03-04T21:02:37.5047962Z * [new tag] ciflow/linux-aarch64/146895 -> ciflow/linux-aarch64/146895 2025-03-04T21:02:37.5049206Z * [new tag] ciflow/linux-aarch64/147073 -> ciflow/linux-aarch64/147073 2025-03-04T21:02:37.5050542Z * [new tag] ciflow/linux-aarch64/147337 -> ciflow/linux-aarch64/147337 2025-03-04T21:02:37.5051803Z * [new tag] ciflow/linux-aarch64/147341 -> ciflow/linux-aarch64/147341 2025-03-04T21:02:37.5052716Z * [new tag] ciflow/linux-aarch64/147359 -> ciflow/linux-aarch64/147359 2025-03-04T21:02:37.5054036Z * [new tag] ciflow/linux-aarch64/147498 -> ciflow/linux-aarch64/147498 2025-03-04T21:02:37.5055345Z * [new tag] ciflow/linux-aarch64/147763 -> ciflow/linux-aarch64/147763 2025-03-04T21:02:37.5056528Z * [new tag] ciflow/linux-aarch64/147817 -> ciflow/linux-aarch64/147817 2025-03-04T21:02:37.5057719Z * [new tag] ciflow/linux-aarch64/147855 -> ciflow/linux-aarch64/147855 2025-03-04T21:02:37.5058900Z * [new tag] ciflow/linux-aarch64/147917 -> ciflow/linux-aarch64/147917 2025-03-04T21:02:37.5060092Z * [new tag] ciflow/linux-aarch64/147945 -> ciflow/linux-aarch64/147945 2025-03-04T21:02:37.5061350Z * [new tag] ciflow/linux-aarch64/147955 -> ciflow/linux-aarch64/147955 2025-03-04T21:02:37.5062703Z * [new tag] ciflow/linux-aarch64/147956 -> ciflow/linux-aarch64/147956 2025-03-04T21:02:37.5063617Z * [new tag] ciflow/linux-aarch64/147957 -> ciflow/linux-aarch64/147957 2025-03-04T21:02:37.5065054Z * [new tag] ciflow/linux-aarch64/147958 -> ciflow/linux-aarch64/147958 2025-03-04T21:02:37.5066245Z * [new tag] ciflow/linux-aarch64/147959 -> ciflow/linux-aarch64/147959 2025-03-04T21:02:37.5067382Z * [new tag] ciflow/linux-aarch64/147964 -> ciflow/linux-aarch64/147964 2025-03-04T21:02:37.5068667Z * [new tag] ciflow/linux-aarch64/148076 -> ciflow/linux-aarch64/148076 2025-03-04T21:02:37.5069857Z * [new tag] ciflow/linux-aarch64/148163 -> ciflow/linux-aarch64/148163 2025-03-04T21:02:37.5071013Z * [new tag] ciflow/linux-aarch64/148173 -> ciflow/linux-aarch64/148173 2025-03-04T21:02:37.5072200Z * [new tag] ciflow/linux-aarch64/148403 -> ciflow/linux-aarch64/148403 2025-03-04T21:02:37.5073760Z * [new tag] ciflow/mps/102148 -> ciflow/mps/102148 2025-03-04T21:02:37.5074821Z * [new tag] ciflow/mps/119496 -> ciflow/mps/119496 2025-03-04T21:02:37.5075883Z * [new tag] ciflow/mps/120076 -> ciflow/mps/120076 2025-03-04T21:02:37.5076846Z * [new tag] ciflow/mps/133423 -> ciflow/mps/133423 2025-03-04T21:02:37.5078513Z * [new tag] ciflow/mps/133667 -> ciflow/mps/133667 2025-03-04T21:02:37.5079930Z * [new tag] ciflow/mps/138640 -> ciflow/mps/138640 2025-03-04T21:02:37.5081004Z * [new tag] ciflow/mps/139469 -> ciflow/mps/139469 2025-03-04T21:02:37.5082080Z * [new tag] ciflow/mps/140159 -> ciflow/mps/140159 2025-03-04T21:02:37.5083295Z * [new tag] ciflow/mps/140211 -> ciflow/mps/140211 2025-03-04T21:02:37.5084817Z * [new tag] ciflow/mps/140725 -> ciflow/mps/140725 2025-03-04T21:02:37.5086038Z * [new tag] ciflow/mps/142097 -> ciflow/mps/142097 2025-03-04T21:02:37.5087577Z * [new tag] ciflow/mps/142202 -> ciflow/mps/142202 2025-03-04T21:02:37.5089327Z * [new tag] ciflow/mps/142477 -> ciflow/mps/142477 2025-03-04T21:02:37.5090737Z * [new tag] ciflow/mps/143630 -> ciflow/mps/143630 2025-03-04T21:02:37.5091830Z * [new tag] ciflow/mps/143666 -> ciflow/mps/143666 2025-03-04T21:02:37.5093045Z * [new tag] ciflow/mps/143911 -> ciflow/mps/143911 2025-03-04T21:02:37.5094166Z * [new tag] ciflow/mps/143966 -> ciflow/mps/143966 2025-03-04T21:02:37.5095340Z * [new tag] ciflow/mps/144405 -> ciflow/mps/144405 2025-03-04T21:02:37.5096500Z * [new tag] ciflow/mps/144664 -> ciflow/mps/144664 2025-03-04T21:02:37.5098312Z * [new tag] ciflow/mps/145955 -> ciflow/mps/145955 2025-03-04T21:02:37.5099561Z * [new tag] ciflow/mps/146098 -> ciflow/mps/146098 2025-03-04T21:02:37.5100739Z * [new tag] ciflow/mps/146436 -> ciflow/mps/146436 2025-03-04T21:02:37.5102026Z * [new tag] ciflow/mps/146754 -> ciflow/mps/146754 2025-03-04T21:02:37.5103199Z * [new tag] ciflow/mps/146989 -> ciflow/mps/146989 2025-03-04T21:02:37.5104495Z * [new tag] ciflow/mps/147205 -> ciflow/mps/147205 2025-03-04T21:02:37.5105640Z * [new tag] ciflow/mps/147583 -> ciflow/mps/147583 2025-03-04T21:02:37.5107065Z * [new tag] ciflow/mps/147644 -> ciflow/mps/147644 2025-03-04T21:02:37.5108267Z * [new tag] ciflow/mps/147893 -> ciflow/mps/147893 2025-03-04T21:02:37.5109417Z * [new tag] ciflow/mps/148305 -> ciflow/mps/148305 2025-03-04T21:02:37.5110794Z * [new tag] ciflow/mps/148350 -> ciflow/mps/148350 2025-03-04T21:02:37.5111947Z * [new tag] ciflow/mps/148415 -> ciflow/mps/148415 2025-03-04T21:02:37.5113098Z * [new tag] ciflow/mps/148449 -> ciflow/mps/148449 2025-03-04T21:02:37.5114287Z * [new tag] ciflow/mps/148468 -> ciflow/mps/148468 2025-03-04T21:02:37.5115480Z * [new tag] ciflow/mps/148471 -> ciflow/mps/148471 2025-03-04T21:02:37.5117044Z * [new tag] ciflow/op-benchmark/143733 -> ciflow/op-benchmark/143733 2025-03-04T21:02:37.5118625Z * [new tag] ciflow/periodic/054a2fd -> ciflow/periodic/054a2fd 2025-03-04T21:02:37.5119923Z * [new tag] ciflow/periodic/123020 -> ciflow/periodic/123020 2025-03-04T21:02:37.5121155Z * [new tag] ciflow/periodic/134817 -> ciflow/periodic/134817 2025-03-04T21:02:37.5122091Z * [new tag] ciflow/periodic/140989 -> ciflow/periodic/140989 2025-03-04T21:02:37.5123324Z * [new tag] ciflow/periodic/141309 -> ciflow/periodic/141309 2025-03-04T21:02:37.5124428Z * [new tag] ciflow/periodic/141355 -> ciflow/periodic/141355 2025-03-04T21:02:37.5125547Z * [new tag] ciflow/periodic/141730 -> ciflow/periodic/141730 2025-03-04T21:02:37.5126517Z * [new tag] ciflow/periodic/142179 -> ciflow/periodic/142179 2025-03-04T21:02:37.5127939Z * [new tag] ciflow/periodic/143959 -> ciflow/periodic/143959 2025-03-04T21:02:37.5129097Z * [new tag] ciflow/periodic/144953 -> ciflow/periodic/144953 2025-03-04T21:02:37.5130211Z * [new tag] ciflow/periodic/146264 -> ciflow/periodic/146264 2025-03-04T21:02:37.5131748Z * [new tag] ciflow/periodic/146403 -> ciflow/periodic/146403 2025-03-04T21:02:37.5133219Z * [new tag] ciflow/periodic/146823 -> ciflow/periodic/146823 2025-03-04T21:02:37.5134577Z * [new tag] ciflow/periodic/146903 -> ciflow/periodic/146903 2025-03-04T21:02:37.5135909Z * [new tag] ciflow/periodic/147459 -> ciflow/periodic/147459 2025-03-04T21:02:37.5137031Z * [new tag] ciflow/periodic/147870 -> ciflow/periodic/147870 2025-03-04T21:02:37.5138731Z * [new tag] ciflow/periodic/148351 -> ciflow/periodic/148351 2025-03-04T21:02:37.5140104Z * [new tag] ciflow/periodic/2a6d37d -> ciflow/periodic/2a6d37d 2025-03-04T21:02:37.5141515Z * [new tag] ciflow/periodic/317eeb8 -> ciflow/periodic/317eeb8 2025-03-04T21:02:37.5142875Z * [new tag] ciflow/periodic/3c32 -> ciflow/periodic/3c32 2025-03-04T21:02:37.5144335Z * [new tag] ciflow/periodic/3e98831 -> ciflow/periodic/3e98831 2025-03-04T21:02:37.5145840Z * [new tag] ciflow/periodic/94512-point -> ciflow/periodic/94512-point 2025-03-04T21:02:37.5147452Z * [new tag] ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519 2025-03-04T21:02:37.5148763Z * [new tag] ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275 2025-03-04T21:02:37.5150077Z * [new tag] ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761 2025-03-04T21:02:37.5151487Z * [new tag] ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12 2025-03-04T21:02:37.5152979Z * [new tag] ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0 2025-03-04T21:02:37.5154482Z * [new tag] ciflow/periodic/sha-ec5b83 -> ciflow/periodic/sha-ec5b83 2025-03-04T21:02:37.5155908Z * [new tag] ciflow/riscv64/143979 -> ciflow/riscv64/143979 2025-03-04T21:02:37.5157476Z * [new tag] ciflow/rocm/124424 -> ciflow/rocm/124424 2025-03-04T21:02:37.5158574Z * [new tag] ciflow/rocm/134817 -> ciflow/rocm/134817 2025-03-04T21:02:37.5159860Z * [new tag] ciflow/rocm/137136 -> ciflow/rocm/137136 2025-03-04T21:02:37.5160975Z * [new tag] ciflow/rocm/139469 -> ciflow/rocm/139469 2025-03-04T21:02:37.5165449Z * [new tag] ciflow/rocm/139975 -> ciflow/rocm/139975 2025-03-04T21:02:37.5166319Z * [new tag] ciflow/rocm/140989 -> ciflow/rocm/140989 2025-03-04T21:02:37.5167545Z * [new tag] ciflow/rocm/141309 -> ciflow/rocm/141309 2025-03-04T21:02:37.5168773Z * [new tag] ciflow/rocm/141355 -> ciflow/rocm/141355 2025-03-04T21:02:37.5169892Z * [new tag] ciflow/rocm/142097 -> ciflow/rocm/142097 2025-03-04T21:02:37.5170968Z * [new tag] ciflow/rocm/142859 -> ciflow/rocm/142859 2025-03-04T21:02:37.5171911Z * [new tag] ciflow/rocm/143416 -> ciflow/rocm/143416 2025-03-04T21:02:37.5173662Z * [new tag] ciflow/rocm/143971 -> ciflow/rocm/143971 2025-03-04T21:02:37.5175189Z * [new tag] ciflow/rocm/144120 -> ciflow/rocm/144120 2025-03-04T21:02:37.5176615Z * [new tag] ciflow/rocm/144572 -> ciflow/rocm/144572 2025-03-04T21:02:37.5177779Z * [new tag] ciflow/rocm/144664 -> ciflow/rocm/144664 2025-03-04T21:02:37.5178948Z * [new tag] ciflow/rocm/145475 -> ciflow/rocm/145475 2025-03-04T21:02:37.5180257Z * [new tag] ciflow/rocm/145584 -> ciflow/rocm/145584 2025-03-04T21:02:37.5181388Z * [new tag] ciflow/rocm/145685 -> ciflow/rocm/145685 2025-03-04T21:02:37.5182722Z * [new tag] ciflow/rocm/145946 -> ciflow/rocm/145946 2025-03-04T21:02:37.5184100Z * [new tag] ciflow/rocm/146227 -> ciflow/rocm/146227 2025-03-04T21:02:37.5185324Z * [new tag] ciflow/rocm/146264 -> ciflow/rocm/146264 2025-03-04T21:02:37.5186551Z * [new tag] ciflow/rocm/146448 -> ciflow/rocm/146448 2025-03-04T21:02:37.5187795Z * [new tag] ciflow/rocm/146903 -> ciflow/rocm/146903 2025-03-04T21:02:37.5189058Z * [new tag] ciflow/rocm/147034 -> ciflow/rocm/147034 2025-03-04T21:02:37.5190424Z * [new tag] ciflow/rocm/147243 -> ciflow/rocm/147243 2025-03-04T21:02:37.5191533Z * [new tag] ciflow/rocm/147315 -> ciflow/rocm/147315 2025-03-04T21:02:37.5192702Z * [new tag] ciflow/rocm/147320 -> ciflow/rocm/147320 2025-03-04T21:02:37.5194077Z * [new tag] ciflow/rocm/147382 -> ciflow/rocm/147382 2025-03-04T21:02:37.5195263Z * [new tag] ciflow/rocm/147403 -> ciflow/rocm/147403 2025-03-04T21:02:37.5196583Z * [new tag] ciflow/rocm/147452 -> ciflow/rocm/147452 2025-03-04T21:02:37.5197453Z * [new tag] ciflow/rocm/147459 -> ciflow/rocm/147459 2025-03-04T21:02:37.5198969Z * [new tag] ciflow/rocm/147527 -> ciflow/rocm/147527 2025-03-04T21:02:37.5200198Z * [new tag] ciflow/rocm/147619 -> ciflow/rocm/147619 2025-03-04T21:02:37.5201428Z * [new tag] ciflow/rocm/147630 -> ciflow/rocm/147630 2025-03-04T21:02:37.5202574Z * [new tag] ciflow/rocm/147821 -> ciflow/rocm/147821 2025-03-04T21:02:37.5203758Z * [new tag] ciflow/rocm/147904 -> ciflow/rocm/147904 2025-03-04T21:02:37.5205098Z * [new tag] ciflow/rocm/147993 -> ciflow/rocm/147993 2025-03-04T21:02:37.5206297Z * [new tag] ciflow/rocm/148223 -> ciflow/rocm/148223 2025-03-04T21:02:37.5207591Z * [new tag] ciflow/rocm/148228 -> ciflow/rocm/148228 2025-03-04T21:02:37.5209154Z * [new tag] ciflow/rocm/148355 -> ciflow/rocm/148355 2025-03-04T21:02:37.5210488Z * [new tag] ciflow/rocm/148371 -> ciflow/rocm/148371 2025-03-04T21:02:37.5211570Z * [new tag] ciflow/rocm/148394 -> ciflow/rocm/148394 2025-03-04T21:02:37.5212715Z * [new tag] ciflow/rocm/148432 -> ciflow/rocm/148432 2025-03-04T21:02:37.5214034Z * [new tag] ciflow/rocm/148433 -> ciflow/rocm/148433 2025-03-04T21:02:37.5215230Z * [new tag] ciflow/rocm/148437 -> ciflow/rocm/148437 2025-03-04T21:02:37.5216665Z * [new tag] ciflow/s390/142346 -> ciflow/s390/142346 2025-03-04T21:02:37.5217739Z * [new tag] ciflow/s390/143959 -> ciflow/s390/143959 2025-03-04T21:02:37.5218967Z * [new tag] ciflow/s390/148452 -> ciflow/s390/148452 2025-03-04T21:02:37.5220542Z * [new tag] ciflow/slow/01c7106 -> ciflow/slow/01c7106 2025-03-04T21:02:37.5221851Z * [new tag] ciflow/slow/0577043 -> ciflow/slow/0577043 2025-03-04T21:02:37.5223573Z * [new tag] ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym 2025-03-04T21:02:37.5224282Z * [new tag] ciflow/slow/0e81104 -> ciflow/slow/0e81104 2025-03-04T21:02:37.5225539Z * [new tag] ciflow/slow/139975 -> ciflow/slow/139975 2025-03-04T21:02:37.5226627Z * [new tag] ciflow/slow/146256 -> ciflow/slow/146256 2025-03-04T21:02:37.5227767Z * [new tag] ciflow/slow/146903 -> ciflow/slow/146903 2025-03-04T21:02:37.5229046Z * [new tag] ciflow/slow/1732077 -> ciflow/slow/1732077 2025-03-04T21:02:37.5230835Z * [new tag] ciflow/slow/187eb7c -> ciflow/slow/187eb7c 2025-03-04T21:02:37.5232454Z * [new tag] ciflow/slow/1faef89 -> ciflow/slow/1faef89 2025-03-04T21:02:37.5234056Z * [new tag] ciflow/slow/3920ec1 -> ciflow/slow/3920ec1 2025-03-04T21:02:37.5235283Z * [new tag] ciflow/slow/3b7c6b2 -> ciflow/slow/3b7c6b2 2025-03-04T21:02:37.5236590Z * [new tag] ciflow/slow/59a3759 -> ciflow/slow/59a3759 2025-03-04T21:02:37.5237872Z * [new tag] ciflow/slow/70ef0bb -> ciflow/slow/70ef0bb 2025-03-04T21:02:37.5239178Z * [new tag] ciflow/slow/788ff06 -> ciflow/slow/788ff06 2025-03-04T21:02:37.5241016Z * [new tag] ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym 2025-03-04T21:02:37.5241807Z * [new tag] ciflow/slow/9d85864 -> ciflow/slow/9d85864 2025-03-04T21:02:37.5243235Z * [new tag] ciflow/slow/9ffad5b -> ciflow/slow/9ffad5b 2025-03-04T21:02:37.5244652Z * [new tag] ciflow/slow/a206e8b -> ciflow/slow/a206e8b 2025-03-04T21:02:37.5245912Z * [new tag] ciflow/slow/a837609 -> ciflow/slow/a837609 2025-03-04T21:02:37.5247261Z * [new tag] ciflow/slow/af841f3 -> ciflow/slow/af841f3 2025-03-04T21:02:37.5249148Z * [new tag] ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym 2025-03-04T21:02:37.5250275Z * [new tag] ciflow/trunk/108303 -> ciflow/trunk/108303 2025-03-04T21:02:37.5251186Z * [new tag] ciflow/trunk/113257 -> ciflow/trunk/113257 2025-03-04T21:02:37.5252425Z * [new tag] ciflow/trunk/113258 -> ciflow/trunk/113258 2025-03-04T21:02:37.5253513Z * [new tag] ciflow/trunk/120076 -> ciflow/trunk/120076 2025-03-04T21:02:37.5254629Z * [new tag] ciflow/trunk/121445 -> ciflow/trunk/121445 2025-03-04T21:02:37.5255709Z * [new tag] ciflow/trunk/123020 -> ciflow/trunk/123020 2025-03-04T21:02:37.5256804Z * [new tag] ciflow/trunk/124424 -> ciflow/trunk/124424 2025-03-04T21:02:37.5257964Z * [new tag] ciflow/trunk/124490 -> ciflow/trunk/124490 2025-03-04T21:02:37.5259041Z * [new tag] ciflow/trunk/125469 -> ciflow/trunk/125469 2025-03-04T21:02:37.5260142Z * [new tag] ciflow/trunk/125806 -> ciflow/trunk/125806 2025-03-04T21:02:37.5261557Z * [new tag] ciflow/trunk/125888 -> ciflow/trunk/125888 2025-03-04T21:02:37.5263213Z * [new tag] ciflow/trunk/125995 -> ciflow/trunk/125995 2025-03-04T21:02:37.5264596Z * [new tag] ciflow/trunk/126050 -> ciflow/trunk/126050 2025-03-04T21:02:37.5265941Z * [new tag] ciflow/trunk/126054 -> ciflow/trunk/126054 2025-03-04T21:02:37.5267434Z * [new tag] ciflow/trunk/126635 -> ciflow/trunk/126635 2025-03-04T21:02:37.5268712Z * [new tag] ciflow/trunk/127171 -> ciflow/trunk/127171 2025-03-04T21:02:37.5269976Z * [new tag] ciflow/trunk/127919 -> ciflow/trunk/127919 2025-03-04T21:02:37.5271122Z * [new tag] ciflow/trunk/129352 -> ciflow/trunk/129352 2025-03-04T21:02:37.5272289Z * [new tag] ciflow/trunk/129420 -> ciflow/trunk/129420 2025-03-04T21:02:37.5273508Z * [new tag] ciflow/trunk/130141 -> ciflow/trunk/130141 2025-03-04T21:02:37.5274676Z * [new tag] ciflow/trunk/130752 -> ciflow/trunk/130752 2025-03-04T21:02:37.5275839Z * [new tag] ciflow/trunk/131354 -> ciflow/trunk/131354 2025-03-04T21:02:37.5277016Z * [new tag] ciflow/trunk/131507 -> ciflow/trunk/131507 2025-03-04T21:02:37.5278184Z * [new tag] ciflow/trunk/132021 -> ciflow/trunk/132021 2025-03-04T21:02:37.5279336Z * [new tag] ciflow/trunk/133044 -> ciflow/trunk/133044 2025-03-04T21:02:37.5280529Z * [new tag] ciflow/trunk/133289 -> ciflow/trunk/133289 2025-03-04T21:02:37.5281652Z * [new tag] ciflow/trunk/133296 -> ciflow/trunk/133296 2025-03-04T21:02:37.5282790Z * [new tag] ciflow/trunk/133297 -> ciflow/trunk/133297 2025-03-04T21:02:37.5283978Z * [new tag] ciflow/trunk/133315 -> ciflow/trunk/133315 2025-03-04T21:02:37.5285635Z * [new tag] ciflow/trunk/133392 -> ciflow/trunk/133392 2025-03-04T21:02:37.5286819Z * [new tag] ciflow/trunk/133419 -> ciflow/trunk/133419 2025-03-04T21:02:37.5288039Z * [new tag] ciflow/trunk/133423 -> ciflow/trunk/133423 2025-03-04T21:02:37.5289297Z * [new tag] ciflow/trunk/133667 -> ciflow/trunk/133667 2025-03-04T21:02:37.5290640Z * [new tag] ciflow/trunk/133753 -> ciflow/trunk/133753 2025-03-04T21:02:37.5291759Z * [new tag] ciflow/trunk/134219 -> ciflow/trunk/134219 2025-03-04T21:02:37.5293084Z * [new tag] ciflow/trunk/134515 -> ciflow/trunk/134515 2025-03-04T21:02:37.5294219Z * [new tag] ciflow/trunk/135058 -> ciflow/trunk/135058 2025-03-04T21:02:37.5295489Z * [new tag] ciflow/trunk/135631 -> ciflow/trunk/135631 2025-03-04T21:02:37.5296776Z * [new tag] ciflow/trunk/136780 -> ciflow/trunk/136780 2025-03-04T21:02:37.5298045Z * [new tag] ciflow/trunk/136824 -> ciflow/trunk/136824 2025-03-04T21:02:37.5299344Z * [new tag] ciflow/trunk/136835 -> ciflow/trunk/136835 2025-03-04T21:02:37.5300728Z * [new tag] ciflow/trunk/136993 -> ciflow/trunk/136993 2025-03-04T21:02:37.5302055Z * [new tag] ciflow/trunk/137400 -> ciflow/trunk/137400 2025-03-04T21:02:37.5303301Z * [new tag] ciflow/trunk/137580 -> ciflow/trunk/137580 2025-03-04T21:02:37.5304523Z * [new tag] ciflow/trunk/138213 -> ciflow/trunk/138213 2025-03-04T21:02:37.5305719Z * [new tag] ciflow/trunk/138436 -> ciflow/trunk/138436 2025-03-04T21:02:37.5306864Z * [new tag] ciflow/trunk/138626 -> ciflow/trunk/138626 2025-03-04T21:02:37.5308028Z * [new tag] ciflow/trunk/138834 -> ciflow/trunk/138834 2025-03-04T21:02:37.5309188Z * [new tag] ciflow/trunk/138889 -> ciflow/trunk/138889 2025-03-04T21:02:37.5310387Z * [new tag] ciflow/trunk/138996 -> ciflow/trunk/138996 2025-03-04T21:02:37.5311693Z * [new tag] ciflow/trunk/139070 -> ciflow/trunk/139070 2025-03-04T21:02:37.5312880Z * [new tag] ciflow/trunk/139094 -> ciflow/trunk/139094 2025-03-04T21:02:37.5314049Z * [new tag] ciflow/trunk/139971 -> ciflow/trunk/139971 2025-03-04T21:02:37.5315212Z * [new tag] ciflow/trunk/139975 -> ciflow/trunk/139975 2025-03-04T21:02:37.5316363Z * [new tag] ciflow/trunk/140084 -> ciflow/trunk/140084 2025-03-04T21:02:37.5317506Z * [new tag] ciflow/trunk/140159 -> ciflow/trunk/140159 2025-03-04T21:02:37.5318674Z * [new tag] ciflow/trunk/140211 -> ciflow/trunk/140211 2025-03-04T21:02:37.5319978Z * [new tag] ciflow/trunk/140298 -> ciflow/trunk/140298 2025-03-04T21:02:37.5321049Z * [new tag] ciflow/trunk/140323 -> ciflow/trunk/140323 2025-03-04T21:02:37.5322197Z * [new tag] ciflow/trunk/140365 -> ciflow/trunk/140365 2025-03-04T21:02:37.5323529Z * [new tag] ciflow/trunk/140399 -> ciflow/trunk/140399 2025-03-04T21:02:37.5324698Z * [new tag] ciflow/trunk/140793 -> ciflow/trunk/140793 2025-03-04T21:02:37.5325826Z * [new tag] ciflow/trunk/140979 -> ciflow/trunk/140979 2025-03-04T21:02:37.5326988Z * [new tag] ciflow/trunk/140989 -> ciflow/trunk/140989 2025-03-04T21:02:37.5328204Z * [new tag] ciflow/trunk/141178 -> ciflow/trunk/141178 2025-03-04T21:02:37.5329713Z * [new tag] ciflow/trunk/141257 -> ciflow/trunk/141257 2025-03-04T21:02:37.5330917Z * [new tag] ciflow/trunk/141309 -> ciflow/trunk/141309 2025-03-04T21:02:37.5332071Z * [new tag] ciflow/trunk/141730 -> ciflow/trunk/141730 2025-03-04T21:02:37.5333449Z * [new tag] ciflow/trunk/141796 -> ciflow/trunk/141796 2025-03-04T21:02:37.5334633Z * [new tag] ciflow/trunk/141842 -> ciflow/trunk/141842 2025-03-04T21:02:37.5335909Z * [new tag] ciflow/trunk/141910 -> ciflow/trunk/141910 2025-03-04T21:02:37.5337155Z * [new tag] ciflow/trunk/141914 -> ciflow/trunk/141914 2025-03-04T21:02:37.5338315Z * [new tag] ciflow/trunk/141961 -> ciflow/trunk/141961 2025-03-04T21:02:37.5339536Z * [new tag] ciflow/trunk/142091 -> ciflow/trunk/142091 2025-03-04T21:02:37.5340775Z * [new tag] ciflow/trunk/142092 -> ciflow/trunk/142092 2025-03-04T21:02:37.5341914Z * [new tag] ciflow/trunk/142097 -> ciflow/trunk/142097 2025-03-04T21:02:37.5343084Z * [new tag] ciflow/trunk/142179 -> ciflow/trunk/142179 2025-03-04T21:02:37.5344283Z * [new tag] ciflow/trunk/142272 -> ciflow/trunk/142272 2025-03-04T21:02:37.5345412Z * [new tag] ciflow/trunk/142273 -> ciflow/trunk/142273 2025-03-04T21:02:37.5346706Z * [new tag] ciflow/trunk/142326 -> ciflow/trunk/142326 2025-03-04T21:02:37.5347880Z * [new tag] ciflow/trunk/142346 -> ciflow/trunk/142346 2025-03-04T21:02:37.5349099Z * [new tag] ciflow/trunk/142350 -> ciflow/trunk/142350 2025-03-04T21:02:37.5350285Z * [new tag] ciflow/trunk/142372 -> ciflow/trunk/142372 2025-03-04T21:02:37.5351470Z * [new tag] ciflow/trunk/142477 -> ciflow/trunk/142477 2025-03-04T21:02:37.5352911Z * [new tag] ciflow/trunk/142821 -> ciflow/trunk/142821 2025-03-04T21:02:37.5354093Z * [new tag] ciflow/trunk/142859 -> ciflow/trunk/142859 2025-03-04T21:02:37.5355351Z * [new tag] ciflow/trunk/142865 -> ciflow/trunk/142865 2025-03-04T21:02:37.5356522Z * [new tag] ciflow/trunk/143082 -> ciflow/trunk/143082 2025-03-04T21:02:37.5357964Z * [new tag] ciflow/trunk/143093 -> ciflow/trunk/143093 2025-03-04T21:02:37.5359208Z * [new tag] ciflow/trunk/143220 -> ciflow/trunk/143220 2025-03-04T21:02:37.5360510Z * [new tag] ciflow/trunk/143261 -> ciflow/trunk/143261 2025-03-04T21:02:37.5361593Z * [new tag] ciflow/trunk/143303 -> ciflow/trunk/143303 2025-03-04T21:02:37.5363072Z * [new tag] ciflow/trunk/143313 -> ciflow/trunk/143313 2025-03-04T21:02:37.5364877Z * [new tag] ciflow/trunk/143347 -> ciflow/trunk/143347 2025-03-04T21:02:37.5366168Z * [new tag] ciflow/trunk/143402 -> ciflow/trunk/143402 2025-03-04T21:02:37.5367331Z * [new tag] ciflow/trunk/143416 -> ciflow/trunk/143416 2025-03-04T21:02:37.5368818Z * [new tag] ciflow/trunk/143451 -> ciflow/trunk/143451 2025-03-04T21:02:37.5370013Z * [new tag] ciflow/trunk/143475 -> ciflow/trunk/143475 2025-03-04T21:02:37.5371170Z * [new tag] ciflow/trunk/143630 -> ciflow/trunk/143630 2025-03-04T21:02:37.5372328Z * [new tag] ciflow/trunk/143666 -> ciflow/trunk/143666 2025-03-04T21:02:37.5373460Z * [new tag] ciflow/trunk/143671 -> ciflow/trunk/143671 2025-03-04T21:02:37.5374906Z * [new tag] ciflow/trunk/143689 -> ciflow/trunk/143689 2025-03-04T21:02:37.5376054Z * [new tag] ciflow/trunk/143712 -> ciflow/trunk/143712 2025-03-04T21:02:37.5377385Z * [new tag] ciflow/trunk/143733 -> ciflow/trunk/143733 2025-03-04T21:02:37.5378659Z * [new tag] ciflow/trunk/143822 -> ciflow/trunk/143822 2025-03-04T21:02:37.5379946Z * [new tag] ciflow/trunk/143833 -> ciflow/trunk/143833 2025-03-04T21:02:37.5381409Z * [new tag] ciflow/trunk/143894 -> ciflow/trunk/143894 2025-03-04T21:02:37.5382600Z * [new tag] ciflow/trunk/143896 -> ciflow/trunk/143896 2025-03-04T21:02:37.5383953Z * [new tag] ciflow/trunk/143961 -> ciflow/trunk/143961 2025-03-04T21:02:37.5384857Z * [new tag] ciflow/trunk/143966 -> ciflow/trunk/143966 2025-03-04T21:02:37.5386177Z * [new tag] ciflow/trunk/144017 -> ciflow/trunk/144017 2025-03-04T21:02:37.5387495Z * [new tag] ciflow/trunk/144019 -> ciflow/trunk/144019 2025-03-04T21:02:37.5388672Z * [new tag] ciflow/trunk/144120 -> ciflow/trunk/144120 2025-03-04T21:02:37.5389957Z * [new tag] ciflow/trunk/144138 -> ciflow/trunk/144138 2025-03-04T21:02:37.5391132Z * [new tag] ciflow/trunk/144172 -> ciflow/trunk/144172 2025-03-04T21:02:37.5392440Z * [new tag] ciflow/trunk/144177 -> ciflow/trunk/144177 2025-03-04T21:02:37.5393823Z * [new tag] ciflow/trunk/144268 -> ciflow/trunk/144268 2025-03-04T21:02:37.5395010Z * [new tag] ciflow/trunk/144272 -> ciflow/trunk/144272 2025-03-04T21:02:37.5396178Z * [new tag] ciflow/trunk/144293 -> ciflow/trunk/144293 2025-03-04T21:02:37.5397336Z * [new tag] ciflow/trunk/144452 -> ciflow/trunk/144452 2025-03-04T21:02:37.5398696Z * [new tag] ciflow/trunk/144468 -> ciflow/trunk/144468 2025-03-04T21:02:37.5399896Z * [new tag] ciflow/trunk/144557 -> ciflow/trunk/144557 2025-03-04T21:02:37.5401057Z * [new tag] ciflow/trunk/144572 -> ciflow/trunk/144572 2025-03-04T21:02:37.5402687Z * [new tag] ciflow/trunk/144590 -> ciflow/trunk/144590 2025-03-04T21:02:37.5404161Z * [new tag] ciflow/trunk/144616 -> ciflow/trunk/144616 2025-03-04T21:02:37.5405369Z * [new tag] ciflow/trunk/144620 -> ciflow/trunk/144620 2025-03-04T21:02:37.5406528Z * [new tag] ciflow/trunk/144664 -> ciflow/trunk/144664 2025-03-04T21:02:37.5407972Z * [new tag] ciflow/trunk/144708 -> ciflow/trunk/144708 2025-03-04T21:02:37.5409236Z * [new tag] ciflow/trunk/144721 -> ciflow/trunk/144721 2025-03-04T21:02:37.5410385Z * [new tag] ciflow/trunk/144733 -> ciflow/trunk/144733 2025-03-04T21:02:37.5411734Z * [new tag] ciflow/trunk/144763 -> ciflow/trunk/144763 2025-03-04T21:02:37.5412926Z * [new tag] ciflow/trunk/144771 -> ciflow/trunk/144771 2025-03-04T21:02:37.5414224Z * [new tag] ciflow/trunk/144844 -> ciflow/trunk/144844 2025-03-04T21:02:37.5415411Z * [new tag] ciflow/trunk/144880 -> ciflow/trunk/144880 2025-03-04T21:02:37.5418036Z * [new tag] ciflow/trunk/144925 -> ciflow/trunk/144925 2025-03-04T21:02:37.5419184Z * [new tag] ciflow/trunk/144953 -> ciflow/trunk/144953 2025-03-04T21:02:37.5420354Z * [new tag] ciflow/trunk/144975 -> ciflow/trunk/144975 2025-03-04T21:02:37.5421537Z * [new tag] ciflow/trunk/144992 -> ciflow/trunk/144992 2025-03-04T21:02:37.5422816Z * [new tag] ciflow/trunk/145061 -> ciflow/trunk/145061 2025-03-04T21:02:37.5424082Z * [new tag] ciflow/trunk/145116 -> ciflow/trunk/145116 2025-03-04T21:02:37.5425268Z * [new tag] ciflow/trunk/145119 -> ciflow/trunk/145119 2025-03-04T21:02:37.5428535Z * [new tag] ciflow/trunk/145136 -> ciflow/trunk/145136 2025-03-04T21:02:37.5428964Z * [new tag] ciflow/trunk/145153 -> ciflow/trunk/145153 2025-03-04T21:02:37.5430370Z * [new tag] ciflow/trunk/145224 -> ciflow/trunk/145224 2025-03-04T21:02:37.5431552Z * [new tag] ciflow/trunk/145241 -> ciflow/trunk/145241 2025-03-04T21:02:37.5432905Z * [new tag] ciflow/trunk/145254 -> ciflow/trunk/145254 2025-03-04T21:02:37.5433791Z * [new tag] ciflow/trunk/145331 -> ciflow/trunk/145331 2025-03-04T21:02:37.5435301Z * [new tag] ciflow/trunk/145406 -> ciflow/trunk/145406 2025-03-04T21:02:37.5436469Z * [new tag] ciflow/trunk/145523 -> ciflow/trunk/145523 2025-03-04T21:02:37.5437763Z * [new tag] ciflow/trunk/145559 -> ciflow/trunk/145559 2025-03-04T21:02:37.5438933Z * [new tag] ciflow/trunk/145677 -> ciflow/trunk/145677 2025-03-04T21:02:37.5440104Z * [new tag] ciflow/trunk/145717 -> ciflow/trunk/145717 2025-03-04T21:02:37.5441279Z * [new tag] ciflow/trunk/145936 -> ciflow/trunk/145936 2025-03-04T21:02:37.5442470Z * [new tag] ciflow/trunk/145946 -> ciflow/trunk/145946 2025-03-04T21:02:37.5443640Z * [new tag] ciflow/trunk/145979 -> ciflow/trunk/145979 2025-03-04T21:02:37.5444817Z * [new tag] ciflow/trunk/146051 -> ciflow/trunk/146051 2025-03-04T21:02:37.5446134Z * [new tag] ciflow/trunk/146069 -> ciflow/trunk/146069 2025-03-04T21:02:37.5447933Z * [new tag] ciflow/trunk/146090 -> ciflow/trunk/146090 2025-03-04T21:02:37.5449193Z * [new tag] ciflow/trunk/146098 -> ciflow/trunk/146098 2025-03-04T21:02:37.5450398Z * [new tag] ciflow/trunk/146115 -> ciflow/trunk/146115 2025-03-04T21:02:37.5451578Z * [new tag] ciflow/trunk/146176 -> ciflow/trunk/146176 2025-03-04T21:02:37.5452919Z * [new tag] ciflow/trunk/146182 -> ciflow/trunk/146182 2025-03-04T21:02:37.5454093Z * [new tag] ciflow/trunk/146256 -> ciflow/trunk/146256 2025-03-04T21:02:37.5455262Z * [new tag] ciflow/trunk/146275 -> ciflow/trunk/146275 2025-03-04T21:02:37.5456558Z * [new tag] ciflow/trunk/146289 -> ciflow/trunk/146289 2025-03-04T21:02:37.5457721Z * [new tag] ciflow/trunk/146335 -> ciflow/trunk/146335 2025-03-04T21:02:37.5458893Z * [new tag] ciflow/trunk/146421 -> ciflow/trunk/146421 2025-03-04T21:02:37.5460295Z * [new tag] ciflow/trunk/146489 -> ciflow/trunk/146489 2025-03-04T21:02:37.5461777Z * [new tag] ciflow/trunk/146517 -> ciflow/trunk/146517 2025-03-04T21:02:37.5465242Z * [new tag] ciflow/trunk/146530 -> ciflow/trunk/146530 2025-03-04T21:02:37.5466503Z * [new tag] ciflow/trunk/146561 -> ciflow/trunk/146561 2025-03-04T21:02:37.5467638Z * [new tag] ciflow/trunk/146573 -> ciflow/trunk/146573 2025-03-04T21:02:37.5469082Z * [new tag] ciflow/trunk/146582 -> ciflow/trunk/146582 2025-03-04T21:02:37.5470196Z * [new tag] ciflow/trunk/146661 -> ciflow/trunk/146661 2025-03-04T21:02:37.5471391Z * [new tag] ciflow/trunk/146718 -> ciflow/trunk/146718 2025-03-04T21:02:37.5472579Z * [new tag] ciflow/trunk/146777 -> ciflow/trunk/146777 2025-03-04T21:02:37.5473974Z * [new tag] ciflow/trunk/146807 -> ciflow/trunk/146807 2025-03-04T21:02:37.5475160Z * [new tag] ciflow/trunk/146823 -> ciflow/trunk/146823 2025-03-04T21:02:37.5476335Z * [new tag] ciflow/trunk/146826 -> ciflow/trunk/146826 2025-03-04T21:02:37.5477498Z * [new tag] ciflow/trunk/146827 -> ciflow/trunk/146827 2025-03-04T21:02:37.5478697Z * [new tag] ciflow/trunk/146845 -> ciflow/trunk/146845 2025-03-04T21:02:37.5479864Z * [new tag] ciflow/trunk/146870 -> ciflow/trunk/146870 2025-03-04T21:02:37.5481129Z * [new tag] ciflow/trunk/146873 -> ciflow/trunk/146873 2025-03-04T21:02:37.5482446Z * [new tag] ciflow/trunk/146874 -> ciflow/trunk/146874 2025-03-04T21:02:37.5483436Z * [new tag] ciflow/trunk/146903 -> ciflow/trunk/146903 2025-03-04T21:02:37.5484729Z * [new tag] ciflow/trunk/146928 -> ciflow/trunk/146928 2025-03-04T21:02:37.5486121Z * [new tag] ciflow/trunk/146970 -> ciflow/trunk/146970 2025-03-04T21:02:37.5487279Z * [new tag] ciflow/trunk/147014 -> ciflow/trunk/147014 2025-03-04T21:02:37.5488524Z * [new tag] ciflow/trunk/147072 -> ciflow/trunk/147072 2025-03-04T21:02:37.5489742Z * [new tag] ciflow/trunk/147105 -> ciflow/trunk/147105 2025-03-04T21:02:37.5490877Z * [new tag] ciflow/trunk/147155 -> ciflow/trunk/147155 2025-03-04T21:02:37.5492046Z * [new tag] ciflow/trunk/147272 -> ciflow/trunk/147272 2025-03-04T21:02:37.5493221Z * [new tag] ciflow/trunk/147314 -> ciflow/trunk/147314 2025-03-04T21:02:37.5494448Z * [new tag] ciflow/trunk/147320 -> ciflow/trunk/147320 2025-03-04T21:02:37.5495746Z * [new tag] ciflow/trunk/147334 -> ciflow/trunk/147334 2025-03-04T21:02:37.5496960Z * [new tag] ciflow/trunk/147349 -> ciflow/trunk/147349 2025-03-04T21:02:37.5498155Z * [new tag] ciflow/trunk/147368 -> ciflow/trunk/147368 2025-03-04T21:02:37.5499343Z * [new tag] ciflow/trunk/147403 -> ciflow/trunk/147403 2025-03-04T21:02:37.5500565Z * [new tag] ciflow/trunk/147422 -> ciflow/trunk/147422 2025-03-04T21:02:37.5501747Z * [new tag] ciflow/trunk/147448 -> ciflow/trunk/147448 2025-03-04T21:02:37.5502941Z * [new tag] ciflow/trunk/147452 -> ciflow/trunk/147452 2025-03-04T21:02:37.5504107Z * [new tag] ciflow/trunk/147481 -> ciflow/trunk/147481 2025-03-04T21:02:37.5505446Z * [new tag] ciflow/trunk/147498 -> ciflow/trunk/147498 2025-03-04T21:02:37.5506761Z * [new tag] ciflow/trunk/147518 -> ciflow/trunk/147518 2025-03-04T21:02:37.5507932Z * [new tag] ciflow/trunk/147574 -> ciflow/trunk/147574 2025-03-04T21:02:37.5509126Z * [new tag] ciflow/trunk/147583 -> ciflow/trunk/147583 2025-03-04T21:02:37.5510298Z * [new tag] ciflow/trunk/147660 -> ciflow/trunk/147660 2025-03-04T21:02:37.5511488Z * [new tag] ciflow/trunk/147664 -> ciflow/trunk/147664 2025-03-04T21:02:37.5512653Z * [new tag] ciflow/trunk/147741 -> ciflow/trunk/147741 2025-03-04T21:02:37.5513980Z * [new tag] ciflow/trunk/147742 -> ciflow/trunk/147742 2025-03-04T21:02:37.5515173Z * [new tag] ciflow/trunk/147752 -> ciflow/trunk/147752 2025-03-04T21:02:37.5516369Z * [new tag] ciflow/trunk/147797 -> ciflow/trunk/147797 2025-03-04T21:02:37.5517551Z * [new tag] ciflow/trunk/147798 -> ciflow/trunk/147798 2025-03-04T21:02:37.5518891Z * [new tag] ciflow/trunk/147808 -> ciflow/trunk/147808 2025-03-04T21:02:37.5520075Z * [new tag] ciflow/trunk/147817 -> ciflow/trunk/147817 2025-03-04T21:02:37.5521677Z * [new tag] ciflow/trunk/147820 -> ciflow/trunk/147820 2025-03-04T21:02:37.5522879Z * [new tag] ciflow/trunk/147821 -> ciflow/trunk/147821 2025-03-04T21:02:37.5524054Z * [new tag] ciflow/trunk/147836 -> ciflow/trunk/147836 2025-03-04T21:02:37.5525242Z * [new tag] ciflow/trunk/147862 -> ciflow/trunk/147862 2025-03-04T21:02:37.5526418Z * [new tag] ciflow/trunk/147870 -> ciflow/trunk/147870 2025-03-04T21:02:37.5527611Z * [new tag] ciflow/trunk/147881 -> ciflow/trunk/147881 2025-03-04T21:02:37.5529471Z * [new tag] ciflow/trunk/147897 -> ciflow/trunk/147897 2025-03-04T21:02:37.5530590Z * [new tag] ciflow/trunk/147910 -> ciflow/trunk/147910 2025-03-04T21:02:37.5531744Z * [new tag] ciflow/trunk/147917 -> ciflow/trunk/147917 2025-03-04T21:02:37.5532933Z * [new tag] ciflow/trunk/147945 -> ciflow/trunk/147945 2025-03-04T21:02:37.5534113Z * [new tag] ciflow/trunk/147955 -> ciflow/trunk/147955 2025-03-04T21:02:37.5535279Z * [new tag] ciflow/trunk/147956 -> ciflow/trunk/147956 2025-03-04T21:02:37.5536452Z * [new tag] ciflow/trunk/147957 -> ciflow/trunk/147957 2025-03-04T21:02:37.5537696Z * [new tag] ciflow/trunk/147958 -> ciflow/trunk/147958 2025-03-04T21:02:37.5538840Z * [new tag] ciflow/trunk/147959 -> ciflow/trunk/147959 2025-03-04T21:02:37.5540035Z * [new tag] ciflow/trunk/147962 -> ciflow/trunk/147962 2025-03-04T21:02:37.5541166Z * [new tag] ciflow/trunk/147964 -> ciflow/trunk/147964 2025-03-04T21:02:37.5542337Z * [new tag] ciflow/trunk/147994 -> ciflow/trunk/147994 2025-03-04T21:02:37.5543536Z * [new tag] ciflow/trunk/147997 -> ciflow/trunk/147997 2025-03-04T21:02:37.5544946Z * [new tag] ciflow/trunk/148049 -> ciflow/trunk/148049 2025-03-04T21:02:37.5546130Z * [new tag] ciflow/trunk/148076 -> ciflow/trunk/148076 2025-03-04T21:02:37.5547487Z * [new tag] ciflow/trunk/148083 -> ciflow/trunk/148083 2025-03-04T21:02:37.5549981Z * [new tag] ciflow/trunk/148131 -> ciflow/trunk/148131 2025-03-04T21:02:37.5551859Z * [new tag] ciflow/trunk/148163 -> ciflow/trunk/148163 2025-03-04T21:02:37.5552157Z * [new tag] ciflow/trunk/148173 -> ciflow/trunk/148173 2025-03-04T21:02:37.5552494Z * [new tag] ciflow/trunk/148180 -> ciflow/trunk/148180 2025-03-04T21:02:37.5554041Z * [new tag] ciflow/trunk/148188 -> ciflow/trunk/148188 2025-03-04T21:02:37.5555239Z * [new tag] ciflow/trunk/148261 -> ciflow/trunk/148261 2025-03-04T21:02:37.5556418Z * [new tag] ciflow/trunk/148266 -> ciflow/trunk/148266 2025-03-04T21:02:37.5557582Z * [new tag] ciflow/trunk/148279 -> ciflow/trunk/148279 2025-03-04T21:02:37.5558983Z * [new tag] ciflow/trunk/148281 -> ciflow/trunk/148281 2025-03-04T21:02:37.5560115Z * [new tag] ciflow/trunk/148290 -> ciflow/trunk/148290 2025-03-04T21:02:37.5561416Z * [new tag] ciflow/trunk/148292 -> ciflow/trunk/148292 2025-03-04T21:02:37.5562799Z * [new tag] ciflow/trunk/148305 -> ciflow/trunk/148305 2025-03-04T21:02:37.5563959Z * [new tag] ciflow/trunk/148343 -> ciflow/trunk/148343 2025-03-04T21:02:37.5565105Z * [new tag] ciflow/trunk/148350 -> ciflow/trunk/148350 2025-03-04T21:02:37.5566312Z * [new tag] ciflow/trunk/148364 -> ciflow/trunk/148364 2025-03-04T21:02:37.5567512Z * [new tag] ciflow/trunk/148366 -> ciflow/trunk/148366 2025-03-04T21:02:37.5568936Z * [new tag] ciflow/trunk/148371 -> ciflow/trunk/148371 2025-03-04T21:02:37.5570075Z * [new tag] ciflow/trunk/148388 -> ciflow/trunk/148388 2025-03-04T21:02:37.5571348Z * [new tag] ciflow/trunk/148421 -> ciflow/trunk/148421 2025-03-04T21:02:37.5572543Z * [new tag] ciflow/trunk/148423 -> ciflow/trunk/148423 2025-03-04T21:02:37.5573713Z * [new tag] ciflow/trunk/148450 -> ciflow/trunk/148450 2025-03-04T21:02:37.5575094Z * [new tag] ciflow/trunk/148480 -> ciflow/trunk/148480 2025-03-04T21:02:37.5575949Z * [new tag] ciflow/trunk/148483 -> ciflow/trunk/148483 2025-03-04T21:02:37.5577415Z * [new tag] ciflow/trunk/70978 -> ciflow/trunk/70978 2025-03-04T21:02:37.5578749Z * [new tag] ciflow/trunk/70979 -> ciflow/trunk/70979 2025-03-04T21:02:37.5580305Z * [new tag] ciflow/unstable/123 -> ciflow/unstable/123 2025-03-04T21:02:37.5581529Z * [new tag] ciflow/unstable/146104 -> ciflow/unstable/146104 2025-03-04T21:02:37.5582595Z * [new tag] ciflow/unstable/146264 -> ciflow/unstable/146264 2025-03-04T21:02:37.5583848Z * [new tag] ciflow/unstable/147320 -> ciflow/unstable/147320 2025-03-04T21:02:37.5585221Z * [new tag] ciflow/xpu/137566 -> ciflow/xpu/137566 2025-03-04T21:02:37.5586325Z * [new tag] ciflow/xpu/137580 -> ciflow/xpu/137580 2025-03-04T21:02:37.5587857Z * [new tag] ciflow/xpu/138889 -> ciflow/xpu/138889 2025-03-04T21:02:37.5588957Z * [new tag] ciflow/xpu/138996 -> ciflow/xpu/138996 2025-03-04T21:02:37.5590052Z * [new tag] ciflow/xpu/139469 -> ciflow/xpu/139469 2025-03-04T21:02:37.5591143Z * [new tag] ciflow/xpu/139971 -> ciflow/xpu/139971 2025-03-04T21:02:37.5592236Z * [new tag] ciflow/xpu/140365 -> ciflow/xpu/140365 2025-03-04T21:02:37.5593339Z * [new tag] ciflow/xpu/140372 -> ciflow/xpu/140372 2025-03-04T21:02:37.5594436Z * [new tag] ciflow/xpu/140686 -> ciflow/xpu/140686 2025-03-04T21:02:37.5595730Z * [new tag] ciflow/xpu/140972 -> ciflow/xpu/140972 2025-03-04T21:02:37.5596814Z * [new tag] ciflow/xpu/142040 -> ciflow/xpu/142040 2025-03-04T21:02:37.5598295Z * [new tag] ciflow/xpu/142097 -> ciflow/xpu/142097 2025-03-04T21:02:37.5599824Z * [new tag] ciflow/xpu/143597 -> ciflow/xpu/143597 2025-03-04T21:02:37.5601163Z * [new tag] ciflow/xpu/143833 -> ciflow/xpu/143833 2025-03-04T21:02:37.5602505Z * [new tag] ciflow/xpu/144240 -> ciflow/xpu/144240 2025-03-04T21:02:37.5603649Z * [new tag] ciflow/xpu/144452 -> ciflow/xpu/144452 2025-03-04T21:02:37.5604795Z * [new tag] ciflow/xpu/144664 -> ciflow/xpu/144664 2025-03-04T21:02:37.5605967Z * [new tag] ciflow/xpu/146098 -> ciflow/xpu/146098 2025-03-04T21:02:37.5607248Z * [new tag] ciflow/xpu/147161 -> ciflow/xpu/147161 2025-03-04T21:02:37.5608619Z * [new tag] ciflow/xpu/147349 -> ciflow/xpu/147349 2025-03-04T21:02:37.5609864Z * [new tag] ciflow/xpu/147355 -> ciflow/xpu/147355 2025-03-04T21:02:37.5610985Z * [new tag] ciflow/xpu/147403 -> ciflow/xpu/147403 2025-03-04T21:02:37.5612168Z * [new tag] ciflow/xpu/147448 -> ciflow/xpu/147448 2025-03-04T21:02:37.5613327Z * [new tag] ciflow/xpu/147498 -> ciflow/xpu/147498 2025-03-04T21:02:37.5614478Z * [new tag] ciflow/xpu/147507 -> ciflow/xpu/147507 2025-03-04T21:02:37.5615630Z * [new tag] ciflow/xpu/147583 -> ciflow/xpu/147583 2025-03-04T21:02:37.5616926Z * [new tag] ciflow/xpu/147593 -> ciflow/xpu/147593 2025-03-04T21:02:37.5618092Z * [new tag] ciflow/xpu/147664 -> ciflow/xpu/147664 2025-03-04T21:02:37.5619254Z * [new tag] ciflow/xpu/147727 -> ciflow/xpu/147727 2025-03-04T21:02:37.5620424Z * [new tag] ciflow/xpu/147821 -> ciflow/xpu/147821 2025-03-04T21:02:37.5621791Z * [new tag] ciflow/xpu/147945 -> ciflow/xpu/147945 2025-03-04T21:02:37.5622756Z * [new tag] ciflow/xpu/147955 -> ciflow/xpu/147955 2025-03-04T21:02:37.5624068Z * [new tag] ciflow/xpu/147956 -> ciflow/xpu/147956 2025-03-04T21:02:37.5625213Z * [new tag] ciflow/xpu/147957 -> ciflow/xpu/147957 2025-03-04T21:02:37.5626374Z * [new tag] ciflow/xpu/147958 -> ciflow/xpu/147958 2025-03-04T21:02:37.5627551Z * [new tag] ciflow/xpu/147959 -> ciflow/xpu/147959 2025-03-04T21:02:37.5628700Z * [new tag] ciflow/xpu/147962 -> ciflow/xpu/147962 2025-03-04T21:02:37.5629914Z * [new tag] ciflow/xpu/148076 -> ciflow/xpu/148076 2025-03-04T21:02:37.5631428Z * [new tag] ciflow/xpu/148081 -> ciflow/xpu/148081 2025-03-04T21:02:37.5632537Z * [new tag] ciflow/xpu/148305 -> ciflow/xpu/148305 2025-03-04T21:02:37.5633731Z * [new tag] ciflow/xpu/148313 -> ciflow/xpu/148313 2025-03-04T21:02:37.5634864Z * [new tag] ciflow/xpu/148366 -> ciflow/xpu/148366 2025-03-04T21:02:37.5636037Z * [new tag] ciflow/xpu/148403 -> ciflow/xpu/148403 2025-03-04T21:02:37.5637190Z * [new tag] ciflow/xpu/148423 -> ciflow/xpu/148423 2025-03-04T21:02:37.5638382Z * [new tag] cslpull75 -> cslpull75 2025-03-04T21:02:37.5639589Z * [new tag] cslpull76 -> cslpull76 2025-03-04T21:02:37.5640741Z * [new tag] cslpull77 -> cslpull77 2025-03-04T21:02:37.5641962Z * [new tag] cslpull78 -> cslpull78 2025-03-04T21:02:37.5643378Z * [new tag] cslpull79 -> cslpull79 2025-03-04T21:02:37.5644942Z * [new tag] cslpull80 -> cslpull80 2025-03-04T21:02:37.5646216Z * [new tag] cslpull81 -> cslpull81 2025-03-04T21:02:37.5647503Z * [new tag] cslpull82 -> cslpull82 2025-03-04T21:02:37.5648891Z * [new tag] cslpull83 -> cslpull83 2025-03-04T21:02:37.5650275Z * [new tag] cslpull84 -> cslpull84 2025-03-04T21:02:37.5651460Z * [new tag] cslpull85 -> cslpull85 2025-03-04T21:02:37.5652725Z * [new tag] cslpull86 -> cslpull86 2025-03-04T21:02:37.5654014Z * [new tag] cslpull87 -> cslpull87 2025-03-04T21:02:37.5655291Z * [new tag] cslpull88 -> cslpull88 2025-03-04T21:02:37.5656521Z * [new tag] cslpull89 -> cslpull89 2025-03-04T21:02:37.5657593Z * [new tag] cslpull90 -> cslpull90 2025-03-04T21:02:37.5659203Z * [new tag] cslpull91 -> cslpull91 2025-03-04T21:02:37.5660393Z * [new tag] cslpull92 -> cslpull92 2025-03-04T21:02:37.5661916Z * [new tag] flight_5 -> flight_5 2025-03-04T21:02:37.5663354Z * [new tag] flight_5.1 -> flight_5.1 2025-03-04T21:02:37.5664667Z * [new tag] flight_5.2 -> flight_5.2 2025-03-04T21:02:37.5665825Z * [new tag] flight_5.3 -> flight_5.3 2025-03-04T21:02:37.5667019Z * [new tag] forpull1 -> forpull1 2025-03-04T21:02:37.5668648Z * [new tag] malfet/tag-2ef5611 -> malfet/tag-2ef5611 2025-03-04T21:02:37.5669894Z * [new tag] malfet/tag-317b1a0 -> malfet/tag-317b1a0 2025-03-04T21:02:37.5671120Z * [new tag] malfet/tag-ec6f767 -> malfet/tag-ec6f767 2025-03-04T21:02:37.5672621Z * [new tag] nightly-binary -> nightly-binary 2025-03-04T21:02:37.5673420Z * [new tag] sqzhang_flight4_plus -> sqzhang_flight4_plus 2025-03-04T21:02:37.5674925Z * [new tag] sqzhang_flight_3 -> sqzhang_flight_3 2025-03-04T21:02:37.5676134Z * [new tag] v0.1.1 -> v0.1.1 2025-03-04T21:02:37.5677363Z * [new tag] v0.1.10 -> v0.1.10 2025-03-04T21:02:37.5678549Z * [new tag] v0.1.11 -> v0.1.11 2025-03-04T21:02:37.5679804Z * [new tag] v0.1.12 -> v0.1.12 2025-03-04T21:02:37.5680999Z * [new tag] v0.1.2 -> v0.1.2 2025-03-04T21:02:37.5682213Z * [new tag] v0.1.3 -> v0.1.3 2025-03-04T21:02:37.5683499Z * [new tag] v0.1.4 -> v0.1.4 2025-03-04T21:02:37.5684757Z * [new tag] v0.1.5 -> v0.1.5 2025-03-04T21:02:37.5685963Z * [new tag] v0.1.6 -> v0.1.6 2025-03-04T21:02:37.5687135Z * [new tag] v0.1.7 -> v0.1.7 2025-03-04T21:02:37.5688390Z * [new tag] v0.1.8 -> v0.1.8 2025-03-04T21:02:37.5689609Z * [new tag] v0.1.9 -> v0.1.9 2025-03-04T21:02:37.5690848Z * [new tag] v0.2.0 -> v0.2.0 2025-03-04T21:02:37.5692131Z * [new tag] v0.3.0 -> v0.3.0 2025-03-04T21:02:37.5693576Z * [new tag] v0.3.1 -> v0.3.1 2025-03-04T21:02:37.5694849Z * [new tag] v0.4.0 -> v0.4.0 2025-03-04T21:02:37.5696115Z * [new tag] v0.4.1 -> v0.4.1 2025-03-04T21:02:37.5697333Z * [new tag] v1.0.0 -> v1.0.0 2025-03-04T21:02:37.5698764Z * [new tag] v1.0.0a0 -> v1.0.0a0 2025-03-04T21:02:37.5700020Z * [new tag] v1.0.1 -> v1.0.1 2025-03-04T21:02:37.5701384Z * [new tag] v1.0rc0 -> v1.0rc0 2025-03-04T21:02:37.5702437Z * [new tag] v1.0rc1 -> v1.0rc1 2025-03-04T21:02:37.5703740Z * [new tag] v1.1.0 -> v1.1.0 2025-03-04T21:02:37.5705032Z * [new tag] v1.1.0a0 -> v1.1.0a0 2025-03-04T21:02:37.5706484Z * [new tag] v1.10.0 -> v1.10.0 2025-03-04T21:02:37.5707826Z * [new tag] v1.10.0-rc1 -> v1.10.0-rc1 2025-03-04T21:02:37.5709129Z * [new tag] v1.10.0-rc2 -> v1.10.0-rc2 2025-03-04T21:02:37.5710687Z * [new tag] v1.10.0-rc3 -> v1.10.0-rc3 2025-03-04T21:02:37.5712069Z * [new tag] v1.10.1 -> v1.10.1 2025-03-04T21:02:37.5713194Z * [new tag] v1.10.1-rc1 -> v1.10.1-rc1 2025-03-04T21:02:37.5714258Z * [new tag] v1.10.2 -> v1.10.2 2025-03-04T21:02:37.5715344Z * [new tag] v1.10.2-rc1 -> v1.10.2-rc1 2025-03-04T21:02:37.5716653Z * [new tag] v1.11.0 -> v1.11.0 2025-03-04T21:02:37.5718033Z * [new tag] v1.11.0-rc1 -> v1.11.0-rc1 2025-03-04T21:02:37.5719412Z * [new tag] v1.11.0-rc2 -> v1.11.0-rc2 2025-03-04T21:02:37.5720767Z * [new tag] v1.11.0-rc3 -> v1.11.0-rc3 2025-03-04T21:02:37.5722032Z * [new tag] v1.11.0-rc4 -> v1.11.0-rc4 2025-03-04T21:02:37.5723360Z * [new tag] v1.11.0-rc5 -> v1.11.0-rc5 2025-03-04T21:02:37.5724453Z * [new tag] v1.11.0-rc6 -> v1.11.0-rc6 2025-03-04T21:02:37.5725600Z * [new tag] v1.11.0-rc7 -> v1.11.0-rc7 2025-03-04T21:02:37.5726860Z * [new tag] v1.12.0 -> v1.12.0 2025-03-04T21:02:37.5728342Z * [new tag] v1.12.0-rc1 -> v1.12.0-rc1 2025-03-04T21:02:37.5729617Z * [new tag] v1.12.0-rc2 -> v1.12.0-rc2 2025-03-04T21:02:37.5730872Z * [new tag] v1.12.0-rc3 -> v1.12.0-rc3 2025-03-04T21:02:37.5732216Z * [new tag] v1.12.0-rc4 -> v1.12.0-rc4 2025-03-04T21:02:37.5733565Z * [new tag] v1.12.0-rc5 -> v1.12.0-rc5 2025-03-04T21:02:37.5734905Z * [new tag] v1.12.0-rc6 -> v1.12.0-rc6 2025-03-04T21:02:37.5736005Z * [new tag] v1.12.0-rc7 -> v1.12.0-rc7 2025-03-04T21:02:37.5737071Z * [new tag] v1.12.0-rc8 -> v1.12.0-rc8 2025-03-04T21:02:37.5738176Z * [new tag] v1.12.1 -> v1.12.1 2025-03-04T21:02:37.5739580Z * [new tag] v1.12.1-rc1 -> v1.12.1-rc1 2025-03-04T21:02:37.5740956Z * [new tag] v1.12.1-rc2 -> v1.12.1-rc2 2025-03-04T21:02:37.5742403Z * [new tag] v1.12.1-rc3 -> v1.12.1-rc3 2025-03-04T21:02:37.5743777Z * [new tag] v1.12.1-rc4 -> v1.12.1-rc4 2025-03-04T21:02:37.5744700Z * [new tag] v1.12.1-rc5 -> v1.12.1-rc5 2025-03-04T21:02:37.5753210Z * [new tag] v1.13.0 -> v1.13.0 2025-03-04T21:02:37.5753562Z * [new tag] v1.13.0-rc1 -> v1.13.0-rc1 2025-03-04T21:02:37.5753811Z * [new tag] v1.13.0-rc2 -> v1.13.0-rc2 2025-03-04T21:02:37.5753961Z * [new tag] v1.13.0-rc3 -> v1.13.0-rc3 2025-03-04T21:02:37.5754235Z * [new tag] v1.13.0-rc4 -> v1.13.0-rc4 2025-03-04T21:02:37.5754383Z * [new tag] v1.13.0-rc5 -> v1.13.0-rc5 2025-03-04T21:02:37.5754536Z * [new tag] v1.13.0-rc6 -> v1.13.0-rc6 2025-03-04T21:02:37.5755013Z * [new tag] v1.13.1 -> v1.13.1 2025-03-04T21:02:37.5756566Z * [new tag] v1.13.1-rc1 -> v1.13.1-rc1 2025-03-04T21:02:37.5757559Z * [new tag] v1.2.0 -> v1.2.0 2025-03-04T21:02:37.5758969Z * [new tag] v1.2.0a0 -> v1.2.0a0 2025-03-04T21:02:37.5760203Z * [new tag] v1.3.0 -> v1.3.0 2025-03-04T21:02:37.5761680Z * [new tag] v1.3.0a0 -> v1.3.0a0 2025-03-04T21:02:37.5764335Z * [new tag] v1.3.1 -> v1.3.1 2025-03-04T21:02:37.5765566Z * [new tag] v1.4.0 -> v1.4.0 2025-03-04T21:02:37.5766819Z * [new tag] v1.4.0a0 -> v1.4.0a0 2025-03-04T21:02:37.5767975Z * [new tag] v1.4.1 -> v1.4.1 2025-03-04T21:02:37.5769365Z * [new tag] v1.5.0 -> v1.5.0 2025-03-04T21:02:37.5770705Z * [new tag] v1.5.0-rc1 -> v1.5.0-rc1 2025-03-04T21:02:37.5771979Z * [new tag] v1.5.0-rc2 -> v1.5.0-rc2 2025-03-04T21:02:37.5773321Z * [new tag] v1.5.0-rc3 -> v1.5.0-rc3 2025-03-04T21:02:37.5774494Z * [new tag] v1.5.0-rc4 -> v1.5.0-rc4 2025-03-04T21:02:37.5775563Z * [new tag] v1.5.0-rc5 -> v1.5.0-rc5 2025-03-04T21:02:37.5777044Z * [new tag] v1.5.1 -> v1.5.1 2025-03-04T21:02:37.5778116Z * [new tag] v1.5.1-rc1 -> v1.5.1-rc1 2025-03-04T21:02:37.5779357Z * [new tag] v1.6.0 -> v1.6.0 2025-03-04T21:02:37.5780556Z * [new tag] v1.6.0-rc1 -> v1.6.0-rc1 2025-03-04T21:02:37.5781863Z * [new tag] v1.6.0-rc2 -> v1.6.0-rc2 2025-03-04T21:02:37.5783159Z * [new tag] v1.6.0-rc3 -> v1.6.0-rc3 2025-03-04T21:02:37.5784550Z * [new tag] v1.6.0-rc4 -> v1.6.0-rc4 2025-03-04T21:02:37.5785814Z * [new tag] v1.6.0-rc5 -> v1.6.0-rc5 2025-03-04T21:02:37.5787091Z * [new tag] v1.6.0-rc6 -> v1.6.0-rc6 2025-03-04T21:02:37.5788544Z * [new tag] v1.6.0-rc7 -> v1.6.0-rc7 2025-03-04T21:02:37.5789839Z * [new tag] v1.7.0 -> v1.7.0 2025-03-04T21:02:37.5791143Z * [new tag] v1.7.0-rc1 -> v1.7.0-rc1 2025-03-04T21:02:37.5792619Z * [new tag] v1.7.0-rc2 -> v1.7.0-rc2 2025-03-04T21:02:37.5794389Z * [new tag] v1.7.0-rc3 -> v1.7.0-rc3 2025-03-04T21:02:37.5795449Z * [new tag] v1.7.0-rc4 -> v1.7.0-rc4 2025-03-04T21:02:37.5796769Z * [new tag] v1.7.1 -> v1.7.1 2025-03-04T21:02:37.5798182Z * [new tag] v1.7.1-rc1 -> v1.7.1-rc1 2025-03-04T21:02:37.5799525Z * [new tag] v1.7.1-rc2 -> v1.7.1-rc2 2025-03-04T21:02:37.5800616Z * [new tag] v1.7.1-rc3 -> v1.7.1-rc3 2025-03-04T21:02:37.5801949Z * [new tag] v1.8.0 -> v1.8.0 2025-03-04T21:02:37.5803058Z * [new tag] v1.8.0-rc1 -> v1.8.0-rc1 2025-03-04T21:02:37.5804343Z * [new tag] v1.8.0-rc2 -> v1.8.0-rc2 2025-03-04T21:02:37.5805645Z * [new tag] v1.8.0-rc3 -> v1.8.0-rc3 2025-03-04T21:02:37.5806916Z * [new tag] v1.8.0-rc4 -> v1.8.0-rc4 2025-03-04T21:02:37.5808126Z * [new tag] v1.8.0-rc5 -> v1.8.0-rc5 2025-03-04T21:02:37.5809216Z * [new tag] v1.8.1 -> v1.8.1 2025-03-04T21:02:37.5810576Z * [new tag] v1.8.1-rc1 -> v1.8.1-rc1 2025-03-04T21:02:37.5811638Z * [new tag] v1.8.1-rc2 -> v1.8.1-rc2 2025-03-04T21:02:37.5812735Z * [new tag] v1.8.1-rc3 -> v1.8.1-rc3 2025-03-04T21:02:37.5814558Z * [new tag] v1.8.2 -> v1.8.2 2025-03-04T21:02:37.5815667Z * [new tag] v1.8.2-rc1 -> v1.8.2-rc1 2025-03-04T21:02:37.5816989Z * [new tag] v1.9.0 -> v1.9.0 2025-03-04T21:02:37.5818328Z * [new tag] v1.9.0-rc1 -> v1.9.0-rc1 2025-03-04T21:02:37.5819689Z * [new tag] v1.9.0-rc2 -> v1.9.0-rc2 2025-03-04T21:02:37.5820985Z * [new tag] v1.9.0-rc3 -> v1.9.0-rc3 2025-03-04T21:02:37.5822128Z * [new tag] v1.9.0-rc4 -> v1.9.0-rc4 2025-03-04T21:02:37.5823443Z * [new tag] v1.9.1 -> v1.9.1 2025-03-04T21:02:37.5824976Z * [new tag] v1.9.1-rc1 -> v1.9.1-rc1 2025-03-04T21:02:37.5826166Z * [new tag] v1.9.1-rc2 -> v1.9.1-rc2 2025-03-04T21:02:37.5827497Z * [new tag] v2.0.0 -> v2.0.0 2025-03-04T21:02:37.5828774Z * [new tag] v2.0.0-rc1 -> v2.0.0-rc1 2025-03-04T21:02:37.5830110Z * [new tag] v2.0.0-rc2 -> v2.0.0-rc2 2025-03-04T21:02:37.5831460Z * [new tag] v2.0.0-rc3 -> v2.0.0-rc3 2025-03-04T21:02:37.5832778Z * [new tag] v2.0.0-rc4 -> v2.0.0-rc4 2025-03-04T21:02:37.5834143Z * [new tag] v2.0.0-rc5 -> v2.0.0-rc5 2025-03-04T21:02:37.5835266Z * [new tag] v2.0.0-rc6 -> v2.0.0-rc6 2025-03-04T21:02:37.5836476Z * [new tag] v2.0.1 -> v2.0.1 2025-03-04T21:02:37.5837843Z * [new tag] v2.0.1-rc1 -> v2.0.1-rc1 2025-03-04T21:02:37.5838927Z * [new tag] v2.0.1-rc2 -> v2.0.1-rc2 2025-03-04T21:02:37.5840139Z * [new tag] v2.0.1-rc3 -> v2.0.1-rc3 2025-03-04T21:02:37.5841206Z * [new tag] v2.0.1-rc4 -> v2.0.1-rc4 2025-03-04T21:02:37.5843021Z * [new tag] v2.1.0 -> v2.1.0 2025-03-04T21:02:37.5844296Z * [new tag] v2.1.0-rc1 -> v2.1.0-rc1 2025-03-04T21:02:37.5845701Z * [new tag] v2.1.0-rc2 -> v2.1.0-rc2 2025-03-04T21:02:37.5846874Z * [new tag] v2.1.0-rc3 -> v2.1.0-rc3 2025-03-04T21:02:37.5848252Z * [new tag] v2.1.0-rc4 -> v2.1.0-rc4 2025-03-04T21:02:37.5849609Z * [new tag] v2.1.0-rc5 -> v2.1.0-rc5 2025-03-04T21:02:37.5850659Z * [new tag] v2.1.0-rc6 -> v2.1.0-rc6 2025-03-04T21:02:37.5851963Z * [new tag] v2.1.1 -> v2.1.1 2025-03-04T21:02:37.5853244Z * [new tag] v2.1.1-rc1 -> v2.1.1-rc1 2025-03-04T21:02:37.5854496Z * [new tag] v2.1.1-rc2 -> v2.1.1-rc2 2025-03-04T21:02:37.5855880Z * [new tag] v2.1.1-rc3 -> v2.1.1-rc3 2025-03-04T21:02:37.5857162Z * [new tag] v2.1.1-rc4 -> v2.1.1-rc4 2025-03-04T21:02:37.5858417Z * [new tag] v2.1.1-rc5 -> v2.1.1-rc5 2025-03-04T21:02:37.5859367Z * [new tag] v2.1.1-rc6 -> v2.1.1-rc6 2025-03-04T21:02:37.5860741Z * [new tag] v2.1.2 -> v2.1.2 2025-03-04T21:02:37.5862380Z * [new tag] v2.1.2-rc1 -> v2.1.2-rc1 2025-03-04T21:02:37.5863563Z * [new tag] v2.1.2-rc2 -> v2.1.2-rc2 2025-03-04T21:02:37.5864711Z * [new tag] v2.1.2-rc3 -> v2.1.2-rc3 2025-03-04T21:02:37.5866011Z * [new tag] v2.2.0 -> v2.2.0 2025-03-04T21:02:37.5867414Z * [new tag] v2.2.0-rc1 -> v2.2.0-rc1 2025-03-04T21:02:37.5868722Z * [new tag] v2.2.0-rc2 -> v2.2.0-rc2 2025-03-04T21:02:37.5869950Z * [new tag] v2.2.0-rc3 -> v2.2.0-rc3 2025-03-04T21:02:37.5871195Z * [new tag] v2.2.0-rc4 -> v2.2.0-rc4 2025-03-04T21:02:37.5872467Z * [new tag] v2.2.0-rc5 -> v2.2.0-rc5 2025-03-04T21:02:37.5873849Z * [new tag] v2.2.0-rc6 -> v2.2.0-rc6 2025-03-04T21:02:37.5874902Z * [new tag] v2.2.0-rc7 -> v2.2.0-rc7 2025-03-04T21:02:37.5876509Z * [new tag] v2.2.0-rc8 -> v2.2.0-rc8 2025-03-04T21:02:37.5877830Z * [new tag] v2.2.1 -> v2.2.1 2025-03-04T21:02:37.5879131Z * [new tag] v2.2.1-rc1 -> v2.2.1-rc1 2025-03-04T21:02:37.5880185Z * [new tag] v2.2.1-rc2 -> v2.2.1-rc2 2025-03-04T21:02:37.5881242Z * [new tag] v2.2.1-rc3 -> v2.2.1-rc3 2025-03-04T21:02:37.5882348Z * [new tag] v2.2.2 -> v2.2.2 2025-03-04T21:02:37.5883837Z * [new tag] v2.2.2-rc1 -> v2.2.2-rc1 2025-03-04T21:02:37.5884914Z * [new tag] v2.2.2-rc2 -> v2.2.2-rc2 2025-03-04T21:02:37.5886185Z * [new tag] v2.2.2-rc3 -> v2.2.2-rc3 2025-03-04T21:02:37.5887394Z * [new tag] v2.3.0 -> v2.3.0 2025-03-04T21:02:37.5888876Z * [new tag] v2.3.0-rc1 -> v2.3.0-rc1 2025-03-04T21:02:37.5890199Z * [new tag] v2.3.0-rc10 -> v2.3.0-rc10 2025-03-04T21:02:37.5891467Z * [new tag] v2.3.0-rc11 -> v2.3.0-rc11 2025-03-04T21:02:37.5892552Z * [new tag] v2.3.0-rc12 -> v2.3.0-rc12 2025-03-04T21:02:37.5893857Z * [new tag] v2.3.0-rc2 -> v2.3.0-rc2 2025-03-04T21:02:37.5895189Z * [new tag] v2.3.0-rc3 -> v2.3.0-rc3 2025-03-04T21:02:37.5896467Z * [new tag] v2.3.0-rc4 -> v2.3.0-rc4 2025-03-04T21:02:37.5897768Z * [new tag] v2.3.0-rc5 -> v2.3.0-rc5 2025-03-04T21:02:37.5898928Z * [new tag] v2.3.0-rc6 -> v2.3.0-rc6 2025-03-04T21:02:37.5900234Z * [new tag] v2.3.0-rc7 -> v2.3.0-rc7 2025-03-04T21:02:37.5901522Z * [new tag] v2.3.0-rc8 -> v2.3.0-rc8 2025-03-04T21:02:37.5902586Z * [new tag] v2.3.0-rc9 -> v2.3.0-rc9 2025-03-04T21:02:37.5903723Z * [new tag] v2.3.1 -> v2.3.1 2025-03-04T21:02:37.5904973Z * [new tag] v2.3.1-rc1 -> v2.3.1-rc1 2025-03-04T21:02:37.5906291Z * [new tag] v2.3.1-rc2 -> v2.3.1-rc2 2025-03-04T21:02:37.5907691Z * [new tag] v2.3.1-rc3 -> v2.3.1-rc3 2025-03-04T21:02:37.5909048Z * [new tag] v2.4.0 -> v2.4.0 2025-03-04T21:02:37.5910358Z * [new tag] v2.4.0-rc1 -> v2.4.0-rc1 2025-03-04T21:02:37.5911720Z * [new tag] v2.4.0-rc2 -> v2.4.0-rc2 2025-03-04T21:02:37.5913004Z * [new tag] v2.4.0-rc3 -> v2.4.0-rc3 2025-03-04T21:02:37.5914455Z * [new tag] v2.4.0-rc4 -> v2.4.0-rc4 2025-03-04T21:02:37.5915802Z * [new tag] v2.4.0-rc5 -> v2.4.0-rc5 2025-03-04T21:02:37.5917077Z * [new tag] v2.4.0-rc6 -> v2.4.0-rc6 2025-03-04T21:02:37.5918451Z * [new tag] v2.4.0-rc7 -> v2.4.0-rc7 2025-03-04T21:02:37.5919798Z * [new tag] v2.4.0-rc8 -> v2.4.0-rc8 2025-03-04T21:02:37.5921160Z * [new tag] v2.4.0-rc9 -> v2.4.0-rc9 2025-03-04T21:02:37.5922282Z * [new tag] v2.4.1 -> v2.4.1 2025-03-04T21:02:37.5923645Z * [new tag] v2.4.1-rc1 -> v2.4.1-rc1 2025-03-04T21:02:37.5925043Z * [new tag] v2.4.1-rc2 -> v2.4.1-rc2 2025-03-04T21:02:37.5926390Z * [new tag] v2.4.1-rc3 -> v2.4.1-rc3 2025-03-04T21:02:37.5927650Z * [new tag] v2.5.0 -> v2.5.0 2025-03-04T21:02:37.5929129Z * [new tag] v2.5.0-rc1 -> v2.5.0-rc1 2025-03-04T21:02:37.5930357Z * [new tag] v2.5.0-rc10 -> v2.5.0-rc10 2025-03-04T21:02:37.5931516Z * [new tag] v2.5.0-rc2 -> v2.5.0-rc2 2025-03-04T21:02:37.5932806Z * [new tag] v2.5.0-rc3 -> v2.5.0-rc3 2025-03-04T21:02:37.5934109Z * [new tag] v2.5.0-rc4 -> v2.5.0-rc4 2025-03-04T21:02:37.5935460Z * [new tag] v2.5.0-rc5 -> v2.5.0-rc5 2025-03-04T21:02:37.5936842Z * [new tag] v2.5.0-rc6 -> v2.5.0-rc6 2025-03-04T21:02:37.5938150Z * [new tag] v2.5.0-rc7 -> v2.5.0-rc7 2025-03-04T21:02:37.5939563Z * [new tag] v2.5.0-rc8 -> v2.5.0-rc8 2025-03-04T21:02:37.5940875Z * [new tag] v2.5.0-rc9 -> v2.5.0-rc9 2025-03-04T21:02:37.5941945Z * [new tag] v2.5.1 -> v2.5.1 2025-03-04T21:02:37.5943007Z * [new tag] v2.5.1-rc1 -> v2.5.1-rc1 2025-03-04T21:02:37.5944103Z * [new tag] v2.6.0 -> v2.6.0 2025-03-04T21:02:37.5945616Z * [new tag] v2.6.0-rc1 -> v2.6.0-rc1 2025-03-04T21:02:37.5947015Z * [new tag] v2.6.0-rc2 -> v2.6.0-rc2 2025-03-04T21:02:37.5948324Z * [new tag] v2.6.0-rc3 -> v2.6.0-rc3 2025-03-04T21:02:37.5949638Z * [new tag] v2.6.0-rc4 -> v2.6.0-rc4 2025-03-04T21:02:37.5951243Z * [new tag] v2.6.0-rc5 -> v2.6.0-rc5 2025-03-04T21:02:37.5952637Z * [new tag] v2.6.0-rc6 -> v2.6.0-rc6 2025-03-04T21:02:37.5953998Z * [new tag] v2.6.0-rc7 -> v2.6.0-rc7 2025-03-04T21:02:37.5955368Z * [new tag] v2.6.0-rc8 -> v2.6.0-rc8 2025-03-04T21:02:37.5956761Z * [new tag] v2.6.0-rc9 -> v2.6.0-rc9 2025-03-04T21:02:37.5958007Z * [new tag] whc_flight_1 -> whc_flight_1 2025-03-04T21:02:37.5959531Z * [new tag] whc_flight_2 -> whc_flight_2 2025-03-04T21:02:37.5960748Z * [new tag] whc_flight_4 -> whc_flight_4 2025-03-04T21:02:37.6786972Z [command]/usr/bin/git rev-parse --verify --quiet 1b7498080987913ecb3aff6253c5e88f3540d911^{object} 2025-03-04T21:02:37.6817153Z 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:02:37.6823199Z ##[endgroup] 2025-03-04T21:02:37.6823929Z ##[group]Determining the checkout info 2025-03-04T21:02:37.6825525Z ##[endgroup] 2025-03-04T21:02:37.6831352Z [command]/usr/bin/git sparse-checkout disable 2025-03-04T21:02:37.6875223Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig 2025-03-04T21:02:37.6907849Z ##[group]Checking out the ref 2025-03-04T21:02:37.6912309Z [command]/usr/bin/git checkout --progress --force 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:02:38.7196566Z Updating files: 78% (13017/16541) 2025-03-04T21:02:38.7416801Z Updating files: 79% (13068/16541) 2025-03-04T21:02:38.7625491Z Updating files: 80% (13233/16541) 2025-03-04T21:02:38.7749175Z Updating files: 81% (13399/16541) 2025-03-04T21:02:38.7875492Z Updating files: 82% (13564/16541) 2025-03-04T21:02:38.8000499Z Updating files: 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files: 99% (16376/16541) 2025-03-04T21:02:39.0493413Z Updating files: 100% (16541/16541) 2025-03-04T21:02:39.0493883Z Updating files: 100% (16541/16541), done. 2025-03-04T21:02:39.0740532Z Note: switching to '1b7498080987913ecb3aff6253c5e88f3540d911'. 2025-03-04T21:02:39.0740905Z 2025-03-04T21:02:39.0741378Z You are in 'detached HEAD' state. You can look around, make experimental 2025-03-04T21:02:39.0741915Z changes and commit them, and you can discard any commits you make in this 2025-03-04T21:02:39.0742445Z state without impacting any branches by switching back to a branch. 2025-03-04T21:02:39.0742749Z 2025-03-04T21:02:39.0742961Z If you want to create a new branch to retain commits you create, you may 2025-03-04T21:02:39.0743451Z do so (now or later) by using -c with the switch command. Example: 2025-03-04T21:02:39.0743731Z 2025-03-04T21:02:39.0743863Z git switch -c 2025-03-04T21:02:39.0744095Z 2025-03-04T21:02:39.0744225Z Or undo this operation with: 2025-03-04T21:02:39.0744405Z 2025-03-04T21:02:39.0744508Z git switch - 2025-03-04T21:02:39.0744646Z 2025-03-04T21:02:39.0744886Z Turn off this advice by setting config variable advice.detachedHead to false 2025-03-04T21:02:39.0745216Z 2025-03-04T21:02:39.0745546Z HEAD is now at 1b749808098 Update on "[dynamo] remove internal stack trace for fullgraph=True graph breaks" 2025-03-04T21:02:39.0866903Z ##[endgroup] 2025-03-04T21:02:39.0867785Z ##[group]Setting up auth for fetching submodules 2025-03-04T21:02:39.0874091Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic *** 2025-03-04T21:02:39.0923896Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf 2025-03-04T21:02:39.0956692Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com: 2025-03-04T21:02:39.0991141Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com: 2025-03-04T21:02:39.1020147Z ##[endgroup] 2025-03-04T21:02:39.1020568Z ##[group]Fetching submodules 2025-03-04T21:02:39.1023390Z [command]/usr/bin/git submodule sync --recursive 2025-03-04T21:02:39.1400555Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive 2025-03-04T21:02:39.1762007Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni' 2025-03-04T21:02:39.1765616Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16' 2025-03-04T21:02:39.1977322Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv' 2025-03-04T21:02:39.1980576Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK' 2025-03-04T21:02:39.1984638Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX' 2025-03-04T21:02:39.1989153Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator' 2025-03-04T21:02:39.1993086Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK' 2025-03-04T21:02:39.2002996Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark' 2025-03-04T21:02:39.2007464Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel' 2025-03-04T21:02:39.2012117Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib' 2025-03-04T21:02:39.2016499Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo' 2025-03-04T21:02:39.2021032Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend' 2025-03-04T21:02:39.2025644Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass' 2025-03-04T21:02:39.2034974Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen' 2025-03-04T21:02:39.2039929Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm' 2025-03-04T21:02:39.2046066Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention' 2025-03-04T21:02:39.2051144Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers' 2025-03-04T21:02:39.2055995Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt' 2025-03-04T21:02:39.2069987Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp' 2025-03-04T21:02:39.2075056Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo' 2025-03-04T21:02:39.2080329Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest' 2025-03-04T21:02:39.2085537Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep' 2025-03-04T21:02:39.2091488Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi' 2025-03-04T21:02:39.2097076Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto' 2025-03-04T21:02:39.2107844Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai' 2025-03-04T21:02:39.2113313Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc' 2025-03-04T21:02:39.2119068Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann' 2025-03-04T21:02:39.2124746Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx' 2025-03-04T21:02:39.2131113Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp' 2025-03-04T21:02:39.2141791Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft' 2025-03-04T21:02:39.2147804Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf' 2025-03-04T21:02:39.2153812Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd' 2025-03-04T21:02:39.2160139Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool' 2025-03-04T21:02:39.2166893Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11' 2025-03-04T21:02:39.2173492Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy' 2025-03-04T21:02:39.2186041Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef' 2025-03-04T21:02:39.2193776Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe' 2025-03-04T21:02:39.2233802Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'... 2025-03-04T21:02:39.5533439Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'... 2025-03-04T21:02:39.5534156Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'... 2025-03-04T21:02:39.5534817Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'... 2025-03-04T21:02:39.5563710Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'... 2025-03-04T21:02:42.8856776Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'... 2025-03-04T21:02:42.9046693Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'... 2025-03-04T21:02:42.9048348Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'... 2025-03-04T21:02:42.9049604Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'... 2025-03-04T21:02:42.9050902Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'... 2025-03-04T21:02:42.9052231Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'... 2025-03-04T21:02:42.9053457Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'... 2025-03-04T21:02:42.9054826Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'... 2025-03-04T21:02:42.9056040Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'... 2025-03-04T21:02:42.9057314Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'... 2025-03-04T21:02:42.9058642Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'... 2025-03-04T21:02:42.9059979Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'... 2025-03-04T21:02:42.9061519Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'... 2025-03-04T21:02:42.9062877Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'... 2025-03-04T21:02:42.9064261Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'... 2025-03-04T21:02:42.9065674Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'... 2025-03-04T21:02:42.9067047Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'... 2025-03-04T21:02:42.9068831Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'... 2025-03-04T21:02:42.9858259Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'... 2025-03-04T21:02:43.2343586Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'... 2025-03-04T21:02:51.1340543Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'... 2025-03-04T21:02:51.1342073Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'... 2025-03-04T21:02:51.1343572Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'... 2025-03-04T21:02:51.1344903Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'... 2025-03-04T21:02:51.1346340Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'... 2025-03-04T21:02:51.1347783Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'... 2025-03-04T21:02:51.1349110Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'... 2025-03-04T21:02:51.1350432Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'... 2025-03-04T21:02:51.1351838Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'... 2025-03-04T21:02:51.1353245Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'... 2025-03-04T21:02:51.2341982Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'... 2025-03-04T21:02:56.3040427Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'... 2025-03-04T21:02:56.3237263Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f' 2025-03-04T21:02:56.3393384Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3' 2025-03-04T21:02:56.3513332Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1' 2025-03-04T21:02:56.3823509Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73' 2025-03-04T21:02:56.4278561Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1' 2025-03-04T21:02:56.4716689Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191' 2025-03-04T21:02:57.3979875Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883' 2025-03-04T21:02:57.4267263Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415' 2025-03-04T21:02:57.7312670Z Submodule path 'third_party/composable_kernel': checked out '8086bbe3a78d931eb96fe12fdc014082e18d18d3' 2025-03-04T21:02:57.7885648Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006' 2025-03-04T21:02:57.9002933Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99' 2025-03-04T21:02:57.9416824Z Submodule path 'third_party/cudnn_frontend': checked out '91b7532f3386768bba4f444ee7672b497f34da8a' 2025-03-04T21:02:58.6369836Z Submodule path 'third_party/cutlass': checked out 'afa1772203677c5118fcd82537a9c8fefbcc7008' 2025-03-04T21:02:58.9206166Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c' 2025-03-04T21:02:59.0339310Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e' 2025-03-04T21:02:59.0363774Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit' 2025-03-04T21:02:59.0365781Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo' 2025-03-04T21:02:59.0369370Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass' 2025-03-04T21:02:59.0372784Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest' 2025-03-04T21:02:59.0376273Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch' 2025-03-04T21:02:59.0407556Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'... 2025-03-04T21:02:59.8429637Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'... 2025-03-04T21:02:59.8431217Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'... 2025-03-04T21:02:59.9430425Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'... 2025-03-04T21:03:01.4599830Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'... 2025-03-04T21:03:01.5121483Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81' 2025-03-04T21:03:01.6234692Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3' 2025-03-04T21:03:02.1351645Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6' 2025-03-04T21:03:02.2068747Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796' 2025-03-04T21:03:02.2223614Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1' 2025-03-04T21:03:02.3117926Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5' 2025-03-04T21:03:02.3141903Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel' 2025-03-04T21:03:02.3144385Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass' 2025-03-04T21:03:02.3175331Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'... 2025-03-04T21:03:04.6264231Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'... 2025-03-04T21:03:04.9448088Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33' 2025-03-04T21:03:05.6291566Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420' 2025-03-04T21:03:05.7964218Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b' 2025-03-04T21:03:05.8353373Z Submodule path 'third_party/fmt': checked out '123913715afeb8a437e6388b4473fcc4753e1c9a' 2025-03-04T21:03:05.8807421Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350' 2025-03-04T21:03:05.9148425Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509' 2025-03-04T21:03:05.9667986Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59' 2025-03-04T21:03:05.9831384Z Submodule path 'third_party/ideep': checked out 'e026f3b0318087fe19e2b062e8edf55bfe7a522c' 2025-03-04T21:03:05.9851431Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn' 2025-03-04T21:03:05.9883581Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'... 2025-03-04T21:03:18.9019957Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af' 2025-03-04T21:03:18.9362823Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42' 2025-03-04T21:03:19.0325887Z Submodule path 'third_party/kineto': checked out 'a054a4be0db117c579a21747debf19c863631f26' 2025-03-04T21:03:19.0349744Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog' 2025-03-04T21:03:19.0351555Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt' 2025-03-04T21:03:19.0355130Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest' 2025-03-04T21:03:19.0386545Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'... 2025-03-04T21:03:19.7714536Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'... 2025-03-04T21:03:20.4507307Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'... 2025-03-04T21:03:20.5463703Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e' 2025-03-04T21:03:20.5484094Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-03-04T21:03:20.5487500Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-03-04T21:03:20.5491285Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-03-04T21:03:20.5494923Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-03-04T21:03:20.5498892Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-03-04T21:03:20.5502504Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-03-04T21:03:20.5506261Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-03-04T21:03:20.5510176Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-03-04T21:03:20.5541748Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'... 2025-03-04T21:03:21.8272326Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'... 2025-03-04T21:03:21.8273996Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'... 2025-03-04T21:03:21.8275128Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'... 2025-03-04T21:03:21.8276824Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'... 2025-03-04T21:03:21.8646105Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'... 2025-03-04T21:03:22.0511346Z Cloning into 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'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T21:03:47.9737042Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-03-04T21:03:47.9789692Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-03-04T21:03:47.9844834Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T21:03:47.9916070Z Entering 'third_party/pocketfft' 2025-03-04T21:03:47.9967465Z Entering 'third_party/protobuf' 2025-03-04T21:03:48.0021759Z Entering 'third_party/protobuf/third_party/benchmark' 2025-03-04T21:03:48.0072284Z Entering 'third_party/protobuf/third_party/googletest' 2025-03-04T21:03:48.0125793Z Entering 'third_party/psimd' 2025-03-04T21:03:48.0184022Z Entering 'third_party/pthreadpool' 2025-03-04T21:03:48.0235519Z Entering 'third_party/pybind11' 2025-03-04T21:03:48.0288846Z Entering 'third_party/python-peachpy' 2025-03-04T21:03:48.0345200Z Entering 'third_party/sleef' 2025-03-04T21:03:48.0398988Z Entering 'third_party/tensorpipe' 2025-03-04T21:03:48.0456623Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T21:03:48.0506077Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T21:03:48.0554755Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T21:03:48.0604342Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T21:03:48.0652584Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T21:03:48.0733086Z [command]/usr/bin/git submodule foreach --recursive git config --local --add 'url.https://github.com/.insteadOf' 'org-21003710@github.com:' 2025-03-04T21:03:48.1098165Z Entering 'android/libs/fbjni' 2025-03-04T21:03:48.1149641Z Entering 'third_party/FP16' 2025-03-04T21:03:48.1201259Z Entering 'third_party/FXdiv' 2025-03-04T21:03:48.1253462Z Entering 'third_party/NNPACK' 2025-03-04T21:03:48.1303400Z Entering 'third_party/NVTX' 2025-03-04T21:03:48.1354352Z Entering 'third_party/VulkanMemoryAllocator' 2025-03-04T21:03:48.1408873Z Entering 'third_party/XNNPACK' 2025-03-04T21:03:48.1476409Z Entering 'third_party/benchmark' 2025-03-04T21:03:48.1526498Z Entering 'third_party/composable_kernel' 2025-03-04T21:03:48.1583557Z Entering 'third_party/cpp-httplib' 2025-03-04T21:03:48.1633554Z Entering 'third_party/cpuinfo' 2025-03-04T21:03:48.1684654Z Entering 'third_party/cudnn_frontend' 2025-03-04T21:03:48.1735221Z Entering 'third_party/cutlass' 2025-03-04T21:03:48.1794062Z Entering 'third_party/eigen' 2025-03-04T21:03:48.1845749Z Entering 'third_party/fbgemm' 2025-03-04T21:03:48.1897717Z Entering 'third_party/fbgemm/third_party/asmjit' 2025-03-04T21:03:48.1948912Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2025-03-04T21:03:48.2005681Z Entering 'third_party/fbgemm/third_party/cutlass' 2025-03-04T21:03:48.2063461Z Entering 'third_party/fbgemm/third_party/googletest' 2025-03-04T21:03:48.2117330Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2025-03-04T21:03:48.2172833Z Entering 'third_party/flash-attention' 2025-03-04T21:03:48.2223801Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-03-04T21:03:48.2283548Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-03-04T21:03:48.2344742Z Entering 'third_party/flatbuffers' 2025-03-04T21:03:48.2399185Z Entering 'third_party/fmt' 2025-03-04T21:03:48.2449974Z Entering 'third_party/gemmlowp/gemmlowp' 2025-03-04T21:03:48.2502903Z Entering 'third_party/gloo' 2025-03-04T21:03:48.2554833Z Entering 'third_party/googletest' 2025-03-04T21:03:48.2607290Z Entering 'third_party/ideep' 2025-03-04T21:03:48.2656827Z Entering 'third_party/ideep/mkl-dnn' 2025-03-04T21:03:48.2719632Z Entering 'third_party/ittapi' 2025-03-04T21:03:48.2771260Z Entering 'third_party/kineto' 2025-03-04T21:03:48.2820887Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-03-04T21:03:48.2889095Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-03-04T21:03:48.2941152Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-03-04T21:03:48.2991733Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-03-04T21:03:48.3040908Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-03-04T21:03:48.3089679Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-03-04T21:03:48.3144713Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-03-04T21:03:48.3198178Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-03-04T21:03:48.3248037Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-03-04T21:03:48.3302068Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-03-04T21:03:48.3355343Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-03-04T21:03:48.3405972Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-03-04T21:03:48.3460009Z Entering 'third_party/kleidiai' 2025-03-04T21:03:48.3512409Z Entering 'third_party/mimalloc' 2025-03-04T21:03:48.3565062Z Entering 'third_party/nlohmann' 2025-03-04T21:03:48.3616977Z Entering 'third_party/onnx' 2025-03-04T21:03:48.3683092Z Entering 'third_party/onnx/third_party/pybind11' 2025-03-04T21:03:48.3739437Z Entering 'third_party/opentelemetry-cpp' 2025-03-04T21:03:48.3795834Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-03-04T21:03:48.3847981Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-03-04T21:03:48.3899420Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-03-04T21:03:48.3949860Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-03-04T21:03:48.4004004Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-03-04T21:03:48.4054001Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-03-04T21:03:48.4107073Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T21:03:48.4155961Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-03-04T21:03:48.4209076Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-03-04T21:03:48.4262493Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T21:03:48.4334528Z Entering 'third_party/pocketfft' 2025-03-04T21:03:48.4385533Z Entering 'third_party/protobuf' 2025-03-04T21:03:48.4438572Z Entering 'third_party/protobuf/third_party/benchmark' 2025-03-04T21:03:48.4489562Z Entering 'third_party/protobuf/third_party/googletest' 2025-03-04T21:03:48.4543811Z Entering 'third_party/psimd' 2025-03-04T21:03:48.4595867Z Entering 'third_party/pthreadpool' 2025-03-04T21:03:48.4646400Z Entering 'third_party/pybind11' 2025-03-04T21:03:48.4699287Z Entering 'third_party/python-peachpy' 2025-03-04T21:03:48.4751847Z Entering 'third_party/sleef' 2025-03-04T21:03:48.4802478Z Entering 'third_party/tensorpipe' 2025-03-04T21:03:48.4852450Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T21:03:48.4904286Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T21:03:48.4955222Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T21:03:48.5009537Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T21:03:48.5058251Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T21:03:48.5131427Z ##[endgroup] 2025-03-04T21:03:48.5177241Z [command]/usr/bin/git log -1 --format=%H 2025-03-04T21:03:48.5207359Z 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:03:48.5426843Z Prepare all required actions 2025-03-04T21:03:48.5427357Z Getting action download info 2025-03-04T21:03:48.6859884Z ##[group]Run ./.github/actions/setup-linux 2025-03-04T21:03:48.6860212Z env: 2025-03-04T21:03:48.6860447Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:48.6860720Z ##[endgroup] 2025-03-04T21:03:48.6902686Z ##[group]Run set -euo pipefail 2025-03-04T21:03:48.6903036Z set -euo pipefail 2025-03-04T21:03:48.6903344Z function get_ec2_metadata() { 2025-03-04T21:03:48.6903729Z  # Pulled from instance metadata endpoint for EC2 2025-03-04T21:03:48.6904343Z  # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html 2025-03-04T21:03:48.6904895Z  category=$1 2025-03-04T21:03:48.6905263Z  # If it is GCP runner (runner name contains gcp), do not run this 2025-03-04T21:03:48.6905699Z  runner_name_str=i-01112049b7e156298 2025-03-04T21:03:48.6906089Z  if [[ -f /.inarc ]]; then 2025-03-04T21:03:48.6906450Z  echo "ARC Runner, no info on ec2 metadata" 2025-03-04T21:03:48.6906844Z  elif [[ $runner_name_str == *"gcp"* ]]; then 2025-03-04T21:03:48.6907309Z  echo "Runner is from Google Cloud Platform, No info on ec2 metadata" 2025-03-04T21:03:48.6907732Z  else 2025-03-04T21:03:48.6908545Z  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}" 2025-03-04T21:03:48.6909393Z  fi 2025-03-04T21:03:48.6909634Z } 2025-03-04T21:03:48.6909917Z echo "ami-id: $(get_ec2_metadata ami-id)" 2025-03-04T21:03:48.6910351Z echo "instance-id: $(get_ec2_metadata instance-id)" 2025-03-04T21:03:48.6910821Z echo "instance-type: $(get_ec2_metadata instance-type)" 2025-03-04T21:03:48.6911239Z echo "system info $(uname -a)" 2025-03-04T21:03:48.6923079Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:48.6923449Z env: 2025-03-04T21:03:48.6923674Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:48.6923937Z ##[endgroup] 2025-03-04T21:03:48.7098877Z ami-id: ami-05b10e08d247fb927 2025-03-04T21:03:48.7214366Z instance-id: i-01112049b7e156298 2025-03-04T21:03:48.7328076Z instance-type: g5.4xlarge 2025-03-04T21:03:48.7340830Z system info Linux ip-10-0-51-51.ec2.internal 6.1.128-136.201.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Mon Feb 10 16:18:01 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux 2025-03-04T21:03:48.7368684Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T21:03:48.7369566Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T21:03:48.7378595Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:48.7378997Z env: 2025-03-04T21:03:48.7379228Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:48.7379501Z ##[endgroup] 2025-03-04T21:03:48.7443327Z ##[group]Run if systemctl is-active --quiet docker; then 2025-03-04T21:03:48.7443837Z if systemctl is-active --quiet docker; then 2025-03-04T21:03:48.7444464Z  echo "Docker daemon is running..."; 2025-03-04T21:03:48.7444918Z else 2025-03-04T21:03:48.7445411Z  echo "Starting docker deamon..." && sudo systemctl start docker; 2025-03-04T21:03:48.7457730Z fi 2025-03-04T21:03:48.7466703Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:48.7467072Z env: 2025-03-04T21:03:48.7467297Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:48.7467561Z ##[endgroup] 2025-03-04T21:03:48.7556661Z Docker daemon is running... 2025-03-04T21:03:48.7599954Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T21:03:48.7600258Z with: 2025-03-04T21:03:48.7600473Z shell: bash 2025-03-04T21:03:48.7600904Z timeout_minutes: 5 2025-03-04T21:03:48.7601160Z max_attempts: 3 2025-03-04T21:03:48.7601408Z retry_wait_seconds: 30 2025-03-04T21:03:48.7603464Z command: AWS_ACCOUNT_ID=$(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 2025-03-04T21:03:48.7605520Z polling_interval_seconds: 1 2025-03-04T21:03:48.7605807Z warning_on_retry: true 2025-03-04T21:03:48.7606081Z continue_on_error: false 2025-03-04T21:03:48.7606341Z env: 2025-03-04T21:03:48.7606570Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:48.7606843Z AWS_RETRY_MODE: standard 2025-03-04T21:03:48.7607109Z AWS_MAX_ATTEMPTS: 5 2025-03-04T21:03:48.7607372Z AWS_DEFAULT_REGION: us-east-1 2025-03-04T21:03:48.7607648Z ##[endgroup] 2025-03-04T21:03:50.0065190Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T21:03:50.0065811Z Configure a credential helper to remove this warning. See 2025-03-04T21:03:50.0066639Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T21:03:50.0067150Z 2025-03-04T21:03:50.0067451Z Login Succeeded 2025-03-04T21:03:50.8435062Z Command completed after 1 attempt(s). 2025-03-04T21:03:50.8518062Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T21:03:50.8518578Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T21:03:50.8519035Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T21:03:50.8529161Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:50.8529532Z env: 2025-03-04T21:03:50.8529763Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:50.8530037Z ##[endgroup] 2025-03-04T21:03:50.8628960Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T21:03:50.8629499Z # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T21:03:50.8629919Z # shellcheck disable=SC2046 2025-03-04T21:03:50.8630321Z docker stop $(docker ps -q) || true 2025-03-04T21:03:50.8630668Z # Prune all of the docker images 2025-03-04T21:03:50.8630998Z docker system prune -af 2025-03-04T21:03:50.8639012Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:50.8639378Z env: 2025-03-04T21:03:50.8639604Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:50.8639871Z ##[endgroup] 2025-03-04T21:03:50.8938705Z "docker stop" requires at least 1 argument. 2025-03-04T21:03:50.8939261Z See 'docker stop --help'. 2025-03-04T21:03:50.8939544Z 2025-03-04T21:03:50.8939796Z Usage: docker stop [OPTIONS] CONTAINER [CONTAINER...] 2025-03-04T21:03:50.8940176Z 2025-03-04T21:03:50.8940335Z Stop one or more running containers 2025-03-04T21:03:50.9260033Z Total reclaimed space: 0B 2025-03-04T21:03:50.9315944Z ##[group]Run set +e 2025-03-04T21:03:50.9316262Z set +e 2025-03-04T21:03:50.9316568Z set -x 2025-03-04T21:03:50.9316900Z  2025-03-04T21:03:50.9317280Z PT_DOMAIN=download.pytorch.org 2025-03-04T21:03:50.9318119Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400, 2025-03-04T21:03:50.9318934Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last 2025-03-04T21:03:50.9319598Z # one is returned at random 2025-03-04T21:03:50.9320105Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1) 2025-03-04T21:03:50.9320596Z  2025-03-04T21:03:50.9321132Z if [ -z "${RESOLVED_IP}" ]; then 2025-03-04T21:03:50.9333987Z  echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..." 2025-03-04T21:03:50.9334501Z  RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1) 2025-03-04T21:03:50.9334890Z  2025-03-04T21:03:50.9335133Z  if [ -z "${RESOLVED_IP}" ]; then 2025-03-04T21:03:50.9335513Z  echo "Couldn't resolve ${PT_DOMAIN}, exiting..." 2025-03-04T21:03:50.9335863Z  exit 1 2025-03-04T21:03:50.9336098Z  fi 2025-03-04T21:03:50.9336315Z fi 2025-03-04T21:03:50.9336524Z  2025-03-04T21:03:50.9336786Z if grep -r "${PT_DOMAIN}" /etc/hosts; then 2025-03-04T21:03:50.9337155Z  # Clean up any old records first 2025-03-04T21:03:50.9337515Z  sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts 2025-03-04T21:03:50.9337840Z fi 2025-03-04T21:03:50.9338061Z  2025-03-04T21:03:50.9338383Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts 2025-03-04T21:03:50.9338779Z cat /etc/hosts 2025-03-04T21:03:50.9347575Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:50.9347946Z env: 2025-03-04T21:03:50.9348177Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:50.9348447Z ##[endgroup] 2025-03-04T21:03:50.9377560Z + PT_DOMAIN=download.pytorch.org 2025-03-04T21:03:50.9383665Z ++ dig -4 +short download.pytorch.org 2025-03-04T21:03:50.9384243Z ++ tail -n1 2025-03-04T21:03:50.9785683Z + RESOLVED_IP=18.160.10.76 2025-03-04T21:03:50.9786002Z + '[' -z 18.160.10.76 ']' 2025-03-04T21:03:50.9786312Z + grep -r download.pytorch.org /etc/hosts 2025-03-04T21:03:50.9804464Z + echo '18.160.10.76 download.pytorch.org' 2025-03-04T21:03:50.9805114Z + sudo tee -a /etc/hosts 2025-03-04T21:03:51.3734045Z 18.160.10.76 download.pytorch.org 2025-03-04T21:03:51.3756262Z + cat /etc/hosts 2025-03-04T21:03:51.3769253Z 127.0.0.1 localhost localhost.localdomain localhost4 localhost4.localdomain4 2025-03-04T21:03:51.3774666Z ::1 localhost6 localhost6.localdomain6 2025-03-04T21:03:51.3775035Z 18.160.10.76 download.pytorch.org 2025-03-04T21:03:51.3923560Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main 2025-03-04T21:03:51.3924020Z with: 2025-03-04T21:03:51.3924827Z docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:51.3925717Z docker-build-dir: .ci/docker 2025-03-04T21:03:51.3926014Z working-directory: . 2025-03-04T21:03:51.3926371Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:51.3926764Z force-push: false 2025-03-04T21:03:51.3928679Z env: 2025-03-04T21:03:51.3928898Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:51.3929167Z ##[endgroup] 2025-03-04T21:03:51.3957882Z ##[group]Run set -ex 2025-03-04T21:03:51.3958192Z set -ex 2025-03-04T21:03:51.3958427Z  2025-03-04T21:03:51.3958837Z # If the docker build directory or the build script doesn't exist, the action will 2025-03-04T21:03:51.3959505Z # gracefully return the docker image name as it is. Pulling docker image in Linux 2025-03-04T21:03:51.3960056Z # job could then download the pre-built image as usual 2025-03-04T21:03:51.3960560Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then 2025-03-04T21:03:51.3961024Z  echo "skip=true" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.3961790Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.3962192Z  2025-03-04T21:03:51.3962550Z  echo "There is no Docker build script in ${REPO_NAME} repo, skipping..." 2025-03-04T21:03:51.3963030Z  exit 0 2025-03-04T21:03:51.3963275Z else 2025-03-04T21:03:51.3963550Z  echo "skip=false" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.3963883Z fi 2025-03-04T21:03:51.3964116Z  2025-03-04T21:03:51.3964469Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then 2025-03-04T21:03:51.3965046Z  # The docker image name already includes the ECR prefix and tag, so we can just 2025-03-04T21:03:51.3965567Z  # use it as it is, but first let's extract the tag 2025-03-04T21:03:51.3966045Z  DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}') 2025-03-04T21:03:51.3966545Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.3967024Z  echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.3967425Z else 2025-03-04T21:03:51.3967753Z  DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}") 2025-03-04T21:03:51.3968305Z  echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.3968918Z  echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.3969465Z fi 2025-03-04T21:03:51.3979577Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:51.3979948Z env: 2025-03-04T21:03:51.3980169Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:51.3980445Z REPO_NAME: pytorch 2025-03-04T21:03:51.3981282Z DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:51.3982154Z DOCKER_BUILD_DIR: .ci/docker 2025-03-04T21:03:51.3982528Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:51.3982916Z ##[endgroup] 2025-03-04T21:03:51.4014938Z + [[ ! -d .ci/docker ]] 2025-03-04T21:03:51.4015431Z + [[ ! -f .ci/docker/build.sh ]] 2025-03-04T21:03:51.4015738Z + echo skip=false 2025-03-04T21:03:51.4016995Z + [[ 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e == *\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* ]] 2025-03-04T21:03:51.4022454Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:51.4023340Z ++ awk -F '[:,]' '{print $2}' 2025-03-04T21:03:51.4047945Z + DOCKER_TAG=e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:51.4048383Z + echo docker-tag=e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:51.4049314Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:51.4100310Z ##[group]Run set +e 2025-03-04T21:03:51.4100649Z set +e 2025-03-04T21:03:51.4100891Z set -x 2025-03-04T21:03:51.4101123Z  2025-03-04T21:03:51.4101343Z login() { 2025-03-04T21:03:51.4101827Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-03-04T21:03:51.4102324Z } 2025-03-04T21:03:51.4102545Z  2025-03-04T21:03:51.4102765Z retry () { 2025-03-04T21:03:51.4103046Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-03-04T21:03:51.4103359Z } 2025-03-04T21:03:51.4103578Z  2025-03-04T21:03:51.4103824Z retry login "${DOCKER_REGISTRY}" 2025-03-04T21:03:51.4104125Z  2025-03-04T21:03:51.4104355Z START_TIME=$(date +%s) 2025-03-04T21:03:51.4104653Z # Wait up to 120 minutes 2025-03-04T21:03:51.4105017Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do 2025-03-04T21:03:51.4105494Z  # Check if image already exists, if it does then skip building it 2025-03-04T21:03:51.4105962Z  if docker manifest inspect "${DOCKER_IMAGE}"; then 2025-03-04T21:03:51.4106321Z  exit 0 2025-03-04T21:03:51.4106574Z  fi 2025-03-04T21:03:51.4106796Z  2025-03-04T21:03:51.4107178Z  # NB: This flag is used by Docker build workflow to push the image to ECR, so we can 2025-03-04T21:03:51.4107804Z  # use this to differentiate between the Docker build and regular build jobs. For the 2025-03-04T21:03:51.4108426Z  # latter, it will wait for the Docker images to become available before continuing 2025-03-04T21:03:51.4108924Z  if [ "${DOCKER_PUSH:-false}" == "true" ]; then 2025-03-04T21:03:51.4109322Z  # It's a Docker build job, let's build the image 2025-03-04T21:03:51.4109664Z  break 2025-03-04T21:03:51.4109908Z  else 2025-03-04T21:03:51.4110252Z  # It's a regular build job, wait for the image to become available 2025-03-04T21:03:51.4110656Z  sleep 300 2025-03-04T21:03:51.4110911Z  fi 2025-03-04T21:03:51.4111141Z done 2025-03-04T21:03:51.4111368Z  2025-03-04T21:03:51.4111715Z # NB: This part requires a full checkout. Otherwise, the merge base will 2025-03-04T21:03:51.4112264Z # be empty. The default action would be to continue rebuild the image 2025-03-04T21:03:51.4112758Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then 2025-03-04T21:03:51.4113197Z  # if we're on the base branch then use the parent commit 2025-03-04T21:03:51.4113594Z  MERGE_BASE=$(git rev-parse HEAD~) 2025-03-04T21:03:51.4113902Z else 2025-03-04T21:03:51.4114234Z  # otherwise we're on a PR, so use the most recent base commit 2025-03-04T21:03:51.4114697Z  MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION") 2025-03-04T21:03:51.4115051Z fi 2025-03-04T21:03:51.4115271Z  2025-03-04T21:03:51.4115517Z if [[ -z "${MERGE_BASE}" ]]; then 2025-03-04T21:03:51.4115879Z  echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.4116399Z  2025-03-04T21:03:51.4117001Z  echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..." 2025-03-04T21:03:51.4117533Z  exit 0 2025-03-04T21:03:51.4117771Z fi 2025-03-04T21:03:51.4118002Z  2025-03-04T21:03:51.4118320Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then 2025-03-04T21:03:51.4118968Z  echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit" 2025-03-04T21:03:51.4119523Z  exit 1 2025-03-04T21:03:51.4119759Z fi 2025-03-04T21:03:51.4119984Z  2025-03-04T21:03:51.4120345Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}") 2025-03-04T21:03:51.4120973Z # If no image exists but the hash is the same as the previous hash then we should error out here 2025-03-04T21:03:51.4121540Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then 2025-03-04T21:03:51.4122200Z  echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch" 2025-03-04T21:03:51.4122927Z  echo " Will re-build docker image to store in local cache, TTS may be longer" 2025-03-04T21:03:51.4123376Z fi 2025-03-04T21:03:51.4123601Z  2025-03-04T21:03:51.4123877Z echo "rebuild=true" >> "${GITHUB_OUTPUT}" 2025-03-04T21:03:51.4132947Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:51.4133350Z env: 2025-03-04T21:03:51.4133584Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:51.4133875Z DOCKER_BUILD_DIR: .ci/docker 2025-03-04T21:03:51.4134234Z BASE_REVISION: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:03:51.4135154Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:51.4136061Z DOCKER_TAG: e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:51.4136518Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:51.4136899Z DOCKER_PUSH: 2025-03-04T21:03:51.4137149Z ##[endgroup] 2025-03-04T21:03:51.4171412Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:51.4171875Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:51.4174851Z + aws ecr get-login-password --region us-east-1 2025-03-04T21:03:51.4175865Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:51.9190022Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T21:03:51.9190728Z Configure a credential helper to remove this warning. See 2025-03-04T21:03:51.9191393Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T21:03:51.9191806Z 2025-03-04T21:03:51.9191978Z Login Succeeded 2025-03-04T21:03:51.9217829Z ++ date +%s 2025-03-04T21:03:51.9229762Z + START_TIME=1741122231 2025-03-04T21:03:51.9234332Z ++ date +%s 2025-03-04T21:03:51.9245786Z + [[ 1741115031 -lt 1741122231 ]] 2025-03-04T21:03:51.9246719Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:52.1507212Z { 2025-03-04T21:03:52.1508270Z "schemaVersion": 2, 2025-03-04T21:03:52.1508692Z "mediaType": "application/vnd.docker.distribution.manifest.v2+json", 2025-03-04T21:03:52.1509190Z "config": { 2025-03-04T21:03:52.1509572Z "mediaType": "application/vnd.docker.container.image.v1+json", 2025-03-04T21:03:52.1509982Z "size": 52686, 2025-03-04T21:03:52.1510407Z "digest": 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"sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T21:03:52.1672143Z }, 2025-03-04T21:03:52.1672336Z { 2025-03-04T21:03:52.1672660Z "mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip", 2025-03-04T21:03:52.1673065Z "size": 32, 2025-03-04T21:03:52.1673470Z "digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1" 2025-03-04T21:03:52.1673930Z } 2025-03-04T21:03:52.1674128Z ] 2025-03-04T21:03:52.1674323Z } 2025-03-04T21:03:52.1712863Z ##[group]Run set -eux 2025-03-04T21:03:52.1713154Z set -eux 2025-03-04T21:03:52.1714020Z aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token | jq --raw-output '.SecretString' | jq -r .docker_hub_readonly_token | docker login --username pytorchbot --password-stdin 2025-03-04T21:03:52.1724670Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:52.1725043Z env: 2025-03-04T21:03:52.1725268Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:52.1725545Z ##[endgroup] 2025-03-04T21:03:52.1759200Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token 2025-03-04T21:03:52.1760014Z + jq --raw-output .SecretString 2025-03-04T21:03:52.1760837Z + jq -r .docker_hub_readonly_token 2025-03-04T21:03:52.1762699Z + docker login --username pytorchbot --password-stdin 2025-03-04T21:03:52.7501974Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T21:03:52.7502570Z Configure a credential helper to remove this warning. See 2025-03-04T21:03:52.7503137Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T21:03:52.7503624Z 2025-03-04T21:03:52.7503786Z Login Succeeded 2025-03-04T21:03:52.7615083Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/} 2025-03-04T21:03:52.7615457Z tag=${ECR_DOCKER_IMAGE##*/} 2025-03-04T21:03:52.7615848Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}" 2025-03-04T21:03:52.7624703Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:52.7625074Z env: 2025-03-04T21:03:52.7625301Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:52.7626145Z ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:52.7627188Z ##[endgroup] 2025-03-04T21:03:52.7662464Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks-e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:52.7719221Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main 2025-03-04T21:03:52.7719667Z with: 2025-03-04T21:03:52.7720453Z docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:52.7721403Z docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:52.7721789Z env: 2025-03-04T21:03:52.7722019Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:52.7722291Z ##[endgroup] 2025-03-04T21:03:52.7747652Z ##[group]Run set -x 2025-03-04T21:03:52.7747927Z set -x 2025-03-04T21:03:52.7748164Z set +e 2025-03-04T21:03:52.7748395Z  2025-03-04T21:03:52.7748632Z login() { 2025-03-04T21:03:52.7749100Z  aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1" 2025-03-04T21:03:52.7749598Z } 2025-03-04T21:03:52.7749818Z  2025-03-04T21:03:52.7750062Z retry () { 2025-03-04T21:03:52.7750354Z  $* || (sleep 1 && $*) || (sleep 2 && $*) 2025-03-04T21:03:52.7750669Z } 2025-03-04T21:03:52.7750894Z  2025-03-04T21:03:52.7751147Z retry login "${DOCKER_REGISTRY}" 2025-03-04T21:03:52.7751461Z  2025-03-04T21:03:52.7751681Z set -e 2025-03-04T21:03:52.7752021Z # ignore output since only exit code is used for conditional 2025-03-04T21:03:52.7752504Z # only pull docker image if it's not available locally 2025-03-04T21:03:52.7753032Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then 2025-03-04T21:03:52.7753519Z  retry docker pull "${DOCKER_IMAGE}" 2025-03-04T21:03:52.7753889Z fi 2025-03-04T21:03:52.7763214Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:03:52.7763583Z env: 2025-03-04T21:03:52.7763808Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:03:52.7764639Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:52.7765578Z DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:52.7765961Z ##[endgroup] 2025-03-04T21:03:52.7794298Z + set +e 2025-03-04T21:03:52.7794750Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:52.7795196Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:52.7798235Z + aws ecr get-login-password --region us-east-1 2025-03-04T21:03:52.7799146Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com 2025-03-04T21:03:53.2995233Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json. 2025-03-04T21:03:53.2995949Z Configure a credential helper to remove this warning. See 2025-03-04T21:03:53.2996512Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store 2025-03-04T21:03:53.2996898Z 2025-03-04T21:03:53.2997057Z Login Succeeded 2025-03-04T21:03:53.3020766Z + set -e 2025-03-04T21:03:53.3021656Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:53.3171243Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:53.3172678Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:03:53.5458292Z e4800fd93ba7d48bf4197a488fd32c12de647b0e: Pulling from pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks 2025-03-04T21:03:53.5461833Z 80888bc6716f: Pulling fs layer 2025-03-04T21:03:53.5462258Z fbcd35dc5bc3: Pulling fs layer 2025-03-04T21:03:53.5463001Z c7232af9ae05: Pulling fs layer 2025-03-04T21:03:53.5463472Z db6cdef1932a: Pulling fs layer 2025-03-04T21:03:53.5463821Z 56dc85502937: Pulling fs layer 2025-03-04T21:03:53.5464269Z 30c0ea6140d0: Pulling fs layer 2025-03-04T21:03:53.5464705Z 71bdb1a72c2d: Pulling fs layer 2025-03-04T21:03:53.5465016Z 4829486be7c3: Pulling fs layer 2025-03-04T21:03:53.5465305Z 1f4e68d7b5e4: Pulling fs layer 2025-03-04T21:03:53.5465704Z 7c373e2d9b7e: Pulling fs layer 2025-03-04T21:03:53.5466141Z 622381141745: Pulling fs layer 2025-03-04T21:03:53.5466546Z e3590e4c540f: Pulling fs layer 2025-03-04T21:03:53.5466964Z aa7bdda81edf: Pulling fs layer 2025-03-04T21:03:53.5467378Z 9f9fefb5046a: Pulling fs layer 2025-03-04T21:03:53.5467791Z e4186ba0d494: Pulling fs layer 2025-03-04T21:03:53.5468167Z db6cdef1932a: Waiting 2025-03-04T21:03:53.5468434Z 56dc85502937: Waiting 2025-03-04T21:03:53.5468739Z 4d0730fe2407: Pulling fs layer 2025-03-04T21:03:53.5469131Z 00ff3e436638: Pulling fs layer 2025-03-04T21:03:53.5469536Z 30c0ea6140d0: Waiting 2025-03-04T21:03:53.5469919Z f389b7bb3dbc: Pulling fs layer 2025-03-04T21:03:53.5470885Z a252197d2b71: Pulling fs layer 2025-03-04T21:03:53.5471312Z 4829486be7c3: Waiting 2025-03-04T21:03:53.5471683Z 65a216d1be48: Pulling fs layer 2025-03-04T21:03:53.5472050Z e265fc00a638: Pulling fs layer 2025-03-04T21:03:53.5472449Z 268644de4c3a: Pulling fs layer 2025-03-04T21:03:53.5472868Z 7dd6b48df5d3: Pulling fs layer 2025-03-04T21:03:53.5473281Z 617552105e2b: Pulling fs layer 2025-03-04T21:03:53.5473679Z 71bdb1a72c2d: Waiting 2025-03-04T21:03:53.5473949Z 2834a2b2342c: Pulling fs layer 2025-03-04T21:03:53.5474239Z 76a0ad9d05e9: Pulling fs layer 2025-03-04T21:03:53.5474705Z 59ca559b9d88: Pulling fs layer 2025-03-04T21:03:53.5475080Z c63808a61495: Pulling fs layer 2025-03-04T21:03:53.5475570Z ad8b390aef2e: Pulling fs layer 2025-03-04T21:03:53.5490790Z 622381141745: Waiting 2025-03-04T21:03:53.5491236Z 475565e4c86d: Pulling fs layer 2025-03-04T21:03:53.5491717Z cd07fae473e4: Pulling fs layer 2025-03-04T21:03:53.5492370Z 7c373e2d9b7e: Waiting 2025-03-04T21:03:53.5492784Z f5604a190caf: Pulling fs layer 2025-03-04T21:03:53.5493245Z 9f9fefb5046a: Waiting 2025-03-04T21:03:53.5493626Z 59ca559b9d88: Waiting 2025-03-04T21:03:53.5494014Z bf0779a5af46: Pulling fs layer 2025-03-04T21:03:53.5494498Z 3a4c33aae4fa: Pulling fs layer 2025-03-04T21:03:53.5494952Z d91b2ac229a3: Pulling fs layer 2025-03-04T21:03:53.5495397Z 00ff3e436638: Waiting 2025-03-04T21:03:53.5495799Z a252197d2b71: Waiting 2025-03-04T21:03:53.5496199Z 65a216d1be48: Waiting 2025-03-04T21:03:53.5496598Z e4186ba0d494: Waiting 2025-03-04T21:03:53.5497340Z ddbeef574b46: Pulling fs layer 2025-03-04T21:03:53.5497793Z 94a55c9da9f6: Pulling fs layer 2025-03-04T21:03:53.5498230Z aa7bdda81edf: Waiting 2025-03-04T21:03:53.5498550Z 2732bb0bde0a: Pulling fs layer 2025-03-04T21:03:53.5498843Z ed3c15f0cb6d: Pulling fs layer 2025-03-04T21:03:53.5499488Z 5aa3189882c0: Pulling fs layer 2025-03-04T21:03:53.5499907Z 268644de4c3a: Waiting 2025-03-04T21:03:53.5500290Z 9d56755a4336: Pulling fs layer 2025-03-04T21:03:53.5500992Z 76a0ad9d05e9: Waiting 2025-03-04T21:03:53.5501385Z 289b1f9140de: Pulling fs layer 2025-03-04T21:03:53.5501773Z 475565e4c86d: Waiting 2025-03-04T21:03:53.5502031Z 2834a2b2342c: Waiting 2025-03-04T21:03:53.5502287Z d91b2ac229a3: Waiting 2025-03-04T21:03:53.5502554Z 1a19b9ebf73a: Pulling fs layer 2025-03-04T21:03:53.5502896Z b9c871e2218d: Pulling fs layer 2025-03-04T21:03:53.5503201Z 3e438d169ab9: Pulling fs layer 2025-03-04T21:03:53.5503539Z ddbeef574b46: Waiting 2025-03-04T21:03:53.5503808Z c8151f73c550: Pulling fs layer 2025-03-04T21:03:53.5504129Z 94a55c9da9f6: Waiting 2025-03-04T21:03:53.5504385Z f5604a190caf: Waiting 2025-03-04T21:03:53.5504653Z e6abce57861b: Pulling fs layer 2025-03-04T21:03:53.5505196Z 181a6d443194: Pulling fs layer 2025-03-04T21:03:53.5505481Z ed3c15f0cb6d: Waiting 2025-03-04T21:03:53.5505747Z a4f66d4bc954: Pulling fs layer 2025-03-04T21:03:53.5506323Z bf0779a5af46: Waiting 2025-03-04T21:03:53.5506589Z 56c9d3d8c176: Pulling fs layer 2025-03-04T21:03:53.5507091Z 2c4565619bd3: Pulling fs layer 2025-03-04T21:03:53.5507374Z 617552105e2b: Waiting 2025-03-04T21:03:53.5507646Z 8163be9f2fc1: Pulling fs layer 2025-03-04T21:03:53.5507945Z 93e47cba5617: Pulling fs layer 2025-03-04T21:03:53.5508217Z c63808a61495: Waiting 2025-03-04T21:03:53.5508628Z 1a19b9ebf73a: Waiting 2025-03-04T21:03:53.5508894Z 15595d813536: Pulling fs layer 2025-03-04T21:03:53.5509261Z b9c871e2218d: Waiting 2025-03-04T21:03:53.5509543Z 3e438d169ab9: Waiting 2025-03-04T21:03:53.5509821Z 4ce74c7e346b: Pulling fs layer 2025-03-04T21:03:53.5510125Z 4f4fb700ef54: Pulling fs layer 2025-03-04T21:03:53.5510389Z c8151f73c550: Waiting 2025-03-04T21:03:53.5510819Z 389263d39fc8: Pulling fs layer 2025-03-04T21:03:53.5511116Z 7dd6b48df5d3: Waiting 2025-03-04T21:03:53.5511380Z e265fc00a638: Waiting 2025-03-04T21:03:53.5511672Z 57f38fb7ba59: Pulling fs layer 2025-03-04T21:03:53.5512046Z 56c9d3d8c176: Waiting 2025-03-04T21:03:53.5512313Z 1cb21e96a9e2: Pulling fs layer 2025-03-04T21:03:53.5512613Z c65c25a44412: Pulling fs layer 2025-03-04T21:03:53.5512904Z 334af9be5f15: Pulling fs layer 2025-03-04T21:03:53.5513202Z a68ab8ed2202: Pulling fs layer 2025-03-04T21:03:53.5513507Z ecff154b7961: Pulling fs layer 2025-03-04T21:03:53.5513865Z 97244b0b2fb2: Pulling fs layer 2025-03-04T21:03:53.5514146Z 2732bb0bde0a: Waiting 2025-03-04T21:03:53.5514412Z e41e958aa2f3: Pulling fs layer 2025-03-04T21:03:53.5514691Z 181a6d443194: Waiting 2025-03-04T21:03:53.5514949Z 9155937b2d4b: Pulling fs layer 2025-03-04T21:03:53.5515242Z b6ec22f6cdfe: Pulling fs layer 2025-03-04T21:03:53.5515522Z 15595d813536: Waiting 2025-03-04T21:03:53.5515775Z 4f4fb700ef54: Waiting 2025-03-04T21:03:53.5516042Z 5dc326dbf4a2: Pulling fs layer 2025-03-04T21:03:53.5516343Z 2d9aeb8c5240: Pulling fs layer 2025-03-04T21:03:53.5516640Z 8aec19867d7f: Pulling fs layer 2025-03-04T21:03:53.5516917Z 389263d39fc8: Waiting 2025-03-04T21:03:53.5517161Z 4ce74c7e346b: Waiting 2025-03-04T21:03:53.5517424Z e2a360747d0d: Pulling fs layer 2025-03-04T21:03:53.5517721Z 4fefa8e1b32c: Pulling fs layer 2025-03-04T21:03:53.5518013Z 4305d8ab3009: Pulling fs layer 2025-03-04T21:03:53.5518303Z b081a7af2ce1: Pulling fs layer 2025-03-04T21:03:53.5518594Z 49f24dd0c353: Pulling fs layer 2025-03-04T21:03:53.5518875Z 9155937b2d4b: Waiting 2025-03-04T21:03:53.5519115Z 57f38fb7ba59: Waiting 2025-03-04T21:03:53.5519362Z 1cb21e96a9e2: Waiting 2025-03-04T21:03:53.5519614Z e6abce57861b: Waiting 2025-03-04T21:03:53.5519875Z c65c25a44412: Waiting 2025-03-04T21:03:53.5520115Z 4305d8ab3009: Waiting 2025-03-04T21:03:53.5520358Z b081a7af2ce1: Waiting 2025-03-04T21:03:53.5520601Z 49f24dd0c353: Waiting 2025-03-04T21:03:53.5520844Z 2c4565619bd3: Waiting 2025-03-04T21:03:53.5521091Z 5dc326dbf4a2: Waiting 2025-03-04T21:03:53.5521336Z ecff154b7961: Waiting 2025-03-04T21:03:53.5521586Z 2d9aeb8c5240: Waiting 2025-03-04T21:03:53.5521829Z 9d56755a4336: Waiting 2025-03-04T21:03:53.5522071Z 97244b0b2fb2: Waiting 2025-03-04T21:03:53.5522319Z e2a360747d0d: Waiting 2025-03-04T21:03:53.5522563Z 4fefa8e1b32c: Waiting 2025-03-04T21:03:53.5522813Z 289b1f9140de: Waiting 2025-03-04T21:03:53.5523050Z b6ec22f6cdfe: Waiting 2025-03-04T21:03:53.6918943Z fbcd35dc5bc3: Verifying Checksum 2025-03-04T21:03:53.6919311Z fbcd35dc5bc3: Download complete 2025-03-04T21:03:53.7778046Z db6cdef1932a: Verifying Checksum 2025-03-04T21:03:53.7778412Z db6cdef1932a: Download complete 2025-03-04T21:03:53.9211777Z 56dc85502937: Verifying Checksum 2025-03-04T21:03:53.9212134Z 56dc85502937: Download complete 2025-03-04T21:03:53.9285502Z 80888bc6716f: Verifying Checksum 2025-03-04T21:03:53.9285940Z 80888bc6716f: Download complete 2025-03-04T21:03:54.0098231Z 71bdb1a72c2d: Verifying Checksum 2025-03-04T21:03:54.0098718Z 71bdb1a72c2d: Download complete 2025-03-04T21:03:54.0977061Z 4829486be7c3: Verifying Checksum 2025-03-04T21:03:54.0977841Z 4829486be7c3: Download complete 2025-03-04T21:03:54.1759703Z c7232af9ae05: Verifying Checksum 2025-03-04T21:03:54.1760056Z c7232af9ae05: Download complete 2025-03-04T21:03:54.1800861Z 1f4e68d7b5e4: Verifying Checksum 2025-03-04T21:03:54.1801621Z 1f4e68d7b5e4: Download complete 2025-03-04T21:03:54.2861741Z 622381141745: Verifying Checksum 2025-03-04T21:03:54.2862271Z 622381141745: Download complete 2025-03-04T21:03:54.3535065Z e3590e4c540f: Verifying Checksum 2025-03-04T21:03:54.3536011Z e3590e4c540f: Download complete 2025-03-04T21:03:55.0035719Z 80888bc6716f: Pull complete 2025-03-04T21:03:55.3022616Z fbcd35dc5bc3: Pull complete 2025-03-04T21:03:56.0465077Z c7232af9ae05: Pull complete 2025-03-04T21:03:56.0689409Z 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4fefa8e1b32c: Pull complete 2025-03-04T21:10:33.7486045Z 4305d8ab3009: Pull complete 2025-03-04T21:10:34.3653308Z b081a7af2ce1: Pull complete 2025-03-04T21:10:34.5848754Z 49f24dd0c353: Pull complete 2025-03-04T21:10:35.5188463Z Digest: sha256:10bcf94a2effce3256f6633d4b69f9e7f22aa569173ddbb4eab262e4cf671bbc 2025-03-04T21:10:35.5589952Z Status: Downloaded newer image for 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:10:35.5791882Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:10:35.5851279Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T21:10:35.5852169Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT" 2025-03-04T21:10:35.5865821Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:10:35.5866204Z env: 2025-03-04T21:10:35.5866441Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:10:35.5866718Z ##[endgroup] 2025-03-04T21:10:35.6122162Z ##[group]Run pytorch/test-infra/.github/actions/setup-nvidia@main 2025-03-04T21:10:35.6122587Z with: 2025-03-04T21:10:35.6122825Z driver-version: 550.54.15 2025-03-04T21:10:35.6123098Z env: 2025-03-04T21:10:35.6123318Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:10:35.6123600Z ##[endgroup] 2025-03-04T21:10:35.6272437Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482 2025-03-04T21:10:35.6272846Z with: 2025-03-04T21:10:35.6273069Z timeout_minutes: 10 2025-03-04T21:10:35.6273320Z max_attempts: 3 2025-03-04T21:10:35.6296337Z command: # Is it disgusting to have a full shell script here in this github action? Sure # But is it the best way to make it so that this action relies on nothing else? Absolutely set -eou pipefail DISTRIBUTION=$(. /etc/os-release;echo $ID$VERSION_ID) DRIVER_FN="NVIDIA-Linux-x86_64-${DRIVER_VERSION}.run" install_nvidia_docker2_amzn2() { ( set -x # Needed for yum-config-manager sudo yum install -y yum-utils if [[ "${DISTRIBUTION}" == "amzn2023" ]] ; then YUM_REPO_URL="https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo" else # Amazon Linux 2 YUM_REPO_URL="https://nvidia.github.io/nvidia-docker/${DISTRIBUTION}/nvidia-docker.repo" fi sudo yum-config-manager --add-repo "${YUM_REPO_URL}" sudo yum install -y nvidia-docker2 nvidia-container-toolkit-1.16.2 sudo systemctl restart docker ) } install_nvidia_docker2_ubuntu20() { ( set -x # Install nvidia-driver package if not installed status="$(dpkg-query -W --showformat='${db:Status-Status}' nvidia-docker2 2>&1)" if [ ! $? = 0 ] || [ ! "$status" = installed ]; then sudo apt-get install -y nvidia-docker2 nvidia-container-toolkit-1.16.2 sudo systemctl restart docker fi ) } pre_install_nvidia_driver_amzn2() { ( # Purge any nvidia driver installed from RHEL repo sudo yum remove -y nvidia-driver-latest-dkms ) } install_nvidia_driver_common() { ( # Try to gather more information about the runner and its existing NVIDIA driver if any echo "Before installing NVIDIA driver" lspci lsmod modinfo nvidia || true HAS_NVIDIA_DRIVER=0 # Check if NVIDIA driver has already been installed if [ -x "$(command -v nvidia-smi)" ]; then set +e # The driver exists, check its version next. Also check only the first GPU if there are more than one of them # so that the same driver version is not print over multiple lines INSTALLED_DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0) NVIDIA_SMI_STATUS=$? if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then echo "Failed to get NVIDIA driver version ($INSTALLED_DRIVER_VERSION). Continuing" elif [ "$INSTALLED_DRIVER_VERSION" != "$DRIVER_VERSION" ]; then echo "NVIDIA driver ($INSTALLED_DRIVER_VERSION) has been installed, but we expect to have $DRIVER_VERSION instead. Continuing" else HAS_NVIDIA_DRIVER=1 echo "NVIDIA driver ($INSTALLED_DRIVER_VERSION) has already been installed. Skipping NVIDIA driver installation" fi set -e fi if [ "$HAS_NVIDIA_DRIVER" -eq 0 ]; then # CAUTION: this may need to be updated in future if [ "${DISTRIBUTION}" != ubuntu20.04 ]; then sudo yum groupinstall -y "Development Tools" # ensure our kernel install is the same as our underlying kernel, # groupinstall "Development Tools" has a habit of mismatching kernel headers sudo yum install -y "kernel-devel-uname-r == $(uname -r)" sudo modprobe backlight fi sudo curl -fsL -o /tmp/nvidia_driver "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN" set +e sudo /bin/bash /tmp/nvidia_driver -s --no-drm NVIDIA_INSTALLATION_STATUS=$? RESET_GPU=0 if [ "$NVIDIA_INSTALLATION_STATUS" -ne 0 ]; then sudo cat /var/log/nvidia-installer.log # Fail to install NVIDIA driver, try to reset the GPU RESET_GPU=1 elif [ -x "$(command -v nvidia-smi)" ]; then # Check again if nvidia-smi works even if the driver installation completes successfully INSTALLED_DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0) NVIDIA_SMI_STATUS=$? if [ "$NVIDIA_SMI_STATUS" -ne 0 ] && [ "$NVIDIA_SMI_STATUS" -ne 14 ]; then RESET_GPU=1 fi fi if [ "$RESET_GPU" -eq 1 ]; then NVIDIA_DEVICES=$(lspci -D | grep -i NVIDIA | cut -d' ' -f1) # The GPU can get stuck in a failure state if somehow the test crashs the GPU microcode. When this # happens, we'll try to reset all NVIDIA devices https://github.com/pytorch/pytorch/issues/88388 for PCI_ID in $NVIDIA_DEVICES; do DEVICE_ENABLED=$(cat /sys/bus/pci/devices/$PCI_ID/enable) echo "Reseting $PCI_ID (enabled state: $DEVICE_ENABLED)" # This requires sudo permission of course echo "1" | sudo tee /sys/bus/pci/devices/$PCI_ID/reset sleep 1 done fi sudo rm -fv /tmp/nvidia_driver set -e fi ) } post_install_nvidia_driver_common() { ( sudo modprobe nvidia || true echo "After installing NVIDIA driver" lspci lsmod modinfo nvidia || true ( set +e nvidia-smi # NB: Annoyingly, nvidia-smi command returns successfully with return code 0 even in # the case where the driver has already crashed as it still can get the driver version # and some basic information like the bus ID. However, the rest of the information # would be missing (ERR!), for example: # # +-----------------------------------------------------------------------------+ # | NVIDIA-SMI 525.89.02 Driver Version: 525.89.02 CUDA Version: 12.0 | # |-------------------------------+----------------------+----------------------+ # | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | # | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | # | | | MIG M. | # |===============================+======================+======================| # | 0 ERR! Off | 00000000:00:1E.0 Off | ERR! | # |ERR! ERR! ERR! ERR! / ERR! | 4184MiB / 23028MiB | ERR! Default | # | | | ERR! | # +-------------------------------+----------------------+----------------------+ # # +-----------------------------------------------------------------------------+ # | Processes: | # | GPU GI CI PID Type Process name GPU Memory | # | ID ID Usage | # |=============================================================================| # +-----------------------------------------------------------------------------+ # # This should be reported as a failure instead as it will guarantee to fail when # Docker tries to run with --gpus all # # So, the correct check here is to query one of the missing piece of info like # GPU name, so that the command can fail accordingly nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0 NVIDIA_SMI_STATUS=$? # Allowable exit statuses for nvidia-smi, see: https://github.com/NVIDIA/gpu-operator/issues/285 if [ "$NVIDIA_SMI_STATUS" -eq 0 ] || [ "$NVIDIA_SMI_STATUS" -eq 14 ]; then echo "INFO: Ignoring allowed status ${NVIDIA_SMI_STATUS}" else echo "ERROR: nvidia-smi exited with unresolved status ${NVIDIA_SMI_STATUS}" exit ${NVIDIA_SMI_STATUS} fi set -e ) ) } install_nvidia_driver_amzn2() { ( set -x pre_install_nvidia_driver_amzn2 install_nvidia_driver_common post_install_nvidia_driver_common ) } install_nvidia_driver_ubuntu20() { ( set -x install_nvidia_driver_common post_install_nvidia_driver_common ) } echo "== Installing nvidia driver ${DRIVER_FN} ==" case "${DISTRIBUTION}" in amzn*) install_nvidia_driver_amzn2 ;; ubuntu20.04) install_nvidia_driver_ubuntu20 ;; *) echo "ERROR: Unknown distribution ${DISTRIBUTION}" exit 1 ;; esac # Install container toolkit based on distribution echo "== Installing nvidia container toolkit for ${DISTRIBUTION} ==" case "${DISTRIBUTION}" in amzn*) install_nvidia_docker2_amzn2 ;; ubuntu20.04) install_nvidia_docker2_ubuntu20 ;; *) echo "ERROR: Unknown distribution ${DISTRIBUTION}" exit 1 ;; esac echo "GPU_FLAG=--gpus all -e NVIDIA_DRIVER_CAPABILITIES=all" >> "${GITHUB_ENV}" # Fix https://github.com/NVIDIA/nvidia-docker/issues/1648 on runners with # more than one GPUs. This just needs to be run once. The command fails # on subsequent runs and complains that the mode is already on, but that's # ok sudo nvidia-persistenced || true # This should show persistence mode ON nvidia-smi 2025-03-04T21:10:35.6319775Z retry_wait_seconds: 10 2025-03-04T21:10:35.6320061Z polling_interval_seconds: 1 2025-03-04T21:10:35.6320350Z warning_on_retry: true 2025-03-04T21:10:35.6320619Z continue_on_error: false 2025-03-04T21:10:35.6320888Z env: 2025-03-04T21:10:35.6321106Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:10:35.6321378Z DRIVER_VERSION: 550.54.15 2025-03-04T21:10:35.6321644Z ##[endgroup] 2025-03-04T21:10:35.7349130Z == Installing nvidia driver NVIDIA-Linux-x86_64-550.54.15.run == 2025-03-04T21:10:35.7350910Z + pre_install_nvidia_driver_amzn2 2025-03-04T21:10:35.7352469Z + sudo yum remove -y nvidia-driver-latest-dkms 2025-03-04T21:10:36.2929283Z No match for argument: nvidia-driver-latest-dkms 2025-03-04T21:10:36.2929788Z No packages marked for removal. 2025-03-04T21:10:36.2996930Z Dependencies resolved. 2025-03-04T21:10:36.3008074Z Nothing to do. 2025-03-04T21:10:36.3008907Z Complete! 2025-03-04T21:10:36.3870460Z + install_nvidia_driver_common 2025-03-04T21:10:36.3873984Z + echo 'Before installing NVIDIA driver' 2025-03-04T21:10:36.3874323Z + lspci 2025-03-04T21:10:36.3876582Z Before installing NVIDIA driver 2025-03-04T21:10:36.4394559Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] 2025-03-04T21:10:36.4395225Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II] 2025-03-04T21:10:36.4396145Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08) 2025-03-04T21:10:36.4396703Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111 2025-03-04T21:10:36.4397225Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller 2025-03-04T21:10:36.4397888Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA) 2025-03-04T21:10:36.4398418Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-03-04T21:10:36.4398939Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller 2025-03-04T21:10:36.4399530Z + lsmod 2025-03-04T21:10:36.4438609Z Module Size Used by 2025-03-04T21:10:36.4439104Z xt_conntrack 16384 1 2025-03-04T21:10:36.4439400Z nft_chain_nat 16384 3 2025-03-04T21:10:36.4439686Z xt_MASQUERADE 20480 1 2025-03-04T21:10:36.4440017Z nf_nat 57344 2 nft_chain_nat,xt_MASQUERADE 2025-03-04T21:10:36.4440391Z nf_conntrack_netlink 57344 0 2025-03-04T21:10:36.4440816Z nf_conntrack 184320 4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE 2025-03-04T21:10:36.4441280Z nf_defrag_ipv6 24576 1 nf_conntrack 2025-03-04T21:10:36.4441620Z nf_defrag_ipv4 16384 1 nf_conntrack 2025-03-04T21:10:36.4441936Z xfrm_user 57344 1 2025-03-04T21:10:36.4442226Z xfrm_algo 16384 1 xfrm_user 2025-03-04T21:10:36.4442557Z xt_addrtype 16384 2 2025-03-04T21:10:36.4442859Z nft_compat 20480 4 2025-03-04T21:10:36.4443196Z nf_tables 311296 57 nft_compat,nft_chain_nat 2025-03-04T21:10:36.4443647Z nfnetlink 20480 4 nft_compat,nf_conntrack_netlink,nf_tables 2025-03-04T21:10:36.4444050Z br_netfilter 36864 0 2025-03-04T21:10:36.4444357Z bridge 323584 1 br_netfilter 2025-03-04T21:10:36.4444683Z stp 16384 1 bridge 2025-03-04T21:10:36.4445004Z llc 16384 2 bridge,stp 2025-03-04T21:10:36.4445316Z overlay 167936 0 2025-03-04T21:10:36.4445594Z tls 135168 0 2025-03-04T21:10:36.4445880Z nls_ascii 16384 1 2025-03-04T21:10:36.4446161Z nls_cp437 20480 1 2025-03-04T21:10:36.4446431Z vfat 24576 1 2025-03-04T21:10:36.4446707Z fat 86016 1 vfat 2025-03-04T21:10:36.4446999Z ena 180224 0 2025-03-04T21:10:36.4447272Z i8042 45056 0 2025-03-04T21:10:36.4447555Z serio 28672 3 i8042 2025-03-04T21:10:36.4448040Z button 24576 0 2025-03-04T21:10:36.4448339Z ghash_clmulni_intel 16384 0 2025-03-04T21:10:36.4448627Z sunrpc 696320 1 2025-03-04T21:10:36.4448912Z sch_fq_codel 20480 17 2025-03-04T21:10:36.4449195Z dm_mod 188416 0 2025-03-04T21:10:36.4449473Z fuse 163840 1 2025-03-04T21:10:36.4449759Z configfs 57344 1 2025-03-04T21:10:36.4450035Z loop 36864 0 2025-03-04T21:10:36.4450320Z dax 45056 1 dm_mod 2025-03-04T21:10:36.4450624Z dmi_sysfs 20480 0 2025-03-04T21:10:36.4450907Z crc32_pclmul 16384 0 2025-03-04T21:10:36.4451178Z crc32c_intel 24576 0 2025-03-04T21:10:36.4451458Z efivarfs 24576 1 2025-03-04T21:10:36.4451736Z + modinfo nvidia 2025-03-04T21:10:36.4456747Z filename: /lib/modules/6.1.128-136.201.amzn2023.x86_64/kernel/drivers/video/nvidia.ko 2025-03-04T21:10:36.4457315Z alias: char-major-195-* 2025-03-04T21:10:36.4457608Z version: 550.54.15 2025-03-04T21:10:36.4457889Z supported: external 2025-03-04T21:10:36.4458156Z license: NVIDIA 2025-03-04T21:10:36.4458455Z firmware: nvidia/550.54.15/gsp_tu10x.bin 2025-03-04T21:10:36.4458815Z firmware: nvidia/550.54.15/gsp_ga10x.bin 2025-03-04T21:10:36.4459156Z srcversion: 833721318DA517F0C2FEC97 2025-03-04T21:10:36.4459623Z alias: pci:v000010DEd*sv*sd*bc06sc80i00* 2025-03-04T21:10:36.4459993Z alias: pci:v000010DEd*sv*sd*bc03sc02i00* 2025-03-04T21:10:36.4460351Z alias: pci:v000010DEd*sv*sd*bc03sc00i00* 2025-03-04T21:10:36.4460689Z depends: i2c-core,drm 2025-03-04T21:10:36.4460966Z retpoline: Y 2025-03-04T21:10:36.4462058Z name: nvidia 2025-03-04T21:10:36.4462457Z vermagic: 6.1.128-136.201.amzn2023.x86_64 SMP preempt mod_unload modversions 2025-03-04T21:10:36.4462957Z parm: NvSwitchRegDwords:NvSwitch regkey (charp) 2025-03-04T21:10:36.4463436Z parm: NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp) 2025-03-04T21:10:36.4464081Z parm: NVreg_ResmanDebugLevel:int 2025-03-04T21:10:36.4464496Z parm: NVreg_RmLogonRC:int 2025-03-04T21:10:36.4464829Z parm: NVreg_ModifyDeviceFiles:int 2025-03-04T21:10:36.4465180Z parm: NVreg_DeviceFileUID:int 2025-03-04T21:10:36.4465515Z parm: NVreg_DeviceFileGID:int 2025-03-04T21:10:36.4465859Z parm: NVreg_DeviceFileMode:int 2025-03-04T21:10:36.4466259Z parm: NVreg_InitializeSystemMemoryAllocations:int 2025-03-04T21:10:36.4466679Z parm: NVreg_UsePageAttributeTable:int 2025-03-04T21:10:36.4467040Z parm: NVreg_EnablePCIeGen3:int 2025-03-04T21:10:36.4467369Z parm: NVreg_EnableMSI:int 2025-03-04T21:10:36.4467689Z parm: NVreg_TCEBypassMode:int 2025-03-04T21:10:36.4468039Z parm: NVreg_EnableStreamMemOPs:int 2025-03-04T21:10:36.4468436Z parm: NVreg_RestrictProfilingToAdminUsers:int 2025-03-04T21:10:36.4468862Z parm: NVreg_PreserveVideoMemoryAllocations:int 2025-03-04T21:10:36.4469288Z parm: NVreg_EnableS0ixPowerManagement:int 2025-03-04T21:10:36.4469727Z parm: NVreg_S0ixPowerManagementVideoMemoryThreshold:int 2025-03-04T21:10:36.4470166Z parm: NVreg_DynamicPowerManagement:int 2025-03-04T21:10:36.4470623Z parm: NVreg_DynamicPowerManagementVideoMemoryThreshold:int 2025-03-04T21:10:36.4471075Z parm: NVreg_EnableGpuFirmware:int 2025-03-04T21:10:36.4471441Z parm: NVreg_EnableGpuFirmwareLogs:int 2025-03-04T21:10:36.4471831Z parm: NVreg_OpenRmEnableUnsupportedGpus:int 2025-03-04T21:10:36.4472234Z parm: NVreg_EnableUserNUMAManagement:int 2025-03-04T21:10:36.4472601Z parm: NVreg_MemoryPoolSize:int 2025-03-04T21:10:36.4472950Z parm: NVreg_KMallocHeapMaxSize:int 2025-03-04T21:10:36.4473310Z parm: NVreg_VMallocHeapMaxSize:int 2025-03-04T21:10:36.4473662Z parm: NVreg_IgnoreMMIOCheck:int 2025-03-04T21:10:36.4474003Z parm: NVreg_NvLinkDisable:int 2025-03-04T21:10:36.4474392Z parm: NVreg_EnablePCIERelaxedOrderingMode:int 2025-03-04T21:10:36.4474813Z parm: NVreg_RegisterPCIDriver:int 2025-03-04T21:10:36.4475310Z parm: NVreg_EnableResizableBar:int 2025-03-04T21:10:36.4475785Z parm: NVreg_EnableDbgBreakpoint:int 2025-03-04T21:10:36.4476173Z parm: NVreg_EnableNonblockingOpen:int 2025-03-04T21:10:36.4476540Z parm: NVreg_RegistryDwords:charp 2025-03-04T21:10:36.4476913Z parm: NVreg_RegistryDwordsPerDevice:charp 2025-03-04T21:10:36.4477275Z parm: NVreg_RmMsg:charp 2025-03-04T21:10:36.4477596Z parm: NVreg_GpuBlacklist:charp 2025-03-04T21:10:36.4477954Z parm: NVreg_TemporaryFilePath:charp 2025-03-04T21:10:36.4478316Z parm: NVreg_ExcludedGpus:charp 2025-03-04T21:10:36.4478663Z parm: NVreg_DmaRemapPeerMmio:int 2025-03-04T21:10:36.4479029Z parm: NVreg_RmNvlinkBandwidth:charp 2025-03-04T21:10:36.4479394Z parm: NVreg_ImexChannelCount:int 2025-03-04T21:10:36.4479747Z parm: rm_firmware_active:charp 2025-03-04T21:10:36.4480083Z + HAS_NVIDIA_DRIVER=0 2025-03-04T21:10:36.4480363Z ++ command -v nvidia-smi 2025-03-04T21:10:36.4480656Z + '[' -x /usr/bin/nvidia-smi ']' 2025-03-04T21:10:36.4480946Z + set +e 2025-03-04T21:10:36.4481290Z ++ nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0 2025-03-04T21:10:38.6888797Z + INSTALLED_DRIVER_VERSION=550.54.15 2025-03-04T21:10:38.6889184Z + NVIDIA_SMI_STATUS=0 2025-03-04T21:10:38.6889743Z + '[' 0 -ne 0 ']' 2025-03-04T21:10:38.6889992Z + '[' 550.54.15 '!=' 550.54.15 ']' 2025-03-04T21:10:38.6890278Z + HAS_NVIDIA_DRIVER=1 2025-03-04T21:10:38.6890851Z + echo 'NVIDIA driver (550.54.15) has already been installed. Skipping NVIDIA driver installation' 2025-03-04T21:10:38.6891393Z + set -e 2025-03-04T21:10:38.6891611Z + '[' 1 -eq 0 ']' 2025-03-04T21:10:38.6891894Z + post_install_nvidia_driver_common 2025-03-04T21:10:38.6892726Z NVIDIA driver (550.54.15) has already been installed. Skipping NVIDIA driver installation 2025-03-04T21:10:38.6895239Z + sudo modprobe nvidia 2025-03-04T21:10:38.8068890Z + echo 'After installing NVIDIA driver' 2025-03-04T21:10:38.8069240Z + lspci 2025-03-04T21:10:38.8069488Z After installing NVIDIA driver 2025-03-04T21:10:38.8187789Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma] 2025-03-04T21:10:38.8188345Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II] 2025-03-04T21:10:38.8188925Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08) 2025-03-04T21:10:38.8189472Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111 2025-03-04T21:10:38.8189987Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller 2025-03-04T21:10:38.8190555Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA) 2025-03-04T21:10:38.8191070Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1) 2025-03-04T21:10:38.8191600Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller 2025-03-04T21:10:38.8192029Z + lsmod 2025-03-04T21:10:38.8220534Z Module Size Used by 2025-03-04T21:10:38.8220852Z nvidia_uvm 4706304 0 2025-03-04T21:10:38.8221324Z nvidia 54071296 1 nvidia_uvm 2025-03-04T21:10:38.8222004Z drm 602112 1 nvidia 2025-03-04T21:10:38.8235859Z drm_panel_orientation_quirks 32768 1 drm 2025-03-04T21:10:38.8236375Z backlight 24576 1 drm 2025-03-04T21:10:38.8236801Z i2c_core 110592 2 nvidia,drm 2025-03-04T21:10:38.8237234Z xt_conntrack 16384 1 2025-03-04T21:10:38.8237622Z nft_chain_nat 16384 3 2025-03-04T21:10:38.8238012Z xt_MASQUERADE 20480 1 2025-03-04T21:10:38.8238488Z nf_nat 57344 2 nft_chain_nat,xt_MASQUERADE 2025-03-04T21:10:38.8239018Z nf_conntrack_netlink 57344 0 2025-03-04T21:10:38.8239660Z nf_conntrack 184320 4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE 2025-03-04T21:10:38.8240446Z nf_defrag_ipv6 24576 1 nf_conntrack 2025-03-04T21:10:38.8240939Z nf_defrag_ipv4 16384 1 nf_conntrack 2025-03-04T21:10:38.8241422Z xfrm_user 57344 1 2025-03-04T21:10:38.8241857Z xfrm_algo 16384 1 xfrm_user 2025-03-04T21:10:38.8242318Z xt_addrtype 16384 2 2025-03-04T21:10:38.8242743Z nft_compat 20480 4 2025-03-04T21:10:38.8243269Z nf_tables 311296 57 nft_compat,nft_chain_nat 2025-03-04T21:10:38.8244034Z nfnetlink 20480 4 nft_compat,nf_conntrack_netlink,nf_tables 2025-03-04T21:10:38.8244646Z br_netfilter 36864 0 2025-03-04T21:10:38.8245094Z bridge 323584 1 br_netfilter 2025-03-04T21:10:38.8245580Z stp 16384 1 bridge 2025-03-04T21:10:38.8246028Z llc 16384 2 bridge,stp 2025-03-04T21:10:38.8246402Z overlay 167936 0 2025-03-04T21:10:38.8246683Z tls 135168 0 2025-03-04T21:10:38.8246960Z nls_ascii 16384 1 2025-03-04T21:10:38.8247251Z nls_cp437 20480 1 2025-03-04T21:10:38.8247522Z vfat 24576 1 2025-03-04T21:10:38.8247993Z fat 86016 1 vfat 2025-03-04T21:10:38.8248283Z ena 180224 0 2025-03-04T21:10:38.8248618Z i8042 45056 0 2025-03-04T21:10:38.8249198Z serio 28672 3 i8042 2025-03-04T21:10:38.8249487Z button 24576 0 2025-03-04T21:10:38.8249768Z ghash_clmulni_intel 16384 0 2025-03-04T21:10:38.8250059Z sunrpc 696320 1 2025-03-04T21:10:38.8250337Z sch_fq_codel 20480 17 2025-03-04T21:10:38.8250617Z dm_mod 188416 0 2025-03-04T21:10:38.8250892Z fuse 163840 1 2025-03-04T21:10:38.8251164Z configfs 57344 1 2025-03-04T21:10:38.8251438Z loop 36864 0 2025-03-04T21:10:38.8251715Z dax 45056 1 dm_mod 2025-03-04T21:10:38.8252008Z dmi_sysfs 20480 0 2025-03-04T21:10:38.8252460Z crc32_pclmul 16384 0 2025-03-04T21:10:38.8252813Z crc32c_intel 24576 0 2025-03-04T21:10:38.8253098Z efivarfs 24576 1 2025-03-04T21:10:38.8253419Z + modinfo nvidia 2025-03-04T21:10:38.8253830Z filename: /lib/modules/6.1.128-136.201.amzn2023.x86_64/kernel/drivers/video/nvidia.ko 2025-03-04T21:10:38.8254370Z alias: char-major-195-* 2025-03-04T21:10:38.8254658Z version: 550.54.15 2025-03-04T21:10:38.8254930Z supported: external 2025-03-04T21:10:38.8255196Z license: NVIDIA 2025-03-04T21:10:38.8255485Z firmware: nvidia/550.54.15/gsp_tu10x.bin 2025-03-04T21:10:38.8255847Z firmware: nvidia/550.54.15/gsp_ga10x.bin 2025-03-04T21:10:38.8256190Z srcversion: 833721318DA517F0C2FEC97 2025-03-04T21:10:38.8256591Z alias: pci:v000010DEd*sv*sd*bc06sc80i00* 2025-03-04T21:10:38.8256951Z alias: pci:v000010DEd*sv*sd*bc03sc02i00* 2025-03-04T21:10:38.8257304Z alias: pci:v000010DEd*sv*sd*bc03sc00i00* 2025-03-04T21:10:38.8257655Z depends: i2c-core,drm 2025-03-04T21:10:38.8257958Z retpoline: Y 2025-03-04T21:10:38.8258202Z name: nvidia 2025-03-04T21:10:38.8258585Z vermagic: 6.1.128-136.201.amzn2023.x86_64 SMP preempt mod_unload modversions 2025-03-04T21:10:38.8259078Z parm: NvSwitchRegDwords:NvSwitch regkey (charp) 2025-03-04T21:10:38.8259553Z parm: NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp) 2025-03-04T21:10:38.8259998Z parm: NVreg_ResmanDebugLevel:int 2025-03-04T21:10:38.8260331Z parm: NVreg_RmLogonRC:int 2025-03-04T21:10:38.8260673Z parm: NVreg_ModifyDeviceFiles:int 2025-03-04T21:10:38.8261050Z parm: NVreg_DeviceFileUID:int 2025-03-04T21:10:38.8261703Z parm: NVreg_DeviceFileGID:int 2025-03-04T21:10:38.8262035Z parm: NVreg_DeviceFileMode:int 2025-03-04T21:10:38.8262422Z parm: NVreg_InitializeSystemMemoryAllocations:int 2025-03-04T21:10:38.8262830Z parm: NVreg_UsePageAttributeTable:int 2025-03-04T21:10:38.8263189Z parm: NVreg_EnablePCIeGen3:int 2025-03-04T21:10:38.8263511Z parm: NVreg_EnableMSI:int 2025-03-04T21:10:38.8263820Z parm: NVreg_TCEBypassMode:int 2025-03-04T21:10:38.8264163Z parm: NVreg_EnableStreamMemOPs:int 2025-03-04T21:10:38.8264554Z parm: NVreg_RestrictProfilingToAdminUsers:int 2025-03-04T21:10:38.8264974Z parm: NVreg_PreserveVideoMemoryAllocations:int 2025-03-04T21:10:38.8265375Z parm: NVreg_EnableS0ixPowerManagement:int 2025-03-04T21:10:38.8265820Z parm: NVreg_S0ixPowerManagementVideoMemoryThreshold:int 2025-03-04T21:10:38.8266308Z parm: NVreg_DynamicPowerManagement:int 2025-03-04T21:10:38.8266750Z parm: NVreg_DynamicPowerManagementVideoMemoryThreshold:int 2025-03-04T21:10:38.8267183Z parm: NVreg_EnableGpuFirmware:int 2025-03-04T21:10:38.8267545Z parm: NVreg_EnableGpuFirmwareLogs:int 2025-03-04T21:10:38.8267952Z parm: NVreg_OpenRmEnableUnsupportedGpus:int 2025-03-04T21:10:38.8268350Z parm: NVreg_EnableUserNUMAManagement:int 2025-03-04T21:10:38.8268706Z parm: NVreg_MemoryPoolSize:int 2025-03-04T21:10:38.8269053Z parm: NVreg_KMallocHeapMaxSize:int 2025-03-04T21:10:38.8269409Z parm: NVreg_VMallocHeapMaxSize:int 2025-03-04T21:10:38.8269921Z parm: NVreg_IgnoreMMIOCheck:int 2025-03-04T21:10:38.8270260Z parm: NVreg_NvLinkDisable:int 2025-03-04T21:10:38.8270633Z parm: NVreg_EnablePCIERelaxedOrderingMode:int 2025-03-04T21:10:38.8271020Z parm: NVreg_RegisterPCIDriver:int 2025-03-04T21:10:38.8271367Z parm: NVreg_EnableResizableBar:int 2025-03-04T21:10:38.8271724Z parm: NVreg_EnableDbgBreakpoint:int 2025-03-04T21:10:38.8272089Z parm: NVreg_EnableNonblockingOpen:int 2025-03-04T21:10:38.8272442Z parm: NVreg_RegistryDwords:charp 2025-03-04T21:10:38.8272909Z parm: NVreg_RegistryDwordsPerDevice:charp 2025-03-04T21:10:38.8273404Z parm: NVreg_RmMsg:charp 2025-03-04T21:10:38.8273718Z parm: NVreg_GpuBlacklist:charp 2025-03-04T21:10:38.8274063Z parm: NVreg_TemporaryFilePath:charp 2025-03-04T21:10:38.8274407Z parm: NVreg_ExcludedGpus:charp 2025-03-04T21:10:38.8274743Z parm: NVreg_DmaRemapPeerMmio:int 2025-03-04T21:10:38.8275098Z parm: NVreg_RmNvlinkBandwidth:charp 2025-03-04T21:10:38.8275444Z parm: NVreg_ImexChannelCount:int 2025-03-04T21:10:38.8275774Z parm: rm_firmware_active:charp 2025-03-04T21:10:38.8276076Z + set +e 2025-03-04T21:10:38.8276280Z + nvidia-smi 2025-03-04T21:10:40.3915835Z Tue Mar 4 21:10:40 2025 2025-03-04T21:10:40.3916375Z +-----------------------------------------------------------------------------------------+ 2025-03-04T21:10:40.3916913Z | NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 | 2025-03-04T21:10:40.3917442Z |-----------------------------------------+------------------------+----------------------+ 2025-03-04T21:10:40.3917962Z | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | 2025-03-04T21:10:40.3918518Z | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | 2025-03-04T21:10:40.3918979Z | | | MIG M. | 2025-03-04T21:10:40.3919355Z |=========================================+========================+======================| 2025-03-04T21:10:40.4010507Z | 0 NVIDIA A10G Off | 00000000:00:1E.0 Off | 0 | 2025-03-04T21:10:40.4011118Z | 0% 26C P0 58W / 300W | 0MiB / 23028MiB | 5% Default | 2025-03-04T21:10:40.4011540Z | | | N/A | 2025-03-04T21:10:40.4011972Z +-----------------------------------------+------------------------+----------------------+ 2025-03-04T21:10:40.4012409Z 2025-03-04T21:10:40.4012908Z +-----------------------------------------------------------------------------------------+ 2025-03-04T21:10:40.4013394Z | Processes: | 2025-03-04T21:10:40.4013888Z | GPU GI CI PID Type Process name GPU Memory | 2025-03-04T21:10:40.4014347Z | ID ID Usage | 2025-03-04T21:10:40.4014720Z |=========================================================================================| 2025-03-04T21:10:40.4015771Z | No running processes found | 2025-03-04T21:10:40.4016268Z +-----------------------------------------------------------------------------------------+ 2025-03-04T21:10:41.0082100Z + nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0 2025-03-04T21:10:42.5699221Z NVIDIA A10G 2025-03-04T21:10:43.0231164Z + NVIDIA_SMI_STATUS=0 2025-03-04T21:10:43.0231491Z + '[' 0 -eq 0 ']' 2025-03-04T21:10:43.0231770Z + echo 'INFO: Ignoring allowed status 0' 2025-03-04T21:10:43.0232095Z + set -e 2025-03-04T21:10:43.0232332Z INFO: Ignoring allowed status 0 2025-03-04T21:10:43.0240238Z == Installing nvidia container toolkit for amzn2023 == 2025-03-04T21:10:43.0244015Z + sudo yum install -y yum-utils 2025-03-04T21:10:43.4223492Z Last metadata expiration check: 0:11:09 ago on Tue Mar 4 20:59:34 2025. 2025-03-04T21:10:43.4463315Z Package dnf-utils-4.3.0-13.amzn2023.0.5.noarch is already installed. 2025-03-04T21:10:43.4847830Z Dependencies resolved. 2025-03-04T21:10:43.5026444Z Nothing to do. 2025-03-04T21:10:43.5026714Z Complete! 2025-03-04T21:10:43.6265564Z + [[ amzn2023 == \a\m\z\n\2\0\2\3 ]] 2025-03-04T21:10:43.6266198Z + YUM_REPO_URL=https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-03-04T21:10:43.6267413Z + sudo yum-config-manager --add-repo https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-03-04T21:10:43.9556398Z Adding repo from: https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo 2025-03-04T21:10:44.0202461Z + sudo yum install -y nvidia-docker2 nvidia-container-toolkit-1.16.2 2025-03-04T21:10:44.6138717Z nvidia-container-toolkit 15 kB/s | 833 B 00:00 2025-03-04T21:10:44.6380502Z Package nvidia-docker2-2.14.0-1.noarch is already installed. 2025-03-04T21:10:44.6769615Z Dependencies resolved. 2025-03-04T21:10:44.6939786Z ================================================================================ 2025-03-04T21:10:44.6940248Z Package Arch Version Repository Size 2025-03-04T21:10:44.6940652Z ================================================================================ 2025-03-04T21:10:44.6940971Z Downgrading: 2025-03-04T21:10:44.6941381Z nvidia-container-toolkit x86_64 1.16.2-1 nvidia-container-toolkit 1.2 M 2025-03-04T21:10:44.6942005Z nvidia-container-toolkit-base x86_64 1.16.2-1 nvidia-container-toolkit 5.6 M 2025-03-04T21:10:44.6942381Z 2025-03-04T21:10:44.6942482Z Transaction Summary 2025-03-04T21:10:44.6942757Z ================================================================================ 2025-03-04T21:10:44.6943100Z Downgrade 2 Packages 2025-03-04T21:10:44.6943270Z 2025-03-04T21:10:44.6943386Z Total download size: 6.8 M 2025-03-04T21:10:44.6944306Z Downloading Packages: 2025-03-04T21:10:44.7686974Z (1/2): nvidia-container-toolkit-1.16.2-1.x86_64 17 MB/s | 1.2 MB 00:00 2025-03-04T21:10:44.8974159Z (2/2): nvidia-container-toolkit-base-1.16.2-1.x 28 MB/s | 5.6 MB 00:00 2025-03-04T21:10:44.8984115Z -------------------------------------------------------------------------------- 2025-03-04T21:10:44.8987497Z Total 34 MB/s | 6.8 MB 00:00 2025-03-04T21:10:44.8990543Z Running transaction check 2025-03-04T21:10:44.9089068Z Transaction check succeeded. 2025-03-04T21:10:44.9089722Z Running transaction test 2025-03-04T21:10:44.9384410Z Transaction test succeeded. 2025-03-04T21:10:44.9387004Z Running transaction 2025-03-04T21:10:45.6437060Z Preparing : 1/1 2025-03-04T21:10:45.7824475Z Downgrading : nvidia-container-toolkit-base-1.16.2-1.x86_64 1/4 2025-03-04T21:10:45.8018307Z Downgrading : nvidia-container-toolkit-1.16.2-1.x86_64 2/4 2025-03-04T21:10:45.8941104Z Running scriptlet: nvidia-container-toolkit-1.16.2-1.x86_64 2/4 2025-03-04T21:10:45.8941938Z Cleanup : nvidia-container-toolkit-1.17.4-1.x86_64 3/4 2025-03-04T21:10:45.9180959Z Running scriptlet: nvidia-container-toolkit-1.17.4-1.x86_64 3/4 2025-03-04T21:10:45.9405924Z Cleanup : nvidia-container-toolkit-base-1.17.4-1.x86_64 4/4 2025-03-04T21:11:34.0609191Z Running scriptlet: nvidia-container-toolkit-1.16.2-1.x86_64 4/4 2025-03-04T21:11:34.0610451Z Verifying : nvidia-container-toolkit-1.16.2-1.x86_64 1/4 2025-03-04T21:11:34.0611590Z Verifying : nvidia-container-toolkit-1.17.4-1.x86_64 2/4 2025-03-04T21:11:34.0613184Z Verifying : nvidia-container-toolkit-base-1.16.2-1.x86_64 3/4 2025-03-04T21:11:34.2353553Z Verifying : nvidia-container-toolkit-base-1.17.4-1.x86_64 4/4 2025-03-04T21:11:34.2353943Z 2025-03-04T21:11:34.2354052Z Downgraded: 2025-03-04T21:11:34.2354440Z nvidia-container-toolkit-1.16.2-1.x86_64 2025-03-04T21:11:34.2355040Z nvidia-container-toolkit-base-1.16.2-1.x86_64 2025-03-04T21:11:34.2355395Z 2025-03-04T21:11:34.2355498Z Complete! 2025-03-04T21:11:34.2859620Z + sudo systemctl restart docker 2025-03-04T21:11:40.4780943Z Tue Mar 4 21:11:40 2025 2025-03-04T21:11:40.4781846Z +-----------------------------------------------------------------------------------------+ 2025-03-04T21:11:40.4782385Z | NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 | 2025-03-04T21:11:40.4782897Z |-----------------------------------------+------------------------+----------------------+ 2025-03-04T21:11:40.4783436Z | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | 2025-03-04T21:11:40.4783989Z | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | 2025-03-04T21:11:40.4784445Z | | | MIG M. | 2025-03-04T21:11:40.4784807Z |=========================================+========================+======================| 2025-03-04T21:11:40.4905260Z | 0 NVIDIA A10G On | 00000000:00:1E.0 Off | 0 | 2025-03-04T21:11:40.4905750Z | 0% 27C P0 59W / 300W | 0MiB / 23028MiB | 5% Default | 2025-03-04T21:11:40.4906153Z | | | N/A | 2025-03-04T21:11:40.4906572Z +-----------------------------------------+------------------------+----------------------+ 2025-03-04T21:11:40.4907007Z 2025-03-04T21:11:40.4907547Z +-----------------------------------------------------------------------------------------+ 2025-03-04T21:11:40.4908007Z | Processes: | 2025-03-04T21:11:40.4908481Z | GPU GI CI PID Type Process name GPU Memory | 2025-03-04T21:11:40.4908928Z | ID ID Usage | 2025-03-04T21:11:40.4909295Z |=========================================================================================| 2025-03-04T21:11:40.4912393Z | No running processes found | 2025-03-04T21:11:40.4912935Z +-----------------------------------------------------------------------------------------+ 2025-03-04T21:11:40.7694704Z Command completed after 1 attempt(s). 2025-03-04T21:11:40.7790369Z Prepare all required actions 2025-03-04T21:11:40.7839966Z ##[group]Run ./.github/actions/get-workflow-job-id 2025-03-04T21:11:40.7840320Z with: 2025-03-04T21:11:40.7840730Z github-token: *** 2025-03-04T21:11:40.7840980Z env: 2025-03-04T21:11:40.7841217Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:11:40.7841563Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:11:40.7841929Z ##[endgroup] 2025-03-04T21:11:40.7862804Z ##[group]Run set -eux 2025-03-04T21:11:40.7863097Z set -eux 2025-03-04T21:11:40.7863541Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-03-04T21:11:40.7878404Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:11:40.7878788Z env: 2025-03-04T21:11:40.7879019Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:11:40.7879369Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:11:40.7879881Z GITHUB_TOKEN: *** 2025-03-04T21:11:40.7880125Z ##[endgroup] 2025-03-04T21:11:40.7911993Z + python3 .github/scripts/get_workflow_job_id.py 13661696663 i-01112049b7e156298 2025-03-04T21:11:41.7696433Z setting job-id=38197724420 2025-03-04T21:11:41.7697208Z setting job-name=cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:11:41.7808087Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7 2025-03-04T21:11:41.7808863Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7 2025-03-04T21:11:41.7809430Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 & 2025-03-04T21:11:41.7809899Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}" 2025-03-04T21:11:41.7819930Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:11:41.7820306Z env: 2025-03-04T21:11:41.7820537Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:11:41.7820877Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:11:41.7821231Z JOB_ID: 38197724420 2025-03-04T21:11:41.7821717Z JOB_NAME: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:11:41.7822240Z WORKFLOW_NAME: inductor 2025-03-04T21:11:41.7822516Z WORKFLOW_RUN_ID: 13661696663 2025-03-04T21:11:41.7822823Z ##[endgroup] 2025-03-04T21:11:42.0571338Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T21:11:42.4423270Z Collecting psutil==5.9.1 2025-03-04T21:11:42.4643629Z Downloading psutil-5.9.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (281 kB) 2025-03-04T21:11:42.5044555Z Collecting nvidia-ml-py==11.525.84 2025-03-04T21:11:42.5073965Z Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB) 2025-03-04T21:11:42.5691348Z Collecting dataclasses_json==0.6.7 2025-03-04T21:11:42.5720879Z Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB) 2025-03-04T21:11:42.6977538Z Collecting marshmallow<4.0.0,>=3.18.0 2025-03-04T21:11:42.7010124Z Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB) 2025-03-04T21:11:42.7226201Z Collecting typing-inspect<1,>=0.4.0 2025-03-04T21:11:42.7258136Z Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB) 2025-03-04T21:11:42.7790208Z Collecting packaging>=17.0 2025-03-04T21:11:42.7821338Z Downloading packaging-24.2-py3-none-any.whl (65 kB) 2025-03-04T21:11:42.8022045Z Collecting mypy-extensions>=0.3.0 2025-03-04T21:11:42.8052367Z Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB) 2025-03-04T21:11:42.8470228Z Collecting typing-extensions>=3.7.4 2025-03-04T21:11:42.8500570Z Downloading typing_extensions-4.12.2-py3-none-any.whl (37 kB) 2025-03-04T21:11:42.9350029Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json 2025-03-04T21:11:43.1815767Z Successfully installed dataclasses-json-0.6.7 marshmallow-3.26.1 mypy-extensions-1.0.0 nvidia-ml-py-11.525.84 packaging-24.2 psutil-5.9.1 typing-extensions-4.12.2 typing-inspect-0.9.0 2025-03-04T21:11:43.3672542Z Prepare all required actions 2025-03-04T21:11:43.3673058Z Getting action download info 2025-03-04T21:11:43.5301575Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6) 2025-03-04T21:11:43.7659038Z Download action repository 'actions/download-artifact@v4' (SHA:cc203385981b70ca67e1cc392babf9cc229d5806) 2025-03-04T21:11:44.0437721Z ##[group]Run ./.github/actions/download-build-artifacts 2025-03-04T21:11:44.0438142Z with: 2025-03-04T21:11:44.0438564Z name: linux-focal-cuda12.4-py3.10-gcc9-sm86 2025-03-04T21:11:44.0438961Z s3-bucket: gha-artifacts 2025-03-04T21:11:44.0439240Z env: 2025-03-04T21:11:44.0439547Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:11:44.0439964Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:11:44.0440400Z ##[endgroup] 2025-03-04T21:11:44.0474395Z ##[group]Run seemethere/download-artifact-s3@v4 2025-03-04T21:11:44.0474738Z with: 2025-03-04T21:11:44.0475221Z name: linux-focal-cuda12.4-py3.10-gcc9-sm86 2025-03-04T21:11:44.0475560Z s3-bucket: gha-artifacts 2025-03-04T21:11:44.0475840Z region: us-east-1 2025-03-04T21:11:44.0476078Z env: 2025-03-04T21:11:44.0476303Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:11:44.0476644Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:11:44.0477002Z ##[endgroup] 2025-03-04T21:11:44.5140966Z (node:53782) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-03-04T21:11:44.5141443Z 2025-03-04T21:11:44.5141648Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-03-04T21:11:44.5142175Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-03-04T21:11:44.5142740Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-03-04T21:11:44.7135643Z Found 1 objects with prefix pytorch/pytorch/13661696663/linux-focal-cuda12.4-py3.10-gcc9-sm86/ 2025-03-04T21:11:44.7136398Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-03-04T21:12:04.4265449Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip 2025-03-04T21:12:04.4270609Z Artifact download has finished successfully 2025-03-04T21:12:04.4630887Z ##[group]Run unzip -o artifacts.zip 2025-03-04T21:12:04.4631239Z unzip -o artifacts.zip 2025-03-04T21:12:04.4640071Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:12:04.4640454Z env: 2025-03-04T21:12:04.4640686Z 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inflating: build/bin/cuda_optional_test 2025-03-04T21:12:16.0961338Z inflating: build/bin/cuda_reportMemoryUsage_test 2025-03-04T21:12:16.1015205Z inflating: build/bin/cuda_allocatorTraceTracker_test 2025-03-04T21:12:16.1081004Z inflating: build/bin/cuda_stream_test 2025-03-04T21:12:16.1136605Z inflating: build/bin/cuda_packedtensoraccessor_test 2025-03-04T21:12:16.1191061Z inflating: build/bin/cuda_cudnn_test 2025-03-04T21:12:16.1246534Z inflating: build/bin/cuda_vectorized_test 2025-03-04T21:12:16.1261550Z inflating: build/bin/tutorial_tensorexpr 2025-03-04T21:12:16.1331961Z inflating: build/bin/ProcessGroupGlooTest 2025-03-04T21:12:16.1394148Z inflating: build/bin/ProcessGroupGlooAsyncTest 2025-03-04T21:12:16.1463631Z inflating: build/bin/ProcessGroupNCCLTest 2025-03-04T21:12:16.1530594Z inflating: build/bin/ProcessGroupNCCLErrorsTest 2025-03-04T21:12:16.2372447Z inflating: build/bin/test_tensorexpr 2025-03-04T21:12:16.2960327Z inflating: build/bin/test_jit 2025-03-04T21:12:16.2961703Z creating: .additional_ci_files/ 2025-03-04T21:12:16.3060201Z inflating: .additional_ci_files/test-times.json 2025-03-04T21:12:16.3446409Z inflating: .additional_ci_files/test-class-times.json 2025-03-04T21:12:16.3484420Z ##[group]Run rm artifacts.zip 2025-03-04T21:12:16.3484750Z rm artifacts.zip 2025-03-04T21:12:16.3493814Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:12:16.3494195Z env: 2025-03-04T21:12:16.3494428Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:16.3494775Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:16.3495151Z ##[endgroup] 2025-03-04T21:12:16.5093491Z ##[group]Run df -H 2025-03-04T21:12:16.5093760Z df -H 2025-03-04T21:12:16.5102505Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:12:16.5102880Z env: 2025-03-04T21:12:16.5103108Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:16.5103452Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:16.5103808Z ##[endgroup] 2025-03-04T21:12:16.5155700Z Filesystem Size Used Avail Use% Mounted on 2025-03-04T21:12:16.5156129Z devtmpfs 4.2M 0 4.2M 0% /dev 2025-03-04T21:12:16.5156480Z tmpfs 34G 0 34G 0% /dev/shm 2025-03-04T21:12:16.5156823Z tmpfs 14G 553k 14G 1% /run 2025-03-04T21:12:16.5157155Z /dev/nvme0n1p1 161G 62G 100G 39% / 2025-03-04T21:12:16.5157487Z tmpfs 34G 13k 34G 1% /tmp 2025-03-04T21:12:16.5157841Z /dev/nvme0n1p128 11M 1.4M 9.2M 13% /boot/efi 2025-03-04T21:12:16.5158212Z tmpfs 6.7G 0 6.7G 0% /run/user/0 2025-03-04T21:12:16.5190417Z Prepare all required actions 2025-03-04T21:12:16.5190800Z Getting action download info 2025-03-04T21:12:16.6713711Z ##[group]Run ./.github/actions/download-td-artifacts 2025-03-04T21:12:16.6714062Z with: 2025-03-04T21:12:16.6714269Z env: 2025-03-04T21:12:16.6714488Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:16.6714823Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:16.6715364Z ##[endgroup] 2025-03-04T21:12:16.6746815Z ##[group]Run seemethere/download-artifact-s3@v4 2025-03-04T21:12:16.6747158Z with: 2025-03-04T21:12:16.6747376Z name: td_results 2025-03-04T21:12:16.6747626Z s3-bucket: gha-artifacts 2025-03-04T21:12:16.6747901Z region: us-east-1 2025-03-04T21:12:16.6748136Z env: 2025-03-04T21:12:16.6748358Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:16.6748694Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:16.6749052Z ##[endgroup] 2025-03-04T21:12:17.1264144Z (node:53810) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023. 2025-03-04T21:12:17.1264865Z 2025-03-04T21:12:17.1265162Z Please migrate your code to use AWS SDK for JavaScript (v3). 2025-03-04T21:12:17.1265936Z For more information, check the migration guide at https://a.co/7PzMCcy 2025-03-04T21:12:17.1266667Z (Use `node --trace-warnings ...` to show where the warning was created) 2025-03-04T21:12:17.2187825Z Found 0 objects with prefix pytorch/pytorch/13661696663/td_results/ 2025-03-04T21:12:17.2194298Z Artifact download has finished successfully 2025-03-04T21:12:17.2540634Z ##[group]Run mkdir -p .additional_ci_files 2025-03-04T21:12:17.2541019Z mkdir -p .additional_ci_files 2025-03-04T21:12:17.2541456Z mv td_results.json .additional_ci_files/td_results.json || true 2025-03-04T21:12:17.2551661Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:12:17.2552043Z env: 2025-03-04T21:12:17.2552271Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:17.2552635Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:17.2552999Z ##[endgroup] 2025-03-04T21:12:17.2614755Z mv: cannot stat 'td_results.json': No such file or directory 2025-03-04T21:12:17.2650781Z ##[group]Run .github/scripts/parse_ref.py 2025-03-04T21:12:17.2651164Z .github/scripts/parse_ref.py 2025-03-04T21:12:17.2660082Z shell: /usr/bin/bash -e {0} 2025-03-04T21:12:17.2660359Z env: 2025-03-04T21:12:17.2660602Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:17.2660944Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:17.2661582Z ##[endgroup] 2025-03-04T21:12:17.2992366Z Prepare all required actions 2025-03-04T21:12:17.2993330Z Getting action download info 2025-03-04T21:12:17.4056703Z ##[group]Run ./.github/actions/filter-test-configs 2025-03-04T21:12:17.4057046Z with: 2025-03-04T21:12:17.4057440Z github-token: *** 2025-03-04T21:12:17.4059080Z test-matrix: {"include": [{"config": "inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}]} 2025-03-04T21:12:17.4060981Z job-name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:12:17.4061779Z env: 2025-03-04T21:12:17.4062155Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:17.4062502Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:17.4062861Z ##[endgroup] 2025-03-04T21:12:17.4100168Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T21:12:17.4100483Z with: 2025-03-04T21:12:17.4100705Z shell: bash 2025-03-04T21:12:17.4100950Z timeout_minutes: 10 2025-03-04T21:12:17.4101205Z max_attempts: 5 2025-03-04T21:12:17.4101452Z retry_wait_seconds: 30 2025-03-04T21:12:17.4102198Z command: set -eux # PyYAML 6.0 doesn't work with MacOS x86 anymore # This must run on Python-3.7 (AmazonLinux2) so can't use request=3.32.2 python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-03-04T21:12:17.4103062Z polling_interval_seconds: 1 2025-03-04T21:12:17.4103358Z warning_on_retry: true 2025-03-04T21:12:17.4103632Z continue_on_error: false 2025-03-04T21:12:17.4104082Z env: 2025-03-04T21:12:17.4104305Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:17.4104647Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:17.4105166Z GITHUB_TOKEN: *** 2025-03-04T21:12:17.4105413Z ##[endgroup] 2025-03-04T21:12:17.5094523Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1 2025-03-04T21:12:17.7416304Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T21:12:17.8673782Z Collecting requests==2.27.1 2025-03-04T21:12:17.8902784Z Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB) 2025-03-04T21:12:18.0611313Z Collecting pyyaml==6.0.1 2025-03-04T21:12:18.0667543Z Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB) 2025-03-04T21:12:18.0904819Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10) 2025-03-04T21:12:18.0912708Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10) 2025-03-04T21:12:18.4258944Z Collecting charset-normalizer~=2.0.0 2025-03-04T21:12:18.4293982Z Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB) 2025-03-04T21:12:18.4764833Z Collecting certifi>=2017.4.17 2025-03-04T21:12:18.4799611Z Downloading certifi-2025.1.31-py3-none-any.whl (166 kB) 2025-03-04T21:12:18.5668276Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml 2025-03-04T21:12:18.6790267Z Successfully installed certifi-2025.1.31 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1 2025-03-04T21:12:19.4888910Z Command completed after 1 attempt(s). 2025-03-04T21:12:19.4962727Z ##[group]Run set -x 2025-03-04T21:12:19.4963144Z set -x 2025-03-04T21:12:19.4963394Z  2025-03-04T21:12:19.4963782Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-03-04T21:12:19.4964254Z # in runner workspace 2025-03-04T21:12:19.4964648Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py" 2025-03-04T21:12:19.4974335Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:12:19.4975003Z env: 2025-03-04T21:12:19.4975313Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:19.4975841Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:19.4986849Z ##[endgroup] 2025-03-04T21:12:19.5016861Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py 2025-03-04T21:12:19.5263431Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}" 2025-03-04T21:12:19.5263849Z echo "Workflow: ${GITHUB_WORKFLOW}" 2025-03-04T21:12:19.5264203Z echo "Job name: ${JOB_NAME}" 2025-03-04T21:12:19.5264501Z  2025-03-04T21:12:19.5264882Z # Use relative path here as this could be checked out anywhere, not necessarily 2025-03-04T21:12:19.5265351Z # in runner workspace 2025-03-04T21:12:19.5265777Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \ 2025-03-04T21:12:19.5266267Z  --workflow "${GITHUB_WORKFLOW}" \ 2025-03-04T21:12:19.5266626Z  --job-name "${JOB_NAME}" \ 2025-03-04T21:12:19.5268341Z  --test-matrix "{"include": [{"config": "inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}]}" \ 2025-03-04T21:12:19.5270064Z  --selected-test-configs "" \ 2025-03-04T21:12:19.5270408Z  --pr-number "${PR_NUMBER}" \ 2025-03-04T21:12:19.5270739Z  --tag "${TAG}" \ 2025-03-04T21:12:19.5271046Z  --event-name "${EVENT_NAME}" \ 2025-03-04T21:12:19.5271553Z  --schedule "${SCHEDULE}" \ 2025-03-04T21:12:19.5271878Z  --branch "${HEAD_BRANCH}" 2025-03-04T21:12:19.5280835Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:12:19.5281209Z env: 2025-03-04T21:12:19.5281434Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:19.5281775Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:19.5282314Z GITHUB_TOKEN: *** 2025-03-04T21:12:19.5282779Z JOB_NAME: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:12:19.5283289Z PR_NUMBER: 2025-03-04T21:12:19.5283531Z TAG: ciflow/inductor/148205 2025-03-04T21:12:19.5283817Z EVENT_NAME: push 2025-03-04T21:12:19.5284053Z SCHEDULE: 2025-03-04T21:12:19.5284279Z HEAD_BRANCH: 2025-03-04T21:12:19.5284515Z ##[endgroup] 2025-03-04T21:12:19.5313044Z Workflow: inductor 2025-03-04T21:12:19.5313555Z Job name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:12:19.7540785Z INFO:root:Found no test-config label on the PR, so all test configs are included 2025-03-04T21:12:19.8807604Z ##[group]Run echo "Filtered matrix:" 2025-03-04T21:12:19.8808151Z echo "Filtered matrix:" 2025-03-04T21:12:19.8809828Z echo "{"include": [{"config": "inductor_huggingface", "shard": 1, "num_shards": 1, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_timm", "shard": 1, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_timm", "shard": 2, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_torchbench", "shard": 1, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}, {"config": "inductor_torchbench", "shard": 2, "num_shards": 2, "runner": "linux.g5.4xlarge.nvidia.gpu"}]}" 2025-03-04T21:12:19.8811489Z  2025-03-04T21:12:19.8811715Z echo 2025-03-04T21:12:19.8811999Z echo "Is the current job unstable? False" 2025-03-04T21:12:19.8812360Z  2025-03-04T21:12:19.8812576Z echo 2025-03-04T21:12:19.8813040Z echo "Is keep-going label set? False" 2025-03-04T21:12:19.8813414Z  2025-03-04T21:12:19.8813634Z echo 2025-03-04T21:12:19.8813885Z echo "Renabled issues? " 2025-03-04T21:12:19.8822497Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:12:19.8822865Z env: 2025-03-04T21:12:19.8823092Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:19.8823432Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:19.8823787Z ##[endgroup] 2025-03-04T21:12:19.8852069Z Filtered matrix: 2025-03-04T21:12:19.8854776Z {include: [{config: inductor_huggingface, shard: 1, num_shards: 1, runner: linux.g5.4xlarge.nvidia.gpu}, {config: inductor_timm, shard: 1, num_shards: 2, runner: linux.g5.4xlarge.nvidia.gpu}, {config: inductor_timm, shard: 2, num_shards: 2, runner: linux.g5.4xlarge.nvidia.gpu}, {config: inductor_torchbench, shard: 1, num_shards: 2, runner: linux.g5.4xlarge.nvidia.gpu}, {config: inductor_torchbench, shard: 2, num_shards: 2, runner: linux.g5.4xlarge.nvidia.gpu}]} 2025-03-04T21:12:19.8857413Z 2025-03-04T21:12:19.8857604Z Is the current job unstable? False 2025-03-04T21:12:19.8857935Z 2025-03-04T21:12:19.8858113Z Is keep-going label set? False 2025-03-04T21:12:19.8858425Z 2025-03-04T21:12:19.8858581Z Renabled issues? 2025-03-04T21:12:19.8908587Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-03-04T21:12:19.8909112Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}" 2025-03-04T21:12:19.8917247Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T21:12:19.8917620Z env: 2025-03-04T21:12:19.8917848Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:19.8918189Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:19.8918545Z JOB_TIMEOUT: 240 2025-03-04T21:12:19.8918788Z ##[endgroup] 2025-03-04T21:12:19.8995769Z ##[group]Run set -x 2025-03-04T21:12:19.8996305Z set -x 2025-03-04T21:12:19.8996545Z  2025-03-04T21:12:19.8996820Z if [[ $TEST_CONFIG == 'multigpu' ]]; then 2025-03-04T21:12:19.8997222Z  TEST_COMMAND=.ci/pytorch/multigpu-test.sh 2025-03-04T21:12:19.8997623Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then 2025-03-04T21:12:19.8997992Z  TEST_COMMAND=.ci/onnx/test.sh 2025-03-04T21:12:19.8998302Z else 2025-03-04T21:12:19.8998572Z  TEST_COMMAND=.ci/pytorch/test.sh 2025-03-04T21:12:19.8998884Z fi 2025-03-04T21:12:19.8999111Z  2025-03-04T21:12:19.8999393Z # Leaving 1GB for the runner and other things 2025-03-04T21:12:19.8999956Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo) 2025-03-04T21:12:19.9000771Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap 2025-03-04T21:12:19.9001447Z # comes from https://github.com/pytorch/test-infra/pull/6058 2025-03-04T21:12:19.9001989Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3)) 2025-03-04T21:12:19.9002397Z  2025-03-04T21:12:19.9002681Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-03-04T21:12:19.9003038Z  SHM_OPTS= 2025-03-04T21:12:19.9003303Z  JENKINS_USER= 2025-03-04T21:12:19.9003663Z  # ensure that docker container cleanly exits in 12 hours 2025-03-04T21:12:19.9004129Z  # if for some reason cleanup action doesn't stop container 2025-03-04T21:12:19.9004532Z  # when job is cancelled 2025-03-04T21:12:19.9004854Z  DOCKER_SHELL_CMD="sleep 12h" 2025-03-04T21:12:19.9005158Z  2025-03-04T21:12:19.9005528Z  # since some steps are skipped on s390x, if they are necessary, run them here 2025-03-04T21:12:19.9006061Z  env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T21:12:19.9006506Z  env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}" 2025-03-04T21:12:19.9006864Z else 2025-03-04T21:12:19.9007134Z  SHM_OPTS="--shm-size=${SHM_SIZE}" 2025-03-04T21:12:19.9007591Z  JENKINS_USER="--user jenkins" 2025-03-04T21:12:19.9008343Z  DOCKER_SHELL_CMD= 2025-03-04T21:12:19.9008737Z fi 2025-03-04T21:12:19.9019196Z  2025-03-04T21:12:19.9019567Z # detached container should get cleaned up by teardown_ec2_linux 2025-03-04T21:12:19.9020101Z # TODO: Stop building test binaries as part of the build phase 2025-03-04T21:12:19.9020689Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice 2025-03-04T21:12:19.9021210Z # shellcheck disable=SC2086,SC2090 2025-03-04T21:12:19.9021557Z container_name=$(docker run \ 2025-03-04T21:12:19.9021877Z  ${GPU_FLAG:-} \ 2025-03-04T21:12:19.9022196Z  ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \ 2025-03-04T21:12:19.9022554Z  -e BUILD_ENVIRONMENT \ 2025-03-04T21:12:19.9022861Z  -e PR_NUMBER \ 2025-03-04T21:12:19.9023145Z  -e GITHUB_ACTIONS \ 2025-03-04T21:12:19.9023445Z  -e GITHUB_REPOSITORY \ 2025-03-04T21:12:19.9023748Z  -e GITHUB_WORKFLOW \ 2025-03-04T21:12:19.9024045Z  -e GITHUB_JOB \ 2025-03-04T21:12:19.9024327Z  -e GITHUB_RUN_ID \ 2025-03-04T21:12:19.9024619Z  -e GITHUB_RUN_NUMBER \ 2025-03-04T21:12:19.9024925Z  -e GITHUB_RUN_ATTEMPT \ 2025-03-04T21:12:19.9025227Z  -e JOB_ID \ 2025-03-04T21:12:19.9025497Z  -e JOB_NAME \ 2025-03-04T21:12:19.9025772Z  -e BASE_SHA \ 2025-03-04T21:12:19.9026038Z  -e BRANCH \ 2025-03-04T21:12:19.9026299Z  -e SHA1 \ 2025-03-04T21:12:19.9026568Z  -e AWS_DEFAULT_REGION \ 2025-03-04T21:12:19.9027030Z  -e IN_WHEEL_TEST \ 2025-03-04T21:12:19.9027331Z  -e SHARD_NUMBER \ 2025-03-04T21:12:19.9027704Z  -e TEST_CONFIG \ 2025-03-04T21:12:19.9027996Z  -e NUM_TEST_SHARDS \ 2025-03-04T21:12:19.9028303Z  -e REENABLED_ISSUES \ 2025-03-04T21:12:19.9028618Z  -e CONTINUE_THROUGH_ERROR \ 2025-03-04T21:12:19.9028943Z  -e VERBOSE_TEST_LOGS \ 2025-03-04T21:12:19.9029246Z  -e TEST_SHOWLOCALS \ 2025-03-04T21:12:19.9029548Z  -e NO_TEST_TIMEOUT \ 2025-03-04T21:12:19.9029840Z  -e NO_TD \ 2025-03-04T21:12:19.9030111Z  -e TD_DISTRIBUTED \ 2025-03-04T21:12:19.9030404Z  -e PR_LABELS \ 2025-03-04T21:12:19.9030717Z  -e MAX_JOBS="$(nproc --ignore=2)" \ 2025-03-04T21:12:19.9031059Z  -e SCCACHE_BUCKET \ 2025-03-04T21:12:19.9031357Z  -e SCCACHE_REGION \ 2025-03-04T21:12:19.9031645Z  -e XLA_CUDA \ 2025-03-04T21:12:19.9031952Z  -e XLA_CLANG_CACHE_S3_BUCKET_NAME \ 2025-03-04T21:12:19.9032326Z  -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \ 2025-03-04T21:12:19.9032711Z  -e PYTORCH_TEST_RERUN_DISABLED_TESTS \ 2025-03-04T21:12:19.9033091Z  -e SKIP_SCCACHE_INITIALIZATION=1 \ 2025-03-04T21:12:19.9033443Z  -e HUGGING_FACE_HUB_TOKEN \ 2025-03-04T21:12:19.9033786Z  -e SCRIBE_GRAPHQL_ACCESS_TOKEN \ 2025-03-04T21:12:19.9034116Z  -e DASHBOARD_TAG \ 2025-03-04T21:12:19.9034407Z  -e IS_A100_RUNNER \ 2025-03-04T21:12:19.9034715Z  -e ARTIFACTS_FILE_SUFFIX \ 2025-03-04T21:12:19.9035085Z  --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \ 2025-03-04T21:12:19.9035504Z  --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \ 2025-03-04T21:12:19.9035914Z  --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \ 2025-03-04T21:12:19.9036311Z  --security-opt seccomp=unconfined \ 2025-03-04T21:12:19.9036660Z  --cap-add=SYS_PTRACE \ 2025-03-04T21:12:19.9036970Z  --ipc=host \ 2025-03-04T21:12:19.9037248Z  ${SHM_OPTS} \ 2025-03-04T21:12:19.9037517Z  --tty \ 2025-03-04T21:12:19.9037765Z  --detach \ 2025-03-04T21:12:19.9038048Z  --name="${container_name}" \ 2025-03-04T21:12:19.9038374Z  ${JENKINS_USER} \ 2025-03-04T21:12:19.9038729Z  -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \ 2025-03-04T21:12:19.9039133Z  -w /var/lib/jenkins/workspace \ 2025-03-04T21:12:19.9039461Z  "${DOCKER_IMAGE}" \ 2025-03-04T21:12:19.9039752Z  ${DOCKER_SHELL_CMD} 2025-03-04T21:12:19.9040030Z ) 2025-03-04T21:12:19.9040335Z # Propagate download.pytorch.org IP to container 2025-03-04T21:12:19.9040986Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts" 2025-03-04T21:12:19.9041667Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}" 2025-03-04T21:12:19.9042074Z  2025-03-04T21:12:19.9042354Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then 2025-03-04T21:12:19.9042920Z  docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt" 2025-03-04T21:12:19.9043421Z fi 2025-03-04T21:12:19.9043644Z  2025-03-04T21:12:19.9044122Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}" 2025-03-04T21:12:19.9054061Z shell: /usr/bin/bash -e {0} 2025-03-04T21:12:19.9054351Z env: 2025-03-04T21:12:19.9054587Z GIT_DEFAULT_BRANCH: main 2025-03-04T21:12:19.9054935Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:19.9055398Z BUILD_ENVIRONMENT: linux-focal-cuda12.4-py3.10-gcc9-sm86 2025-03-04T21:12:19.9055773Z PR_NUMBER: 2025-03-04T21:12:19.9056039Z GITHUB_REPOSITORY: pytorch/pytorch 2025-03-04T21:12:19.9056359Z GITHUB_WORKFLOW: inductor 2025-03-04T21:12:19.9056637Z GITHUB_JOB: test 2025-03-04T21:12:19.9057033Z GITHUB_RUN_ID: 13661696663 2025-03-04T21:12:19.9057397Z GITHUB_RUN_NUMBER: 120837 2025-03-04T21:12:19.9057683Z GITHUB_RUN_ATTEMPT: 1 2025-03-04T21:12:19.9057945Z JOB_ID: 38197724420 2025-03-04T21:12:19.9058425Z JOB_NAME: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:12:19.9058941Z BRANCH: 2025-03-04T21:12:19.9059210Z SHA1: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:19.9059586Z BASE_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:19.9059946Z TEST_CONFIG: inductor_huggingface 2025-03-04T21:12:19.9060247Z SHARD_NUMBER: 1 2025-03-04T21:12:19.9060493Z NUM_TEST_SHARDS: 1 2025-03-04T21:12:19.9060751Z REENABLED_ISSUES: 2025-03-04T21:12:19.9061020Z CONTINUE_THROUGH_ERROR: False 2025-03-04T21:12:19.9061633Z VERBOSE_TEST_LOGS: False 2025-03-04T21:12:19.9061909Z TEST_SHOWLOCALS: False 2025-03-04T21:12:19.9062185Z NO_TEST_TIMEOUT: False 2025-03-04T21:12:19.9062458Z NO_TD: False 2025-03-04T21:12:19.9062717Z TD_DISTRIBUTED: False 2025-03-04T21:12:19.9063051Z SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2 2025-03-04T21:12:19.9063424Z SCCACHE_REGION: us-east-1 2025-03-04T21:12:19.9063702Z SHM_SIZE: 2g 2025-03-04T21:12:19.9064501Z DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:12:19.9065345Z XLA_CUDA: 2025-03-04T21:12:19.9065718Z XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla 2025-03-04T21:12:19.9066178Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0 2025-03-04T21:12:19.9066513Z PYTORCH_TEST_RERUN_DISABLED_TESTS: 0 2025-03-04T21:12:19.9066831Z DASHBOARD_TAG: 2025-03-04T21:12:19.9067252Z HUGGING_FACE_HUB_TOKEN: *** 2025-03-04T21:12:19.9067671Z SCRIBE_GRAPHQL_ACCESS_TOKEN: *** 2025-03-04T21:12:19.9067974Z IS_A100_RUNNER: 0 2025-03-04T21:12:19.9068447Z ARTIFACTS_FILE_SUFFIX: test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420 2025-03-04T21:12:19.9068972Z ##[endgroup] 2025-03-04T21:12:19.9099444Z + [[ inductor_huggingface == \m\u\l\t\i\g\p\u ]] 2025-03-04T21:12:19.9099862Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *onnx* ]] 2025-03-04T21:12:19.9100240Z + TEST_COMMAND=.ci/pytorch/test.sh 2025-03-04T21:12:19.9103358Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo 2025-03-04T21:12:19.9128022Z + TOTAL_AVAILABLE_MEMORY_IN_GB='61.094 ' 2025-03-04T21:12:19.9128351Z + TOTAL_MEMORY_WITH_SWAP=64 2025-03-04T21:12:19.9128730Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *\s\3\9\0\x* ]] 2025-03-04T21:12:19.9129118Z + SHM_OPTS=--shm-size=2g 2025-03-04T21:12:19.9129405Z + JENKINS_USER='--user jenkins' 2025-03-04T21:12:19.9129692Z + DOCKER_SHELL_CMD= 2025-03-04T21:12:19.9138653Z +++ nproc --ignore=2 2025-03-04T21:12:19.9168740Z ++ docker run --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all -e BUILD_ENVIRONMENT -e PR_NUMBER -e GITHUB_ACTIONS -e GITHUB_REPOSITORY -e GITHUB_WORKFLOW -e GITHUB_JOB -e GITHUB_RUN_ID -e GITHUB_RUN_NUMBER -e GITHUB_RUN_ATTEMPT -e JOB_ID -e JOB_NAME -e BASE_SHA -e BRANCH -e SHA1 -e AWS_DEFAULT_REGION -e IN_WHEEL_TEST -e SHARD_NUMBER -e TEST_CONFIG -e NUM_TEST_SHARDS -e REENABLED_ISSUES -e CONTINUE_THROUGH_ERROR -e VERBOSE_TEST_LOGS -e TEST_SHOWLOCALS -e NO_TEST_TIMEOUT -e NO_TD -e TD_DISTRIBUTED -e PR_LABELS -e MAX_JOBS=14 -e SCCACHE_BUCKET -e SCCACHE_REGION -e XLA_CUDA -e XLA_CLANG_CACHE_S3_BUCKET_NAME -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK -e PYTORCH_TEST_RERUN_DISABLED_TESTS -e SKIP_SCCACHE_INITIALIZATION=1 -e HUGGING_FACE_HUB_TOKEN -e SCRIBE_GRAPHQL_ACCESS_TOKEN -e DASHBOARD_TAG -e IS_A100_RUNNER -e ARTIFACTS_FILE_SUFFIX --memory=61g --memory-swap=64g --env-file=/tmp/github_env_13661696663 --security-opt seccomp=unconfined --cap-add=SYS_PTRACE --ipc=host --shm-size=2g --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/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 2025-03-04T21:12:32.3347698Z + container_name=bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T21:12:32.3350601Z + grep download.pytorch.org /etc/hosts 2025-03-04T21:12:32.3354572Z + docker exec -i bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 sudo bash -c '/bin/cat >> /etc/hosts' 2025-03-04T21:12:32.5143886Z + echo DOCKER_CONTAINER_ID=bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T21:12:32.5144532Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *\s\3\9\0\x* ]] 2025-03-04T21:12:32.5148876Z ++ echo dist/torch-2.7.0a0+git1b74980-cp310-cp310-linux_x86_64.whl 2025-03-04T21:12:32.5152164Z + docker exec -t bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 sh -c 'python3 -m pip install dist/torch-2.7.0a0+git1b74980-cp310-cp310-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh' 2025-03-04T21:12:32.9767973Z Processing ./dist/torch-2.7.0a0+git1b74980-cp310-cp310-linux_x86_64.whl (from torch==2.7.0a0+git1b74980) 2025-03-04T21:12:33.3283016Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.16.1) 2025-03-04T21:12:33.3285788Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (4.12.2) 2025-03-04T21:12:33.3744068Z Collecting sympy==1.13.3 (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) 2025-03-04T21:12:33.3757261Z Using cached sympy-1.13.3-py3-none-any.whl.metadata (12 kB) 2025-03-04T21:12:33.3778176Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2.8.8) 2025-03-04T21:12:33.3781704Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.1.5) 2025-03-04T21:12:33.3784729Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (2024.10.0) 2025-03-04T21:12:33.3795207Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from sympy==1.13.3->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (1.3.0) 2025-03-04T21:12:33.3809170Z Requirement already satisfied: opt-einsum>=3.3 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.3.0) 2025-03-04T21:12:33.3827416Z Requirement already satisfied: numpy>=1.7 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from opt-einsum>=3.3->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (1.22.4) 2025-03-04T21:12:33.4205815Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from jinja2->torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980) (3.0.2) 2025-03-04T21:12:33.4286870Z Using cached sympy-1.13.3-py3-none-any.whl (6.2 MB) 2025-03-04T21:12:34.0569435Z Installing collected packages: sympy, torch 2025-03-04T21:12:34.0569819Z Attempting uninstall: sympy 2025-03-04T21:12:34.0580887Z Found existing installation: sympy 1.13.1 2025-03-04T21:12:34.2562274Z Uninstalling sympy-1.13.1: 2025-03-04T21:12:35.4622335Z Successfully uninstalled sympy-1.13.1 2025-03-04T21:12:50.0956072Z ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. 2025-03-04T21:12:50.0957073Z timm 1.0.14 requires torchvision, which is not installed. 2025-03-04T21:12:50.0957658Z Successfully installed sympy-1.13.3 torch-2.7.0a0+git1b74980 2025-03-04T21:12:50.2036607Z + export TERM=vt100 2025-03-04T21:12:50.2036913Z + TERM=vt100 2025-03-04T21:12:50.2039780Z ++ dirname .ci/pytorch/test.sh 2025-03-04T21:12:50.2050298Z + source .ci/pytorch/common.sh 2025-03-04T21:12:50.2054059Z +++ dirname .ci/pytorch/common.sh 2025-03-04T21:12:50.2063561Z ++ source .ci/pytorch/common_utils.sh 2025-03-04T21:12:50.2065772Z +++ declare -f -t trap_add 2025-03-04T21:12:50.2071808Z ++ set -ex -o pipefail 2025-03-04T21:12:50.2072148Z ++ [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *rocm* ]] 2025-03-04T21:12:50.2072607Z ++ BUILD_TEST_LIBTORCH=0 2025-03-04T21:12:50.2073480Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *rocm* ]] 2025-03-04T21:12:50.2073949Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *s390x* ]] 2025-03-04T21:12:50.2074326Z + [[ -d /var/lib/jenkins/workspace ]] 2025-03-04T21:12:50.2076620Z ++ stat -c %u /var/lib/jenkins/workspace 2025-03-04T21:12:50.2096068Z + WORKSPACE_ORIGINAL_OWNER_ID=1000 2025-03-04T21:12:50.2096404Z + trap_add cleanup_workspace EXIT 2025-03-04T21:12:50.2096833Z + trap_add_cmd=cleanup_workspace 2025-03-04T21:12:50.2097119Z + shift 2025-03-04T21:12:50.2097368Z + for trap_add_name in "$@" 2025-03-04T21:12:50.2103789Z +++ trap -p EXIT 2025-03-04T21:12:50.2107403Z ++ eval 'extract_trap_cmd ' 2025-03-04T21:12:50.2107787Z +++ extract_trap_cmd 2025-03-04T21:12:50.2108043Z +++ printf '%s\n' '' 2025-03-04T21:12:50.2108314Z ++ printf '%s\n' cleanup_workspace 2025-03-04T21:12:50.2110656Z + trap -- ' 2025-03-04T21:12:50.2110907Z cleanup_workspace' EXIT 2025-03-04T21:12:50.2111230Z + sudo chown -R jenkins /var/lib/jenkins/workspace 2025-03-04T21:12:51.0006790Z + git config --global --add safe.directory /var/lib/jenkins/workspace 2025-03-04T21:12:51.0028968Z + echo 'Environment variables:' 2025-03-04T21:12:51.0029301Z Environment variables: 2025-03-04T21:12:51.0029573Z + env 2025-03-04T21:12:51.0039866Z INSTALLED_DB=yes 2025-03-04T21:12:51.0040440Z NV_LIBCUBLAS_VERSION=12.4.5.8-1 2025-03-04T21:12:51.0040891Z NVIDIA_VISIBLE_DEVICES=all 2025-03-04T21:12:51.0041247Z NV_NVML_DEV_VERSION=12.4.127-1 2025-03-04T21:12:51.0041861Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T21:12:51.0042316Z CONTINUE_THROUGH_ERROR=False 2025-03-04T21:12:51.0042664Z NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.21.5-1+cuda12.4 2025-03-04T21:12:51.0043039Z NV_LIBNCCL_DEV_PACKAGE_VERSION=2.21.5-1 2025-03-04T21:12:51.0043432Z BUILD_ENVIRONMENT=linux-focal-cuda12.4-py3.10-gcc9-sm86 2025-03-04T21:12:51.0043804Z HOSTNAME=bc0ac25a1209 2025-03-04T21:12:51.0044369Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0044972Z GITHUB_ACTION=__self 2025-03-04T21:12:51.0045247Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-03-04T21:12:51.0049834Z NVIDIA_REQUIRE_CUDA=cuda>=12.4 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536 2025-03-04T21:12:51.0053917Z NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-4=12.4.5.8-1 2025-03-04T21:12:51.0054282Z NV_NVTX_VERSION=12.4.127-1 2025-03-04T21:12:51.0054555Z GITHUB_RUN_NUMBER=120837 2025-03-04T21:12:51.0054834Z TEST_CONFIG=inductor_huggingface 2025-03-04T21:12:51.0055435Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-03-04T21:12:51.0055919Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-03-04T21:12:51.0056228Z IS_A100_RUNNER=0 2025-03-04T21:12:51.0056487Z NV_CUDA_CUDART_DEV_VERSION=12.4.127-1 2025-03-04T21:12:51.0056799Z NV_LIBCUSPARSE_VERSION=12.3.1.170-1 2025-03-04T21:12:51.0057305Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-03-04T21:12:51.0057603Z NV_LIBNPP_VERSION=12.2.5.30-1 2025-03-04T21:12:51.0057912Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot] 2025-03-04T21:12:51.0058282Z CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache 2025-03-04T21:12:51.0058627Z GITHUB_REF_TYPE=tag 2025-03-04T21:12:51.0058884Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-03-04T21:12:51.0059160Z NCCL_VERSION=2.21.5-1 2025-03-04T21:12:51.0059451Z BASE_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:51.0059772Z XLA_CUDA= 2025-03-04T21:12:51.0060098Z HUGGING_FACE_HUB_TOKEN=*** 2025-03-04T21:12:51.0062499Z *** 2025-03-04T21:12:51.0062737Z CARGO_NET_GIT_FETCH_WITH_CLI=true 2025-03-04T21:12:51.0063058Z GITHUB_REPOSITORY_ID=65600975 2025-03-04T21:12:51.0063347Z GITHUB_ACTIONS=true 2025-03-04T21:12:51.0063600Z NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:51.0063930Z NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-4=12.4.127-1 2025-03-04T21:12:51.0064295Z NV_LIBNPP_PACKAGE=libnpp-12-4=12.2.5.30-1 2025-03-04T21:12:51.0064646Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:51.0064993Z NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev 2025-03-04T21:12:51.0065365Z GITHUB_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:51.0065940Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/tags/ciflow/inductor/148205 2025-03-04T21:12:51.0066467Z UCC_HOME=/usr 2025-03-04T21:12:51.0066711Z NV_LIBCUBLAS_DEV_VERSION=12.4.5.8-1 2025-03-04T21:12:51.0067015Z VERBOSE_TEST_LOGS=False 2025-03-04T21:12:51.0067283Z NVIDIA_PRODUCT_NAME=CUDA 2025-03-04T21:12:51.0067602Z NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-4 2025-03-04T21:12:51.0067977Z GITHUB_REF=refs/tags/ciflow/inductor/148205 2025-03-04T21:12:51.0068317Z NV_CUDA_CUDART_VERSION=12.4.127-1 2025-03-04T21:12:51.0068609Z SHARD_NUMBER=1 2025-03-04T21:12:51.0068853Z GITHUB_REF_PROTECTED=false 2025-03-04T21:12:51.0069126Z HOME=/var/lib/jenkins 2025-03-04T21:12:51.0069417Z GITHUB_API_URL=https://api.github.com 2025-03-04T21:12:51.0069751Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-03-04T21:12:51.0070102Z UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb 2025-03-04T21:12:51.0070441Z CUDA_VERSION=12.4.1 2025-03-04T21:12:51.0070724Z NV_LIBCUBLAS_PACKAGE=libcublas-12-4=12.4.5.8-1 2025-03-04T21:12:51.0071045Z NUM_TEST_SHARDS=1 2025-03-04T21:12:51.0071273Z UCX_HOME=/usr 2025-03-04T21:12:51.0081473Z NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-4=12.4.1-1 2025-03-04T21:12:51.0082194Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0083002Z JOB_NAME=cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:12:51.0083794Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0084545Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-03-04T21:12:51.0085017Z GITHUB_EVENT_NAME=push 2025-03-04T21:12:51.0085266Z DASHBOARD_TAG= 2025-03-04T21:12:51.0085501Z GITHUB_RUN_ID=13661696663 2025-03-04T21:12:51.0085804Z NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-4=12.2.5.30-1 2025-03-04T21:12:51.0086163Z NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-4 2025-03-04T21:12:51.0086808Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0087444Z GITHUB_ACTOR=pytorch-bot[bot] 2025-03-04T21:12:51.0087866Z NV_LIBNPP_DEV_VERSION=12.2.5.30-1 2025-03-04T21:12:51.0088146Z PR_NUMBER= 2025-03-04T21:12:51.0088376Z GITHUB_RUN_ATTEMPT=1 2025-03-04T21:12:51.0088633Z ANACONDA_PYTHON_VERSION=3.10 2025-03-04T21:12:51.0089156Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-03-04T21:12:51.0089619Z TERM=vt100 2025-03-04T21:12:51.0089874Z NV_LIBCUSPARSE_DEV_VERSION=12.3.1.170-1 2025-03-04T21:12:51.0090189Z INSTALLED_VISION=yes 2025-03-04T21:12:51.0090434Z BRANCH= 2025-03-04T21:12:51.0090665Z SCCACHE_REGION=us-east-1 2025-03-04T21:12:51.0090947Z OPENSSL_ROOT_DIR=/opt/openssl 2025-03-04T21:12:51.0091253Z LIBRARY_PATH=/usr/local/cuda/lib64/stubs 2025-03-04T21:12:51.0091572Z CUDA_PATH=/usr/local/cuda 2025-03-04T21:12:51.0092079Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-03-04T21:12:51.0092635Z GITHUB_SERVER_URL=https://github.com 2025-03-04T21:12:51.0093003Z UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b 2025-03-04T21:12:51.0093356Z REENABLED_ISSUES= 2025-03-04T21:12:51.0093592Z SHLVL=1 2025-03-04T21:12:51.0093806Z MAX_JOBS=14 2025-03-04T21:12:51.0094044Z NV_CUDA_LIB_VERSION=12.4.1-1 2025-03-04T21:12:51.0094306Z NVARCH=x86_64 2025-03-04T21:12:51.0094550Z GITHUB_ACTOR_ID=54816060 2025-03-04T21:12:51.0094890Z GITHUB_WORKFLOW_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:51.0095291Z GITHUB_REF_NAME=ciflow/inductor/148205 2025-03-04T21:12:51.0095626Z NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-4 2025-03-04T21:12:51.0096069Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-03-04T21:12:51.0096488Z GITHUB_JOB=test 2025-03-04T21:12:51.0096758Z NV_LIBNCCL_PACKAGE=libnccl2=2.21.5-1+cuda12.4 2025-03-04T21:12:51.0097161Z LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 2025-03-04T21:12:51.0097535Z NO_TEST_TIMEOUT=False 2025-03-04T21:12:51.0097794Z TD_DISTRIBUTED=False 2025-03-04T21:12:51.0098071Z NV_CUDA_NSIGHT_COMPUTE_VERSION=12.4.1-1 2025-03-04T21:12:51.0098401Z GITHUB_REPOSITORY=pytorch/pytorch 2025-03-04T21:12:51.0098711Z NV_NVPROF_VERSION=12.4.127-1 2025-03-04T21:12:51.0098995Z GITHUB_RETENTION_DAYS=90 2025-03-04T21:12:51.0099271Z OPENSSL_DIR=/opt/openssl 2025-03-04T21:12:51.0099545Z GITHUB_ACTION_REPOSITORY= 2025-03-04T21:12:51.0100281Z PATH=/opt/cache/bin:/opt/conda/envs/py_3.10/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T21:12:51.0101031Z GITHUB_BASE_REF= 2025-03-04T21:12:51.0101500Z ARTIFACTS_FILE_SUFFIX=test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420 2025-03-04T21:12:51.0102036Z NV_LIBNCCL_PACKAGE_NAME=libnccl2 2025-03-04T21:12:51.0102319Z CI=true 2025-03-04T21:12:51.0102552Z NV_LIBNCCL_PACKAGE_VERSION=2.21.5-1 2025-03-04T21:12:51.0102870Z GITHUB_REPOSITORY_OWNER=pytorch 2025-03-04T21:12:51.0103153Z JOB_ID=38197724420 2025-03-04T21:12:51.0103399Z INSTALLED_PROTOBUF=yes 2025-03-04T21:12:51.0103656Z GITHUB_HEAD_REF= 2025-03-04T21:12:51.0103897Z GITHUB_ACTION_REF= 2025-03-04T21:12:51.0104197Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-03-04T21:12:51.0104548Z TEST_SHOWLOCALS=False 2025-03-04T21:12:51.0104815Z GITHUB_WORKFLOW=inductor 2025-03-04T21:12:51.0105098Z DEBIAN_FRONTEND=noninteractive 2025-03-04T21:12:51.0105705Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0106313Z NO_TD=False 2025-03-04T21:12:51.0106548Z SKIP_SCCACHE_INITIALIZATION=1 2025-03-04T21:12:51.0106831Z _=/usr/bin/env 2025-03-04T21:12:51.0107166Z ++ python -c 'import site; print(site.getsitepackages()[0])' 2025-03-04T21:12:51.0221066Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch 2025-03-04T21:12:51.0221848Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/bin 2025-03-04T21:12:51.0222645Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/lib 2025-03-04T21:12:51.0223393Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/test 2025-03-04T21:12:51.0223933Z + BUILD_DIR=build 2025-03-04T21:12:51.0224198Z + BUILD_RENAMED_DIR=build_renamed 2025-03-04T21:12:51.0224499Z + BUILD_BIN_DIR=build/bin 2025-03-04T21:12:51.0224764Z + SHARD_NUMBER=1 2025-03-04T21:12:51.0225181Z + NUM_TEST_SHARDS=1 2025-03-04T21:12:51.0225572Z + export TORCH_SERIALIZATION_DEBUG=1 2025-03-04T21:12:51.0225893Z + TORCH_SERIALIZATION_DEBUG=1 2025-03-04T21:12:51.0226180Z + export VALGRIND=ON 2025-03-04T21:12:51.0226430Z + VALGRIND=ON 2025-03-04T21:12:51.0226812Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *clang9* ]] 2025-03-04T21:12:51.0227406Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *xpu* ]] 2025-03-04T21:12:51.0227868Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *s390x* ]] 2025-03-04T21:12:51.0228284Z + [[ 0 == \1 ]] 2025-03-04T21:12:51.0228514Z + [[ False == \1 ]] 2025-03-04T21:12:51.0228902Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *bazel* ]] 2025-03-04T21:12:51.0229294Z ++ realpath build/custom_test_artifacts 2025-03-04T21:12:51.0241762Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts 2025-03-04T21:12:51.0242255Z + [[ -n '' ]] 2025-03-04T21:12:51.0242509Z + echo 'Environment variables' 2025-03-04T21:12:51.0242818Z Environment variables 2025-03-04T21:12:51.0243069Z + env 2025-03-04T21:12:51.0250832Z INSTALLED_DB=yes 2025-03-04T21:12:51.0251795Z NV_LIBCUBLAS_VERSION=12.4.5.8-1 2025-03-04T21:12:51.0252210Z NVIDIA_VISIBLE_DEVICES=all 2025-03-04T21:12:51.0252513Z NV_NVML_DEV_VERSION=12.4.127-1 2025-03-04T21:12:51.0252936Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T21:12:51.0253385Z CONTINUE_THROUGH_ERROR=False 2025-03-04T21:12:51.0253732Z NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.21.5-1+cuda12.4 2025-03-04T21:12:51.0254128Z NV_LIBNCCL_DEV_PACKAGE_VERSION=2.21.5-1 2025-03-04T21:12:51.0254527Z BUILD_ENVIRONMENT=linux-focal-cuda12.4-py3.10-gcc9-sm86 2025-03-04T21:12:51.0254910Z HOSTNAME=bc0ac25a1209 2025-03-04T21:12:51.0255483Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0256093Z GITHUB_ACTION=__self 2025-03-04T21:12:51.0256380Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0 2025-03-04T21:12:51.0260378Z NVIDIA_REQUIRE_CUDA=cuda>=12.4 brand=tesla,driver>=470,driver<471 brand=unknown,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=geforce,driver>=470,driver<471 brand=geforcertx,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=titan,driver>=470,driver<471 brand=titanrtx,driver>=470,driver<471 brand=tesla,driver>=525,driver<526 brand=unknown,driver>=525,driver<526 brand=nvidia,driver>=525,driver<526 brand=nvidiartx,driver>=525,driver<526 brand=geforce,driver>=525,driver<526 brand=geforcertx,driver>=525,driver<526 brand=quadro,driver>=525,driver<526 brand=quadrortx,driver>=525,driver<526 brand=titan,driver>=525,driver<526 brand=titanrtx,driver>=525,driver<526 brand=tesla,driver>=535,driver<536 brand=unknown,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=geforce,driver>=535,driver<536 brand=geforcertx,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=titan,driver>=535,driver<536 brand=titanrtx,driver>=535,driver<536 2025-03-04T21:12:51.0265665Z NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-4=12.4.5.8-1 2025-03-04T21:12:51.0266045Z NV_NVTX_VERSION=12.4.127-1 2025-03-04T21:12:51.0266329Z GITHUB_RUN_NUMBER=120837 2025-03-04T21:12:51.0266621Z TEST_CONFIG=inductor_huggingface 2025-03-04T21:12:51.0266938Z GITHUB_REPOSITORY_OWNER_ID=21003710 2025-03-04T21:12:51.0267284Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all 2025-03-04T21:12:51.0267606Z IS_A100_RUNNER=0 2025-03-04T21:12:51.0267876Z NV_CUDA_CUDART_DEV_VERSION=12.4.127-1 2025-03-04T21:12:51.0268209Z NV_LIBCUSPARSE_VERSION=12.3.1.170-1 2025-03-04T21:12:51.0268726Z SCRIBE_GRAPHQL_ACCESS_TOKEN=*** 2025-03-04T21:12:51.0269036Z NV_LIBNPP_VERSION=12.2.5.30-1 2025-03-04T21:12:51.0269359Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot] 2025-03-04T21:12:51.0269741Z CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache 2025-03-04T21:12:51.0270095Z GITHUB_REF_TYPE=tag 2025-03-04T21:12:51.0270612Z TORCH_CUDA_ARCH_LIST=Maxwell 2025-03-04T21:12:51.0270912Z NCCL_VERSION=2.21.5-1 2025-03-04T21:12:51.0271359Z BASE_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:51.0271704Z XLA_CUDA= 2025-03-04T21:12:51.0272048Z HUGGING_FACE_HUB_TOKEN=*** 2025-03-04T21:12:51.0272569Z *** 2025-03-04T21:12:51.0272809Z CARGO_NET_GIT_FETCH_WITH_CLI=true 2025-03-04T21:12:51.0273123Z GITHUB_REPOSITORY_ID=65600975 2025-03-04T21:12:51.0273413Z GITHUB_ACTIONS=true 2025-03-04T21:12:51.0273689Z NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T21:12:51.0274030Z NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-4=12.4.127-1 2025-03-04T21:12:51.0274420Z NV_LIBNPP_PACKAGE=libnpp-12-4=12.2.5.30-1 2025-03-04T21:12:51.0274794Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:51.0275158Z NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev 2025-03-04T21:12:51.0275532Z GITHUB_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:51.0276133Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/tags/ciflow/inductor/148205 2025-03-04T21:12:51.0276693Z UCC_HOME=/usr 2025-03-04T21:12:51.0276956Z TORCH_SERIALIZATION_DEBUG=1 2025-03-04T21:12:51.0277261Z NV_LIBCUBLAS_DEV_VERSION=12.4.5.8-1 2025-03-04T21:12:51.0277575Z VERBOSE_TEST_LOGS=False 2025-03-04T21:12:51.0277861Z NVIDIA_PRODUCT_NAME=CUDA 2025-03-04T21:12:51.0278204Z NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-4 2025-03-04T21:12:51.0278601Z GITHUB_REF=refs/tags/ciflow/inductor/148205 2025-03-04T21:12:51.0278949Z NV_CUDA_CUDART_VERSION=12.4.127-1 2025-03-04T21:12:51.0279239Z SHARD_NUMBER=1 2025-03-04T21:12:51.0279491Z GITHUB_REF_PROTECTED=false 2025-03-04T21:12:51.0279771Z HOME=/var/lib/jenkins 2025-03-04T21:12:51.0280070Z GITHUB_API_URL=https://api.github.com 2025-03-04T21:12:51.0280410Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0 2025-03-04T21:12:51.0280763Z UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb 2025-03-04T21:12:51.0281116Z CUDA_VERSION=12.4.1 2025-03-04T21:12:51.0281412Z NV_LIBCUBLAS_PACKAGE=libcublas-12-4=12.4.5.8-1 2025-03-04T21:12:51.0281740Z NUM_TEST_SHARDS=1 2025-03-04T21:12:51.0281985Z UCX_HOME=/usr 2025-03-04T21:12:51.0282346Z NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-4=12.4.1-1 2025-03-04T21:12:51.0283085Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0283921Z JOB_NAME=cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T21:12:51.0284731Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0285554Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json 2025-03-04T21:12:51.0286055Z GITHUB_EVENT_NAME=push 2025-03-04T21:12:51.0286314Z DASHBOARD_TAG= 2025-03-04T21:12:51.0286564Z GITHUB_RUN_ID=13661696663 2025-03-04T21:12:51.0286888Z NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-4=12.2.5.30-1 2025-03-04T21:12:51.0287272Z NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-4 2025-03-04T21:12:51.0288083Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0288743Z GITHUB_ACTOR=pytorch-bot[bot] 2025-03-04T21:12:51.0289053Z NV_LIBNPP_DEV_VERSION=12.2.5.30-1 2025-03-04T21:12:51.0289366Z PR_NUMBER= 2025-03-04T21:12:51.0289597Z GITHUB_RUN_ATTEMPT=1 2025-03-04T21:12:51.0289850Z VALGRIND=ON 2025-03-04T21:12:51.0290097Z ANACONDA_PYTHON_VERSION=3.10 2025-03-04T21:12:51.0290442Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql 2025-03-04T21:12:51.0290794Z TERM=vt100 2025-03-04T21:12:51.0291060Z NV_LIBCUSPARSE_DEV_VERSION=12.3.1.170-1 2025-03-04T21:12:51.0291386Z INSTALLED_VISION=yes 2025-03-04T21:12:51.0291637Z BRANCH= 2025-03-04T21:12:51.0291867Z SCCACHE_REGION=us-east-1 2025-03-04T21:12:51.0292150Z OPENSSL_ROOT_DIR=/opt/openssl 2025-03-04T21:12:51.0292458Z LIBRARY_PATH=/usr/local/cuda/lib64/stubs 2025-03-04T21:12:51.0292782Z CUDA_PATH=/usr/local/cuda 2025-03-04T21:12:51.0293391Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux 2025-03-04T21:12:51.0294025Z GITHUB_SERVER_URL=https://github.com 2025-03-04T21:12:51.0294399Z UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b 2025-03-04T21:12:51.0294749Z REENABLED_ISSUES= 2025-03-04T21:12:51.0294989Z SHLVL=1 2025-03-04T21:12:51.0295211Z MAX_JOBS=14 2025-03-04T21:12:51.0295494Z NV_CUDA_LIB_VERSION=12.4.1-1 2025-03-04T21:12:51.0295773Z NVARCH=x86_64 2025-03-04T21:12:51.0296003Z GITHUB_ACTOR_ID=54816060 2025-03-04T21:12:51.0296344Z GITHUB_WORKFLOW_SHA=1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T21:12:51.0296745Z GITHUB_REF_NAME=ciflow/inductor/148205 2025-03-04T21:12:51.0297081Z NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-4 2025-03-04T21:12:51.0297528Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla 2025-03-04T21:12:51.0297951Z GITHUB_JOB=test 2025-03-04T21:12:51.0298226Z NV_LIBNCCL_PACKAGE=libnccl2=2.21.5-1+cuda12.4 2025-03-04T21:12:51.0298637Z LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64 2025-03-04T21:12:51.0299029Z NO_TEST_TIMEOUT=False 2025-03-04T21:12:51.0299302Z TD_DISTRIBUTED=False 2025-03-04T21:12:51.0299579Z NV_CUDA_NSIGHT_COMPUTE_VERSION=12.4.1-1 2025-03-04T21:12:51.0299914Z GITHUB_REPOSITORY=pytorch/pytorch 2025-03-04T21:12:51.0300220Z NV_NVPROF_VERSION=12.4.127-1 2025-03-04T21:12:51.0300511Z GITHUB_RETENTION_DAYS=90 2025-03-04T21:12:51.0300785Z OPENSSL_DIR=/opt/openssl 2025-03-04T21:12:51.0301062Z GITHUB_ACTION_REPOSITORY= 2025-03-04T21:12:51.0301793Z PATH=/opt/cache/bin:/opt/conda/envs/py_3.10/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T21:12:51.0302551Z GITHUB_BASE_REF= 2025-03-04T21:12:51.0303022Z ARTIFACTS_FILE_SUFFIX=test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420 2025-03-04T21:12:51.0303563Z NV_LIBNCCL_PACKAGE_NAME=libnccl2 2025-03-04T21:12:51.0303847Z CI=true 2025-03-04T21:12:51.0304083Z NV_LIBNCCL_PACKAGE_VERSION=2.21.5-1 2025-03-04T21:12:51.0304404Z GITHUB_REPOSITORY_OWNER=pytorch 2025-03-04T21:12:51.0304687Z JOB_ID=38197724420 2025-03-04T21:12:51.0304939Z INSTALLED_PROTOBUF=yes 2025-03-04T21:12:51.0305197Z GITHUB_HEAD_REF= 2025-03-04T21:12:51.0305437Z GITHUB_ACTION_REF= 2025-03-04T21:12:51.0305741Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2 2025-03-04T21:12:51.0306101Z TEST_SHOWLOCALS=False 2025-03-04T21:12:51.0306369Z GITHUB_WORKFLOW=inductor 2025-03-04T21:12:51.0306657Z DEBIAN_FRONTEND=noninteractive 2025-03-04T21:12:51.0307269Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_a77ad9a2-b3a8-4025-8bbc-2320104c2625 2025-03-04T21:12:51.0307876Z NO_TD=False 2025-03-04T21:12:51.0308117Z SKIP_SCCACHE_INITIALIZATION=1 2025-03-04T21:12:51.0308402Z _=/usr/bin/env 2025-03-04T21:12:51.0308649Z + echo 'Testing pytorch' 2025-03-04T21:12:51.0308917Z Testing pytorch 2025-03-04T21:12:51.0309177Z + export LANG=C.UTF-8 2025-03-04T21:12:51.0309428Z + LANG=C.UTF-8 2025-03-04T21:12:51.0309660Z + PR_NUMBER= 2025-03-04T21:12:51.0309932Z + [[ inductor_huggingface == \d\e\f\a\u\l\t ]] 2025-03-04T21:12:51.0310314Z + [[ inductor_huggingface == \d\i\s\t\r\i\b\u\t\e\d ]] 2025-03-04T21:12:51.0310679Z + [[ inductor_huggingface == \s\l\o\w ]] 2025-03-04T21:12:51.0311096Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *slow-gradcheck* ]] 2025-03-04T21:12:51.0311562Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *cuda* ]] 2025-03-04T21:12:51.0311960Z + export PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda 2025-03-04T21:12:51.0312310Z + PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda 2025-03-04T21:12:51.0312644Z + [[ inductor_huggingface == *crossref* ]] 2025-03-04T21:12:51.0313028Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *rocm* ]] 2025-03-04T21:12:51.0313446Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *xpu* ]] 2025-03-04T21:12:51.0313869Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *-bazel-* ]] 2025-03-04T21:12:51.0314263Z + pip_install --user ninja==1.10.2 2025-03-04T21:12:51.0314658Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T21:12:51.0315237Z + python3 -m pip install --progress-bar off --user ninja==1.10.2 2025-03-04T21:12:51.5599351Z Collecting ninja==1.10.2 2025-03-04T21:12:51.5952563Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB) 2025-03-04T21:12:51.6062539Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB) 2025-03-04T21:12:52.2111739Z Installing collected packages: ninja 2025-03-04T21:12:52.2192083Z  WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH. 2025-03-04T21:12:52.2193043Z Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location. 2025-03-04T21:12:52.2249257Z Successfully installed ninja-1.10.2 2025-03-04T21:12:52.3266215Z + export PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/conda/envs/py_3.10/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T21:12:52.3267757Z + PATH=/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/conda/envs/py_3.10/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T21:12:52.3268725Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *aarch64* ]] 2025-03-04T21:12:52.3269114Z + install_tlparse 2025-03-04T21:12:52.3269408Z + pip_install --user tlparse==0.3.30 2025-03-04T21:12:52.3269825Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T21:12:52.3270330Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30 2025-03-04T21:12:52.7890732Z Collecting tlparse==0.3.30 2025-03-04T21:12:52.8213677Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB) 2025-03-04T21:12:52.8306815Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB) 2025-03-04T21:12:53.4443569Z Installing collected packages: tlparse 2025-03-04T21:12:53.4800597Z Successfully installed tlparse-0.3.30 2025-03-04T21:12:53.5800977Z ++ python -m site --user-base 2025-03-04T21:12:53.5988300Z + PATH=/var/lib/jenkins/.local/bin:/var/lib/jenkins/.local/bin:/opt/cache/bin:/opt/conda/envs/py_3.10/bin:/opt/conda/bin:/usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin 2025-03-04T21:12:53.5989419Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *asan* ]] 2025-03-04T21:12:53.5989864Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *-debug* ]] 2025-03-04T21:12:53.5990313Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *-bazel-* ]] 2025-03-04T21:12:53.5990906Z + echo 'We are not in debug mode: linux-focal-cuda12.4-py3.10-gcc9-sm86. Expect the assertion to pass' 2025-03-04T21:12:53.5991618Z We are not in debug mode: linux-focal-cuda12.4-py3.10-gcc9-sm86. Expect the assertion to pass 2025-03-04T21:12:53.5994482Z + cd test 2025-03-04T21:12:53.5995346Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)' 2025-03-04T21:12:55.3153166Z + [[ inductor_huggingface == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]] 2025-03-04T21:12:55.3153652Z + [[ inductor_huggingface == \n\o\g\p\u\_\A\V\X\5\1\2 ]] 2025-03-04T21:12:55.3158337Z + DYNAMO_BENCHMARK_FLAGS=() 2025-03-04T21:12:55.3160247Z + [[ inductor_huggingface == *pr_time_benchmarks* ]] 2025-03-04T21:12:55.3160853Z + [[ inductor_huggingface == *dynamo_eager* ]] 2025-03-04T21:12:55.3161472Z + [[ inductor_huggingface == *aot_eager* ]] 2025-03-04T21:12:55.3161859Z + [[ inductor_huggingface == *aot_inductor* ]] 2025-03-04T21:12:55.3162222Z + [[ inductor_huggingface == *inductor* ]] 2025-03-04T21:12:55.3162567Z + [[ inductor_huggingface != *perf* ]] 2025-03-04T21:12:55.3162932Z + DYNAMO_BENCHMARK_FLAGS+=(--inductor) 2025-03-04T21:12:55.3163265Z + [[ inductor_huggingface == *dynamic* ]] 2025-03-04T21:12:55.3163609Z + [[ inductor_huggingface == *cpu* ]] 2025-03-04T21:12:55.3163940Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda) 2025-03-04T21:12:55.3194268Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *libtorch* ]] 2025-03-04T21:12:55.3195105Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *-bazel-* ]] 2025-03-04T21:12:55.3197644Z + cd test 2025-03-04T21:12:55.3198219Z + python -c 'import torch; print(torch.__config__.show())' 2025-03-04T21:12:56.8457370Z PyTorch built with: 2025-03-04T21:12:56.8457681Z - GCC 9.4 2025-03-04T21:12:56.8457918Z - C++ Version: 201703 2025-03-04T21:12:56.8458483Z - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2025-03-04T21:12:56.8459247Z - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2025-03-04T21:12:56.8459701Z - OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-03-04T21:12:56.8460055Z - LAPACK is enabled (usually provided by MKL) 2025-03-04T21:12:56.8460402Z - NNPACK is enabled 2025-03-04T21:12:56.8460687Z - CPU capability usage: AVX2 2025-03-04T21:12:56.8460990Z - CUDA Runtime 12.4 2025-03-04T21:12:56.8461582Z - NVCC architecture flags: -gencode;arch=compute_86,code=sm_86 2025-03-04T21:12:56.8462230Z - CuDNN 90.1 2025-03-04T21:12:56.8462595Z - Magma 2.6.1 2025-03-04T21:12:56.8466973Z - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, COMMIT_SHA=1b7498080987913ecb3aff6253c5e88f3540d911, CUDA_VERSION=12.4, CUDNN_VERSION=9.1.0, CXX_COMPILER=/opt/cache/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=1 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DLIBKINETO_NOXPUPTI=ON -DUSE_FBGEMM -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-unknown-pragmas -Wno-unused-parameter -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wsuggest-override -Wno-psabi -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Werror -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, FORCE_FALLBACK_CUDA_MPI=1, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, TORCH_VERSION=2.7.0, USE_CUDA=ON, USE_CUDNN=ON, USE_CUSPARSELT=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_GLOO=ON, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=ON, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, USE_ROCM_KERNEL_ASSERT=OFF, 2025-03-04T21:12:56.8471379Z 2025-03-04T21:12:57.2035266Z + cd test 2025-03-04T21:12:57.2035640Z + python -c 'import torch; print(torch.__config__.parallel_info())' 2025-03-04T21:12:58.6026863Z ATen/Parallel: 2025-03-04T21:12:58.6027659Z at::get_num_threads() : 8 2025-03-04T21:12:58.6028330Z at::get_num_interop_threads() : 16 2025-03-04T21:12:58.6028982Z OpenMP 201511 (a.k.a. OpenMP 4.5) 2025-03-04T21:12:58.6029468Z omp_get_max_threads() : 8 2025-03-04T21:12:58.6030025Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications 2025-03-04T21:12:58.6030603Z mkl_get_max_threads() : 8 2025-03-04T21:12:58.6031021Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af) 2025-03-04T21:12:58.6031478Z std::thread::hardware_concurrency() : 16 2025-03-04T21:12:58.6031823Z Environment variables: 2025-03-04T21:12:58.6032102Z OMP_NUM_THREADS : [not set] 2025-03-04T21:12:58.6032396Z MKL_NUM_THREADS : [not set] 2025-03-04T21:12:58.6032698Z ATen parallel backend: OpenMP 2025-03-04T21:12:58.6032892Z 2025-03-04T21:12:58.9066093Z + [[ inductor_huggingface == *numpy_2* ]] 2025-03-04T21:12:58.9066539Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *aarch64* ]] 2025-03-04T21:12:58.9066944Z + [[ inductor_huggingface == *backward* ]] 2025-03-04T21:12:58.9067285Z + [[ inductor_huggingface == *xla* ]] 2025-03-04T21:12:58.9067622Z + [[ inductor_huggingface == *executorch* ]] 2025-03-04T21:12:58.9067991Z + [[ inductor_huggingface == \j\i\t\_\l\e\g\a\c\y ]] 2025-03-04T21:12:58.9068408Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *libtorch* ]] 2025-03-04T21:12:58.9068805Z + [[ inductor_huggingface == distributed ]] 2025-03-04T21:12:58.9076998Z + [[ inductor_huggingface == *inductor_distributed* ]] 2025-03-04T21:12:58.9077434Z + [[ inductor_huggingface == *inductor-halide* ]] 2025-03-04T21:12:58.9077986Z + [[ inductor_huggingface == *inductor-triton-cpu* ]] 2025-03-04T21:12:58.9078418Z + [[ inductor_huggingface == *inductor-micro-benchmark* ]] 2025-03-04T21:12:58.9078829Z + [[ inductor_huggingface == *huggingface* ]] 2025-03-04T21:12:58.9079171Z + install_torchvision 2025-03-04T21:12:58.9079435Z + local orig_preload 2025-03-04T21:12:58.9079693Z + local commit 2025-03-04T21:12:58.9079945Z ++ get_pinned_commit vision 2025-03-04T21:12:58.9080258Z ++ cat .github/ci_commit_pins/vision.txt 2025-03-04T21:12:58.9090405Z + commit=d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T21:12:58.9090762Z + orig_preload= 2025-03-04T21:12:58.9091009Z + '[' -n '' ']' 2025-03-04T21:12:58.9091565Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T21:12:58.9092253Z + pip_install_pkg='python3 -m pip install --progress-bar off' 2025-03-04T21:12:58.9093029Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T21:12:59.3064659Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T21:12:59.3068751Z Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-qrz9ox52 2025-03-04T21:12:59.3100434Z Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-qrz9ox52 2025-03-04T21:13:00.8314999Z Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f' 2025-03-04T21:13:00.8345916Z Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T21:13:02.2356173Z Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T21:13:02.6025102Z Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f 2025-03-04T21:13:05.3030710Z Preparing metadata (setup.py) ... [?25l- \ done 2025-03-04T21:13:05.3064584Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchvision==0.19.0a0+d23a6e1) (1.22.4) 2025-03-04T21:13:05.3068083Z Requirement already satisfied: torch in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchvision==0.19.0a0+d23a6e1) (2.7.0a0+git1b74980) 2025-03-04T21:13:05.3072741Z Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchvision==0.19.0a0+d23a6e1) (11.0.0) 2025-03-04T21:13:05.3139520Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.16.1) 2025-03-04T21:13:05.3143942Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (4.12.2) 2025-03-04T21:13:05.3148036Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (1.13.3) 2025-03-04T21:13:05.3151598Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2.8.8) 2025-03-04T21:13:05.3154858Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (3.1.5) 2025-03-04T21:13:05.3159243Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchvision==0.19.0a0+d23a6e1) (2024.10.0) 2025-03-04T21:13:05.3174870Z Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from sympy==1.13.3->torch->torchvision==0.19.0a0+d23a6e1) (1.3.0) 2025-03-04T21:13:05.3651592Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from jinja2->torch->torchvision==0.19.0a0+d23a6e1) (3.0.2) 2025-03-04T21:13:05.3717705Z Building wheels for collected packages: torchvision 2025-03-04T21:14:25.6034830Z Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / done 2025-03-04T21:14:25.6067977Z [?25h Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp310-cp310-linux_x86_64.whl size=2073642 sha256=79e7d34e557bba1908d29d5e3f09b6eb8d54196c90043ff9babddc0e2707cc3a 2025-03-04T21:14:25.6069653Z Stored in directory: /var/lib/jenkins/.cache/pip/wheels/0e/56/35/02931e71eb23fd2b85591c7ec05b733ca7c8b328a2fd151f96 2025-03-04T21:14:25.6105021Z Successfully built torchvision 2025-03-04T21:14:26.1291305Z Installing collected packages: torchvision 2025-03-04T21:14:26.5481451Z Successfully installed torchvision-0.19.0a0+d23a6e1 2025-03-04T21:14:26.7152354Z + '[' -n '' ']' 2025-03-04T21:14:26.7152651Z + id=0 2025-03-04T21:14:26.7152920Z + test_dynamo_benchmark huggingface 0 2025-03-04T21:14:26.7159026Z ++ pwd 2025-03-04T21:14:26.7162658Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports 2025-03-04T21:14:26.7163207Z + local suite=huggingface 2025-03-04T21:14:26.7163490Z + shift 2025-03-04T21:14:26.7163717Z + local shard_id=0 2025-03-04T21:14:26.7163970Z + shift 2025-03-04T21:14:26.7164241Z + [[ inductor_huggingface == *perf_compare* ]] 2025-03-04T21:14:26.7164611Z + [[ inductor_huggingface == *perf* ]] 2025-03-04T21:14:26.7164955Z + [[ inductor_huggingface == *cpu* ]] 2025-03-04T21:14:26.7165346Z + [[ inductor_huggingface == *aot_inductor* ]] 2025-03-04T21:14:26.7166049Z + test_single_dynamo_benchmark inference huggingface 0 --inference --bfloat16 2025-03-04T21:14:26.7166670Z ++ pwd 2025-03-04T21:14:26.7170118Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports 2025-03-04T21:14:26.7170626Z + mkdir -p /var/lib/jenkins/workspace/test/test-reports 2025-03-04T21:14:26.7223500Z + local name=inference 2025-03-04T21:14:26.7223914Z + shift 2025-03-04T21:14:26.7224243Z + local suite=huggingface 2025-03-04T21:14:26.7224609Z + shift 2025-03-04T21:14:26.7224914Z + local shard_id=0 2025-03-04T21:14:26.7225234Z + shift 2025-03-04T21:14:26.7225535Z + partition_flags=() 2025-03-04T21:14:26.7225848Z + local partition_flags 2025-03-04T21:14:26.7226109Z + [[ -n 1 ]] 2025-03-04T21:14:26.7226339Z + [[ -n 0 ]] 2025-03-04T21:14:26.7226752Z + partition_flags=(--total-partitions "$NUM_TEST_SHARDS" --partition-id "$shard_id") 2025-03-04T21:14:26.7227362Z + [[ inductor_huggingface == *perf_compare* ]] 2025-03-04T21:14:26.7227722Z + [[ inductor_huggingface == *perf* ]] 2025-03-04T21:14:26.7228060Z + [[ inductor_huggingface == *_avx2* ]] 2025-03-04T21:14:26.7228399Z + [[ inductor_huggingface == *_avx512* ]] 2025-03-04T21:14:26.7229840Z + python benchmarks/dynamo/huggingface.py --ci --accuracy --timing --explain --print-compilation-time --inductor --device cuda --inference --bfloat16 --total-partitions 1 --partition-id 0 --output /var/lib/jenkins/workspace/test/test-reports/inference_huggingface.csv 2025-03-04T21:14:30.5762507Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:14:30.5763739Z warnings.warn( 2025-03-04T21:14:30.7236365Z 2025-03-04T21:14:30.7236702Z config.json: 0% 0.00/694 [00:00 will be ignored 2025-03-04T21:39:52.0108314Z Compilation time (from dynamo_timed): 33.311733021 2025-03-04T21:39:52.0116277Z pass 2025-03-04T21:39:52.0493153Z TIMING: entire_frame_compile:24.84369 gc:0.00244 _recursive_pre_grad_passes:0.01507 pad_mm_benchmark:0.6182 _recursive_joint_graph_passes:1.82934 _recursive_post_grad_passes:0.62955 async_compile.wait:5.37976 code_gen:12.37037 inductor_compile:18.98454 backend_compile:20.63033 entire_backward_compile:8.46805 total_wall_time:33.31173 2025-03-04T21:39:52.0495138Z STATS: call_* op count: 585 | FakeTensorMode.__torch_dispatch__:40901 | FakeTensor.__torch_dispatch__:6387 | ProxyTorchDispatchMode.__torch_dispatch__:20178 2025-03-04T21:39:52.0496140Z Dynamo produced 2 graphs covering 585 ops with 5 graph breaks (4 unique) 2025-03-04T21:39:58.7163307Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:39:58.7164625Z warnings.warn( 2025-03-04T21:39:58.9424541Z 2025-03-04T21:40:01.8820628Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:40:01.8820991Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:40:01.8821332Z cuda train AllenaiLongformerBase 2025-03-04T21:40:09.2677463Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] Graph break from `Tensor.item()`, consider setting: 2025-03-04T21:40:09.2678542Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] torch._dynamo.config.capture_scalar_outputs = True 2025-03-04T21:40:09.2679369Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] or: 2025-03-04T21:40:09.2680151Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1 2025-03-04T21:40:09.2681083Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] to include these operations in the captured graph. 2025-03-04T21:40:09.2681860Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] 2025-03-04T21:40:09.2682584Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] Graph break: from user code at: 2025-03-04T21:40:09.2683836Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:40:09.2685103Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] pred = mod(**cloned_inputs) 2025-03-04T21:40:09.2686346Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 1835, in forward 2025-03-04T21:40:09.2687576Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] outputs = self.longformer( 2025-03-04T21:40:09.2688946Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 1738, in forward 2025-03-04T21:40:09.2690217Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] encoder_outputs = self.encoder( 2025-03-04T21:40:09.2691458Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 1291, in forward 2025-03-04T21:40:09.2699880Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] is_global_attn = is_index_global_attn.flatten().any().item() 2025-03-04T21:40:09.2700692Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] 2025-03-04T21:40:09.2701293Z W0304 21:40:09.266000 11329 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] 2025-03-04T21:41:14.9644760Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:41:14.9645862Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 1318, in torch_dynamo_resume_in_forward_at_1291 2025-03-04T21:41:14.9647161Z layer_outputs = layer_module( 2025-03-04T21:41:14.9648032Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 1246, in forward 2025-03-04T21:41:14.9648924Z self_attn_outputs = self.attention( 2025-03-04T21:41:14.9649643Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 1182, in forward 2025-03-04T21:41:14.9650350Z self_outputs = self.self( 2025-03-04T21:41:14.9651033Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 571, in forward 2025-03-04T21:41:14.9651787Z attn_scores = self._sliding_chunks_query_key_matmul( 2025-03-04T21:41:14.9652635Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 836, in _sliding_chunks_query_key_matmul 2025-03-04T21:41:14.9653587Z query = self._chunk(query, window_overlap, getattr(self.config, "onnx_export", False)) 2025-03-04T21:41:14.9654437Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 778, in _chunk 2025-03-04T21:41:14.9655238Z return hidden_states.as_strided(size=chunk_size, stride=chunk_stride) 2025-03-04T21:41:14.9655565Z 2025-03-04T21:41:15.5393125Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:41:15.5394040Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 1734, in forward 2025-03-04T21:41:15.5394784Z embedding_output = self.embeddings( 2025-03-04T21:41:15.5395496Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/longformer/modeling_longformer.py", line 470, in forward 2025-03-04T21:41:15.5396245Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:41:15.5396509Z 2025-03-04T21:41:15.5551470Z W0304 21:41:15.554000 11329 site-packages/torch/_logging/_internal.py:1134] [18/0] Profiler function will be ignored 2025-03-04T21:42:05.8099205Z Compilation time (from dynamo_timed): 107.96918742599999 2025-03-04T21:42:05.8138658Z pass 2025-03-04T21:42:05.8571237Z TIMING: entire_frame_compile:90.80508 gc:0.00974 _recursive_pre_grad_passes:0.05799 _recursive_joint_graph_passes:3.10986 inductor_compile:54.27066 backend_compile:76.60223 _recursive_post_grad_passes:1.92006 async_compile.precompile:0.2874 async_compile.wait:7.24397 code_gen:33.74156 pad_mm_benchmark:0.42425 entire_backward_compile:17.16411 total_wall_time:107.96919 2025-03-04T21:42:05.8573034Z STATS: call_* op count: 2772 | FakeTensorMode.__torch_dispatch__:119549 | FakeTensor.__torch_dispatch__:17385 | ProxyTorchDispatchMode.__torch_dispatch__:59644 2025-03-04T21:42:05.8573871Z Dynamo produced 7 graphs covering 2772 ops with 9 graph breaks (5 unique) 2025-03-04T21:42:15.7805120Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:42:15.7806402Z warnings.warn( 2025-03-04T21:42:16.0359982Z 2025-03-04T21:42:20.1207823Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:42:20.1208326Z loading model: 0it [00:04, ?it/s] 2025-03-04T21:42:20.1208666Z cuda train BartForCausalLM 2025-03-04T21:42:28.6266379Z 2025-03-04T21:42:28.6267386Z class GraphModule(torch.nn.Module): 2025-03-04T21:42:28.6270285Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:42:28.6272515Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:42:28.6273303Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:42:28.6274435Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:42:28.6275622Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:42:28.6276806Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:42:28.6277524Z 2025-03-04T21:42:28.6277796Z # No stacktrace found for following nodes 2025-03-04T21:42:28.6278383Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:42:28.6278908Z 2025-03-04T21:42:28.6279664Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1364 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:42:28.6280642Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); input_ids = None 2025-03-04T21:42:28.6281107Z 2025-03-04T21:42:28.6281880Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1375 in forward, code: inputs_embeds = self.embed_tokens(input) * self.embed_scale 2025-03-04T21:42:28.6283648Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(l_cloned_inputs_input_ids_, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_cloned_inputs_input_ids_ = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:42:28.6285021Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:42:28.6285481Z 2025-03-04T21:42:28.6286143Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:42:28.6287088Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:42:28.6287589Z 2025-03-04T21:42:28.6288425Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:42:28.6289257Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:42:28.6289672Z 2025-03-04T21:42:28.6290406Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:42:28.6291290Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:42:28.6292489Z positions_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:42:28.6293593Z 2025-03-04T21:42:28.6294320Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1416 in forward, code: positions = positions.to(inputs_embeds.device) 2025-03-04T21:42:28.6295470Z positions_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T21:42:28.6296068Z 2025-03-04T21:42:28.6296779Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1418 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T21:42:28.6297809Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + positions_2; inputs_embeds = positions_2 = None 2025-03-04T21:42:28.6298354Z 2025-03-04T21:42:28.6299113Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1419 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:42:28.6301387Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:42:28.6303099Z 2025-03-04T21:42:28.6304006Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1421 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:42:28.6305386Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:42:28.6306083Z 2025-03-04T21:42:28.6306787Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1450 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:42:28.6307619Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:42:28.6307977Z 2025-03-04T21:42:28.6308691Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1451 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T21:42:28.6309591Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:42:28.6310001Z 2025-03-04T21:42:28.6310145Z 2025-03-04T21:42:28.6310278Z class GraphModule(torch.nn.Module): 2025-03-04T21:42:28.6312030Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:42:28.6313824Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:42:28.6314594Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:42:28.6315705Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:42:28.6316866Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:42:28.6318039Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:42:28.6318872Z 2025-03-04T21:42:28.6319143Z # No stacktrace found for following nodes 2025-03-04T21:42:28.6319798Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:42:28.6320337Z 2025-03-04T21:42:28.6321082Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1364 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:42:28.6322066Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); input_ids = None 2025-03-04T21:42:28.6322537Z 2025-03-04T21:42:28.6323314Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1375 in forward, code: inputs_embeds = self.embed_tokens(input) * self.embed_scale 2025-03-04T21:42:28.6325084Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(l_cloned_inputs_input_ids_, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_cloned_inputs_input_ids_ = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:42:28.6326519Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:42:28.6326977Z 2025-03-04T21:42:28.6327637Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:42:28.6328731Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:42:28.6329230Z 2025-03-04T21:42:28.6329860Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:42:28.6330684Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:42:28.6331105Z 2025-03-04T21:42:28.6331829Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:42:28.6332715Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:42:28.6333913Z positions_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:42:28.6335006Z 2025-03-04T21:42:28.6335745Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1416 in forward, code: positions = positions.to(inputs_embeds.device) 2025-03-04T21:42:28.6336796Z positions_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T21:42:28.6337331Z 2025-03-04T21:42:28.6338045Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1418 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T21:42:28.6339080Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + positions_2; inputs_embeds = positions_2 = None 2025-03-04T21:42:28.6339622Z 2025-03-04T21:42:28.6340377Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1419 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:42:28.6342725Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:42:28.6344530Z 2025-03-04T21:42:28.6345423Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1421 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:42:28.6346787Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:42:28.6347469Z 2025-03-04T21:42:28.6348166Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1450 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:42:28.6348989Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:42:28.6349338Z 2025-03-04T21:42:28.6350045Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1451 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T21:42:28.6350950Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:42:28.6351361Z 2025-03-04T21:42:29.5231058Z 2025-03-04T21:42:29.5231782Z class GraphModule(torch.nn.Module): 2025-03-04T21:42:29.5234240Z def forward(self, L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:42:29.5236004Z l_input_ids_ = L_input_ids_ 2025-03-04T21:42:29.5236717Z l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:42:29.5237847Z l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:42:29.5239028Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:42:29.5240219Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:42:29.5240948Z 2025-03-04T21:42:29.5241712Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1364 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:42:29.5242661Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); input_ids = None 2025-03-04T21:42:29.5243086Z 2025-03-04T21:42:29.5243858Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1375 in forward, code: inputs_embeds = self.embed_tokens(input) * self.embed_scale 2025-03-04T21:42:29.5245524Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(l_input_ids_, l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_input_ids_ = l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:42:29.5247158Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:42:29.5247765Z 2025-03-04T21:42:29.5248639Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:42:29.5249581Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:42:29.5250082Z 2025-03-04T21:42:29.5250712Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:42:29.5251534Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:42:29.5251947Z 2025-03-04T21:42:29.5252671Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:42:29.5253557Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:42:29.5254770Z positions_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:42:29.5255864Z 2025-03-04T21:42:29.5256598Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1416 in forward, code: positions = positions.to(inputs_embeds.device) 2025-03-04T21:42:29.5257640Z positions_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T21:42:29.5258169Z 2025-03-04T21:42:29.5258876Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1418 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T21:42:29.5259907Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + positions_2; inputs_embeds = positions_2 = None 2025-03-04T21:42:29.5260446Z 2025-03-04T21:42:29.5261732Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1419 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:42:29.5264010Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:42:29.5265735Z 2025-03-04T21:42:29.5266626Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1421 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:42:29.5268000Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:42:29.5268687Z 2025-03-04T21:42:29.5269380Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1450 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:42:29.5270200Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:42:29.5270542Z 2025-03-04T21:42:29.5271429Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1451 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T21:42:29.5272427Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:42:29.5272830Z 2025-03-04T21:42:34.3007404Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:42:34.3008344Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py", line 135, in forward 2025-03-04T21:42:34.3009031Z return super().forward(positions + self.offset) 2025-03-04T21:42:34.3009277Z 2025-03-04T21:42:40.7133790Z Compilation time (from dynamo_timed): 5.506051413000001 2025-03-04T21:42:40.7160717Z pass 2025-03-04T21:42:40.8428394Z TIMING: entire_frame_compile:4.11589 gc:0.00505 _recursive_pre_grad_passes:0.00529 _recursive_joint_graph_passes:0.51365 inductor_compile:2.72118 backend_compile:3.28908 _recursive_post_grad_passes:0.07561 async_compile.precompile:0.15375 async_compile.wait:0.80784 code_gen:1.8881 pad_mm_benchmark:0.27861 entire_backward_compile:1.39016 total_wall_time:5.50605 2025-03-04T21:42:40.8430679Z STATS: call_* op count: 39 | FakeTensorMode.__torch_dispatch__:3879 | FakeTensor.__torch_dispatch__:620 | ProxyTorchDispatchMode.__torch_dispatch__:1670 2025-03-04T21:42:40.8431479Z Dynamo produced 5 graphs covering 39 ops with 6 graph breaks (5 unique) 2025-03-04T21:42:46.4029443Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:42:46.4031777Z warnings.warn( 2025-03-04T21:42:46.6596500Z 2025-03-04T21:42:54.4669185Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:42:54.4669656Z loading model: 0it [00:07, ?it/s] 2025-03-04T21:42:54.4670074Z cuda train BartForConditionalGeneration 2025-03-04T21:43:11.0526767Z 2025-03-04T21:43:11.0527637Z class GraphModule(torch.nn.Module): 2025-03-04T21:43:11.0530334Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:43:11.0532589Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T21:43:11.0533094Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:43:11.0533850Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:43:11.0534952Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:43:11.0536107Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:43:11.0537272Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:43:11.0537985Z 2025-03-04T21:43:11.0538242Z # No stacktrace found for following nodes 2025-03-04T21:43:11.0538818Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:43:11.0539348Z 2025-03-04T21:43:11.0540535Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:104 in shift_tokens_right, code: shifted_input_ids = input_ids.new_zeros(input_ids.shape) 2025-03-04T21:43:11.0541726Z shifted_input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.new_zeros((1, 1024)) 2025-03-04T21:43:11.0542173Z 2025-03-04T21:43:11.0542943Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:105 in shift_tokens_right, code: shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() 2025-03-04T21:43:11.0544085Z getitem: "i64[1, 1023][1024, 1]cuda:0" = l_cloned_inputs_labels_[(slice(None, None, None), slice(None, -1, None))]; l_cloned_inputs_labels_ = None 2025-03-04T21:43:11.0544776Z clone: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T21:43:11.0545435Z shifted_input_ids[(slice(None, None, None), slice(1, None, None))] = clone; setitem = shifted_input_ids; clone = setitem = None 2025-03-04T21:43:11.0545985Z 2025-03-04T21:43:11.0546754Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:106 in shift_tokens_right, code: shifted_input_ids[:, 0] = decoder_start_token_id 2025-03-04T21:43:11.0547775Z shifted_input_ids[(slice(None, None, None), 0)] = 2; setitem_1 = shifted_input_ids; setitem_1 = None 2025-03-04T21:43:11.0548248Z 2025-03-04T21:43:11.0549129Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:111 in shift_tokens_right, code: shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) 2025-03-04T21:43:11.0550095Z eq: "b8[1, 1024][1024, 1]cuda:0" = shifted_input_ids == -100 2025-03-04T21:43:11.0550706Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = shifted_input_ids.masked_fill_(eq, 1); shifted_input_ids = eq = masked_fill_ = None 2025-03-04T21:43:11.0551242Z 2025-03-04T21:43:11.0551985Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1145 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T21:43:11.0554424Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T21:43:11.0554932Z 2025-03-04T21:43:11.0555707Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1152 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:43:11.0557365Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:43:11.0558627Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:43:11.0559076Z 2025-03-04T21:43:11.0559731Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:43:11.0560666Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:43:11.0561500Z 2025-03-04T21:43:11.0562120Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:43:11.0562930Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:43:11.0563337Z 2025-03-04T21:43:11.0564054Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:43:11.0564926Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:43:11.0566226Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:43:11.0567405Z 2025-03-04T21:43:11.0568248Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1155 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T21:43:11.0569262Z embed_pos_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T21:43:11.0569763Z 2025-03-04T21:43:11.0570460Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1157 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:43:11.0571477Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T21:43:11.0572009Z 2025-03-04T21:43:11.0572756Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1158 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:43:11.0574998Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:43:11.0576695Z 2025-03-04T21:43:11.0577586Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1159 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:43:11.0578958Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:43:11.0579637Z 2025-03-04T21:43:11.0580322Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1191 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:43:11.0581135Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:43:11.0581472Z 2025-03-04T21:43:11.0582228Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1192 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:43:11.0583172Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:43:11.0583574Z 2025-03-04T21:43:11.0583749Z 2025-03-04T21:43:11.0583869Z class GraphModule(torch.nn.Module): 2025-03-04T21:43:11.0585729Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:43:11.0587610Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T21:43:11.0588028Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:43:11.0588914Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:43:11.0590088Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:43:11.0591239Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:43:11.0592402Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:43:11.0593117Z 2025-03-04T21:43:11.0593372Z # No stacktrace found for following nodes 2025-03-04T21:43:11.0593951Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:43:11.0594487Z 2025-03-04T21:43:11.0595287Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:104 in shift_tokens_right, code: shifted_input_ids = input_ids.new_zeros(input_ids.shape) 2025-03-04T21:43:11.0596321Z shifted_input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.new_zeros((1, 1024)) 2025-03-04T21:43:11.0596769Z 2025-03-04T21:43:11.0597534Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:105 in shift_tokens_right, code: shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() 2025-03-04T21:43:11.0598674Z getitem: "i64[1, 1023][1024, 1]cuda:0" = l_cloned_inputs_labels_[(slice(None, None, None), slice(None, -1, None))]; l_cloned_inputs_labels_ = None 2025-03-04T21:43:11.0599364Z clone: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T21:43:11.0600032Z shifted_input_ids[(slice(None, None, None), slice(1, None, None))] = clone; setitem = shifted_input_ids; clone = setitem = None 2025-03-04T21:43:11.0600592Z 2025-03-04T21:43:11.0601352Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:106 in shift_tokens_right, code: shifted_input_ids[:, 0] = decoder_start_token_id 2025-03-04T21:43:11.0602368Z shifted_input_ids[(slice(None, None, None), 0)] = 2; setitem_1 = shifted_input_ids; setitem_1 = None 2025-03-04T21:43:11.0602855Z 2025-03-04T21:43:11.0603694Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:111 in shift_tokens_right, code: shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) 2025-03-04T21:43:11.0615801Z eq: "b8[1, 1024][1024, 1]cuda:0" = shifted_input_ids == -100 2025-03-04T21:43:11.0616441Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = shifted_input_ids.masked_fill_(eq, 1); shifted_input_ids = eq = masked_fill_ = None 2025-03-04T21:43:11.0616982Z 2025-03-04T21:43:11.0617742Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1145 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T21:43:11.0618828Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T21:43:11.0619340Z 2025-03-04T21:43:11.0620119Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1152 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:43:11.0621775Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:43:11.0623168Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:43:11.0623687Z 2025-03-04T21:43:11.0624339Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:43:11.0625270Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:43:11.0625766Z 2025-03-04T21:43:11.0626392Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:43:11.0627208Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:43:11.0627619Z 2025-03-04T21:43:11.0628348Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:43:11.0629227Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:43:11.0630402Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:43:11.0631476Z 2025-03-04T21:43:11.0632200Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1155 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T21:43:11.0633212Z embed_pos_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T21:43:11.0633716Z 2025-03-04T21:43:11.0634427Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1157 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:43:11.0635445Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T21:43:11.0635974Z 2025-03-04T21:43:11.0636724Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1158 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:43:11.0638963Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:43:11.0640664Z 2025-03-04T21:43:11.0641557Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1159 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:43:11.0642911Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:43:11.0643589Z 2025-03-04T21:43:11.0644279Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1191 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:43:11.0645093Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:43:11.0645435Z 2025-03-04T21:43:11.0646281Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1192 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:43:11.0647294Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:43:11.0647692Z 2025-03-04T21:43:11.0647939Z 2025-03-04T21:43:11.0648057Z class GraphModule(torch.nn.Module): 2025-03-04T21:43:11.0649923Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:43:11.0651808Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T21:43:11.0652233Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:43:11.0652979Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:43:11.0654066Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:43:11.0655207Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:43:11.0656360Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:43:11.0657071Z 2025-03-04T21:43:11.0657329Z # No stacktrace found for following nodes 2025-03-04T21:43:11.0657897Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:43:11.0658427Z 2025-03-04T21:43:11.0659216Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:104 in shift_tokens_right, code: shifted_input_ids = input_ids.new_zeros(input_ids.shape) 2025-03-04T21:43:11.0660229Z shifted_input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.new_zeros((1, 1024)) 2025-03-04T21:43:11.0660669Z 2025-03-04T21:43:11.0661727Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:105 in shift_tokens_right, code: shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() 2025-03-04T21:43:11.0662859Z getitem: "i64[1, 1023][1024, 1]cuda:0" = l_cloned_inputs_labels_[(slice(None, None, None), slice(None, -1, None))]; l_cloned_inputs_labels_ = None 2025-03-04T21:43:11.0663546Z clone: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T21:43:11.0664206Z shifted_input_ids[(slice(None, None, None), slice(1, None, None))] = clone; setitem = shifted_input_ids; clone = setitem = None 2025-03-04T21:43:11.0664755Z 2025-03-04T21:43:11.0665513Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:106 in shift_tokens_right, code: shifted_input_ids[:, 0] = decoder_start_token_id 2025-03-04T21:43:11.0666520Z shifted_input_ids[(slice(None, None, None), 0)] = 2; setitem_1 = shifted_input_ids; setitem_1 = None 2025-03-04T21:43:11.0666992Z 2025-03-04T21:43:11.0667819Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:111 in shift_tokens_right, code: shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) 2025-03-04T21:43:11.0668965Z eq: "b8[1, 1024][1024, 1]cuda:0" = shifted_input_ids == -100 2025-03-04T21:43:11.0669688Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = shifted_input_ids.masked_fill_(eq, 1); shifted_input_ids = eq = masked_fill_ = None 2025-03-04T21:43:11.0670226Z 2025-03-04T21:43:11.0670963Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1145 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T21:43:11.0671987Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T21:43:11.0672498Z 2025-03-04T21:43:11.0673265Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1152 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:43:11.0674931Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:43:11.0676198Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:43:11.0676650Z 2025-03-04T21:43:11.0677300Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:43:11.0678231Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:43:11.0678772Z 2025-03-04T21:43:11.0679391Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:43:11.0680201Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:43:11.0680603Z 2025-03-04T21:43:11.0681332Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:43:11.0682203Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:43:11.0683376Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:43:11.0684451Z 2025-03-04T21:43:11.0685172Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1155 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T21:43:11.0686184Z embed_pos_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T21:43:11.0686691Z 2025-03-04T21:43:11.0687380Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1157 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:43:11.0688512Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T21:43:11.0689031Z 2025-03-04T21:43:11.0689779Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1158 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:43:11.0692112Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:43:11.0693894Z 2025-03-04T21:43:11.0694779Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1159 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:43:11.0696129Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:43:11.0696801Z 2025-03-04T21:43:11.0697483Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1191 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:43:11.0698303Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:43:11.0698645Z 2025-03-04T21:43:11.0699395Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1192 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:43:11.0700328Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:43:11.0700725Z 2025-03-04T21:43:11.9524984Z 2025-03-04T21:43:11.9525397Z class GraphModule(torch.nn.Module): 2025-03-04T21:43:11.9527275Z def forward(self, L_labels_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:43:11.9529268Z l_labels_ = L_labels_ 2025-03-04T21:43:11.9529564Z l_input_ids_ = L_input_ids_ 2025-03-04T21:43:11.9530267Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:43:11.9531389Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:43:11.9532561Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:43:11.9533746Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:43:11.9534477Z 2025-03-04T21:43:11.9535292Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:104 in shift_tokens_right, code: shifted_input_ids = input_ids.new_zeros(input_ids.shape) 2025-03-04T21:43:11.9536281Z shifted_input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_labels_.new_zeros((1, 1024)) 2025-03-04T21:43:11.9536699Z 2025-03-04T21:43:11.9537467Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:105 in shift_tokens_right, code: shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() 2025-03-04T21:43:11.9538520Z getitem: "i64[1, 1023][1024, 1]cuda:0" = l_labels_[(slice(None, None, None), slice(None, -1, None))]; l_labels_ = None 2025-03-04T21:43:11.9539124Z clone: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T21:43:11.9540160Z shifted_input_ids[(slice(None, None, None), slice(1, None, None))] = clone; setitem = shifted_input_ids; clone = setitem = None 2025-03-04T21:43:11.9540872Z 2025-03-04T21:43:11.9541634Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:106 in shift_tokens_right, code: shifted_input_ids[:, 0] = decoder_start_token_id 2025-03-04T21:43:11.9542646Z shifted_input_ids[(slice(None, None, None), 0)] = 2; setitem_1 = shifted_input_ids; setitem_1 = None 2025-03-04T21:43:11.9543121Z 2025-03-04T21:43:11.9543950Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:111 in shift_tokens_right, code: shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) 2025-03-04T21:43:11.9544927Z eq: "b8[1, 1024][1024, 1]cuda:0" = shifted_input_ids == -100 2025-03-04T21:43:11.9545540Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = shifted_input_ids.masked_fill_(eq, 1); shifted_input_ids = eq = masked_fill_ = None 2025-03-04T21:43:11.9546094Z 2025-03-04T21:43:11.9546884Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1145 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T21:43:11.9547836Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T21:43:11.9548264Z 2025-03-04T21:43:11.9549040Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1152 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:43:11.9550698Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:43:11.9551972Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:43:11.9552430Z 2025-03-04T21:43:11.9553103Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:43:11.9554038Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:43:11.9554535Z 2025-03-04T21:43:11.9555159Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:43:11.9555976Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:43:11.9556389Z 2025-03-04T21:43:11.9557118Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:43:11.9558003Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:43:11.9559249Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:43:11.9560332Z 2025-03-04T21:43:11.9561322Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1155 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T21:43:11.9562346Z embed_pos_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T21:43:11.9562850Z 2025-03-04T21:43:11.9563693Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1157 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:43:11.9564826Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T21:43:11.9565358Z 2025-03-04T21:43:11.9566110Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1158 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:43:11.9568480Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:43:11.9570196Z 2025-03-04T21:43:11.9571080Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1159 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:43:11.9572447Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:43:11.9573130Z 2025-03-04T21:43:11.9573825Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1191 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:43:11.9574642Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:43:11.9574985Z 2025-03-04T21:43:11.9575774Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1192 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:43:11.9576722Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:43:11.9577127Z 2025-03-04T21:43:11.9577248Z 2025-03-04T21:43:11.9577377Z class GraphModule(torch.nn.Module): 2025-03-04T21:43:11.9579158Z def forward(self, L_labels_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:43:11.9580912Z l_labels_ = L_labels_ 2025-03-04T21:43:11.9581209Z l_input_ids_ = L_input_ids_ 2025-03-04T21:43:11.9581913Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:43:11.9583027Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:43:11.9584192Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:43:11.9585370Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:43:11.9586089Z 2025-03-04T21:43:11.9586976Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:104 in shift_tokens_right, code: shifted_input_ids = input_ids.new_zeros(input_ids.shape) 2025-03-04T21:43:11.9588035Z shifted_input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_labels_.new_zeros((1, 1024)) 2025-03-04T21:43:11.9588450Z 2025-03-04T21:43:11.9589265Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:105 in shift_tokens_right, code: shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() 2025-03-04T21:43:11.9590315Z getitem: "i64[1, 1023][1024, 1]cuda:0" = l_labels_[(slice(None, None, None), slice(None, -1, None))]; l_labels_ = None 2025-03-04T21:43:11.9590916Z clone: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T21:43:11.9591573Z shifted_input_ids[(slice(None, None, None), slice(1, None, None))] = clone; setitem = shifted_input_ids; clone = setitem = None 2025-03-04T21:43:11.9592122Z 2025-03-04T21:43:11.9592884Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:106 in shift_tokens_right, code: shifted_input_ids[:, 0] = decoder_start_token_id 2025-03-04T21:43:11.9593896Z shifted_input_ids[(slice(None, None, None), 0)] = 2; setitem_1 = shifted_input_ids; setitem_1 = None 2025-03-04T21:43:11.9594370Z 2025-03-04T21:43:11.9595200Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:111 in shift_tokens_right, code: shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) 2025-03-04T21:43:11.9596162Z eq: "b8[1, 1024][1024, 1]cuda:0" = shifted_input_ids == -100 2025-03-04T21:43:11.9596774Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = shifted_input_ids.masked_fill_(eq, 1); shifted_input_ids = eq = masked_fill_ = None 2025-03-04T21:43:11.9597314Z 2025-03-04T21:43:11.9598053Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1145 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T21:43:11.9599006Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T21:43:11.9599437Z 2025-03-04T21:43:11.9600212Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1152 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:43:11.9601874Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:43:11.9603133Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:43:11.9603581Z 2025-03-04T21:43:11.9604235Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:43:11.9605170Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:43:11.9605665Z 2025-03-04T21:43:11.9606290Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:43:11.9607106Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:43:11.9607514Z 2025-03-04T21:43:11.9608314Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:43:11.9609191Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:43:11.9610508Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:43:11.9611674Z 2025-03-04T21:43:11.9612406Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1155 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T21:43:11.9613425Z embed_pos_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T21:43:11.9613930Z 2025-03-04T21:43:11.9614631Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1157 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:43:11.9615654Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T21:43:11.9616190Z 2025-03-04T21:43:11.9616942Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1158 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:43:11.9619203Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:43:11.9620911Z 2025-03-04T21:43:11.9621801Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1159 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:43:11.9623167Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:43:11.9623845Z 2025-03-04T21:43:11.9624535Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1191 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:43:11.9625352Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:43:11.9625698Z 2025-03-04T21:43:11.9626452Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1192 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:43:11.9627398Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:43:11.9627800Z 2025-03-04T21:43:12.5771831Z 2025-03-04T21:43:12.5772725Z class GraphModule(torch.nn.Module): 2025-03-04T21:43:12.5774454Z def forward(self, L_decoder_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:43:12.5776237Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T21:43:12.5776589Z l_input_ids_ = L_input_ids_ 2025-03-04T21:43:12.5777191Z l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:43:12.5778567Z l_self_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:43:12.5779721Z l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:43:12.5780720Z l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:43:12.5781352Z 2025-03-04T21:43:12.5782118Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1145 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T21:43:12.5783068Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T21:43:12.5783497Z 2025-03-04T21:43:12.5784284Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1152 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:43:12.5785866Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:43:12.5787045Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:43:12.5787494Z 2025-03-04T21:43:12.5788151Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:43:12.5789093Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:43:12.5789588Z 2025-03-04T21:43:12.5790220Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:43:12.5791039Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:43:12.5791455Z 2025-03-04T21:43:12.5792178Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:43:12.5793052Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:43:12.5794146Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:43:12.5795137Z 2025-03-04T21:43:12.5795863Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1155 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T21:43:12.5796876Z embed_pos_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T21:43:12.5797376Z 2025-03-04T21:43:12.5798076Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1157 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:43:12.5799089Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T21:43:12.5799645Z 2025-03-04T21:43:12.5800415Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1158 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:43:12.5802593Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:43:12.5804203Z 2025-03-04T21:43:12.5805093Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1159 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:43:12.5806455Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:43:12.5807136Z 2025-03-04T21:43:12.5808021Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1191 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:43:12.5808848Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:43:12.5809189Z 2025-03-04T21:43:12.5809952Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1192 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:43:12.5810896Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:43:12.5811300Z 2025-03-04T21:43:12.7166576Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:43:12.7167382Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py", line 135, in forward 2025-03-04T21:43:12.7168146Z return super().forward(positions + self.offset) 2025-03-04T21:43:12.7168435Z 2025-03-04T21:43:14.3934926Z 2025-03-04T21:43:14.3935604Z class GraphModule(torch.nn.Module): 2025-03-04T21:43:14.3937465Z def forward(self, dict_getitem_L_stack0_list_dict_keys_L_stack0_0_: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0", L_decoder_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T21:43:14.3939452Z dict_getitem_l_stack0_list_dict_keys_l_stack0_0_ = dict_getitem_L_stack0_list_dict_keys_L_stack0_0_ 2025-03-04T21:43:14.3939998Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T21:43:14.3940665Z l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:43:14.3941616Z l_self_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:43:14.3942609Z l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:43:14.3943612Z l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:43:14.3944247Z 2025-03-04T21:43:14.3945005Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1364 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:43:14.3945963Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_decoder_input_ids_.view(-1, 1024); input_ids = None 2025-03-04T21:43:14.3946409Z 2025-03-04T21:43:14.3947568Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1375 in forward, code: inputs_embeds = self.embed_tokens(input) * self.embed_scale 2025-03-04T21:43:14.3949356Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(l_decoder_input_ids_, l_self_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_decoder_input_ids_ = l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:43:14.3950602Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:43:14.3951056Z 2025-03-04T21:43:14.3951718Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:131 in forward, code: positions = torch.arange( 2025-03-04T21:43:14.3952661Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:43:14.3953162Z 2025-03-04T21:43:14.3953798Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:133 in forward, code: ).expand(bsz, -1) 2025-03-04T21:43:14.3954617Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T21:43:14.3955029Z 2025-03-04T21:43:14.3955746Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:135 in forward, code: return super().forward(positions + self.offset) 2025-03-04T21:43:14.3956623Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T21:43:14.3957729Z positions_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:43:14.3958743Z 2025-03-04T21:43:14.3959475Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1416 in forward, code: positions = positions.to(inputs_embeds.device) 2025-03-04T21:43:14.3960576Z positions_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T21:43:14.3961239Z 2025-03-04T21:43:14.3961947Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1418 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T21:43:14.3962973Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + positions_2; inputs_embeds = positions_2 = None 2025-03-04T21:43:14.3963508Z 2025-03-04T21:43:14.3964265Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1419 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:43:14.3966344Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:43:14.3967972Z 2025-03-04T21:43:14.3968857Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1421 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:43:14.3970346Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:43:14.3971033Z 2025-03-04T21:43:14.3971827Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1450 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:43:14.3972644Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:43:14.3972988Z 2025-03-04T21:43:14.3973695Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py:1451 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T21:43:14.3974583Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:43:14.3974982Z 2025-03-04T21:43:35.6437313Z Compilation time (from dynamo_timed): 8.331946836 2025-03-04T21:43:35.6491786Z pass 2025-03-04T21:43:35.8536901Z TIMING: entire_frame_compile:6.39355 gc:0.01112 _recursive_pre_grad_passes:0.00682 _recursive_joint_graph_passes:0.57855 inductor_compile:3.94105 backend_compile:4.87428 _recursive_post_grad_passes:0.16086 async_compile.precompile:0.11409 async_compile.wait:0.71327 code_gen:2.34549 pad_mm_benchmark:0.03108 entire_backward_compile:1.93839 total_wall_time:8.33195 2025-03-04T21:43:35.8548191Z STATS: call_* op count: 93 | FakeTensorMode.__torch_dispatch__:8252 | FakeTensor.__torch_dispatch__:1327 | ProxyTorchDispatchMode.__torch_dispatch__:3844 2025-03-04T21:43:35.8549022Z Dynamo produced 7 graphs covering 93 ops with 8 graph breaks (5 unique) 2025-03-04T21:43:41.6251051Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:43:41.6252279Z warnings.warn( 2025-03-04T21:43:41.9567324Z 2025-03-04T21:43:44.2396716Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:43:44.2397276Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:43:44.2397866Z cuda train BertForMaskedLM 2025-03-04T21:44:17.5869082Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:44:17.5869987Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:44:17.5870711Z pred = mod(**cloned_inputs) 2025-03-04T21:44:17.5871353Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1360, in forward 2025-03-04T21:44:17.5872002Z outputs = self.bert( 2025-03-04T21:44:17.5872617Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1006, in forward 2025-03-04T21:44:17.5873278Z embedding_output = self.embeddings( 2025-03-04T21:44:17.5873929Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 232, in forward 2025-03-04T21:44:17.5874645Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:44:17.5874908Z 2025-03-04T21:44:17.7643640Z W0304 21:44:17.763000 12409 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:44:59.7603946Z Compilation time (from dynamo_timed): 68.682500033 2025-03-04T21:44:59.7627872Z pass 2025-03-04T21:44:59.8263632Z TIMING: entire_frame_compile:60.50408 gc:0.00578 _recursive_pre_grad_passes:0.03933 pad_mm_benchmark:0.40103 _recursive_joint_graph_passes:1.75184 _recursive_post_grad_passes:0.61461 async_compile.wait:3.25533 code_gen:18.79041 inductor_compile:33.55428 backend_compile:47.57691 entire_backward_compile:8.17842 total_wall_time:68.6825 2025-03-04T21:44:59.8268617Z STATS: call_* op count: 1401 | FakeTensorMode.__torch_dispatch__:62964 | FakeTensor.__torch_dispatch__:14265 | ProxyTorchDispatchMode.__torch_dispatch__:28569 2025-03-04T21:44:59.8269448Z Dynamo produced 2 graphs covering 1401 ops with 5 graph breaks (4 unique) 2025-03-04T21:45:08.0518856Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:45:08.0520267Z warnings.warn( 2025-03-04T21:45:08.7877445Z 2025-03-04T21:45:10.8293134Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:45:10.8293504Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:45:10.8293857Z cuda train BertForQuestionAnswering 2025-03-04T21:45:43.2314232Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:45:43.2315114Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:45:43.2315844Z pred = mod(**cloned_inputs) 2025-03-04T21:45:43.2316537Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1846, in forward 2025-03-04T21:45:43.2317210Z outputs = self.bert( 2025-03-04T21:45:43.2317824Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1006, in forward 2025-03-04T21:45:43.2318487Z embedding_output = self.embeddings( 2025-03-04T21:45:43.2319137Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 232, in forward 2025-03-04T21:45:43.2319819Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:45:43.2320076Z 2025-03-04T21:45:43.4038044Z W0304 21:45:43.402000 12723 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:46:22.8163343Z Compilation time (from dynamo_timed): 65.47684723 2025-03-04T21:46:22.8192754Z pass 2025-03-04T21:46:22.8854874Z TIMING: entire_frame_compile:57.79833 gc:0.00408 _recursive_pre_grad_passes:0.04109 pad_mm_benchmark:0.37992 _recursive_joint_graph_passes:1.72025 _recursive_post_grad_passes:0.62167 async_compile.wait:1.09379 code_gen:16.39898 inductor_compile:30.95297 backend_compile:45.09674 entire_backward_compile:7.67851 total_wall_time:65.47685 2025-03-04T21:46:22.8856601Z STATS: call_* op count: 1393 | FakeTensorMode.__torch_dispatch__:62458 | FakeTensor.__torch_dispatch__:14108 | ProxyTorchDispatchMode.__torch_dispatch__:28358 2025-03-04T21:46:22.8857455Z Dynamo produced 2 graphs covering 1393 ops with 5 graph breaks (4 unique) 2025-03-04T21:46:31.1210710Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:46:31.1212902Z warnings.warn( 2025-03-04T21:46:31.6000669Z 2025-03-04T21:47:00.2614939Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:47:00.2615355Z loading model: 0it [00:28, ?it/s] 2025-03-04T21:47:00.2615708Z cuda train BlenderbotForCausalLM 2025-03-04T21:47:00.2623043Z Traceback (most recent call last): 2025-03-04T21:47:00.2623680Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model 2025-03-04T21:47:00.2624328Z self.model_iter_fn(model, example_inputs) 2025-03-04T21:47:00.2625057Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in forward_and_backward_pass 2025-03-04T21:47:00.2625764Z pred = mod(**cloned_inputs) 2025-03-04T21:47:00.2626470Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:47:00.2627214Z return self._call_impl(*args, **kwargs) 2025-03-04T21:47:00.2627894Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:47:00.2628588Z return forward_call(*args, **kwargs) 2025-03-04T21:47:00.2629760Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 1531, in forward 2025-03-04T21:47:00.2630646Z outputs = self.model.decoder( 2025-03-04T21:47:00.2631266Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:47:00.2631904Z return self._call_impl(*args, **kwargs) 2025-03-04T21:47:00.2632500Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:47:00.2633094Z return forward_call(*args, **kwargs) 2025-03-04T21:47:00.2633797Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 997, in forward 2025-03-04T21:47:00.2634501Z layer_outputs = decoder_layer( 2025-03-04T21:47:00.2635114Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:47:00.2635760Z return self._call_impl(*args, **kwargs) 2025-03-04T21:47:00.2636357Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:47:00.2636951Z return forward_call(*args, **kwargs) 2025-03-04T21:47:00.2637650Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 397, in forward 2025-03-04T21:47:00.2638454Z hidden_states, self_attn_weights, present_key_value = self.self_attn( 2025-03-04T21:47:00.2639196Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:47:00.2639835Z return self._call_impl(*args, **kwargs) 2025-03-04T21:47:00.2640429Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:47:00.2641031Z return forward_call(*args, **kwargs) 2025-03-04T21:47:00.2641736Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot/modeling_blenderbot.py", line 152, in forward 2025-03-04T21:47:00.2642499Z query_states = self.q_proj(hidden_states) * self.scaling 2025-03-04T21:47:00.2643182Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:47:00.2643814Z return self._call_impl(*args, **kwargs) 2025-03-04T21:47:00.2644405Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:47:00.2645008Z return forward_call(*args, **kwargs) 2025-03-04T21:47:00.2645572Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 125, in forward 2025-03-04T21:47:00.2646176Z return F.linear(input, self.weight, self.bias) 2025-03-04T21:47:00.2648317Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 14.00 MiB. GPU 0 has a total capacity of 21.98 GiB of which 6.44 MiB is free. Process 68396 has 21.96 GiB memory in use. Of the allocated memory 21.61 GiB is allocated by PyTorch, and 27.77 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 2025-03-04T21:47:00.2650146Z 2025-03-04T21:47:00.2650366Z The above exception was the direct cause of the following exception: 2025-03-04T21:47:00.2650674Z 2025-03-04T21:47:00.2650801Z Traceback (most recent call last): 2025-03-04T21:47:00.2651278Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run 2025-03-04T21:47:00.2651755Z ) = runner.load_model( 2025-03-04T21:47:00.2652249Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 458, in load_model 2025-03-04T21:47:00.2652810Z self.validate_model(model, example_inputs) 2025-03-04T21:47:00.2653442Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model 2025-03-04T21:47:00.2654073Z raise RuntimeError("Eager run failed") from e 2025-03-04T21:47:00.2654429Z RuntimeError: Eager run failed 2025-03-04T21:47:00.2654621Z 2025-03-04T21:47:00.2654719Z eager_fail_to_run 2025-03-04T21:47:04.7079817Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:47:04.7081107Z warnings.warn( 2025-03-04T21:47:05.1478279Z 2025-03-04T21:47:06.6584142Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:47:06.6584573Z loading model: 0it [00:01, ?it/s] 2025-03-04T21:47:06.6584924Z cuda train BlenderbotSmallForCausalLM 2025-03-04T21:47:06.6795135Z WARNING:common:fp64 golden ref were not generated for BlenderbotSmallForCausalLM. Setting accuracy check to cosine 2025-03-04T21:47:08.4015283Z 2025-03-04T21:47:08.4015955Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:08.4017902Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:08.4019720Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:47:08.4020486Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:08.4021611Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:08.4022779Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:08.4023955Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:08.4024673Z 2025-03-04T21:47:08.4024932Z # No stacktrace found for following nodes 2025-03-04T21:47:08.4025512Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:47:08.4026033Z 2025-03-04T21:47:08.4026871Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:919 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:08.4027977Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T21:47:08.4028474Z 2025-03-04T21:47:08.4029335Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:929 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:08.4031061Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:08.4032274Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:08.4032697Z 2025-03-04T21:47:08.4033846Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T21:47:08.4035088Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T21:47:08.4035641Z 2025-03-04T21:47:08.4036601Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T21:47:08.4037560Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T21:47:08.4037990Z 2025-03-04T21:47:08.4038791Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T21:47:08.4039797Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T21:47:08.4040182Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T21:47:08.4040643Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T21:47:08.4041194Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T21:47:08.4041633Z 2025-03-04T21:47:08.4042280Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T21:47:08.4043087Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T21:47:08.4043475Z 2025-03-04T21:47:08.4044304Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T21:47:08.4045472Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T21:47:08.4046268Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T21:47:08.4046782Z 2025-03-04T21:47:08.4047524Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:08.4048726Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:08.4049230Z 2025-03-04T21:47:08.4049994Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:08.4051660Z positions_1: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:08.4052762Z 2025-03-04T21:47:08.4053604Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:946 in forward, code: inputs_embeds = self.layernorm_embedding(inputs_embeds) 2025-03-04T21:47:08.4056071Z inputs_embeds_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(inputs_embeds, (512,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); inputs_embeds = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:08.4057811Z 2025-03-04T21:47:08.4058601Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:947 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T21:47:08.4059699Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds_1 + positions_1; inputs_embeds_1 = positions_1 = None 2025-03-04T21:47:08.4060217Z 2025-03-04T21:47:08.4061565Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:949 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:08.4062996Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T21:47:08.4063651Z 2025-03-04T21:47:08.4064426Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:977 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:08.4065334Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:08.4065675Z 2025-03-04T21:47:08.4066465Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:978 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T21:47:08.4067452Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T21:47:08.4067862Z 2025-03-04T21:47:08.4068001Z 2025-03-04T21:47:08.4068128Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:08.4069836Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:08.4071577Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:47:08.4072337Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:08.4073434Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:08.4074598Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:08.4075771Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:08.4076491Z 2025-03-04T21:47:08.4076746Z # No stacktrace found for following nodes 2025-03-04T21:47:08.4077323Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:47:08.4077859Z 2025-03-04T21:47:08.4078676Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:919 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:08.4079975Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T21:47:08.4080489Z 2025-03-04T21:47:08.4081460Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:929 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:08.4083191Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:08.4084410Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:08.4084845Z 2025-03-04T21:47:08.4085668Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T21:47:08.4086823Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T21:47:08.4087337Z 2025-03-04T21:47:08.4088187Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T21:47:08.4089154Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T21:47:08.4089591Z 2025-03-04T21:47:08.4090384Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T21:47:08.4091277Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T21:47:08.4091673Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T21:47:08.4092142Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T21:47:08.4092700Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T21:47:08.4093148Z 2025-03-04T21:47:08.4093795Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T21:47:08.4094611Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T21:47:08.4095003Z 2025-03-04T21:47:08.4095825Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T21:47:08.4096999Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T21:47:08.4097800Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T21:47:08.4098316Z 2025-03-04T21:47:08.4099058Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:08.4100093Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:08.4100595Z 2025-03-04T21:47:08.4101357Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:08.4103152Z positions_1: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:08.4104360Z 2025-03-04T21:47:08.4105221Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:946 in forward, code: inputs_embeds = self.layernorm_embedding(inputs_embeds) 2025-03-04T21:47:08.4107606Z inputs_embeds_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(inputs_embeds, (512,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); inputs_embeds = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:08.4109331Z 2025-03-04T21:47:08.4110136Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:947 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T21:47:08.4111243Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds_1 + positions_1; inputs_embeds_1 = positions_1 = None 2025-03-04T21:47:08.4111764Z 2025-03-04T21:47:08.4112736Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:949 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:08.4114153Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T21:47:08.4114807Z 2025-03-04T21:47:08.4115591Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:977 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:08.4116553Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:08.4116895Z 2025-03-04T21:47:08.4117683Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:978 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T21:47:08.4118670Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T21:47:08.4119087Z 2025-03-04T21:47:09.2897312Z 2025-03-04T21:47:09.2898069Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:09.2900390Z def forward(self, L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:09.2902249Z l_input_ids_ = L_input_ids_ 2025-03-04T21:47:09.2902976Z l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:09.2904139Z l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:09.2905355Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:09.2906938Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:09.2907824Z 2025-03-04T21:47:09.2908680Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:919 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:09.2909717Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T21:47:09.2910137Z 2025-03-04T21:47:09.2911010Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:929 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:09.2912759Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:09.2913993Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:09.2914425Z 2025-03-04T21:47:09.2915241Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T21:47:09.2916344Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T21:47:09.2916851Z 2025-03-04T21:47:09.2917593Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T21:47:09.2918553Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T21:47:09.2918988Z 2025-03-04T21:47:09.2919777Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T21:47:09.2920660Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T21:47:09.2921044Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T21:47:09.2921507Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T21:47:09.2922055Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T21:47:09.2922495Z 2025-03-04T21:47:09.2923144Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T21:47:09.2923957Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T21:47:09.2924345Z 2025-03-04T21:47:09.2925173Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T21:47:09.2926341Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T21:47:09.2927141Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T21:47:09.2927660Z 2025-03-04T21:47:09.2928780Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:09.2929819Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:09.2930528Z 2025-03-04T21:47:09.2931291Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:09.2932968Z positions_1: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:09.2934081Z 2025-03-04T21:47:09.2934929Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:946 in forward, code: inputs_embeds = self.layernorm_embedding(inputs_embeds) 2025-03-04T21:47:09.2937259Z inputs_embeds_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(inputs_embeds, (512,), l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); inputs_embeds = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:09.2938956Z 2025-03-04T21:47:09.2939753Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:947 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T21:47:09.2949427Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds_1 + positions_1; inputs_embeds_1 = positions_1 = None 2025-03-04T21:47:09.2950002Z 2025-03-04T21:47:09.2951011Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:949 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:09.2952463Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T21:47:09.2953123Z 2025-03-04T21:47:09.2953912Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:977 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:09.2954824Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:09.2955168Z 2025-03-04T21:47:09.2955971Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:978 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T21:47:09.2956969Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T21:47:09.2957397Z 2025-03-04T21:47:14.3569066Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:47:14.3570024Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py", line 90, in forward 2025-03-04T21:47:14.3570783Z return super().forward(positions) 2025-03-04T21:47:14.3571003Z 2025-03-04T21:47:18.9948353Z Compilation time (from dynamo_timed): 5.847465949 2025-03-04T21:47:18.9959801Z pass 2025-03-04T21:47:19.0228960Z TIMING: entire_frame_compile:4.53929 gc:0.00737 _recursive_pre_grad_passes:0.00614 _recursive_joint_graph_passes:0.43886 inductor_compile:2.88573 backend_compile:3.61877 _recursive_post_grad_passes:0.08006 async_compile.precompile:0.23085 async_compile.wait:0.73579 code_gen:1.95073 pad_mm_benchmark:0.20117 entire_backward_compile:1.30818 total_wall_time:5.84747 2025-03-04T21:47:19.0230927Z STATS: call_* op count: 58 | FakeTensorMode.__torch_dispatch__:4460 | FakeTensor.__torch_dispatch__:672 | ProxyTorchDispatchMode.__torch_dispatch__:1896 2025-03-04T21:47:19.0231745Z Dynamo produced 6 graphs covering 58 ops with 6 graph breaks (5 unique) 2025-03-04T21:47:24.6015164Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:47:24.6016383Z warnings.warn( 2025-03-04T21:47:24.8401838Z 2025-03-04T21:47:26.9754201Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:47:26.9754895Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:47:26.9755636Z cuda train BlenderbotSmallForConditionalGeneration 2025-03-04T21:47:27.0582474Z WARNING:common:fp64 golden ref were not generated for BlenderbotSmallForConditionalGeneration. Setting accuracy check to cosine 2025-03-04T21:47:29.1553300Z 2025-03-04T21:47:29.1554133Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:29.1557605Z def forward(self, L_cloned_inputs_labels_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:29.1559688Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T21:47:29.1560183Z l_cloned_inputs_decoder_input_ids_ = L_cloned_inputs_decoder_input_ids_ 2025-03-04T21:47:29.1560757Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:47:29.1561822Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:29.1562962Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:29.1564154Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:29.1565353Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:29.1566091Z 2025-03-04T21:47:29.1566363Z # No stacktrace found for following nodes 2025-03-04T21:47:29.1566961Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:47:29.1567513Z 2025-03-04T21:47:29.1568632Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:714 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:29.1569915Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T21:47:29.1570429Z 2025-03-04T21:47:29.1571379Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:721 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:29.1573609Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:29.1575009Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:29.1575452Z 2025-03-04T21:47:29.1576221Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:29.1577326Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:29.1577839Z 2025-03-04T21:47:29.1578625Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:29.1580312Z embed_pos: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:29.1581440Z 2025-03-04T21:47:29.1582244Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:725 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:47:29.1583333Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T21:47:29.1583842Z 2025-03-04T21:47:29.1584689Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:726 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:47:29.1587047Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (512,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:29.1588990Z 2025-03-04T21:47:29.1590073Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:727 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:29.1591529Z hidden_states_2: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:47:29.1592204Z 2025-03-04T21:47:29.1593006Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:750 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:29.1593936Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:29.1594295Z 2025-03-04T21:47:29.1595156Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:751 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:47:29.1596197Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:47:29.1596608Z 2025-03-04T21:47:29.1596769Z 2025-03-04T21:47:29.1596906Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:29.1599048Z def forward(self, L_cloned_inputs_labels_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:29.1601144Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T21:47:29.1601615Z l_cloned_inputs_decoder_input_ids_ = L_cloned_inputs_decoder_input_ids_ 2025-03-04T21:47:29.1602107Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:47:29.1602871Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:29.1603981Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:29.1605145Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:29.1606315Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:29.1607031Z 2025-03-04T21:47:29.1607289Z # No stacktrace found for following nodes 2025-03-04T21:47:29.1608075Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:47:29.1608614Z 2025-03-04T21:47:29.1609450Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:714 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:29.1610578Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T21:47:29.1611092Z 2025-03-04T21:47:29.1611976Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:721 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:29.1613730Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:29.1614969Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:29.1615407Z 2025-03-04T21:47:29.1616178Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:29.1617282Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:29.1617792Z 2025-03-04T21:47:29.1618579Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:29.1620255Z embed_pos: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:29.1621366Z 2025-03-04T21:47:29.1622350Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:725 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:47:29.1623530Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T21:47:29.1624032Z 2025-03-04T21:47:29.1624881Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:726 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:47:29.1627224Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (512,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:29.1628919Z 2025-03-04T21:47:29.1629903Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:727 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:29.1631345Z hidden_states_2: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:47:29.1632013Z 2025-03-04T21:47:29.1632793Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:750 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:29.1633703Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:29.1634053Z 2025-03-04T21:47:29.1634908Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:751 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:47:29.1635952Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:47:29.1636356Z 2025-03-04T21:47:29.1636484Z 2025-03-04T21:47:29.1636607Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:29.1638629Z def forward(self, L_cloned_inputs_labels_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:29.1640667Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T21:47:29.1641144Z l_cloned_inputs_decoder_input_ids_ = L_cloned_inputs_decoder_input_ids_ 2025-03-04T21:47:29.1641648Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T21:47:29.1642417Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:29.1643526Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:29.1644681Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:29.1645934Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:29.1646731Z 2025-03-04T21:47:29.1647039Z # No stacktrace found for following nodes 2025-03-04T21:47:29.1647618Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T21:47:29.1648283Z 2025-03-04T21:47:29.1649108Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:714 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:29.1650230Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T21:47:29.1650735Z 2025-03-04T21:47:29.1651622Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:721 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:29.1653372Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:29.1654614Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:29.1655053Z 2025-03-04T21:47:29.1655813Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:29.1656852Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:29.1657373Z 2025-03-04T21:47:29.1658174Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:29.1659857Z embed_pos: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:29.1660969Z 2025-03-04T21:47:29.1662253Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:725 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:47:29.1663339Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T21:47:29.1663840Z 2025-03-04T21:47:29.1664695Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:726 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:47:29.1667030Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (512,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:29.1668714Z 2025-03-04T21:47:29.1669857Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:727 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:29.1671294Z hidden_states_2: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:47:29.1672068Z 2025-03-04T21:47:29.1672848Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:750 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:29.1673754Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:29.1674099Z 2025-03-04T21:47:29.1674950Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:751 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:47:29.1675986Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:47:29.1676390Z 2025-03-04T21:47:30.0458136Z 2025-03-04T21:47:30.0458873Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:30.0461579Z def forward(self, L_labels_: "i64[1, 128][128, 1]cuda:0", L_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:30.0463824Z l_labels_ = L_labels_ 2025-03-04T21:47:30.0464158Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T21:47:30.0464590Z l_input_ids_ = L_input_ids_ 2025-03-04T21:47:30.0465379Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:30.0466708Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:30.0468164Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:30.0469449Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:30.0470267Z 2025-03-04T21:47:30.0471193Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:714 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:30.0472387Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T21:47:30.0472820Z 2025-03-04T21:47:30.0473783Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:721 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:30.0475771Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:30.0477105Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:30.0477624Z 2025-03-04T21:47:30.0478815Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:30.0480037Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:30.0480761Z 2025-03-04T21:47:30.0481606Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:30.0483474Z embed_pos: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:30.0484677Z 2025-03-04T21:47:30.0485565Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:725 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:47:30.0486781Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T21:47:30.0487305Z 2025-03-04T21:47:30.0488424Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:726 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:47:30.0491016Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (512,), l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:30.0492906Z 2025-03-04T21:47:30.0493975Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:727 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:30.0495589Z hidden_states_2: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:47:30.0496333Z 2025-03-04T21:47:30.0497193Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:750 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:30.0498188Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:30.0498550Z 2025-03-04T21:47:30.0499471Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:751 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:47:30.0500597Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:47:30.0501005Z 2025-03-04T21:47:30.0501207Z 2025-03-04T21:47:30.0501361Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:30.0503244Z def forward(self, L_labels_: "i64[1, 128][128, 1]cuda:0", L_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:30.0505187Z l_labels_ = L_labels_ 2025-03-04T21:47:30.0505621Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T21:47:30.0506057Z l_input_ids_ = L_input_ids_ 2025-03-04T21:47:30.0506766Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:30.0508001Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:30.0509183Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:30.0510374Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:30.0511103Z 2025-03-04T21:47:30.0512096Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:714 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:30.0513332Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T21:47:30.0513807Z 2025-03-04T21:47:30.0514835Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:721 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:30.0516570Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:30.0517798Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:30.0518233Z 2025-03-04T21:47:30.0518981Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:30.0520014Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:30.0520512Z 2025-03-04T21:47:30.0521275Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:30.0522935Z embed_pos: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:30.0524040Z 2025-03-04T21:47:30.0524832Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:725 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:47:30.0525904Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T21:47:30.0526399Z 2025-03-04T21:47:30.0527235Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:726 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:47:30.0529931Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (512,), l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:30.0531710Z 2025-03-04T21:47:30.0532683Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:727 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:30.0534116Z hidden_states_2: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:47:30.0534781Z 2025-03-04T21:47:30.0535557Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:750 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:30.0536468Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:30.0536820Z 2025-03-04T21:47:30.0537668Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:751 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:47:30.0538697Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:47:30.0539102Z 2025-03-04T21:47:30.1365212Z 2025-03-04T21:47:30.1366011Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:30.1367668Z def forward(self, L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_self_modules_encoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_self_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:30.1369422Z l_input_ids_ = L_input_ids_ 2025-03-04T21:47:30.1370048Z l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:30.1371005Z l_self_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:30.1372009Z l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:30.1373023Z l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:30.1373662Z 2025-03-04T21:47:30.1374518Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:714 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:30.1375554Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T21:47:30.1375976Z 2025-03-04T21:47:30.1376855Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:721 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:30.1378568Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:30.1379719Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:30.1380148Z 2025-03-04T21:47:30.1382121Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:30.1383314Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:30.1383819Z 2025-03-04T21:47:30.1384585Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:30.1386150Z embed_pos: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:30.1387163Z 2025-03-04T21:47:30.1388011Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:725 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T21:47:30.1389092Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T21:47:30.1389585Z 2025-03-04T21:47:30.1390422Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:726 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T21:47:30.1392554Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (512,), l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:30.1394046Z 2025-03-04T21:47:30.1395019Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:727 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:30.1396442Z hidden_states_2: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T21:47:30.1397094Z 2025-03-04T21:47:30.1397866Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:750 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:30.1398765Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:30.1399100Z 2025-03-04T21:47:30.1399944Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:751 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T21:47:30.1400980Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T21:47:30.1401381Z 2025-03-04T21:47:30.2902521Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:47:30.2903419Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py", line 90, in forward 2025-03-04T21:47:30.2904179Z return super().forward(positions) 2025-03-04T21:47:30.2904392Z 2025-03-04T21:47:32.4317035Z 2025-03-04T21:47:32.4317727Z class GraphModule(torch.nn.Module): 2025-03-04T21:47:32.4321092Z def forward(self, dict_getitem_L_stack0_list_dict_keys_L_stack0_0_: "f32[1, 128, 512][65536, 512, 1]cuda:0", L_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 512][512, 1]cuda:0", L_self_modules_decoder_modules_embed_positions_parameters_weight_: "f32[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[512][1]cuda:0", L_self_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[512][1]cuda:0"): 2025-03-04T21:47:32.4323230Z dict_getitem_l_stack0_list_dict_keys_l_stack0_0_ = dict_getitem_L_stack0_list_dict_keys_L_stack0_0_ 2025-03-04T21:47:32.4323777Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T21:47:32.4324457Z l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T21:47:32.4325401Z l_self_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T21:47:32.4326391Z l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T21:47:32.4327400Z l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T21:47:32.4328222Z 2025-03-04T21:47:32.4329072Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:919 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T21:47:32.4330148Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_decoder_input_ids_.view(-1, 128); l_decoder_input_ids_ = None 2025-03-04T21:47:32.4330616Z 2025-03-04T21:47:32.4331487Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:929 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T21:47:32.4333140Z embedding: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_decoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T21:47:32.4334286Z inputs_embeds: "f32[1, 128, 512][65536, 512, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T21:47:32.4334724Z 2025-03-04T21:47:32.4335543Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T21:47:32.4336652Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T21:47:32.4337160Z 2025-03-04T21:47:32.4337910Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T21:47:32.4338877Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T21:47:32.4339318Z 2025-03-04T21:47:32.4340116Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T21:47:32.4341007Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T21:47:32.4341403Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T21:47:32.4341875Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T21:47:32.4342430Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T21:47:32.4342876Z 2025-03-04T21:47:32.4343613Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T21:47:32.4344429Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T21:47:32.4344894Z 2025-03-04T21:47:32.4345725Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T21:47:32.4346899Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T21:47:32.4347701Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T21:47:32.4348227Z 2025-03-04T21:47:32.4348979Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:87 in forward, code: positions = torch.arange( 2025-03-04T21:47:32.4350025Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T21:47:32.4350532Z 2025-03-04T21:47:32.4351295Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:90 in forward, code: return super().forward(positions) 2025-03-04T21:47:32.4352881Z positions_1: "f32[128, 512][512, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T21:47:32.4353905Z 2025-03-04T21:47:32.4354751Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:946 in forward, code: inputs_embeds = self.layernorm_embedding(inputs_embeds) 2025-03-04T21:47:32.4356899Z inputs_embeds_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.layer_norm(inputs_embeds, (512,), l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); inputs_embeds = l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T21:47:32.4358407Z 2025-03-04T21:47:32.4359205Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:947 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T21:47:32.4360371Z hidden_states: "f32[1, 128, 512][65536, 512, 1]cuda:0" = inputs_embeds_1 + positions_1; inputs_embeds_1 = positions_1 = None 2025-03-04T21:47:32.4369206Z 2025-03-04T21:47:32.4370225Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:949 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T21:47:32.4371664Z hidden_states_1: "f32[1, 128, 512][65536, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T21:47:32.4372324Z 2025-03-04T21:47:32.4373103Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:977 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T21:47:32.4374008Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T21:47:32.4374352Z 2025-03-04T21:47:32.4375142Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/blenderbot_small/modeling_blenderbot_small.py:978 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T21:47:32.4376295Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T21:47:32.4376814Z 2025-03-04T21:47:48.8573911Z Compilation time (from dynamo_timed): 8.764620777 2025-03-04T21:47:48.8596972Z pass 2025-03-04T21:47:48.8951883Z TIMING: entire_frame_compile:6.70876 gc:0.00934 _recursive_pre_grad_passes:0.00727 _recursive_joint_graph_passes:0.40277 inductor_compile:4.29981 backend_compile:5.12798 async_compile.precompile:0.03312 async_compile.wait:0.76337 pad_mm_benchmark:0.04188 _recursive_post_grad_passes:0.16238 code_gen:2.55711 entire_backward_compile:2.05586 total_wall_time:8.76462 2025-03-04T21:47:48.8955388Z STATS: call_* op count: 121 | FakeTensorMode.__torch_dispatch__:9082 | FakeTensor.__torch_dispatch__:1409 | ProxyTorchDispatchMode.__torch_dispatch__:4238 2025-03-04T21:47:48.8957011Z Dynamo produced 7 graphs covering 121 ops with 8 graph breaks (5 unique) 2025-03-04T21:47:54.6207213Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:47:54.6208596Z warnings.warn( 2025-03-04T21:47:54.8735439Z 2025-03-04T21:47:57.0137239Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:47:57.0137625Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:47:57.0137955Z cuda train CamemBert 2025-03-04T21:48:30.1587956Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:48:30.1588964Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:48:30.1589773Z pred = mod(**cloned_inputs) 2025-03-04T21:48:30.1590540Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/camembert/modeling_camembert.py", line 979, in forward 2025-03-04T21:48:30.1591329Z outputs = self.roberta( 2025-03-04T21:48:30.1592122Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/camembert/modeling_camembert.py", line 881, in forward 2025-03-04T21:48:30.1592969Z embedding_output = self.embeddings( 2025-03-04T21:48:30.1593777Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/camembert/modeling_camembert.py", line 139, in forward 2025-03-04T21:48:30.1594629Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:48:30.1594971Z 2025-03-04T21:48:30.3375216Z W0304 21:48:30.336000 13507 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:49:12.6215482Z Compilation time (from dynamo_timed): 68.717801527 2025-03-04T21:49:12.6242756Z pass 2025-03-04T21:49:12.6895470Z TIMING: entire_frame_compile:60.89584 gc:0.00508 _recursive_pre_grad_passes:0.03977 pad_mm_benchmark:0.40874 _recursive_joint_graph_passes:1.77035 _recursive_post_grad_passes:0.62612 async_compile.wait:3.03305 code_gen:18.56774 inductor_compile:33.44489 backend_compile:47.95504 entire_backward_compile:7.82196 total_wall_time:68.7178 2025-03-04T21:49:12.6897181Z STATS: call_* op count: 1409 | FakeTensorMode.__torch_dispatch__:63083 | FakeTensor.__torch_dispatch__:14283 | ProxyTorchDispatchMode.__torch_dispatch__:28617 2025-03-04T21:49:12.6898021Z Dynamo produced 2 graphs covering 1409 ops with 5 graph breaks (4 unique) 2025-03-04T21:49:20.9406117Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:49:20.9407315Z warnings.warn( 2025-03-04T21:49:21.2068611Z 2025-03-04T21:49:23.7555250Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:49:23.7556135Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:49:23.7557708Z cuda train DebertaForMaskedLM 2025-03-04T21:49:58.5342172Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:49:58.5344486Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:49:58.5345928Z pred = mod(**cloned_inputs) 2025-03-04T21:49:58.5347170Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta/modeling_deberta.py", line 1062, in forward 2025-03-04T21:49:58.5347865Z outputs = self.deberta( 2025-03-04T21:49:58.5348516Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta/modeling_deberta.py", line 966, in forward 2025-03-04T21:49:58.5349207Z embedding_output = self.embeddings( 2025-03-04T21:49:58.5349894Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta/modeling_deberta.py", line 780, in forward 2025-03-04T21:49:58.5350605Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:49:58.5350877Z 2025-03-04T21:49:58.6989010Z W0304 21:49:58.698000 13800 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:50:34.4191607Z Compilation time (from dynamo_timed): 63.876960823000005 2025-03-04T21:50:34.4221884Z pass 2025-03-04T21:50:34.4815702Z TIMING: entire_frame_compile:56.06094 gc:0.00426 _recursive_pre_grad_passes:0.03998 pad_mm_benchmark:0.8543 _recursive_joint_graph_passes:2.072 _recursive_post_grad_passes:0.88586 async_compile.wait:4.27624 code_gen:18.02486 inductor_compile:32.1233 backend_compile:43.78791 entire_backward_compile:7.81602 total_wall_time:63.87696 2025-03-04T21:50:34.4817552Z STATS: call_* op count: 1650 | FakeTensorMode.__torch_dispatch__:64524 | FakeTensor.__torch_dispatch__:13084 | ProxyTorchDispatchMode.__torch_dispatch__:29501 2025-03-04T21:50:34.4818398Z Dynamo produced 2 graphs covering 1650 ops with 5 graph breaks (4 unique) 2025-03-04T21:50:42.5497062Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:50:42.5498268Z warnings.warn( 2025-03-04T21:50:42.9907334Z 2025-03-04T21:50:45.1588812Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:50:45.1589289Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:50:45.1589745Z cuda train DebertaForQuestionAnswering 2025-03-04T21:51:18.4223635Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:51:18.4224517Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:51:18.4225248Z pred = mod(**cloned_inputs) 2025-03-04T21:51:18.4225980Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta/modeling_deberta.py", line 1388, in forward 2025-03-04T21:51:18.4226672Z outputs = self.deberta( 2025-03-04T21:51:18.4227334Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta/modeling_deberta.py", line 966, in forward 2025-03-04T21:51:18.4228028Z embedding_output = self.embeddings( 2025-03-04T21:51:18.4228707Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta/modeling_deberta.py", line 780, in forward 2025-03-04T21:51:18.4229421Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:51:18.4229682Z 2025-03-04T21:51:18.5752719Z W0304 21:51:18.574000 14203 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:51:51.1434573Z Compilation time (from dynamo_timed): 59.804198446 2025-03-04T21:51:51.1457056Z pass 2025-03-04T21:51:51.2152199Z TIMING: entire_frame_compile:52.17906 gc:0.00475 _recursive_pre_grad_passes:0.03976 pad_mm_benchmark:0.63585 _recursive_joint_graph_passes:1.83189 _recursive_post_grad_passes:0.88768 async_compile.wait:1.48237 code_gen:15.00744 inductor_compile:28.785 backend_compile:40.12802 entire_backward_compile:7.62514 total_wall_time:59.8042 2025-03-04T21:51:51.2156937Z STATS: call_* op count: 1642 | FakeTensorMode.__torch_dispatch__:63976 | ProxyTorchDispatchMode.__torch_dispatch__:29281 | FakeTensor.__torch_dispatch__:12915 2025-03-04T21:51:51.2158166Z Dynamo produced 2 graphs covering 1642 ops with 5 graph breaks (4 unique) 2025-03-04T21:51:59.2967035Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:51:59.2968692Z warnings.warn( 2025-03-04T21:51:59.5952473Z 2025-03-04T21:52:13.7043617Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:52:13.7044323Z loading model: 0it [00:14, ?it/s] 2025-03-04T21:52:13.7045050Z cuda train DebertaV2ForMaskedLM 2025-03-04T21:52:13.7301245Z Compilation time (from dynamo_timed): 0 2025-03-04T21:52:13.7301593Z pass_due_to_skip 2025-03-04T21:52:13.9527080Z TIMING: total_wall_time:0 2025-03-04T21:52:13.9527997Z STATS: call_* op count: 0 2025-03-04T21:52:13.9528797Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique) 2025-03-04T21:52:18.3409034Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:52:18.3410311Z warnings.warn( 2025-03-04T21:52:18.6992657Z 2025-03-04T21:52:29.2401437Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:52:29.2402098Z loading model: 0it [00:10, ?it/s] 2025-03-04T21:52:29.2402787Z cuda train DebertaV2ForQuestionAnswering 2025-03-04T21:52:39.8647134Z ERROR:common: 2025-03-04T21:52:39.8647627Z Traceback (most recent call last): 2025-03-04T21:52:39.8648722Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 2150, in check_accuracy 2025-03-04T21:52:39.8649655Z correct_result = self.run_n_iterations( 2025-03-04T21:52:39.8650543Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1947, in run_n_iterations 2025-03-04T21:52:39.8651389Z model_iter_fn(mod, inputs, collect_outputs=False) 2025-03-04T21:52:39.8652367Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in forward_and_backward_pass 2025-03-04T21:52:39.8653321Z pred = mod(**cloned_inputs) 2025-03-04T21:52:39.8654326Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:52:39.8655328Z return self._call_impl(*args, **kwargs) 2025-03-04T21:52:39.8656270Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:52:39.8657211Z return forward_call(*args, **kwargs) 2025-03-04T21:52:39.8658317Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 1486, in forward 2025-03-04T21:52:39.8659410Z outputs = self.deberta( 2025-03-04T21:52:39.8660341Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:52:39.8661758Z return self._call_impl(*args, **kwargs) 2025-03-04T21:52:39.8662752Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:52:39.8663788Z return forward_call(*args, **kwargs) 2025-03-04T21:52:39.8665008Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 1070, in forward 2025-03-04T21:52:39.8666192Z encoder_outputs = self.encoder( 2025-03-04T21:52:39.8667635Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:52:39.8668948Z return self._call_impl(*args, **kwargs) 2025-03-04T21:52:39.8670001Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:52:39.8671032Z return forward_call(*args, **kwargs) 2025-03-04T21:52:39.8672251Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 514, in forward 2025-03-04T21:52:39.8673449Z output_states = layer_module( 2025-03-04T21:52:39.8674502Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:52:39.8675632Z return self._call_impl(*args, **kwargs) 2025-03-04T21:52:39.8676676Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:52:39.8677716Z return forward_call(*args, **kwargs) 2025-03-04T21:52:39.8678907Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 362, in forward 2025-03-04T21:52:39.8680110Z attention_output = self.attention( 2025-03-04T21:52:39.8681186Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:52:39.8682307Z return self._call_impl(*args, **kwargs) 2025-03-04T21:52:39.8683325Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:52:39.8684349Z return forward_call(*args, **kwargs) 2025-03-04T21:52:39.8685520Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 293, in forward 2025-03-04T21:52:39.8686656Z self_output = self.self( 2025-03-04T21:52:39.8687683Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl 2025-03-04T21:52:39.8688898Z return self._call_impl(*args, **kwargs) 2025-03-04T21:52:39.8689904Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl 2025-03-04T21:52:39.8690898Z return forward_call(*args, **kwargs) 2025-03-04T21:52:39.8692106Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 733, in forward 2025-03-04T21:52:39.8693512Z attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1) 2025-03-04T21:52:39.8694668Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/autograd/function.py", line 575, in apply 2025-03-04T21:52:39.8695759Z return super().apply(*args, **kwargs) # type: ignore[misc] 2025-03-04T21:52:39.8696949Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py", line 113, in forward 2025-03-04T21:52:39.8698029Z output = torch.softmax(output, self.dim) 2025-03-04T21:52:39.8701326Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 24.00 MiB. GPU 0 has a total capacity of 21.98 GiB of which 10.44 MiB is free. Process 70156 has 21.96 GiB memory in use. Of the allocated memory 21.29 GiB is allocated by PyTorch, and 318.25 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) 2025-03-04T21:52:39.8926279Z Compilation time (from dynamo_timed): 0 2025-03-04T21:52:39.9263167Z eager_1st_run_OOM 2025-03-04T21:52:39.9263689Z TIMING: total_wall_time:0 2025-03-04T21:52:39.9264234Z STATS: call_* op count: 0 2025-03-04T21:52:39.9264681Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique) 2025-03-04T21:52:44.3559461Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:52:44.3560856Z warnings.warn( 2025-03-04T21:52:44.7312096Z 2025-03-04T21:52:46.3413877Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:52:46.3414308Z loading model: 0it [00:01, ?it/s] 2025-03-04T21:52:46.3414687Z cuda train DistilBertForMaskedLM 2025-03-04T21:53:04.0469521Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:53:04.0470395Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:53:04.0471111Z pred = mod(**cloned_inputs) 2025-03-04T21:53:04.0471837Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 905, in forward 2025-03-04T21:53:04.0472551Z dlbrt_output = self.distilbert( 2025-03-04T21:53:04.0473264Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 814, in forward 2025-03-04T21:53:04.0474106Z embeddings = self.embeddings(input_ids, inputs_embeds) # (bs, seq_length, dim) 2025-03-04T21:53:04.0474948Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 141, in forward 2025-03-04T21:53:04.0475763Z input_embeds = self.word_embeddings(input_ids) # (bs, max_seq_length, dim) 2025-03-04T21:53:04.0476097Z 2025-03-04T21:53:04.2001688Z W0304 21:53:04.199000 14562 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:53:27.0929432Z Compilation time (from dynamo_timed): 37.037228603 2025-03-04T21:53:27.0941037Z pass 2025-03-04T21:53:27.1340600Z TIMING: entire_frame_compile:33.22069 gc:0.00383 _recursive_pre_grad_passes:0.02372 pad_mm_benchmark:0.30014 _recursive_joint_graph_passes:1.04409 _recursive_post_grad_passes:0.45113 async_compile.wait:3.29539 code_gen:10.46073 inductor_compile:18.23564 backend_compile:26.32861 entire_backward_compile:3.81654 total_wall_time:37.03723 2025-03-04T21:53:27.1343097Z STATS: call_* op count: 752 | FakeTensorMode.__torch_dispatch__:33094 | FakeTensor.__torch_dispatch__:7436 | ProxyTorchDispatchMode.__torch_dispatch__:15043 2025-03-04T21:53:27.1344093Z Dynamo produced 2 graphs covering 752 ops with 5 graph breaks (4 unique) 2025-03-04T21:53:33.9671338Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:53:33.9672743Z warnings.warn( 2025-03-04T21:53:34.2696846Z 2025-03-04T21:53:35.6251951Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:53:35.6252337Z loading model: 0it [00:01, ?it/s] 2025-03-04T21:53:35.6252712Z cuda train DistilBertForQuestionAnswering 2025-03-04T21:53:52.9684569Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:53:52.9685466Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:53:52.9686195Z pred = mod(**cloned_inputs) 2025-03-04T21:53:52.9686911Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 1124, in forward 2025-03-04T21:53:52.9687645Z distilbert_output = self.distilbert( 2025-03-04T21:53:52.9688460Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 814, in forward 2025-03-04T21:53:52.9689311Z embeddings = self.embeddings(input_ids, inputs_embeds) # (bs, seq_length, dim) 2025-03-04T21:53:52.9690525Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 141, in forward 2025-03-04T21:53:52.9691512Z input_embeds = self.word_embeddings(input_ids) # (bs, max_seq_length, dim) 2025-03-04T21:53:52.9691851Z 2025-03-04T21:53:53.1091858Z W0304 21:53:53.108000 14909 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:54:13.4002599Z Compilation time (from dynamo_timed): 34.192502878999996 2025-03-04T21:54:13.4016061Z pass 2025-03-04T21:54:13.4412230Z TIMING: entire_frame_compile:30.50132 gc:0.00351 _recursive_pre_grad_passes:0.02336 pad_mm_benchmark:0.29775 _recursive_joint_graph_passes:1.03212 _recursive_post_grad_passes:0.44681 async_compile.wait:1.24158 code_gen:8.06806 inductor_compile:15.6697 backend_compile:23.70695 entire_backward_compile:3.69118 total_wall_time:34.1925 2025-03-04T21:54:13.4414187Z STATS: call_* op count: 745 | FakeTensorMode.__torch_dispatch__:32628 | FakeTensor.__torch_dispatch__:7291 | ProxyTorchDispatchMode.__torch_dispatch__:14854 2025-03-04T21:54:13.4415042Z Dynamo produced 2 graphs covering 745 ops with 5 graph breaks (4 unique) 2025-03-04T21:54:20.2632968Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:54:20.2634160Z warnings.warn( 2025-03-04T21:54:20.5611666Z 2025-03-04T21:54:22.6440084Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:54:22.6440448Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:54:22.6440786Z cuda train DistillGPT2 2025-03-04T21:54:40.4519882Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:54:40.4520773Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:54:40.4521496Z pred = mod(**cloned_inputs) 2025-03-04T21:54:40.4522137Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 1074, in forward 2025-03-04T21:54:40.4522809Z transformer_outputs = self.transformer( 2025-03-04T21:54:40.4523467Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 837, in forward 2025-03-04T21:54:40.4531973Z inputs_embeds = self.wte(input_ids) 2025-03-04T21:54:40.4532243Z 2025-03-04T21:54:40.5802400Z W0304 21:54:40.579000 15189 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:54:58.1415154Z Compilation time (from dynamo_timed): 31.050900792 2025-03-04T21:54:58.1426620Z pass 2025-03-04T21:54:58.1787608Z TIMING: entire_frame_compile:26.67173 gc:0.00269 _recursive_pre_grad_passes:0.0221 pad_mm_benchmark:0.31007 _recursive_joint_graph_passes:0.92331 _recursive_post_grad_passes:0.2885 async_compile.wait:3.63314 code_gen:10.15011 inductor_compile:16.27181 backend_compile:21.64112 entire_backward_compile:4.37917 total_wall_time:31.0509 2025-03-04T21:54:58.1789314Z STATS: call_* op count: 725 | FakeTensorMode.__torch_dispatch__:27280 | FakeTensor.__torch_dispatch__:6021 | ProxyTorchDispatchMode.__torch_dispatch__:12087 2025-03-04T21:54:58.1790153Z Dynamo produced 2 graphs covering 725 ops with 5 graph breaks (4 unique) 2025-03-04T21:55:04.6929934Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:55:04.6931149Z warnings.warn( 2025-03-04T21:55:04.9887917Z 2025-03-04T21:55:04.9898050Z loading model: 0it [00:00, ?it/s]If you want to use `ElectraForCausalLM` as a standalone, add `is_decoder=True.` 2025-03-04T21:55:05.8557979Z 2025-03-04T21:55:05.8558894Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:55:05.8559299Z cuda train ElectraForCausalLM 2025-03-04T21:55:37.4426874Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:55:37.4429284Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:55:37.4430020Z pred = mod(**cloned_inputs) 2025-03-04T21:55:37.4430701Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/electra/modeling_electra.py", line 1617, in forward 2025-03-04T21:55:37.4431382Z outputs = self.electra( 2025-03-04T21:55:37.4432022Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/electra/modeling_electra.py", line 902, in forward 2025-03-04T21:55:37.4432689Z hidden_states = self.embeddings( 2025-03-04T21:55:37.4433384Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/electra/modeling_electra.py", line 203, in forward 2025-03-04T21:55:37.4434093Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:55:37.4434345Z 2025-03-04T21:55:38.2838512Z Compilation time (from dynamo_timed): 29.266078067 2025-03-04T21:55:38.2859852Z pass 2025-03-04T21:55:38.3050013Z TIMING: entire_frame_compile:21.31857 gc:0.00129 _recursive_pre_grad_passes:0.00884 pad_mm_benchmark:0.3793 _recursive_joint_graph_passes:1.5808 _recursive_post_grad_passes:0.49868 async_compile.wait:0.16598 code_gen:7.07849 inductor_compile:13.81048 backend_compile:15.57729 entire_backward_compile:7.94751 total_wall_time:29.26608 2025-03-04T21:55:38.3051729Z STATS: call_* op count: 377 | FakeTensorMode.__torch_dispatch__:39331 | FakeTensor.__torch_dispatch__:5390 | ProxyTorchDispatchMode.__torch_dispatch__:17991 2025-03-04T21:55:38.3052560Z Dynamo produced 1 graphs covering 377 ops with 4 graph breaks (4 unique) 2025-03-04T21:55:44.9748339Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:55:44.9750686Z warnings.warn( 2025-03-04T21:55:45.2564889Z 2025-03-04T21:55:46.0848453Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:55:46.0848836Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:55:46.0849207Z cuda train ElectraForQuestionAnswering 2025-03-04T21:56:17.3091589Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:56:17.3094571Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:56:17.3095351Z pred = mod(**cloned_inputs) 2025-03-04T21:56:17.3096095Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/electra/modeling_electra.py", line 1385, in forward 2025-03-04T21:56:17.3096815Z discriminator_hidden_states = self.electra( 2025-03-04T21:56:17.3097533Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/electra/modeling_electra.py", line 902, in forward 2025-03-04T21:56:17.3098216Z hidden_states = self.embeddings( 2025-03-04T21:56:17.3098891Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/electra/modeling_electra.py", line 203, in forward 2025-03-04T21:56:17.3099599Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:56:17.3099851Z 2025-03-04T21:56:17.4757566Z W0304 21:56:17.474000 15841 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:57:00.9830064Z Compilation time (from dynamo_timed): 69.923613833 2025-03-04T21:57:00.9853294Z pass 2025-03-04T21:57:01.0002932Z TIMING: entire_frame_compile:62.07996 gc:0.00404 _recursive_pre_grad_passes:0.03926 pad_mm_benchmark:0.37851 _recursive_joint_graph_passes:1.73251 _recursive_post_grad_passes:0.6276 async_compile.wait:4.60528 code_gen:19.97932 inductor_compile:34.74049 backend_compile:49.19879 entire_backward_compile:7.84366 total_wall_time:69.92361 2025-03-04T21:57:01.0005272Z STATS: call_* op count: 1404 | FakeTensorMode.__torch_dispatch__:63017 | FakeTensor.__torch_dispatch__:14251 | ProxyTorchDispatchMode.__torch_dispatch__:28571 2025-03-04T21:57:01.0006148Z Dynamo produced 2 graphs covering 1404 ops with 5 graph breaks (4 unique) 2025-03-04T21:57:09.1901672Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:57:09.1902914Z warnings.warn( 2025-03-04T21:57:09.5258671Z 2025-03-04T21:57:12.2777474Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:57:12.2778128Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:57:12.2778705Z cuda train GPT2ForSequenceClassification 2025-03-04T21:57:43.7271165Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:57:43.7272025Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:57:43.7272743Z pred = mod(**cloned_inputs) 2025-03-04T21:57:43.7273389Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 1426, in forward 2025-03-04T21:57:43.7274063Z transformer_outputs = self.transformer( 2025-03-04T21:57:43.7274727Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 837, in forward 2025-03-04T21:57:43.7275383Z inputs_embeds = self.wte(input_ids) 2025-03-04T21:57:43.7275601Z 2025-03-04T21:57:43.8151273Z W0304 21:57:43.814000 16200 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:58:15.9036581Z Compilation time (from dynamo_timed): 56.027165325 2025-03-04T21:58:15.9058165Z pass 2025-03-04T21:58:15.9854445Z TIMING: entire_frame_compile:48.62817 gc:0.00414 _recursive_pre_grad_passes:0.03623 pad_mm_benchmark:0.3349 _recursive_joint_graph_passes:1.63597 _recursive_post_grad_passes:0.54376 async_compile.wait:4.30915 code_gen:16.82569 inductor_compile:28.48242 backend_compile:38.91328 entire_backward_compile:7.399 total_wall_time:56.02717 2025-03-04T21:58:15.9856161Z STATS: call_* op count: 1399 | FakeTensorMode.__torch_dispatch__:52165 | FakeTensor.__torch_dispatch__:11646 | ProxyTorchDispatchMode.__torch_dispatch__:23402 2025-03-04T21:58:15.9857008Z Dynamo produced 2 graphs covering 1399 ops with 5 graph breaks (4 unique) 2025-03-04T21:58:23.7088858Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:58:23.7091714Z warnings.warn( 2025-03-04T21:58:23.9595584Z 2025-03-04T21:58:25.6122179Z loading model: 0it [00:00, ?it/s]WARNING:common:Model GoogleFnet supports float32 only 2025-03-04T21:58:25.9066581Z 2025-03-04T21:58:25.9067009Z loading model: 0it [00:01, ?it/s] 2025-03-04T21:58:25.9067410Z WARNING:common:Model GoogleFnet supports float32 only 2025-03-04T21:58:25.9078448Z cuda train GoogleFnet 2025-03-04T21:58:27.3015312Z WARNING:common:Model GoogleFnet supports float32 only 2025-03-04T21:58:44.3320503Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:58:44.3321762Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:58:44.3322482Z pred = mod(**cloned_inputs) 2025-03-04T21:58:44.3323460Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/fnet/modeling_fnet.py", line 749, in forward 2025-03-04T21:58:44.3324295Z outputs = self.fnet( 2025-03-04T21:58:44.3324905Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/fnet/modeling_fnet.py", line 581, in forward 2025-03-04T21:58:44.3325564Z embedding_output = self.embeddings( 2025-03-04T21:58:44.3326215Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/fnet/modeling_fnet.py", line 148, in forward 2025-03-04T21:58:44.3326900Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:58:44.3327150Z 2025-03-04T21:58:44.4803637Z W0304 21:58:44.479000 16611 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T21:59:07.8375209Z Compilation time (from dynamo_timed): 37.337853378 2025-03-04T21:59:07.8387034Z pass 2025-03-04T21:59:07.8831640Z TIMING: entire_frame_compile:32.86967 gc:0.0046 _recursive_pre_grad_passes:0.02479 pad_mm_benchmark:0.0477 _recursive_joint_graph_passes:0.54054 _recursive_post_grad_passes:0.2832 async_compile.wait:3.81284 code_gen:11.62038 inductor_compile:19.37046 backend_compile:25.69998 entire_backward_compile:4.46819 total_wall_time:37.33785 2025-03-04T21:59:07.8834864Z STATS: call_* op count: 791 | FakeTensorMode.__torch_dispatch__:28434 | FakeTensor.__torch_dispatch__:7541 | ProxyTorchDispatchMode.__torch_dispatch__:13155 2025-03-04T21:59:07.8835879Z Dynamo produced 2 graphs covering 791 ops with 5 graph breaks (4 unique) 2025-03-04T21:59:14.6902565Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T21:59:14.6903784Z warnings.warn( 2025-03-04T21:59:14.9486845Z 2025-03-04T21:59:17.2502289Z loading model: 0it [00:00, ?it/s] 2025-03-04T21:59:17.2502675Z loading model: 0it [00:02, ?it/s] 2025-03-04T21:59:17.2503025Z cuda train LayoutLMForMaskedLM 2025-03-04T21:59:51.5479501Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T21:59:51.5480359Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T21:59:51.5481067Z pred = mod(**cloned_inputs) 2025-03-04T21:59:51.5481739Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/layoutlm/modeling_layoutlm.py", line 938, in forward 2025-03-04T21:59:51.5482420Z outputs = self.layoutlm( 2025-03-04T21:59:51.5483073Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/layoutlm/modeling_layoutlm.py", line 821, in forward 2025-03-04T21:59:51.5483760Z embedding_output = self.embeddings( 2025-03-04T21:59:51.5484463Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/layoutlm/modeling_layoutlm.py", line 99, in forward 2025-03-04T21:59:51.5485177Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T21:59:51.5485431Z 2025-03-04T21:59:51.7272589Z W0304 21:59:51.726000 16917 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:00:35.9390204Z Compilation time (from dynamo_timed): 71.735781218 2025-03-04T22:00:35.9414811Z pass 2025-03-04T22:00:36.0070063Z TIMING: entire_frame_compile:63.20371 gc:0.00378 _recursive_pre_grad_passes:0.0405 pad_mm_benchmark:0.19887 _recursive_joint_graph_passes:1.56307 _recursive_post_grad_passes:0.61923 async_compile.wait:5.02528 code_gen:20.96812 inductor_compile:36.03394 backend_compile:49.90064 entire_backward_compile:8.53207 total_wall_time:71.73578 2025-03-04T22:00:36.0073640Z STATS: call_* op count: 1447 | FakeTensorMode.__torch_dispatch__:64421 | FakeTensor.__torch_dispatch__:14530 | ProxyTorchDispatchMode.__torch_dispatch__:29173 2025-03-04T22:00:36.0074962Z Dynamo produced 2 graphs covering 1447 ops with 5 graph breaks (4 unique) 2025-03-04T22:00:44.2520113Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:00:44.2521315Z warnings.warn( 2025-03-04T22:00:44.5737123Z 2025-03-04T22:00:46.6186674Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:00:46.6187053Z loading model: 0it [00:02, ?it/s] 2025-03-04T22:00:46.6187408Z cuda train LayoutLMForSequenceClassification 2025-03-04T22:01:19.7293771Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:01:19.7296929Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:01:19.7297770Z pred = mod(**cloned_inputs) 2025-03-04T22:01:19.7298494Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/layoutlm/modeling_layoutlm.py", line 1060, in forward 2025-03-04T22:01:19.7299197Z outputs = self.layoutlm( 2025-03-04T22:01:19.7299978Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/layoutlm/modeling_layoutlm.py", line 821, in forward 2025-03-04T22:01:19.7300676Z embedding_output = self.embeddings( 2025-03-04T22:01:19.7301355Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/layoutlm/modeling_layoutlm.py", line 99, in forward 2025-03-04T22:01:19.7302062Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T22:01:19.7302317Z 2025-03-04T22:01:19.9184452Z W0304 22:01:19.917000 17244 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:02:02.1297757Z Compilation time (from dynamo_timed): 68.937883132 2025-03-04T22:02:02.1321764Z pass 2025-03-04T22:02:02.2002963Z TIMING: entire_frame_compile:60.98683 gc:0.00454 _recursive_pre_grad_passes:0.04031 pad_mm_benchmark:0.38626 _recursive_joint_graph_passes:1.75034 _recursive_post_grad_passes:0.62544 async_compile.wait:2.56034 code_gen:18.25943 inductor_compile:33.13109 backend_compile:47.68693 entire_backward_compile:7.95105 total_wall_time:68.93788 2025-03-04T22:02:02.2005645Z STATS: call_* op count: 1440 | FakeTensorMode.__torch_dispatch__:63969 | FakeTensor.__torch_dispatch__:14417 | ProxyTorchDispatchMode.__torch_dispatch__:28967 2025-03-04T22:02:02.2006954Z Dynamo produced 2 graphs covering 1440 ops with 5 graph breaks (4 unique) 2025-03-04T22:02:10.4954658Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:02:10.4955855Z warnings.warn( 2025-03-04T22:02:10.7710643Z 2025-03-04T22:02:21.2694634Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:02:21.2695048Z loading model: 0it [00:10, ?it/s] 2025-03-04T22:02:21.2695401Z cuda train M2M100ForConditionalGeneration 2025-03-04T22:02:21.3861994Z WARNING:common:fp64 golden ref were not generated for M2M100ForConditionalGeneration. Setting accuracy check to cosine 2025-03-04T22:03:31.6077191Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:03:31.6080043Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:03:31.6082511Z pred = mod(**cloned_inputs) 2025-03-04T22:03:31.6083225Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/m2m_100/modeling_m2m_100.py", line 1275, in forward 2025-03-04T22:03:31.6083917Z outputs = self.model( 2025-03-04T22:03:31.6084957Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/m2m_100/modeling_m2m_100.py", line 1162, in forward 2025-03-04T22:03:31.6085813Z encoder_outputs = self.encoder( 2025-03-04T22:03:31.6086463Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/m2m_100/modeling_m2m_100.py", line 763, in forward 2025-03-04T22:03:31.6087204Z inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:03:31.6087512Z 2025-03-04T22:03:33.3157959Z Compilation time (from dynamo_timed): 67.038792998 2025-03-04T22:03:33.3192803Z pass 2025-03-04T22:03:33.4131197Z TIMING: entire_frame_compile:50.31528 gc:0.00246 _recursive_pre_grad_passes:0.02126 pad_mm_benchmark:0.46406 _recursive_joint_graph_passes:3.13955 _recursive_post_grad_passes:2.21644 async_compile.wait:0.45092 code_gen:15.66238 inductor_compile:31.44202 backend_compile:37.58426 entire_backward_compile:16.72351 total_wall_time:67.03879 2025-03-04T22:03:33.4132996Z STATS: call_* op count: 1296 | FakeTensorMode.__torch_dispatch__:97904 | FakeTensor.__torch_dispatch__:11833 | ProxyTorchDispatchMode.__torch_dispatch__:47008 2025-03-04T22:03:33.4133841Z Dynamo produced 1 graphs covering 1296 ops with 4 graph breaks (4 unique) 2025-03-04T22:03:41.8451276Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:03:41.8452595Z warnings.warn( 2025-03-04T22:03:42.0716848Z 2025-03-04T22:03:46.0779122Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:03:46.0779501Z loading model: 0it [00:04, ?it/s] 2025-03-04T22:03:46.0779846Z cuda train MBartForCausalLM 2025-03-04T22:03:46.1086779Z WARNING:common:fp64 golden ref were not generated for MBartForCausalLM. Setting accuracy check to cosine 2025-03-04T22:03:48.1308952Z 2025-03-04T22:03:48.1309673Z class GraphModule(torch.nn.Module): 2025-03-04T22:03:48.1311728Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:03:48.1313551Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:03:48.1314333Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:03:48.1324151Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:03:48.1325611Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:03:48.1326788Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:03:48.1327502Z 2025-03-04T22:03:48.1327921Z # No stacktrace found for following nodes 2025-03-04T22:03:48.1328497Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:03:48.1329022Z 2025-03-04T22:03:48.1329784Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1224 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:03:48.1330901Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T22:03:48.1332017Z 2025-03-04T22:03:48.1333349Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1235 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:03:48.1335148Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:03:48.1336412Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:03:48.1336864Z 2025-03-04T22:03:48.1337670Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:03:48.1338797Z mask: "f32[1024, 1024][1024, 1]cuda:0" = torch.full((1024, 1024), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:03:48.1339332Z 2025-03-04T22:03:48.1340075Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:03:48.1341035Z mask_cond: "i64[1024][1]cuda:0" = torch.arange(1024, device = device(type='cuda', index=0)) 2025-03-04T22:03:48.1341474Z 2025-03-04T22:03:48.1342270Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:03:48.1343159Z add: "i64[1024][1]cuda:0" = mask_cond + 1 2025-03-04T22:03:48.1343557Z view_1: "i64[1024, 1][1, 1]cuda:0" = add.view(1024, 1); add = None 2025-03-04T22:03:48.1344036Z lt: "b8[1024, 1024][1024, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:03:48.1344612Z masked_fill_: "f32[1024, 1024][1024, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:03:48.1345070Z 2025-03-04T22:03:48.1345714Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:03:48.1346536Z mask_1: "f32[1024, 1024][1024, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:03:48.1346937Z 2025-03-04T22:03:48.1347818Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:03:48.1349010Z getitem: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:03:48.1349863Z causal_4d_mask: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = getitem.expand(1, 1, 1024, 1024); getitem = causal_4d_mask = None 2025-03-04T22:03:48.1350414Z 2025-03-04T22:03:48.1351074Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:03:48.1352022Z arange_1: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:03:48.1352516Z 2025-03-04T22:03:48.1353153Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:03:48.1353980Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange_1.expand(1, -1); arange_1 = None 2025-03-04T22:03:48.1354399Z 2025-03-04T22:03:48.1355233Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:03:48.1356201Z add_1: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:03:48.1357403Z positions_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:03:48.1358495Z 2025-03-04T22:03:48.1359294Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1259 in forward, code: hidden_states = inputs_embeds + positions.to(inputs_embeds.device) 2025-03-04T22:03:48.1360381Z to_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T22:03:48.1361429Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to_1; inputs_embeds = to_1 = None 2025-03-04T22:03:48.1361921Z 2025-03-04T22:03:48.1362685Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1260 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:03:48.1364937Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:03:48.1366637Z 2025-03-04T22:03:48.1367586Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1262 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:03:48.1369104Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:03:48.1369785Z 2025-03-04T22:03:48.1370481Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1290 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:03:48.1371309Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:03:48.1371654Z 2025-03-04T22:03:48.1372371Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1291 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:03:48.1373284Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:03:48.1373699Z 2025-03-04T22:03:48.1373839Z 2025-03-04T22:03:48.1373969Z class GraphModule(torch.nn.Module): 2025-03-04T22:03:48.1375699Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:03:48.1377494Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:03:48.1378413Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:03:48.1379612Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:03:48.1380762Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:03:48.1381920Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:03:48.1382632Z 2025-03-04T22:03:48.1382886Z # No stacktrace found for following nodes 2025-03-04T22:03:48.1383464Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:03:48.1383989Z 2025-03-04T22:03:48.1384731Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1224 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:03:48.1385770Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T22:03:48.1386282Z 2025-03-04T22:03:48.1387070Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1235 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:03:48.1388733Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:03:48.1389992Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:03:48.1390446Z 2025-03-04T22:03:48.1391248Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:03:48.1392354Z mask: "f32[1024, 1024][1024, 1]cuda:0" = torch.full((1024, 1024), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:03:48.1392875Z 2025-03-04T22:03:48.1393618Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:03:48.1394575Z mask_cond: "i64[1024][1]cuda:0" = torch.arange(1024, device = device(type='cuda', index=0)) 2025-03-04T22:03:48.1395013Z 2025-03-04T22:03:48.1395807Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:03:48.1396703Z add: "i64[1024][1]cuda:0" = mask_cond + 1 2025-03-04T22:03:48.1397095Z view_1: "i64[1024, 1][1, 1]cuda:0" = add.view(1024, 1); add = None 2025-03-04T22:03:48.1397576Z lt: "b8[1024, 1024][1024, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:03:48.1398149Z masked_fill_: "f32[1024, 1024][1024, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:03:48.1398604Z 2025-03-04T22:03:48.1399246Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:03:48.1400063Z mask_1: "f32[1024, 1024][1024, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:03:48.1400457Z 2025-03-04T22:03:48.1401365Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:03:48.1402651Z getitem: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:03:48.1403501Z causal_4d_mask: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = getitem.expand(1, 1, 1024, 1024); getitem = causal_4d_mask = None 2025-03-04T22:03:48.1404046Z 2025-03-04T22:03:48.1404704Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:03:48.1405651Z arange_1: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:03:48.1406153Z 2025-03-04T22:03:48.1406788Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:03:48.1407622Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange_1.expand(1, -1); arange_1 = None 2025-03-04T22:03:48.1408130Z 2025-03-04T22:03:48.1408863Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:03:48.1409752Z add_1: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:03:48.1410952Z positions_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:03:48.1412053Z 2025-03-04T22:03:48.1412854Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1259 in forward, code: hidden_states = inputs_embeds + positions.to(inputs_embeds.device) 2025-03-04T22:03:48.1413938Z to_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T22:03:48.1414639Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to_1; inputs_embeds = to_1 = None 2025-03-04T22:03:48.1415127Z 2025-03-04T22:03:48.1415892Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1260 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:03:48.1418208Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:03:48.1419901Z 2025-03-04T22:03:48.1420795Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1262 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:03:48.1422165Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:03:48.1422843Z 2025-03-04T22:03:48.1423685Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1290 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:03:48.1424583Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:03:48.1424927Z 2025-03-04T22:03:48.1425640Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1291 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:03:48.1426550Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:03:48.1426960Z 2025-03-04T22:03:49.0226791Z 2025-03-04T22:03:49.0227515Z class GraphModule(torch.nn.Module): 2025-03-04T22:03:49.0229624Z def forward(self, L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:03:49.0231381Z l_input_ids_ = L_input_ids_ 2025-03-04T22:03:49.0232096Z l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:03:49.0233235Z l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:03:49.0234424Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:03:49.0235623Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:03:49.0236365Z 2025-03-04T22:03:49.0237141Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1224 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:03:49.0238168Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T22:03:49.0238610Z 2025-03-04T22:03:49.0239409Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1235 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:03:49.0241105Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:03:49.0242384Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:03:49.0242844Z 2025-03-04T22:03:49.0243660Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:03:49.0244781Z mask: "f32[1024, 1024][1024, 1]cuda:0" = torch.full((1024, 1024), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:03:49.0245314Z 2025-03-04T22:03:49.0246065Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:03:49.0247032Z mask_cond: "i64[1024][1]cuda:0" = torch.arange(1024, device = device(type='cuda', index=0)) 2025-03-04T22:03:49.0247477Z 2025-03-04T22:03:49.0248827Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:03:49.0249896Z add: "i64[1024][1]cuda:0" = mask_cond + 1 2025-03-04T22:03:49.0250296Z view_1: "i64[1024, 1][1, 1]cuda:0" = add.view(1024, 1); add = None 2025-03-04T22:03:49.0250786Z lt: "b8[1024, 1024][1024, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:03:49.0251371Z masked_fill_: "f32[1024, 1024][1024, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:03:49.0251832Z 2025-03-04T22:03:49.0252485Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:03:49.0253321Z mask_1: "f32[1024, 1024][1024, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:03:49.0253728Z 2025-03-04T22:03:49.0254571Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:03:49.0255781Z getitem: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:03:49.0256647Z causal_4d_mask: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = getitem.expand(1, 1, 1024, 1024); getitem = causal_4d_mask = None 2025-03-04T22:03:49.0257203Z 2025-03-04T22:03:49.0257868Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:03:49.0258825Z arange_1: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:03:49.0259335Z 2025-03-04T22:03:49.0259970Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:03:49.0260832Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange_1.expand(1, -1); arange_1 = None 2025-03-04T22:03:49.0261561Z 2025-03-04T22:03:49.0262305Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:03:49.0263203Z add_1: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:03:49.0264435Z positions_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:03:49.0265554Z 2025-03-04T22:03:49.0266371Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1259 in forward, code: hidden_states = inputs_embeds + positions.to(inputs_embeds.device) 2025-03-04T22:03:49.0267459Z to_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T22:03:49.0268217Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to_1; inputs_embeds = to_1 = None 2025-03-04T22:03:49.0268711Z 2025-03-04T22:03:49.0269472Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1260 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:03:49.0271889Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:03:49.0273725Z 2025-03-04T22:03:49.0274632Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1262 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:03:49.0276012Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:03:49.0276696Z 2025-03-04T22:03:49.0277406Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1290 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:03:49.0278244Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:03:49.0278591Z 2025-03-04T22:03:49.0279307Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1291 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:03:49.0280218Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:03:49.0280636Z 2025-03-04T22:03:49.6670735Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:03:49.6671665Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py", line 135, in forward 2025-03-04T22:03:49.6672355Z return super().forward(positions + self.offset) 2025-03-04T22:03:49.6672604Z 2025-03-04T22:03:59.6096613Z Compilation time (from dynamo_timed): 4.597946816 2025-03-04T22:03:59.6116520Z pass 2025-03-04T22:03:59.6742052Z TIMING: entire_frame_compile:3.66667 gc:0.00654 _recursive_pre_grad_passes:0.00604 _recursive_joint_graph_passes:0.23225 inductor_compile:1.86839 backend_compile:2.76359 _recursive_post_grad_passes:0.06628 async_compile.precompile:0.11328 async_compile.wait:0.42618 code_gen:1.13373 pad_mm_benchmark:0.01444 entire_backward_compile:0.93127 total_wall_time:4.59795 2025-03-04T22:03:59.6743832Z STATS: call_* op count: 60 | FakeTensorMode.__torch_dispatch__:3883 | FakeTensor.__torch_dispatch__:555 | ProxyTorchDispatchMode.__torch_dispatch__:1583 2025-03-04T22:03:59.6744636Z Dynamo produced 6 graphs covering 60 ops with 6 graph breaks (5 unique) 2025-03-04T22:04:05.2194808Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:04:05.2196938Z warnings.warn( 2025-03-04T22:04:05.5342498Z 2025-03-04T22:04:12.5545971Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:04:12.5546345Z loading model: 0it [00:07, ?it/s] 2025-03-04T22:04:12.5546696Z cuda train MBartForConditionalGeneration 2025-03-04T22:04:12.6638431Z WARNING:common:fp64 golden ref were not generated for MBartForConditionalGeneration. Setting accuracy check to cosine 2025-03-04T22:04:16.4321753Z 2025-03-04T22:04:16.4322838Z class GraphModule(torch.nn.Module): 2025-03-04T22:04:16.4326135Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:04:16.4328720Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:04:16.4329152Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:04:16.4329913Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:04:16.4331021Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:04:16.4332185Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:04:16.4333364Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:04:16.4334083Z 2025-03-04T22:04:16.4334353Z # No stacktrace found for following nodes 2025-03-04T22:04:16.4334939Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:04:16.4335481Z 2025-03-04T22:04:16.4336249Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:88 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:04:16.4337294Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.clone(); l_cloned_inputs_labels_ = None 2025-03-04T22:04:16.4337811Z 2025-03-04T22:04:16.4338678Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:93 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:04:16.4339679Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:04:16.4340254Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:04:16.4340754Z 2025-03-04T22:04:16.4341618Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:95 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:04:16.4342607Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:04:16.4343076Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:04:16.4343599Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:04:16.4344135Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:04:16.4344516Z 2025-03-04T22:04:16.4345383Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:96 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:04:16.4346855Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:04:16.4347589Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:04:16.4347991Z 2025-03-04T22:04:16.4348792Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:97 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:04:16.4349860Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:04:16.4350449Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:04:16.4351287Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:04:16.4351939Z 2025-03-04T22:04:16.4352705Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:98 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:04:16.4353929Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:04:16.4354611Z 2025-03-04T22:04:16.4355341Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1010 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:04:16.4356369Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T22:04:16.4356886Z 2025-03-04T22:04:16.4357676Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1017 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:04:16.4359354Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:04:16.4360614Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:04:16.4361403Z 2025-03-04T22:04:16.4362076Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:04:16.4363028Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:04:16.4363524Z 2025-03-04T22:04:16.4364161Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:04:16.4364984Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:04:16.4365397Z 2025-03-04T22:04:16.4366122Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:04:16.4367020Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:04:16.4368288Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:04:16.4369375Z 2025-03-04T22:04:16.4370183Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1021 in forward, code: hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device) 2025-03-04T22:04:16.4371240Z to: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:04:16.4371912Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to; inputs_embeds = to = None 2025-03-04T22:04:16.4372390Z 2025-03-04T22:04:16.4373380Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1022 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:04:16.4376250Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:04:16.4378084Z 2025-03-04T22:04:16.4378985Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1023 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:04:16.4380362Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:04:16.4381046Z 2025-03-04T22:04:16.4381753Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1050 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:04:16.4382592Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:04:16.4382941Z 2025-03-04T22:04:16.4383708Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1051 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:04:16.4384823Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:04:16.4385373Z 2025-03-04T22:04:16.4385623Z 2025-03-04T22:04:16.4385751Z class GraphModule(torch.nn.Module): 2025-03-04T22:04:16.4387633Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:04:16.4389534Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:04:16.4389943Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:04:16.4390697Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:04:16.4391795Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:04:16.4393002Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:04:16.4394174Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:04:16.4394893Z 2025-03-04T22:04:16.4395145Z # No stacktrace found for following nodes 2025-03-04T22:04:16.4395725Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:04:16.4396257Z 2025-03-04T22:04:16.4396994Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:88 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:04:16.4398027Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.clone(); l_cloned_inputs_labels_ = None 2025-03-04T22:04:16.4398539Z 2025-03-04T22:04:16.4399494Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:93 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:04:16.4400587Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:04:16.4401155Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:04:16.4401650Z 2025-03-04T22:04:16.4402514Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:95 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:04:16.4403511Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:04:16.4403918Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:04:16.4404297Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:04:16.4404721Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:04:16.4405105Z 2025-03-04T22:04:16.4405957Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:96 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:04:16.4407047Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:04:16.4407623Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:04:16.4408146Z 2025-03-04T22:04:16.4408943Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:97 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:04:16.4410015Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:04:16.4410615Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:04:16.4411300Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:04:16.4411873Z 2025-03-04T22:04:16.4412633Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:98 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:04:16.4413858Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:04:16.4414541Z 2025-03-04T22:04:16.4415272Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1010 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:04:16.4416305Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T22:04:16.4416825Z 2025-03-04T22:04:16.4417617Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1017 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:04:16.4419296Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:04:16.4420559Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:04:16.4421010Z 2025-03-04T22:04:16.4421756Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:04:16.4422804Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:04:16.4423336Z 2025-03-04T22:04:16.4423971Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:04:16.4433002Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:04:16.4433431Z 2025-03-04T22:04:16.4434186Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:04:16.4435086Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:04:16.4436286Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:04:16.4437369Z 2025-03-04T22:04:16.4438173Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1021 in forward, code: hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device) 2025-03-04T22:04:16.4439227Z to: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:04:16.4439893Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to; inputs_embeds = to = None 2025-03-04T22:04:16.4440370Z 2025-03-04T22:04:16.4441136Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1022 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:04:16.4443387Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:04:16.4445082Z 2025-03-04T22:04:16.4445977Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1023 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:04:16.4447352Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:04:16.4448176Z 2025-03-04T22:04:16.4448883Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1050 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:04:16.4449718Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:04:16.4450069Z 2025-03-04T22:04:16.4450850Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1051 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:04:16.4451804Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:04:16.4452213Z 2025-03-04T22:04:16.4452345Z 2025-03-04T22:04:16.4452468Z class GraphModule(torch.nn.Module): 2025-03-04T22:04:16.4454467Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:04:16.4456451Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:04:16.4456874Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:04:16.4457630Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:04:16.4458728Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:04:16.4459887Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:04:16.4461561Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:04:16.4462292Z 2025-03-04T22:04:16.4462544Z # No stacktrace found for following nodes 2025-03-04T22:04:16.4463123Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:04:16.4463702Z 2025-03-04T22:04:16.4464444Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:88 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:04:16.4465484Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.clone(); l_cloned_inputs_labels_ = None 2025-03-04T22:04:16.4466004Z 2025-03-04T22:04:16.4466855Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:93 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:04:16.4467835Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:04:16.4468400Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:04:16.4468893Z 2025-03-04T22:04:16.4469752Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:95 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:04:16.4470740Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:04:16.4471145Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:04:16.4471527Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:04:16.4471947Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:04:16.4472317Z 2025-03-04T22:04:16.4473167Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:96 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:04:16.4474248Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:04:16.4474819Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:04:16.4475218Z 2025-03-04T22:04:16.4476911Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:97 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:04:16.4477993Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:04:16.4478723Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:04:16.4479400Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:04:16.4479972Z 2025-03-04T22:04:16.4480734Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:98 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:04:16.4481961Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:04:16.4482648Z 2025-03-04T22:04:16.4483448Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1010 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:04:16.4484490Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T22:04:16.4485005Z 2025-03-04T22:04:16.4485793Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1017 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:04:16.4487463Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:04:16.4488840Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:04:16.4489299Z 2025-03-04T22:04:16.4489968Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:04:16.4490918Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:04:16.4491418Z 2025-03-04T22:04:16.4492054Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:04:16.4492882Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:04:16.4493325Z 2025-03-04T22:04:16.4494082Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:04:16.4494978Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:04:16.4496164Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:04:16.4497246Z 2025-03-04T22:04:16.4498046Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1021 in forward, code: hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device) 2025-03-04T22:04:16.4499104Z to: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:04:16.4499777Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to; inputs_embeds = to = None 2025-03-04T22:04:16.4500257Z 2025-03-04T22:04:16.4501113Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1022 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:04:16.4503441Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:04:16.4505133Z 2025-03-04T22:04:16.4506036Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1023 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:04:16.4507412Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:04:16.4508100Z 2025-03-04T22:04:16.4508805Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1050 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:04:16.4509636Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:04:16.4509980Z 2025-03-04T22:04:16.4510755Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1051 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:04:16.4511710Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:04:16.4512113Z 2025-03-04T22:04:17.3342776Z 2025-03-04T22:04:17.3343477Z class GraphModule(torch.nn.Module): 2025-03-04T22:04:17.3345608Z def forward(self, L_labels_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:04:17.3347429Z l_labels_ = L_labels_ 2025-03-04T22:04:17.3347729Z l_input_ids_ = L_input_ids_ 2025-03-04T22:04:17.3348442Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:04:17.3349578Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:04:17.3350767Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:04:17.3351969Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:04:17.3352699Z 2025-03-04T22:04:17.3353464Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:88 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:04:17.3354430Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_labels_.clone(); l_labels_ = None 2025-03-04T22:04:17.3354875Z 2025-03-04T22:04:17.3356129Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:93 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:04:17.3357300Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:04:17.3357877Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:04:17.3358381Z 2025-03-04T22:04:17.3359248Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:95 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:04:17.3360246Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:04:17.3360656Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:04:17.3361043Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:04:17.3361786Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:04:17.3362166Z 2025-03-04T22:04:17.3363031Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:96 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:04:17.3364125Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:04:17.3364740Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:04:17.3365146Z 2025-03-04T22:04:17.3366183Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:97 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:04:17.3367389Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:04:17.3368190Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:04:17.3369171Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:04:17.3369985Z 2025-03-04T22:04:17.3370818Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:98 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:04:17.3372054Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:04:17.3372746Z 2025-03-04T22:04:17.3373487Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1010 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:04:17.3374447Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T22:04:17.3374889Z 2025-03-04T22:04:17.3375698Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1017 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:04:17.3377382Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:04:17.3378654Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:04:17.3379107Z 2025-03-04T22:04:17.3379952Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:04:17.3381014Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:04:17.3381517Z 2025-03-04T22:04:17.3382155Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:04:17.3382990Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:04:17.3383403Z 2025-03-04T22:04:17.3384133Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:04:17.3385040Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:04:17.3386271Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:04:17.3387397Z 2025-03-04T22:04:17.3388209Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1021 in forward, code: hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device) 2025-03-04T22:04:17.3389268Z to: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:04:17.3389940Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to; inputs_embeds = to = None 2025-03-04T22:04:17.3390419Z 2025-03-04T22:04:17.3391187Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1022 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:04:17.3393465Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:04:17.3395184Z 2025-03-04T22:04:17.3396079Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1023 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:04:17.3397452Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:04:17.3398141Z 2025-03-04T22:04:17.3398844Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1050 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:04:17.3399690Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:04:17.3400036Z 2025-03-04T22:04:17.3400813Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1051 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:04:17.3401769Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:04:17.3402170Z 2025-03-04T22:04:17.3402324Z 2025-03-04T22:04:17.3402447Z class GraphModule(torch.nn.Module): 2025-03-04T22:04:17.3404337Z def forward(self, L_labels_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:04:17.3406191Z l_labels_ = L_labels_ 2025-03-04T22:04:17.3406481Z l_input_ids_ = L_input_ids_ 2025-03-04T22:04:17.3407177Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:04:17.3408459Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:04:17.3409633Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:04:17.3410831Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:04:17.3411560Z 2025-03-04T22:04:17.3412300Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:88 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:04:17.3413249Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_labels_.clone(); l_labels_ = None 2025-03-04T22:04:17.3413690Z 2025-03-04T22:04:17.3414546Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:93 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:04:17.3415533Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:04:17.3416102Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:04:17.3416596Z 2025-03-04T22:04:17.3417452Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:95 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:04:17.3418436Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:04:17.3418834Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:04:17.3419209Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:04:17.3419622Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:04:17.3419992Z 2025-03-04T22:04:17.3420844Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:96 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:04:17.3421938Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:04:17.3422509Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:04:17.3422908Z 2025-03-04T22:04:17.3423755Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:97 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:04:17.3424821Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:04:17.3425409Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:04:17.3426172Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:04:17.3426820Z 2025-03-04T22:04:17.3427579Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:98 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:04:17.3428800Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:04:17.3429485Z 2025-03-04T22:04:17.3430215Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1010 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:04:17.3431159Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T22:04:17.3431587Z 2025-03-04T22:04:17.3432376Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1017 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:04:17.3434056Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:04:17.3435323Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:04:17.3435776Z 2025-03-04T22:04:17.3436436Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:04:17.3437380Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:04:17.3437882Z 2025-03-04T22:04:17.3438513Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:04:17.3439345Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:04:17.3439759Z 2025-03-04T22:04:17.3440493Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:04:17.3441381Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:04:17.3442576Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:04:17.3443666Z 2025-03-04T22:04:17.3444478Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1021 in forward, code: hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device) 2025-03-04T22:04:17.3445541Z to: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:04:17.3446219Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to; inputs_embeds = to = None 2025-03-04T22:04:17.3446699Z 2025-03-04T22:04:17.3447460Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1022 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:04:17.3449934Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:04:17.3451731Z 2025-03-04T22:04:17.3452630Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1023 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:04:17.3454003Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:04:17.3454683Z 2025-03-04T22:04:17.3455385Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1050 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:04:17.3456216Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:04:17.3456558Z 2025-03-04T22:04:17.3457322Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1051 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:04:17.3458273Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:04:17.3458672Z 2025-03-04T22:04:18.0838172Z 2025-03-04T22:04:18.0838681Z class GraphModule(torch.nn.Module): 2025-03-04T22:04:18.0840514Z def forward(self, L_decoder_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:04:18.0842266Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T22:04:18.0842627Z l_input_ids_ = L_input_ids_ 2025-03-04T22:04:18.0843252Z l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:04:18.0844255Z l_self_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:04:18.0845262Z l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:04:18.0846293Z l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:04:18.0846939Z 2025-03-04T22:04:18.0847752Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1010 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:04:18.0848894Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T22:04:18.0849339Z 2025-03-04T22:04:18.0850151Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1017 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:04:18.0851760Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:04:18.0853366Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:04:18.0854032Z 2025-03-04T22:04:18.0854707Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:04:18.0855667Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:04:18.0856175Z 2025-03-04T22:04:18.0856823Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:04:18.0857661Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:04:18.0858078Z 2025-03-04T22:04:18.0858827Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:04:18.0859739Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:04:18.0860849Z embed_pos: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:04:18.0862402Z 2025-03-04T22:04:18.0863214Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1021 in forward, code: hidden_states = inputs_embeds + embed_pos.to(inputs_embeds.device) 2025-03-04T22:04:18.0864279Z to: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:04:18.0864961Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to; inputs_embeds = to = None 2025-03-04T22:04:18.0865443Z 2025-03-04T22:04:18.0866225Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1022 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:04:18.0868336Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:04:18.0869879Z 2025-03-04T22:04:18.0870784Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1023 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:04:18.0872172Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:04:18.0872863Z 2025-03-04T22:04:18.0873566Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1050 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:04:18.0874400Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:04:18.0874749Z 2025-03-04T22:04:18.0875526Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1051 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:04:18.0876490Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:04:18.0876898Z 2025-03-04T22:04:18.3021615Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:04:18.3022785Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py", line 135, in forward 2025-03-04T22:04:18.3023634Z return super().forward(positions + self.offset) 2025-03-04T22:04:18.3023887Z 2025-03-04T22:04:20.2113269Z 2025-03-04T22:04:20.2114464Z class GraphModule(torch.nn.Module): 2025-03-04T22:04:20.2116844Z def forward(self, dict_getitem_L_stack0_list_dict_keys_L_stack0_0_: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0", L_decoder_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 1024][1024, 1]cuda:0", L_self_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:04:20.2118962Z dict_getitem_l_stack0_list_dict_keys_l_stack0_0_ = dict_getitem_L_stack0_list_dict_keys_L_stack0_0_ 2025-03-04T22:04:20.2119520Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T22:04:20.2120169Z l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:04:20.2121114Z l_self_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:04:20.2122113Z l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:04:20.2123122Z l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:04:20.2123762Z 2025-03-04T22:04:20.2124545Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1224 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:04:20.2125619Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_decoder_input_ids_.view(-1, 1024); l_decoder_input_ids_ = None 2025-03-04T22:04:20.2126105Z 2025-03-04T22:04:20.2126904Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1235 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:04:20.2130004Z embedding: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:04:20.2131191Z inputs_embeds: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:04:20.2131647Z 2025-03-04T22:04:20.2132473Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:04:20.2133603Z mask: "f32[1024, 1024][1024, 1]cuda:0" = torch.full((1024, 1024), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:04:20.2134133Z 2025-03-04T22:04:20.2134883Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:04:20.2135852Z mask_cond: "i64[1024][1]cuda:0" = torch.arange(1024, device = device(type='cuda', index=0)) 2025-03-04T22:04:20.2136298Z 2025-03-04T22:04:20.2137093Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:04:20.2138299Z add: "i64[1024][1]cuda:0" = mask_cond + 1 2025-03-04T22:04:20.2138841Z view_1: "i64[1024, 1][1, 1]cuda:0" = add.view(1024, 1); add = None 2025-03-04T22:04:20.2139331Z lt: "b8[1024, 1024][1024, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:04:20.2139908Z masked_fill_: "f32[1024, 1024][1024, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:04:20.2140368Z 2025-03-04T22:04:20.2141021Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:04:20.2141850Z mask_1: "f32[1024, 1024][1024, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:04:20.2142254Z 2025-03-04T22:04:20.2143089Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:04:20.2144290Z getitem: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:04:20.2145151Z causal_4d_mask: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = getitem.expand(1, 1, 1024, 1024); getitem = causal_4d_mask = None 2025-03-04T22:04:20.2145713Z 2025-03-04T22:04:20.2146383Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:119 in forward, code: positions = torch.arange( 2025-03-04T22:04:20.2147342Z arange_1: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:04:20.2147848Z 2025-03-04T22:04:20.2148485Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:121 in forward, code: ).expand(bsz, -1) 2025-03-04T22:04:20.2149328Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange_1.expand(1, -1); arange_1 = None 2025-03-04T22:04:20.2149752Z 2025-03-04T22:04:20.2150487Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:123 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:04:20.2151383Z add_1: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:04:20.2152538Z positions_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_self_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_self_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:04:20.2153562Z 2025-03-04T22:04:20.2154380Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1259 in forward, code: hidden_states = inputs_embeds + positions.to(inputs_embeds.device) 2025-03-04T22:04:20.2155532Z to_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T22:04:20.2156296Z hidden_states: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = inputs_embeds + to_1; inputs_embeds = to_1 = None 2025-03-04T22:04:20.2164313Z 2025-03-04T22:04:20.2165112Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1260 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:04:20.2167368Z hidden_states_1: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:04:20.2169121Z 2025-03-04T22:04:20.2170026Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1262 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:04:20.2171403Z hidden_states_2: "f32[1, 1024, 1024][1048576, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:04:20.2172090Z 2025-03-04T22:04:20.2172793Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1290 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:04:20.2173625Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:04:20.2173982Z 2025-03-04T22:04:20.2174704Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mbart/modeling_mbart.py:1291 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:04:20.2175615Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:04:20.2176028Z 2025-03-04T22:04:42.4682773Z Compilation time (from dynamo_timed): 8.884409946 2025-03-04T22:04:42.4715542Z pass 2025-03-04T22:04:42.5768374Z TIMING: entire_frame_compile:6.68913 gc:0.01031 _recursive_pre_grad_passes:0.00768 _recursive_joint_graph_passes:0.41641 inductor_compile:4.26016 backend_compile:5.06158 _recursive_post_grad_passes:0.16821 async_compile.precompile:0.12666 async_compile.wait:0.5061 code_gen:2.48694 pad_mm_benchmark:0.04225 entire_backward_compile:2.19528 total_wall_time:8.88441 2025-03-04T22:04:42.5770180Z STATS: call_* op count: 136 | FakeTensorMode.__torch_dispatch__:9417 | FakeTensor.__torch_dispatch__:1421 | ProxyTorchDispatchMode.__torch_dispatch__:4358 2025-03-04T22:04:42.5771049Z Dynamo produced 8 graphs covering 136 ops with 8 graph breaks (5 unique) 2025-03-04T22:04:48.3537295Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:04:48.3538584Z warnings.warn( 2025-03-04T22:04:48.5927250Z 2025-03-04T22:04:52.7557048Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:04:52.7557639Z loading model: 0it [00:04, ?it/s] 2025-03-04T22:04:52.7558206Z cuda train MT5ForConditionalGeneration 2025-03-04T22:05:42.1787238Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:05:42.1790921Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:05:42.1792041Z pred = mod(**cloned_inputs) 2025-03-04T22:05:42.1793052Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mt5/modeling_mt5.py", line 1722, in forward 2025-03-04T22:05:42.1794050Z encoder_outputs = self.encoder( 2025-03-04T22:05:42.1794808Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mt5/modeling_mt5.py", line 989, in forward 2025-03-04T22:05:42.1795472Z inputs_embeds = self.embed_tokens(input_ids) 2025-03-04T22:05:42.1795713Z 2025-03-04T22:05:42.3424055Z W0304 22:05:42.341000 18307 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:06:26.1305215Z Compilation time (from dynamo_timed): 85.728881127 2025-03-04T22:06:26.1341014Z pass 2025-03-04T22:06:26.2799091Z TIMING: entire_frame_compile:73.15283 gc:0.00335 _recursive_pre_grad_passes:0.04748 pad_mm_benchmark:0.3421 _recursive_joint_graph_passes:2.49135 _recursive_post_grad_passes:0.98539 async_compile.wait:4.11752 code_gen:22.23873 inductor_compile:40.33339 backend_compile:57.26435 entire_backward_compile:12.57605 total_wall_time:85.72888 2025-03-04T22:06:26.2800999Z STATS: call_* op count: 2145 | FakeTensorMode.__torch_dispatch__:98737 | ProxyTorchDispatchMode.__torch_dispatch__:46228 | FakeTensor.__torch_dispatch__:17593 2025-03-04T22:06:26.2801837Z Dynamo produced 2 graphs covering 2145 ops with 5 graph breaks (4 unique) 2025-03-04T22:06:35.2843229Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:06:35.2844449Z warnings.warn( 2025-03-04T22:06:35.5640529Z 2025-03-04T22:06:35.5680865Z loading model: 0it [00:00, ?it/s]If you want to use `MegatronBertForCausalLM` as a standalone, add `is_decoder=True.` 2025-03-04T22:06:40.3676662Z 2025-03-04T22:06:40.3677280Z loading model: 0it [00:04, ?it/s] 2025-03-04T22:06:40.3677717Z cuda train MegatronBertForCausalLM 2025-03-04T22:07:44.4675440Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:07:44.4676611Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:07:44.4677343Z pred = mod(**cloned_inputs) 2025-03-04T22:07:44.4678071Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/megatron_bert/modeling_megatron_bert.py", line 1199, in forward 2025-03-04T22:07:44.4678796Z outputs = self.bert( 2025-03-04T22:07:44.4679480Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/megatron_bert/modeling_megatron_bert.py", line 967, in forward 2025-03-04T22:07:44.4680287Z embedding_output = self.embeddings( 2025-03-04T22:07:44.4681194Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/megatron_bert/modeling_megatron_bert.py", line 179, in forward 2025-03-04T22:07:44.4682061Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T22:07:44.4682326Z 2025-03-04T22:07:44.7000740Z W0304 22:07:44.699000 18655 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:09:14.4997732Z Compilation time (from dynamo_timed): 138.801974075 2025-03-04T22:09:14.5061983Z pass 2025-03-04T22:09:14.7068175Z TIMING: entire_frame_compile:123.9908 gc:0.00553 _recursive_pre_grad_passes:0.07604 pad_mm_benchmark:0.33956 _recursive_joint_graph_passes:2.89467 _recursive_post_grad_passes:1.23196 async_compile.wait:7.99783 code_gen:41.61938 inductor_compile:69.62122 backend_compile:97.63965 entire_backward_compile:14.81117 total_wall_time:138.80197 2025-03-04T22:09:14.7071819Z STATS: call_* op count: 2712 | FakeTensorMode.__torch_dispatch__:122444 | FakeTensor.__torch_dispatch__:27722 | ProxyTorchDispatchMode.__torch_dispatch__:55799 2025-03-04T22:09:14.7073558Z Dynamo produced 2 graphs covering 2712 ops with 5 graph breaks (4 unique) 2025-03-04T22:09:26.9771358Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:09:26.9772619Z warnings.warn( 2025-03-04T22:09:27.2949071Z 2025-03-04T22:09:31.8086679Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:09:31.8087370Z loading model: 0it [00:04, ?it/s] 2025-03-04T22:09:31.8088187Z cuda train MegatronBertForQuestionAnswering 2025-03-04T22:10:35.0996583Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:10:35.0999359Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:10:35.1000104Z pred = mod(**cloned_inputs) 2025-03-04T22:10:35.1001164Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/megatron_bert/modeling_megatron_bert.py", line 1792, in forward 2025-03-04T22:10:35.1002071Z outputs = self.bert( 2025-03-04T22:10:35.1002746Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/megatron_bert/modeling_megatron_bert.py", line 967, in forward 2025-03-04T22:10:35.1003467Z embedding_output = self.embeddings( 2025-03-04T22:10:35.1004185Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/megatron_bert/modeling_megatron_bert.py", line 179, in forward 2025-03-04T22:10:35.1004930Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T22:10:35.1005189Z 2025-03-04T22:10:35.3321527Z W0304 22:10:35.331000 19079 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:11:57.5512368Z Compilation time (from dynamo_timed): 130.735067829 2025-03-04T22:11:57.5569202Z pass 2025-03-04T22:11:57.7626375Z TIMING: entire_frame_compile:116.22118 gc:0.0048 _recursive_pre_grad_passes:0.08133 pad_mm_benchmark:0.54928 _recursive_joint_graph_passes:3.05057 _recursive_post_grad_passes:1.19564 async_compile.wait:1.29151 code_gen:34.28055 inductor_compile:61.96897 backend_compile:90.20439 entire_backward_compile:14.51389 total_wall_time:130.73507 2025-03-04T22:11:57.7628278Z STATS: call_* op count: 2700 | FakeTensorMode.__torch_dispatch__:121816 | FakeTensor.__torch_dispatch__:27557 | ProxyTorchDispatchMode.__torch_dispatch__:55544 2025-03-04T22:11:57.7629129Z Dynamo produced 2 graphs covering 2700 ops with 5 graph breaks (4 unique) 2025-03-04T22:12:09.7545044Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:12:09.7546295Z warnings.warn( 2025-03-04T22:12:10.1161301Z 2025-03-04T22:12:11.8150037Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:12:11.8150549Z loading model: 0it [00:01, ?it/s] 2025-03-04T22:12:11.8150899Z cuda train MobileBertForMaskedLM 2025-03-04T22:14:04.5631275Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:14:04.5632288Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:14:04.5633158Z pred = mod(**cloned_inputs) 2025-03-04T22:14:04.5633988Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mobilebert/modeling_mobilebert.py", line 1089, in forward 2025-03-04T22:14:04.5634826Z outputs = self.mobilebert( 2025-03-04T22:14:04.5635636Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mobilebert/modeling_mobilebert.py", line 895, in forward 2025-03-04T22:14:04.5636474Z embedding_output = self.embeddings( 2025-03-04T22:14:04.5637329Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mobilebert/modeling_mobilebert.py", line 218, in forward 2025-03-04T22:14:04.5638210Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T22:14:04.5638467Z 2025-03-04T22:14:07.2691288Z Compilation time (from dynamo_timed): 107.792144351 2025-03-04T22:14:07.2873339Z pass 2025-03-04T22:14:07.3357550Z TIMING: entire_frame_compile:81.61334 gc:0.00277 _recursive_pre_grad_passes:0.03049 pad_mm_benchmark:0.90546 _recursive_joint_graph_passes:5.14792 _recursive_post_grad_passes:2.88123 async_compile.wait:0.60895 code_gen:22.41698 inductor_compile:45.94083 backend_compile:57.22993 entire_backward_compile:26.1788 total_wall_time:107.79214 2025-03-04T22:14:07.3359701Z STATS: call_* op count: 1449 | FakeTensorMode.__torch_dispatch__:155644 | FakeTensor.__torch_dispatch__:17950 | ProxyTorchDispatchMode.__torch_dispatch__:74428 2025-03-04T22:14:07.3360710Z Dynamo produced 1 graphs covering 1449 ops with 3 graph breaks (3 unique) 2025-03-04T22:14:18.4576527Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:14:18.4577948Z warnings.warn( 2025-03-04T22:14:18.7838947Z 2025-03-04T22:14:20.3489831Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:14:20.3490362Z loading model: 0it [00:01, ?it/s] 2025-03-04T22:14:20.3490818Z cuda train MobileBertForQuestionAnswering 2025-03-04T22:16:12.0570480Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:16:12.0571342Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:16:12.0572058Z pred = mod(**cloned_inputs) 2025-03-04T22:16:12.0572767Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mobilebert/modeling_mobilebert.py", line 1390, in forward 2025-03-04T22:16:12.0573471Z outputs = self.mobilebert( 2025-03-04T22:16:12.0574160Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mobilebert/modeling_mobilebert.py", line 895, in forward 2025-03-04T22:16:12.0574865Z embedding_output = self.embeddings( 2025-03-04T22:16:12.0575563Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/mobilebert/modeling_mobilebert.py", line 218, in forward 2025-03-04T22:16:12.0576291Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T22:16:12.0576538Z 2025-03-04T22:16:14.7200646Z Compilation time (from dynamo_timed): 106.85095660200001 2025-03-04T22:16:14.7367122Z pass 2025-03-04T22:16:14.7702748Z TIMING: entire_frame_compile:80.98286 gc:0.00245 _recursive_pre_grad_passes:0.02778 pad_mm_benchmark:0.9249 _recursive_joint_graph_passes:5.77509 _recursive_post_grad_passes:2.89575 async_compile.wait:0.11216 code_gen:21.64847 inductor_compile:45.08614 backend_compile:56.75341 entire_backward_compile:25.8681 total_wall_time:106.85096 2025-03-04T22:16:14.7704744Z STATS: call_* op count: 1453 | FakeTensorMode.__torch_dispatch__:155448 | ProxyTorchDispatchMode.__torch_dispatch__:74369 | FakeTensor.__torch_dispatch__:17936 2025-03-04T22:16:14.7705689Z Dynamo produced 1 graphs covering 1453 ops with 3 graph breaks (3 unique) 2025-03-04T22:16:25.9017709Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:16:25.9018897Z warnings.warn( 2025-03-04T22:16:26.2191850Z 2025-03-04T22:16:28.7321822Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:16:28.7322213Z loading model: 0it [00:02, ?it/s] 2025-03-04T22:16:28.7322577Z cuda train OPTForCausalLM 2025-03-04T22:16:28.8047555Z WARNING:common:fp64 golden ref were not generated for OPTForCausalLM. Setting accuracy check to cosine 2025-03-04T22:16:31.4932941Z 2025-03-04T22:16:31.4933422Z class GraphModule(torch.nn.Module): 2025-03-04T22:16:31.4934837Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 2048][2048, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50272, 768][768, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[2050, 768][768, 1]cuda:0"): 2025-03-04T22:16:31.4936349Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:16:31.4937240Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:16:31.4938481Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:16:31.4939318Z 2025-03-04T22:16:31.4940091Z # No stacktrace found for following nodes 2025-03-04T22:16:31.4940997Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:16:31.4941644Z 2025-03-04T22:16:31.4942462Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:824 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:16:31.4943567Z input_ids: "i64[1, 2048][2048, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 2048); l_cloned_inputs_input_ids_ = None 2025-03-04T22:16:31.4944179Z 2025-03-04T22:16:31.4944949Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:831 in forward, code: inputs_embeds = self.embed_tokens(input_ids) 2025-03-04T22:16:31.4946732Z inputs_embeds: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:16:31.4947930Z 2025-03-04T22:16:31.4948859Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:850 in forward, code: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) 2025-03-04T22:16:31.4950111Z attention_mask: "f32[1, 2048][2048, 1]cuda:0" = torch.ones(1, 2048, device = device(type='cuda', index=0)) 2025-03-04T22:16:31.4950599Z 2025-03-04T22:16:31.4951492Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:16:31.4952778Z mask: "f32[2048, 2048][2048, 1]cuda:0" = torch.full((2048, 2048), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:16:31.4953341Z 2025-03-04T22:16:31.4954160Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:16:31.4955271Z mask_cond: "i64[2048][1]cuda:0" = torch.arange(2048, device = device(type='cuda', index=0)) 2025-03-04T22:16:31.4955727Z 2025-03-04T22:16:31.4956617Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:16:31.4957638Z add: "i64[2048][1]cuda:0" = mask_cond + 1 2025-03-04T22:16:31.4958083Z view_1: "i64[2048, 1][1, 1]cuda:0" = add.view(2048, 1); add = None 2025-03-04T22:16:31.4958649Z lt: "b8[2048, 2048][2048, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:16:31.4959251Z masked_fill_: "f32[2048, 2048][2048, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:16:31.4959792Z 2025-03-04T22:16:31.4960530Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:16:31.4961759Z mask_1: "f32[2048, 2048][2048, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:16:31.4962236Z 2025-03-04T22:16:31.4963184Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:16:31.4964848Z getitem: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:16:31.4966007Z causal_4d_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = getitem.expand(1, 1, 2048, 2048); getitem = None 2025-03-04T22:16:31.4966695Z 2025-03-04T22:16:31.4967599Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:182 in _expand_mask, code: expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) 2025-03-04T22:16:31.4969065Z getitem_1: "f32[1, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = attention_mask[(slice(None, None, None), None, None, slice(None, None, None))] 2025-03-04T22:16:31.4969878Z expand_1: "f32[1, 1, 2048, 2048][2048, 2048, 0, 1]cuda:0" = getitem_1.expand(1, 1, 2048, 2048); getitem_1 = None 2025-03-04T22:16:31.4970612Z expanded_mask: "f32[1, 1, 2048, 2048][2048, 2048, 0, 1]cuda:0" = expand_1.to(torch.float32); expand_1 = None 2025-03-04T22:16:31.4971106Z 2025-03-04T22:16:31.4971915Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:184 in _expand_mask, code: inverted_mask = 1.0 - expanded_mask 2025-03-04T22:16:31.4972977Z inverted_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = 1.0 - expanded_mask; expanded_mask = None 2025-03-04T22:16:31.4973563Z 2025-03-04T22:16:31.4974427Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:186 in _expand_mask, code: return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) 2025-03-04T22:16:31.4975587Z to_2: "b8[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = inverted_mask.to(torch.bool) 2025-03-04T22:16:31.4976353Z masked_fill: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = inverted_mask.masked_fill(to_2, -3.4028234663852886e+38); inverted_mask = to_2 = None 2025-03-04T22:16:31.4977037Z 2025-03-04T22:16:31.4977894Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:132 in to_4d, code: expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to( 2025-03-04T22:16:31.4979108Z expanded_attn_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = masked_fill.to(device(type='cuda', index=0)); masked_fill = None 2025-03-04T22:16:31.4979693Z 2025-03-04T22:16:31.4980551Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:137 in to_4d, code: expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min) 2025-03-04T22:16:31.4981837Z bool_1: "b8[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = expanded_attn_mask.bool(); expanded_attn_mask = None 2025-03-04T22:16:31.4982791Z expanded_attn_mask_1: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = causal_4d_mask.masked_fill(bool_1, -3.4028234663852886e+38); causal_4d_mask = bool_1 = expanded_attn_mask_1 = None 2025-03-04T22:16:31.4983515Z 2025-03-04T22:16:31.4984214Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:101 in forward, code: attention_mask = attention_mask.long() 2025-03-04T22:16:31.4985151Z attention_mask_1: "i64[1, 2048][2048, 1]cuda:0" = attention_mask.long(); attention_mask = None 2025-03-04T22:16:31.4985613Z 2025-03-04T22:16:31.4986509Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:104 in forward, code: positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 2025-03-04T22:16:31.4987594Z cumsum: "i64[1, 2048][2048, 1]cuda:0" = torch.cumsum(attention_mask_1, dim = 1) 2025-03-04T22:16:31.4988244Z type_as: "i64[1, 2048][2048, 1]cuda:0" = cumsum.type_as(attention_mask_1); cumsum = None 2025-03-04T22:16:31.4988851Z mul: "i64[1, 2048][2048, 1]cuda:0" = type_as * attention_mask_1; type_as = attention_mask_1 = None 2025-03-04T22:16:31.4989500Z long_1: "i64[1, 2048][2048, 1]cuda:0" = mul.long(); mul = None 2025-03-04T22:16:31.4990083Z positions: "i64[1, 2048][2048, 1]cuda:0" = long_1 - 1; long_1 = None 2025-03-04T22:16:31.4990474Z 2025-03-04T22:16:31.4991212Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:107 in forward, code: positions = positions[:, past_key_values_length:] 2025-03-04T22:16:31.4992259Z positions_1: "i64[1, 2048][2048, 1]cuda:0" = positions[(slice(None, None, None), slice(0, None, None))]; positions = None 2025-03-04T22:16:31.4992793Z 2025-03-04T22:16:31.4993525Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:109 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:16:31.4994418Z add_1: "i64[1, 2048][2048, 1]cuda:0" = positions_1 + 2; positions_1 = None 2025-03-04T22:16:31.4995651Z pos_embeds: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:16:31.4996767Z 2025-03-04T22:16:31.4997480Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:865 in forward, code: hidden_states = inputs_embeds + pos_embeds 2025-03-04T22:16:31.4998541Z hidden_states: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = inputs_embeds + pos_embeds; inputs_embeds = pos_embeds = hidden_states = None 2025-03-04T22:16:31.4999126Z 2025-03-04T22:16:31.4999818Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:894 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:16:31.5000644Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:16:31.5001011Z 2025-03-04T22:16:31.5001811Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:895 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:16:31.5002729Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:16:31.5003158Z 2025-03-04T22:16:31.5003311Z 2025-03-04T22:16:31.5003449Z class GraphModule(torch.nn.Module): 2025-03-04T22:16:31.5004548Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 2048][2048, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50272, 768][768, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[2050, 768][768, 1]cuda:0"): 2025-03-04T22:16:31.5005698Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:16:31.5006488Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:16:31.5007606Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:16:31.5008469Z 2025-03-04T22:16:31.5008729Z # No stacktrace found for following nodes 2025-03-04T22:16:31.5009312Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:16:31.5009846Z 2025-03-04T22:16:31.5010569Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:824 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:16:31.5011591Z input_ids: "i64[1, 2048][2048, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 2048); l_cloned_inputs_input_ids_ = None 2025-03-04T22:16:31.5012114Z 2025-03-04T22:16:31.5012926Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:831 in forward, code: inputs_embeds = self.embed_tokens(input_ids) 2025-03-04T22:16:31.5014604Z inputs_embeds: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:16:31.5015700Z 2025-03-04T22:16:31.5016550Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:850 in forward, code: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) 2025-03-04T22:16:31.5017667Z attention_mask: "f32[1, 2048][2048, 1]cuda:0" = torch.ones(1, 2048, device = device(type='cuda', index=0)) 2025-03-04T22:16:31.5018151Z 2025-03-04T22:16:31.5018979Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:16:31.5020112Z mask: "f32[2048, 2048][2048, 1]cuda:0" = torch.full((2048, 2048), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:16:31.5020650Z 2025-03-04T22:16:31.5021403Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:16:31.5022379Z mask_cond: "i64[2048][1]cuda:0" = torch.arange(2048, device = device(type='cuda', index=0)) 2025-03-04T22:16:31.5022833Z 2025-03-04T22:16:31.5023648Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:16:31.5024554Z add: "i64[2048][1]cuda:0" = mask_cond + 1 2025-03-04T22:16:31.5024966Z view_1: "i64[2048, 1][1, 1]cuda:0" = add.view(2048, 1); add = None 2025-03-04T22:16:31.5025456Z lt: "b8[2048, 2048][2048, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:16:31.5026045Z masked_fill_: "f32[2048, 2048][2048, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:16:31.5026513Z 2025-03-04T22:16:31.5027171Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:16:31.5028113Z mask_1: "f32[2048, 2048][2048, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:16:31.5028526Z 2025-03-04T22:16:31.5029364Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:16:31.5030571Z getitem: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:16:31.5031392Z causal_4d_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = getitem.expand(1, 1, 2048, 2048); getitem = None 2025-03-04T22:16:31.5031909Z 2025-03-04T22:16:31.5032721Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:182 in _expand_mask, code: expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) 2025-03-04T22:16:31.5033868Z getitem_1: "f32[1, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = attention_mask[(slice(None, None, None), None, None, slice(None, None, None))] 2025-03-04T22:16:31.5034693Z expand_1: "f32[1, 1, 2048, 2048][2048, 2048, 0, 1]cuda:0" = getitem_1.expand(1, 1, 2048, 2048); getitem_1 = None 2025-03-04T22:16:31.5035352Z expanded_mask: "f32[1, 1, 2048, 2048][2048, 2048, 0, 1]cuda:0" = expand_1.to(torch.float32); expand_1 = None 2025-03-04T22:16:31.5035902Z 2025-03-04T22:16:31.5036588Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:184 in _expand_mask, code: inverted_mask = 1.0 - expanded_mask 2025-03-04T22:16:31.5037553Z inverted_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = 1.0 - expanded_mask; expanded_mask = None 2025-03-04T22:16:31.5038059Z 2025-03-04T22:16:31.5038924Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:186 in _expand_mask, code: return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) 2025-03-04T22:16:31.5039984Z to_2: "b8[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = inverted_mask.to(torch.bool) 2025-03-04T22:16:31.5040748Z masked_fill: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = inverted_mask.masked_fill(to_2, -3.4028234663852886e+38); inverted_mask = to_2 = None 2025-03-04T22:16:31.5041368Z 2025-03-04T22:16:31.5042209Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:132 in to_4d, code: expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to( 2025-03-04T22:16:31.5043412Z expanded_attn_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = masked_fill.to(device(type='cuda', index=0)); masked_fill = None 2025-03-04T22:16:31.5044001Z 2025-03-04T22:16:31.5044870Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:137 in to_4d, code: expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min) 2025-03-04T22:16:31.5046011Z bool_1: "b8[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = expanded_attn_mask.bool(); expanded_attn_mask = None 2025-03-04T22:16:31.5046969Z expanded_attn_mask_1: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = causal_4d_mask.masked_fill(bool_1, -3.4028234663852886e+38); causal_4d_mask = bool_1 = expanded_attn_mask_1 = None 2025-03-04T22:16:31.5047709Z 2025-03-04T22:16:31.5048532Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:101 in forward, code: attention_mask = attention_mask.long() 2025-03-04T22:16:31.5049462Z attention_mask_1: "i64[1, 2048][2048, 1]cuda:0" = attention_mask.long(); attention_mask = None 2025-03-04T22:16:31.5049924Z 2025-03-04T22:16:31.5050821Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:104 in forward, code: positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 2025-03-04T22:16:31.5051920Z cumsum: "i64[1, 2048][2048, 1]cuda:0" = torch.cumsum(attention_mask_1, dim = 1) 2025-03-04T22:16:31.5052493Z type_as: "i64[1, 2048][2048, 1]cuda:0" = cumsum.type_as(attention_mask_1); cumsum = None 2025-03-04T22:16:31.5053096Z mul: "i64[1, 2048][2048, 1]cuda:0" = type_as * attention_mask_1; type_as = attention_mask_1 = None 2025-03-04T22:16:31.5053631Z long_1: "i64[1, 2048][2048, 1]cuda:0" = mul.long(); mul = None 2025-03-04T22:16:31.5054099Z positions: "i64[1, 2048][2048, 1]cuda:0" = long_1 - 1; long_1 = None 2025-03-04T22:16:31.5054488Z 2025-03-04T22:16:31.5055225Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:107 in forward, code: positions = positions[:, past_key_values_length:] 2025-03-04T22:16:31.5056268Z positions_1: "i64[1, 2048][2048, 1]cuda:0" = positions[(slice(None, None, None), slice(0, None, None))]; positions = None 2025-03-04T22:16:31.5056803Z 2025-03-04T22:16:31.5057621Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:109 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:16:31.5058651Z add_1: "i64[1, 2048][2048, 1]cuda:0" = positions_1 + 2; positions_1 = None 2025-03-04T22:16:31.5059870Z pos_embeds: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:16:31.5060967Z 2025-03-04T22:16:31.5062070Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:865 in forward, code: hidden_states = inputs_embeds + pos_embeds 2025-03-04T22:16:31.5063136Z hidden_states: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = inputs_embeds + pos_embeds; inputs_embeds = pos_embeds = hidden_states = None 2025-03-04T22:16:31.5063704Z 2025-03-04T22:16:31.5064392Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:894 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:16:31.5065203Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:16:31.5065545Z 2025-03-04T22:16:31.5066236Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:895 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:16:31.5067153Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:16:31.5067574Z 2025-03-04T22:16:32.3925170Z 2025-03-04T22:16:32.3925986Z class GraphModule(torch.nn.Module): 2025-03-04T22:16:32.3927994Z def forward(self, L_input_ids_: "i64[1, 2048][2048, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50272, 768][768, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[2050, 768][768, 1]cuda:0"): 2025-03-04T22:16:32.3929868Z l_input_ids_ = L_input_ids_ 2025-03-04T22:16:32.3931031Z l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:16:32.3932856Z l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:16:32.3934045Z 2025-03-04T22:16:32.3935272Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:824 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:16:32.3936868Z input_ids: "i64[1, 2048][2048, 1]cuda:0" = l_input_ids_.view(-1, 2048); l_input_ids_ = None 2025-03-04T22:16:32.3937597Z 2025-03-04T22:16:32.3938810Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:831 in forward, code: inputs_embeds = self.embed_tokens(input_ids) 2025-03-04T22:16:32.3941735Z inputs_embeds: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:16:32.3943739Z 2025-03-04T22:16:32.3945233Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:850 in forward, code: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device) 2025-03-04T22:16:32.3947198Z attention_mask: "f32[1, 2048][2048, 1]cuda:0" = torch.ones(1, 2048, device = device(type='cuda', index=0)) 2025-03-04T22:16:32.3948034Z 2025-03-04T22:16:32.3949915Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:16:32.3952212Z mask: "f32[2048, 2048][2048, 1]cuda:0" = torch.full((2048, 2048), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:16:32.3953140Z 2025-03-04T22:16:32.3954405Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:16:32.3956144Z mask_cond: "i64[2048][1]cuda:0" = torch.arange(2048, device = device(type='cuda', index=0)) 2025-03-04T22:16:32.3956889Z 2025-03-04T22:16:32.3958297Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:16:32.3959840Z add: "i64[2048][1]cuda:0" = mask_cond + 1 2025-03-04T22:16:32.3960478Z view_1: "i64[2048, 1][1, 1]cuda:0" = add.view(2048, 1); add = None 2025-03-04T22:16:32.3961610Z lt: "b8[2048, 2048][2048, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:16:32.3962612Z masked_fill_: "f32[2048, 2048][2048, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:16:32.3963394Z 2025-03-04T22:16:32.3964505Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:16:32.3965965Z mask_1: "f32[2048, 2048][2048, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:16:32.3966646Z 2025-03-04T22:16:32.3968225Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:16:32.3970360Z getitem: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:16:32.3971794Z causal_4d_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = getitem.expand(1, 1, 2048, 2048); getitem = None 2025-03-04T22:16:32.3972690Z 2025-03-04T22:16:32.3974152Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:182 in _expand_mask, code: expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype) 2025-03-04T22:16:32.3976164Z getitem_1: "f32[1, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = attention_mask[(slice(None, None, None), None, None, slice(None, None, None))] 2025-03-04T22:16:32.3977439Z expand_1: "f32[1, 1, 2048, 2048][2048, 2048, 0, 1]cuda:0" = getitem_1.expand(1, 1, 2048, 2048); getitem_1 = None 2025-03-04T22:16:32.3978578Z expanded_mask: "f32[1, 1, 2048, 2048][2048, 2048, 0, 1]cuda:0" = expand_1.to(torch.float32); expand_1 = None 2025-03-04T22:16:32.3979399Z 2025-03-04T22:16:32.3980578Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:184 in _expand_mask, code: inverted_mask = 1.0 - expanded_mask 2025-03-04T22:16:32.3982284Z inverted_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = 1.0 - expanded_mask; expanded_mask = None 2025-03-04T22:16:32.3983155Z 2025-03-04T22:16:32.3984704Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:186 in _expand_mask, code: return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min) 2025-03-04T22:16:32.3986561Z to_2: "b8[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = inverted_mask.to(torch.bool) 2025-03-04T22:16:32.3988068Z masked_fill: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = inverted_mask.masked_fill(to_2, -3.4028234663852886e+38); inverted_mask = to_2 = None 2025-03-04T22:16:32.3989263Z 2025-03-04T22:16:32.3990732Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:132 in to_4d, code: expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to( 2025-03-04T22:16:32.3992863Z expanded_attn_mask: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = masked_fill.to(device(type='cuda', index=0)); masked_fill = None 2025-03-04T22:16:32.3993897Z 2025-03-04T22:16:32.3995432Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:137 in to_4d, code: expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min) 2025-03-04T22:16:32.3997466Z bool_1: "b8[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = expanded_attn_mask.bool(); expanded_attn_mask = None 2025-03-04T22:16:32.3999163Z expanded_attn_mask_1: "f32[1, 1, 2048, 2048][4194304, 4194304, 2048, 1]cuda:0" = causal_4d_mask.masked_fill(bool_1, -3.4028234663852886e+38); causal_4d_mask = bool_1 = expanded_attn_mask_1 = None 2025-03-04T22:16:32.4000428Z 2025-03-04T22:16:32.4001650Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:101 in forward, code: attention_mask = attention_mask.long() 2025-03-04T22:16:32.4003297Z attention_mask_1: "i64[1, 2048][2048, 1]cuda:0" = attention_mask.long(); attention_mask = None 2025-03-04T22:16:32.4004092Z 2025-03-04T22:16:32.4005677Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:104 in forward, code: positions = (torch.cumsum(attention_mask, dim=1).type_as(attention_mask) * attention_mask).long() - 1 2025-03-04T22:16:32.4007625Z cumsum: "i64[1, 2048][2048, 1]cuda:0" = torch.cumsum(attention_mask_1, dim = 1) 2025-03-04T22:16:32.4008905Z type_as: "i64[1, 2048][2048, 1]cuda:0" = cumsum.type_as(attention_mask_1); cumsum = None 2025-03-04T22:16:32.4009945Z mul: "i64[1, 2048][2048, 1]cuda:0" = type_as * attention_mask_1; type_as = attention_mask_1 = None 2025-03-04T22:16:32.4010857Z long_1: "i64[1, 2048][2048, 1]cuda:0" = mul.long(); mul = None 2025-03-04T22:16:32.4011642Z positions: "i64[1, 2048][2048, 1]cuda:0" = long_1 - 1; long_1 = None 2025-03-04T22:16:32.4012309Z 2025-03-04T22:16:32.4013606Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:107 in forward, code: positions = positions[:, past_key_values_length:] 2025-03-04T22:16:32.4015452Z positions_1: "i64[1, 2048][2048, 1]cuda:0" = positions[(slice(None, None, None), slice(0, None, None))]; positions = None 2025-03-04T22:16:32.4016374Z 2025-03-04T22:16:32.4017616Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:109 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:16:32.4019243Z add_1: "i64[1, 2048][2048, 1]cuda:0" = positions_1 + 2; positions_1 = None 2025-03-04T22:16:32.4021408Z pos_embeds: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:16:32.4023390Z 2025-03-04T22:16:32.4024603Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:865 in forward, code: hidden_states = inputs_embeds + pos_embeds 2025-03-04T22:16:32.4026657Z hidden_states: "f32[1, 2048, 768][1572864, 768, 1]cuda:0" = inputs_embeds + pos_embeds; inputs_embeds = pos_embeds = hidden_states = None 2025-03-04T22:16:32.4027808Z 2025-03-04T22:16:32.4029029Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:894 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:16:32.4030488Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:16:32.4031096Z 2025-03-04T22:16:32.4032328Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py:895 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:16:32.4033934Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:16:32.4034687Z 2025-03-04T22:16:37.6040381Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:16:37.6041295Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/opt/modeling_opt.py", line 109, in forward 2025-03-04T22:16:37.6042017Z return super().forward(positions + self.offset) 2025-03-04T22:16:37.6042303Z 2025-03-04T22:16:44.9860004Z Compilation time (from dynamo_timed): 5.921347672 2025-03-04T22:16:44.9873245Z pass 2025-03-04T22:16:45.0127221Z TIMING: entire_frame_compile:4.42064 gc:0.00683 _recursive_pre_grad_passes:0.0076 _recursive_joint_graph_passes:0.24847 inductor_compile:3.07744 backend_compile:3.47052 _recursive_post_grad_passes:0.08843 async_compile.precompile:0.10572 async_compile.wait:0.90421 code_gen:2.10393 pad_mm_benchmark:0.01596 entire_backward_compile:1.5007 total_wall_time:5.92135 2025-03-04T22:16:45.0129882Z STATS: call_* op count: 82 | FakeTensorMode.__torch_dispatch__:4839 | ProxyTorchDispatchMode.__torch_dispatch__:2031 | FakeTensor.__torch_dispatch__:703 2025-03-04T22:16:45.0130703Z Dynamo produced 6 graphs covering 82 ops with 6 graph breaks (5 unique) 2025-03-04T22:16:50.7301240Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:16:50.7302454Z warnings.warn( 2025-03-04T22:16:50.9994199Z 2025-03-04T22:16:53.0567065Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:16:53.0567759Z loading model: 0it [00:02, ?it/s] 2025-03-04T22:16:53.0568606Z cuda train PLBartForCausalLM 2025-03-04T22:16:53.0742887Z WARNING:common:fp64 golden ref were not generated for PLBartForCausalLM. Setting accuracy check to cosine 2025-03-04T22:16:54.8753580Z 2025-03-04T22:16:54.8754241Z class GraphModule(torch.nn.Module): 2025-03-04T22:16:54.8756092Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:16:54.8757950Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:16:54.8758736Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:16:54.8759866Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:16:54.8761046Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:16:54.8762951Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:16:54.8763820Z 2025-03-04T22:16:54.8764083Z # No stacktrace found for following nodes 2025-03-04T22:16:54.8764666Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:16:54.8765194Z 2025-03-04T22:16:54.8765965Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:968 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:16:54.8766962Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); input_ids = None 2025-03-04T22:16:54.8767430Z 2025-03-04T22:16:54.8768332Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:979 in forward, code: inputs_embeds = self.embed_tokens(input) * self.embed_scale 2025-03-04T22:16:54.8770125Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(l_cloned_inputs_input_ids_, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_cloned_inputs_input_ids_ = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:16:54.8771503Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:16:54.8771972Z 2025-03-04T22:16:54.8772787Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:16:54.8773956Z mask: "f32[1024, 1024][1024, 1]cuda:0" = torch.full((1024, 1024), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:16:54.8774486Z 2025-03-04T22:16:54.8775236Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:16:54.8776203Z mask_cond: "i64[1024][1]cuda:0" = torch.arange(1024, device = device(type='cuda', index=0)) 2025-03-04T22:16:54.8776649Z 2025-03-04T22:16:54.8777446Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:16:54.8778484Z add: "i64[1024][1]cuda:0" = mask_cond + 1 2025-03-04T22:16:54.8778887Z view_1: "i64[1024, 1][1, 1]cuda:0" = add.view(1024, 1); add = None 2025-03-04T22:16:54.8779552Z lt: "b8[1024, 1024][1024, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:16:54.8780344Z masked_fill_: "f32[1024, 1024][1024, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:16:54.8780977Z 2025-03-04T22:16:54.8781629Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:16:54.8782459Z mask_1: "f32[1024, 1024][1024, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:16:54.8782854Z 2025-03-04T22:16:54.8783705Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:16:54.8784913Z getitem: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:16:54.8785763Z causal_4d_mask: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = getitem.expand(1, 1, 1024, 1024); getitem = causal_4d_mask = None 2025-03-04T22:16:54.8786416Z 2025-03-04T22:16:54.8787091Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:16:54.8788123Z arange_1: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:16:54.8788622Z 2025-03-04T22:16:54.8789251Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:16:54.8790089Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange_1.expand(1, -1); arange_1 = None 2025-03-04T22:16:54.8790502Z 2025-03-04T22:16:54.8791243Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:16:54.8792147Z add_1: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:16:54.8793339Z positions_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:16:54.8794424Z 2025-03-04T22:16:54.8795168Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1020 in forward, code: positions = positions.to(inputs_embeds.device) 2025-03-04T22:16:54.8796222Z positions_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T22:16:54.8796724Z 2025-03-04T22:16:54.8797451Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1022 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:16:54.8798484Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + positions_2; inputs_embeds = positions_2 = None 2025-03-04T22:16:54.8799010Z 2025-03-04T22:16:54.8799795Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1023 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:16:54.8802043Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:16:54.8811869Z 2025-03-04T22:16:54.8812977Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1025 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:16:54.8814447Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:16:54.8815129Z 2025-03-04T22:16:54.8815837Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1054 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:16:54.8816681Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:16:54.8817018Z 2025-03-04T22:16:54.8817865Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1055 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:16:54.8818793Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:16:54.8819289Z 2025-03-04T22:16:54.8819442Z 2025-03-04T22:16:54.8819570Z class GraphModule(torch.nn.Module): 2025-03-04T22:16:54.8821282Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:16:54.8823032Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:16:54.8823793Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:16:54.8824890Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:16:54.8826041Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:16:54.8827204Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:16:54.8827917Z 2025-03-04T22:16:54.8828174Z # No stacktrace found for following nodes 2025-03-04T22:16:54.8828750Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:16:54.8829275Z 2025-03-04T22:16:54.8830030Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:968 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:16:54.8831020Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); input_ids = None 2025-03-04T22:16:54.8831481Z 2025-03-04T22:16:54.8832264Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:979 in forward, code: inputs_embeds = self.embed_tokens(input) * self.embed_scale 2025-03-04T22:16:54.8834073Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(l_cloned_inputs_input_ids_, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_cloned_inputs_input_ids_ = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:16:54.8835447Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:16:54.8835921Z 2025-03-04T22:16:54.8836736Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:16:54.8837852Z mask: "f32[1024, 1024][1024, 1]cuda:0" = torch.full((1024, 1024), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:16:54.8838378Z 2025-03-04T22:16:54.8839123Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:16:54.8840088Z mask_cond: "i64[1024][1]cuda:0" = torch.arange(1024, device = device(type='cuda', index=0)) 2025-03-04T22:16:54.8840534Z 2025-03-04T22:16:54.8841421Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:16:54.8842384Z add: "i64[1024][1]cuda:0" = mask_cond + 1 2025-03-04T22:16:54.8842780Z view_1: "i64[1024, 1][1, 1]cuda:0" = add.view(1024, 1); add = None 2025-03-04T22:16:54.8843263Z lt: "b8[1024, 1024][1024, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:16:54.8843839Z masked_fill_: "f32[1024, 1024][1024, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:16:54.8844297Z 2025-03-04T22:16:54.8844948Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:16:54.8845768Z mask_1: "f32[1024, 1024][1024, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:16:54.8846167Z 2025-03-04T22:16:54.8847000Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:16:54.8848315Z getitem: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:16:54.8849171Z causal_4d_mask: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = getitem.expand(1, 1, 1024, 1024); getitem = causal_4d_mask = None 2025-03-04T22:16:54.8849726Z 2025-03-04T22:16:54.8850401Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:16:54.8851359Z arange_1: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:16:54.8851863Z 2025-03-04T22:16:54.8852513Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:16:54.8853362Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange_1.expand(1, -1); arange_1 = None 2025-03-04T22:16:54.8853778Z 2025-03-04T22:16:54.8854521Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:16:54.8855423Z add_1: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:16:54.8856612Z positions_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:16:54.8857696Z 2025-03-04T22:16:54.8858452Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1020 in forward, code: positions = positions.to(inputs_embeds.device) 2025-03-04T22:16:54.8859501Z positions_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T22:16:54.8860012Z 2025-03-04T22:16:54.8860736Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1022 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:16:54.8862054Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + positions_2; inputs_embeds = positions_2 = None 2025-03-04T22:16:54.8862583Z 2025-03-04T22:16:54.8863585Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1023 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:16:54.8865943Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:16:54.8867622Z 2025-03-04T22:16:54.8868540Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1025 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:16:54.8869919Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:16:54.8870593Z 2025-03-04T22:16:54.8871304Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1054 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:16:54.8872146Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:16:54.8872491Z 2025-03-04T22:16:54.8873218Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1055 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:16:54.8874194Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:16:54.8874612Z 2025-03-04T22:16:55.7678672Z 2025-03-04T22:16:55.7679392Z class GraphModule(torch.nn.Module): 2025-03-04T22:16:55.7681723Z def forward(self, L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:16:55.7683895Z l_input_ids_ = L_input_ids_ 2025-03-04T22:16:55.7684744Z l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:16:55.7686059Z l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:16:55.7687565Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:16:55.7689206Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:16:55.7690095Z 2025-03-04T22:16:55.7691047Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:968 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:16:55.7692159Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); input_ids = None 2025-03-04T22:16:55.7692724Z 2025-03-04T22:16:55.7693667Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:979 in forward, code: inputs_embeds = self.embed_tokens(input) * self.embed_scale 2025-03-04T22:16:55.7695970Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(l_input_ids_, l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_input_ids_ = l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:16:55.7697596Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:16:55.7698160Z 2025-03-04T22:16:55.7699064Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:16:55.7700312Z mask: "f32[1024, 1024][1024, 1]cuda:0" = torch.full((1024, 1024), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:16:55.7700892Z 2025-03-04T22:16:55.7701741Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:16:55.7702838Z mask_cond: "i64[1024][1]cuda:0" = torch.arange(1024, device = device(type='cuda', index=0)) 2025-03-04T22:16:55.7703347Z 2025-03-04T22:16:55.7704237Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:16:55.7705227Z add: "i64[1024][1]cuda:0" = mask_cond + 1 2025-03-04T22:16:55.7705711Z view_1: "i64[1024, 1][1, 1]cuda:0" = add.view(1024, 1); add = None 2025-03-04T22:16:55.7706237Z lt: "b8[1024, 1024][1024, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:16:55.7706872Z masked_fill_: "f32[1024, 1024][1024, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:16:55.7707420Z 2025-03-04T22:16:55.7708169Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:16:55.7709097Z mask_1: "f32[1024, 1024][1024, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:16:55.7709508Z 2025-03-04T22:16:55.7710445Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:16:55.7711820Z getitem: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:16:55.7712770Z causal_4d_mask: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = getitem.expand(1, 1, 1024, 1024); getitem = causal_4d_mask = None 2025-03-04T22:16:55.7713410Z 2025-03-04T22:16:55.7714179Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:16:55.7715239Z arange_1: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:16:55.7715825Z 2025-03-04T22:16:55.7716543Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:16:55.7717478Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange_1.expand(1, -1); arange_1 = None 2025-03-04T22:16:55.7717910Z 2025-03-04T22:16:55.7718745Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:16:55.7719746Z add_1: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:16:55.7721256Z positions_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:16:55.7722590Z 2025-03-04T22:16:55.7723374Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1020 in forward, code: positions = positions.to(inputs_embeds.device) 2025-03-04T22:16:55.7724543Z positions_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T22:16:55.7725071Z 2025-03-04T22:16:55.7725814Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1022 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:16:55.7726882Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + positions_2; inputs_embeds = positions_2 = None 2025-03-04T22:16:55.7727428Z 2025-03-04T22:16:55.7728473Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1023 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:16:55.7730797Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:16:55.7732525Z 2025-03-04T22:16:55.7733457Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1025 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:16:55.7734993Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:16:55.7735676Z 2025-03-04T22:16:55.7736396Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1054 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:16:55.7737250Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:16:55.7737619Z 2025-03-04T22:16:55.7738352Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1055 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:16:55.7739280Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:16:55.7739704Z 2025-03-04T22:17:00.7935412Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:17:00.7936487Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py", line 106, in forward 2025-03-04T22:17:00.7937298Z return super().forward(positions + self.offset) 2025-03-04T22:17:00.7937558Z 2025-03-04T22:17:04.7678520Z Compilation time (from dynamo_timed): 5.815518417 2025-03-04T22:17:04.7689553Z pass 2025-03-04T22:17:04.7998876Z TIMING: entire_frame_compile:4.37731 gc:0.00625 _recursive_pre_grad_passes:0.00613 _recursive_joint_graph_passes:0.50585 inductor_compile:2.82813 backend_compile:3.4717 async_compile.precompile:0.1061 async_compile.wait:0.754 _recursive_post_grad_passes:0.07936 code_gen:1.89594 pad_mm_benchmark:0.26335 entire_backward_compile:1.43821 total_wall_time:5.81552 2025-03-04T22:17:04.8001016Z STATS: call_* op count: 60 | FakeTensorMode.__torch_dispatch__:4413 | FakeTensor.__torch_dispatch__:675 | ProxyTorchDispatchMode.__torch_dispatch__:1868 2025-03-04T22:17:04.8001988Z Dynamo produced 6 graphs covering 60 ops with 6 graph breaks (5 unique) 2025-03-04T22:17:10.4563039Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:17:10.4564319Z warnings.warn( 2025-03-04T22:17:10.8406475Z 2025-03-04T22:17:14.3475756Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:17:14.3476215Z loading model: 0it [00:03, ?it/s] 2025-03-04T22:17:14.3476673Z cuda train PLBartForConditionalGeneration 2025-03-04T22:17:14.4161519Z WARNING:common:fp64 golden ref were not generated for PLBartForConditionalGeneration. Setting accuracy check to cosine 2025-03-04T22:17:16.8196822Z 2025-03-04T22:17:16.8197410Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:16.8199413Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:17:16.8201348Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:17:16.8201777Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:17:16.8202542Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:16.8203653Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:16.8204820Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:17:16.8205994Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:17:16.8206715Z 2025-03-04T22:17:16.8206999Z # No stacktrace found for following nodes 2025-03-04T22:17:16.8207581Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:17:16.8208286Z 2025-03-04T22:17:16.8209064Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:71 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:17:16.8210115Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.clone(); l_cloned_inputs_labels_ = None 2025-03-04T22:17:16.8210637Z 2025-03-04T22:17:16.8211506Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:76 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:17:16.8212513Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:17:16.8213085Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:17:16.8213587Z 2025-03-04T22:17:16.8214465Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:78 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:17:16.8215869Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:17:16.8216458Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:17:16.8216842Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:17:16.8217266Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:17:16.8217646Z 2025-03-04T22:17:16.8218517Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:79 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:17:16.8220000Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:17:16.8220702Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:17:16.8221130Z 2025-03-04T22:17:16.8222310Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:80 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:17:16.8223567Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:17:16.8224397Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:17:16.8225216Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:17:16.8225795Z 2025-03-04T22:17:16.8226568Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:81 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:17:16.8227811Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:17:16.8228492Z 2025-03-04T22:17:16.8229248Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:748 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T22:17:16.8230295Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T22:17:16.8230806Z 2025-03-04T22:17:16.8231600Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:755 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:16.8233265Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:16.8234531Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:17:16.8235005Z 2025-03-04T22:17:16.8235684Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:17:16.8236651Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:16.8237158Z 2025-03-04T22:17:16.8237801Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:17:16.8238639Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:17:16.8239055Z 2025-03-04T22:17:16.8239920Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:17:16.8240903Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:17:16.8242076Z embed_pos: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:16.8243145Z 2025-03-04T22:17:16.8243885Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:758 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T22:17:16.8244902Z embed_pos_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:17:16.8245393Z 2025-03-04T22:17:16.8246116Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:760 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:17:16.8247144Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T22:17:16.8247736Z 2025-03-04T22:17:16.8248957Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:761 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:17:16.8251567Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:17:16.8253271Z 2025-03-04T22:17:16.8254182Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:762 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:16.8255553Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:17:16.8256224Z 2025-03-04T22:17:16.8256929Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:794 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:16.8257763Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:16.8258106Z 2025-03-04T22:17:16.8258888Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:795 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:17:16.8259856Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:17:16.8260258Z 2025-03-04T22:17:16.8260422Z 2025-03-04T22:17:16.8260549Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:16.8262739Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:17:16.8264809Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:17:16.8265387Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:17:16.8266145Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:16.8267246Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:16.8268410Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:17:16.8269584Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:17:16.8270305Z 2025-03-04T22:17:16.8270571Z # No stacktrace found for following nodes 2025-03-04T22:17:16.8271157Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:17:16.8271690Z 2025-03-04T22:17:16.8272444Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:71 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:17:16.8273495Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.clone(); l_cloned_inputs_labels_ = None 2025-03-04T22:17:16.8274015Z 2025-03-04T22:17:16.8274882Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:76 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:17:16.8275933Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:17:16.8276519Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:17:16.8277030Z 2025-03-04T22:17:16.8277916Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:78 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:17:16.8278921Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:17:16.8279331Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:17:16.8279706Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:17:16.8280126Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:17:16.8280503Z 2025-03-04T22:17:16.8281373Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:79 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:17:16.8282482Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:17:16.8283059Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:17:16.8283467Z 2025-03-04T22:17:16.8284278Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:80 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:17:16.8285360Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:17:16.8285963Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:17:16.8286741Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:17:16.8287321Z 2025-03-04T22:17:16.8288302Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:81 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:17:16.8289546Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:17:16.8290231Z 2025-03-04T22:17:16.8290990Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:748 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T22:17:16.8292044Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T22:17:16.8292564Z 2025-03-04T22:17:16.8293372Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:755 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:16.8295057Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:16.8296325Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:17:16.8296797Z 2025-03-04T22:17:16.8297474Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:17:16.8298432Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:16.8298939Z 2025-03-04T22:17:16.8299593Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:17:16.8300445Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:17:16.8300875Z 2025-03-04T22:17:16.8301623Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:17:16.8302529Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:17:16.8303709Z embed_pos: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:16.8304790Z 2025-03-04T22:17:16.8305566Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:758 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T22:17:16.8306621Z embed_pos_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:17:16.8307118Z 2025-03-04T22:17:16.8307847Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:760 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:17:16.8308878Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T22:17:16.8309399Z 2025-03-04T22:17:16.8310257Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:761 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:17:16.8312586Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:17:16.8314275Z 2025-03-04T22:17:16.8315181Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:762 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:16.8316558Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:17:16.8317238Z 2025-03-04T22:17:16.8317947Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:794 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:16.8318784Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:16.8319128Z 2025-03-04T22:17:16.8319899Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:795 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:17:16.8320867Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:17:16.8321264Z 2025-03-04T22:17:16.8321393Z 2025-03-04T22:17:16.8321515Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:16.8323398Z def forward(self, L_cloned_inputs_labels_: "i64[1, 1024][1024, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:17:16.8325301Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:17:16.8325719Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:17:16.8326676Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:16.8327785Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:16.8329052Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:17:16.8330231Z l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:17:16.8330947Z 2025-03-04T22:17:16.8331202Z # No stacktrace found for following nodes 2025-03-04T22:17:16.8331779Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:17:16.8332317Z 2025-03-04T22:17:16.8333073Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:71 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:17:16.8334217Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_labels_.clone(); l_cloned_inputs_labels_ = None 2025-03-04T22:17:16.8334813Z 2025-03-04T22:17:16.8335703Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:76 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:17:16.8336721Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:17:16.8337308Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:17:16.8337810Z 2025-03-04T22:17:16.8338684Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:78 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:17:16.8339688Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:17:16.8349122Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:17:16.8349545Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:17:16.8349979Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:17:16.8350378Z 2025-03-04T22:17:16.8351285Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:79 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:17:16.8352423Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:17:16.8353021Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:17:16.8353456Z 2025-03-04T22:17:16.8354302Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:80 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:17:16.8355411Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:17:16.8356040Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:17:16.8356754Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:17:16.8357360Z 2025-03-04T22:17:16.8358170Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:81 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:17:16.8359440Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:17:16.8360153Z 2025-03-04T22:17:16.8360942Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:748 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T22:17:16.8362300Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 1024); l_cloned_inputs_input_ids_ = None 2025-03-04T22:17:16.8362848Z 2025-03-04T22:17:16.8363672Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:755 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:16.8365369Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:16.8366878Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:17:16.8367504Z 2025-03-04T22:17:16.8368317Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:17:16.8369311Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:16.8369834Z 2025-03-04T22:17:16.8370492Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:17:16.8371344Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:17:16.8371769Z 2025-03-04T22:17:16.8372535Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:17:16.8373451Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:17:16.8374645Z embed_pos: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:16.8375732Z 2025-03-04T22:17:16.8376488Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:758 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T22:17:16.8377525Z embed_pos_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:17:16.8378038Z 2025-03-04T22:17:16.8378783Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:760 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:17:16.8379833Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T22:17:16.8380362Z 2025-03-04T22:17:16.8381141Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:761 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:17:16.8383408Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:17:16.8385113Z 2025-03-04T22:17:16.8386081Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:762 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:16.8387464Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:17:16.8388141Z 2025-03-04T22:17:16.8388850Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:794 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:16.8389691Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:16.8390047Z 2025-03-04T22:17:16.8390919Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:795 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:17:16.8391960Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:17:16.8392366Z 2025-03-04T22:17:17.7207228Z 2025-03-04T22:17:17.7208071Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:17.7210164Z def forward(self, L_labels_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:17:17.7212097Z l_labels_ = L_labels_ 2025-03-04T22:17:17.7212405Z l_input_ids_ = L_input_ids_ 2025-03-04T22:17:17.7213149Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:17.7214287Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:17.7215467Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:17:17.7216708Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:17:17.7217443Z 2025-03-04T22:17:17.7218225Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:71 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:17:17.7219201Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_labels_.clone(); l_labels_ = None 2025-03-04T22:17:17.7219649Z 2025-03-04T22:17:17.7220522Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:76 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:17:17.7221522Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:17:17.7222100Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:17:17.7222599Z 2025-03-04T22:17:17.7223476Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:78 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:17:17.7224489Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:17:17.7224901Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:17:17.7225283Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:17:17.7225704Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:17:17.7226087Z 2025-03-04T22:17:17.7226961Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:79 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:17:17.7228063Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:17:17.7228646Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:17:17.7229051Z 2025-03-04T22:17:17.7230246Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:80 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:17:17.7231548Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:17:17.7232372Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:17:17.7233189Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:17:17.7233780Z 2025-03-04T22:17:17.7234576Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:81 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:17:17.7235950Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:17:17.7236650Z 2025-03-04T22:17:17.7237427Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:748 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T22:17:17.7238414Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T22:17:17.7238854Z 2025-03-04T22:17:17.7239666Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:755 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:17.7241348Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:17.7242633Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:17:17.7243106Z 2025-03-04T22:17:17.7243786Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:17:17.7244749Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:17.7245253Z 2025-03-04T22:17:17.7245918Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:17:17.7246791Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:17:17.7247212Z 2025-03-04T22:17:17.7248107Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:17:17.7249049Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:17:17.7250240Z embed_pos: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:17.7251332Z 2025-03-04T22:17:17.7252084Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:758 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T22:17:17.7253117Z embed_pos_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:17:17.7253616Z 2025-03-04T22:17:17.7254476Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:760 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:17:17.7255590Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T22:17:17.7256115Z 2025-03-04T22:17:17.7256898Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:761 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:17:17.7259198Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:17:17.7260928Z 2025-03-04T22:17:17.7262248Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:762 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:17.7263630Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:17:17.7264305Z 2025-03-04T22:17:17.7265015Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:794 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:17.7265855Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:17.7266201Z 2025-03-04T22:17:17.7266987Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:795 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:17:17.7267962Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:17:17.7268365Z 2025-03-04T22:17:17.7268524Z 2025-03-04T22:17:17.7268645Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:17.7270424Z def forward(self, L_labels_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:17:17.7272173Z l_labels_ = L_labels_ 2025-03-04T22:17:17.7272471Z l_input_ids_ = L_input_ids_ 2025-03-04T22:17:17.7273170Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:17.7274294Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:17.7275469Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:17:17.7276707Z l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:17:17.7277437Z 2025-03-04T22:17:17.7278370Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:71 in shift_tokens_right, code: prev_output_tokens = input_ids.clone() 2025-03-04T22:17:17.7279454Z prev_output_tokens: "i64[1, 1024][1024, 1]cuda:0" = l_labels_.clone(); l_labels_ = None 2025-03-04T22:17:17.7279889Z 2025-03-04T22:17:17.7280757Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:76 in shift_tokens_right, code: prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id) 2025-03-04T22:17:17.7281754Z eq: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens == -100 2025-03-04T22:17:17.7282321Z masked_fill_: "i64[1, 1024][1024, 1]cuda:0" = prev_output_tokens.masked_fill_(eq, 1); eq = masked_fill_ = None 2025-03-04T22:17:17.7282822Z 2025-03-04T22:17:17.7283704Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:78 in shift_tokens_right, code: index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1) 2025-03-04T22:17:17.7284708Z ne: "b8[1, 1024][1024, 1]cuda:0" = prev_output_tokens.ne(1) 2025-03-04T22:17:17.7285125Z sum_1: "i64[1][1]cuda:0" = ne.sum(dim = 1); ne = None 2025-03-04T22:17:17.7285507Z sub: "i64[1][1]cuda:0" = sum_1 - 1; sum_1 = None 2025-03-04T22:17:17.7285930Z index_of_eos: "i64[1, 1][1, 1]cuda:0" = sub.unsqueeze(-1); sub = None 2025-03-04T22:17:17.7286306Z 2025-03-04T22:17:17.7287177Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:79 in shift_tokens_right, code: decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze() 2025-03-04T22:17:17.7288381Z gather: "i64[1, 1][1, 1]cuda:0" = prev_output_tokens.gather(1, index_of_eos); index_of_eos = None 2025-03-04T22:17:17.7288955Z decoder_start_tokens: "i64[][]cuda:0" = gather.squeeze(); gather = None 2025-03-04T22:17:17.7289359Z 2025-03-04T22:17:17.7290175Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:80 in shift_tokens_right, code: prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone() 2025-03-04T22:17:17.7291261Z getitem: "i64[1, 1023][1024, 1]cuda:0" = prev_output_tokens[(slice(None, None, None), slice(None, -1, None))] 2025-03-04T22:17:17.7291855Z clone_1: "i64[1, 1023][1023, 1]cuda:0" = getitem.clone(); getitem = None 2025-03-04T22:17:17.7292536Z prev_output_tokens[(slice(None, None, None), slice(1, None, None))] = clone_1; setitem = prev_output_tokens; clone_1 = setitem = None 2025-03-04T22:17:17.7293106Z 2025-03-04T22:17:17.7293877Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:81 in shift_tokens_right, code: prev_output_tokens[:, 0] = decoder_start_tokens 2025-03-04T22:17:17.7295116Z prev_output_tokens[(slice(None, None, None), 0)] = decoder_start_tokens; setitem_1 = prev_output_tokens; prev_output_tokens = decoder_start_tokens = setitem_1 = None 2025-03-04T22:17:17.7295799Z 2025-03-04T22:17:17.7296567Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:748 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T22:17:17.7297534Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T22:17:17.7297976Z 2025-03-04T22:17:17.7298771Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:755 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:17.7300567Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:17.7301918Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:17:17.7302389Z 2025-03-04T22:17:17.7303071Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:17:17.7304036Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:17.7304534Z 2025-03-04T22:17:17.7305183Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:17:17.7306027Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:17:17.7306445Z 2025-03-04T22:17:17.7307198Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:17:17.7308107Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:17:17.7309290Z embed_pos: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:17.7310366Z 2025-03-04T22:17:17.7311107Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:758 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T22:17:17.7312125Z embed_pos_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:17:17.7312619Z 2025-03-04T22:17:17.7313342Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:760 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:17:17.7314374Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T22:17:17.7314895Z 2025-03-04T22:17:17.7315658Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:761 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:17:17.7317922Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:17:17.7319626Z 2025-03-04T22:17:17.7320530Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:762 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:17.7321900Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:17:17.7322570Z 2025-03-04T22:17:17.7323276Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:794 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:17.7324114Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:17.7324455Z 2025-03-04T22:17:17.7325318Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:795 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:17:17.7326350Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:17:17.7326756Z 2025-03-04T22:17:18.0849663Z 2025-03-04T22:17:18.0850058Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:18.0851749Z def forward(self, L_decoder_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_self_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_self_modules_encoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_self_modules_encoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:17:18.0853436Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T22:17:18.0853792Z l_input_ids_ = L_input_ids_ 2025-03-04T22:17:18.0854395Z l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:18.0855328Z l_self_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:18.0856311Z l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:17:18.0857308Z l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:17:18.0857938Z 2025-03-04T22:17:18.0858715Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:748 in forward, code: input_ids = input_ids.view(-1, input_ids.shape[-1]) 2025-03-04T22:17:18.0859689Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_input_ids_.view(-1, 1024); l_input_ids_ = None 2025-03-04T22:17:18.0860119Z 2025-03-04T22:17:18.0860907Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:755 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:18.0862768Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_encoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:18.0863948Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:17:18.0864418Z 2025-03-04T22:17:18.0865098Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:17:18.0866064Z arange: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:18.0866564Z 2025-03-04T22:17:18.0867211Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:17:18.0868048Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:17:18.0868459Z 2025-03-04T22:17:18.0869207Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:17:18.0870109Z add: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:17:18.0871553Z embed_pos: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:18.0872696Z 2025-03-04T22:17:18.0873441Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:758 in forward, code: embed_pos = embed_pos.to(inputs_embeds.device) 2025-03-04T22:17:18.0874458Z embed_pos_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embed_pos.to(device(type='cuda', index=0)); embed_pos = None 2025-03-04T22:17:18.0874962Z 2025-03-04T22:17:18.0875679Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:760 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:17:18.0876764Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + embed_pos_1; inputs_embeds = embed_pos_1 = None 2025-03-04T22:17:18.0877278Z 2025-03-04T22:17:18.0878050Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:761 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:17:18.0880123Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_encoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_encoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:17:18.0881632Z 2025-03-04T22:17:18.0882537Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:762 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:18.0883896Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:17:18.0884568Z 2025-03-04T22:17:18.0885274Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:794 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:18.0886113Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:18.0886567Z 2025-03-04T22:17:18.0887343Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:795 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:17:18.0888401Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:17:18.0888805Z 2025-03-04T22:17:18.2183322Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:17:18.2184137Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py", line 106, in forward 2025-03-04T22:17:18.2184832Z return super().forward(positions + self.offset) 2025-03-04T22:17:18.2185080Z 2025-03-04T22:17:20.1433676Z 2025-03-04T22:17:20.1434202Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:20.1436155Z def forward(self, dict_getitem_L_stack0_list_dict_keys_L_stack0_0_: "f32[1, 1024, 768][786432, 768, 1]cuda:0", L_decoder_input_ids_: "i64[1, 1024][1024, 1]cuda:0", L_self_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50005, 768][768, 1]cuda:0", L_self_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1026, 768][768, 1]cuda:0", L_self_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[768][1]cuda:0", L_self_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[768][1]cuda:0"): 2025-03-04T22:17:20.1438626Z dict_getitem_l_stack0_list_dict_keys_l_stack0_0_ = dict_getitem_L_stack0_list_dict_keys_L_stack0_0_ 2025-03-04T22:17:20.1439378Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T22:17:20.1440029Z l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:20.1440976Z l_self_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:20.1441972Z l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:17:20.1442980Z l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:17:20.1443624Z 2025-03-04T22:17:20.1444416Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:968 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:17:20.1445403Z input_ids: "i64[1, 1024][1024, 1]cuda:0" = l_decoder_input_ids_.view(-1, 1024); input_ids = None 2025-03-04T22:17:20.1445854Z 2025-03-04T22:17:20.1446645Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:979 in forward, code: inputs_embeds = self.embed_tokens(input) * self.embed_scale 2025-03-04T22:17:20.1448450Z embedding: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(l_decoder_input_ids_, l_self_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_decoder_input_ids_ = l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:20.1449707Z inputs_embeds: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = embedding * 27.712812921102035; embedding = None 2025-03-04T22:17:20.1450181Z 2025-03-04T22:17:20.1451008Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:17:20.1452142Z mask: "f32[1024, 1024][1024, 1]cuda:0" = torch.full((1024, 1024), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:17:20.1452674Z 2025-03-04T22:17:20.1453427Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:17:20.1454399Z mask_cond: "i64[1024][1]cuda:0" = torch.arange(1024, device = device(type='cuda', index=0)) 2025-03-04T22:17:20.1454846Z 2025-03-04T22:17:20.1455651Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:17:20.1456555Z add: "i64[1024][1]cuda:0" = mask_cond + 1 2025-03-04T22:17:20.1456957Z view_1: "i64[1024, 1][1, 1]cuda:0" = add.view(1024, 1); add = None 2025-03-04T22:17:20.1457446Z lt: "b8[1024, 1024][1024, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:17:20.1458032Z masked_fill_: "f32[1024, 1024][1024, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:17:20.1458492Z 2025-03-04T22:17:20.1459153Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:17:20.1459999Z mask_1: "f32[1024, 1024][1024, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:17:20.1460409Z 2025-03-04T22:17:20.1461657Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:17:20.1462983Z getitem: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:17:20.1463846Z causal_4d_mask: "f32[1, 1, 1024, 1024][1048576, 1048576, 1024, 1]cuda:0" = getitem.expand(1, 1, 1024, 1024); getitem = causal_4d_mask = None 2025-03-04T22:17:20.1464403Z 2025-03-04T22:17:20.1465089Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:102 in forward, code: positions = torch.arange( 2025-03-04T22:17:20.1466053Z arange_1: "i64[1024][1]cuda:0" = torch.arange(0, 1024, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:20.1466558Z 2025-03-04T22:17:20.1467213Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:104 in forward, code: ).expand(bsz, -1) 2025-03-04T22:17:20.1468071Z positions: "i64[1, 1024][1024, 1]cuda:0" = arange_1.expand(1, -1); arange_1 = None 2025-03-04T22:17:20.1468494Z 2025-03-04T22:17:20.1469244Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:106 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:17:20.1470154Z add_1: "i64[1, 1024][1024, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:17:20.1471265Z positions_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.embedding(add_1, l_self_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add_1 = l_self_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:20.1472265Z 2025-03-04T22:17:20.1473027Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1020 in forward, code: positions = positions.to(inputs_embeds.device) 2025-03-04T22:17:20.1474084Z positions_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = positions_1.to(device(type='cuda', index=0)); positions_1 = None 2025-03-04T22:17:20.1474602Z 2025-03-04T22:17:20.1475329Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1022 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:17:20.1476369Z hidden_states: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = inputs_embeds + positions_2; inputs_embeds = positions_2 = None 2025-03-04T22:17:20.1476894Z 2025-03-04T22:17:20.1477673Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1023 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:17:20.1479758Z hidden_states_1: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (768,), l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:17:20.1481282Z 2025-03-04T22:17:20.1482198Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1025 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:20.1483568Z hidden_states_2: "f32[1, 1024, 768][786432, 768, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:17:20.1484238Z 2025-03-04T22:17:20.1485036Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1054 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:20.1485954Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:20.1486299Z 2025-03-04T22:17:20.1487019Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/plbart/modeling_plbart.py:1055 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:17:20.1488112Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:17:20.1488525Z 2025-03-04T22:17:35.4859680Z Compilation time (from dynamo_timed): 9.148864589 2025-03-04T22:17:35.4883227Z pass 2025-03-04T22:17:35.5262694Z TIMING: entire_frame_compile:6.83586 gc:0.00945 _recursive_pre_grad_passes:0.00817 _recursive_joint_graph_passes:0.4104 inductor_compile:4.54835 backend_compile:5.1933 async_compile.precompile:0.04221 async_compile.wait:0.74615 pad_mm_benchmark:0.04172 _recursive_post_grad_passes:0.16525 code_gen:2.53259 entire_backward_compile:2.313 total_wall_time:9.14886 2025-03-04T22:17:35.5264626Z STATS: call_* op count: 137 | FakeTensorMode.__torch_dispatch__:9352 | FakeTensor.__torch_dispatch__:1471 | ProxyTorchDispatchMode.__torch_dispatch__:4348 2025-03-04T22:17:35.5265459Z Dynamo produced 8 graphs covering 137 ops with 8 graph breaks (5 unique) 2025-03-04T22:17:41.4487833Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:17:41.4489161Z warnings.warn( 2025-03-04T22:17:41.7242880Z 2025-03-04T22:17:48.3616882Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:17:48.3617260Z loading model: 0it [00:06, ?it/s] 2025-03-04T22:17:48.3617609Z cuda train PegasusForCausalLM 2025-03-04T22:17:48.3917216Z WARNING:common:fp64 golden ref were not generated for PegasusForCausalLM. Setting accuracy check to cosine 2025-03-04T22:17:50.2588744Z 2025-03-04T22:17:50.2589728Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:50.2590957Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:17:50.2592152Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:17:50.2592930Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:50.2594089Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:50.2594798Z 2025-03-04T22:17:50.2595095Z # No stacktrace found for following nodes 2025-03-04T22:17:50.2595695Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:17:50.2596233Z 2025-03-04T22:17:50.2597019Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:976 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:17:50.2598068Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:17:50.2598573Z 2025-03-04T22:17:50.2599382Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:986 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:50.2602110Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:50.2603525Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:17:50.2603972Z 2025-03-04T22:17:50.2604789Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:17:50.2605894Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:17:50.2606403Z 2025-03-04T22:17:50.2607156Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:17:50.2608292Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:17:50.2608738Z 2025-03-04T22:17:50.2609531Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:17:50.2610423Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:17:50.2610822Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:17:50.2611296Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:17:50.2611850Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:17:50.2612298Z 2025-03-04T22:17:50.2612959Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:17:50.2613784Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:17:50.2614179Z 2025-03-04T22:17:50.2615007Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:17:50.2616179Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:17:50.2616982Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:17:50.2617503Z 2025-03-04T22:17:50.2617764Z # No stacktrace found for following nodes 2025-03-04T22:17:50.2618239Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:17:50.2618675Z 2025-03-04T22:17:50.2619359Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:17:50.2620335Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:50.2620841Z 2025-03-04T22:17:50.2621543Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:17:50.2623155Z positions_1: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:50.2624601Z 2025-03-04T22:17:50.2624942Z # No stacktrace found for following nodes 2025-03-04T22:17:50.2625422Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:17:50.2625856Z 2025-03-04T22:17:50.2626599Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1002 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:17:50.2627655Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + positions_1; inputs_embeds = positions_1 = None 2025-03-04T22:17:50.2628185Z 2025-03-04T22:17:50.2629105Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1004 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:50.2630486Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:17:50.2631161Z 2025-03-04T22:17:50.2631882Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1032 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:50.2632734Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:50.2633078Z 2025-03-04T22:17:50.2633812Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1033 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:17:50.2634745Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:17:50.2635163Z 2025-03-04T22:17:50.2635306Z 2025-03-04T22:17:50.2635442Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:50.2636536Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:17:50.2637673Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:17:50.2638428Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:50.2639522Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:50.2640223Z 2025-03-04T22:17:50.2640480Z # No stacktrace found for following nodes 2025-03-04T22:17:50.2641058Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:17:50.2641595Z 2025-03-04T22:17:50.2642359Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:976 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:17:50.2643395Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:17:50.2643895Z 2025-03-04T22:17:50.2644697Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:986 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:50.2646368Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:50.2647692Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:17:50.2648308Z 2025-03-04T22:17:50.2649119Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:17:50.2650214Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:17:50.2650724Z 2025-03-04T22:17:50.2651467Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:17:50.2652420Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:17:50.2652857Z 2025-03-04T22:17:50.2653699Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:17:50.2654593Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:17:50.2654980Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:17:50.2655445Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:17:50.2655996Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:17:50.2656439Z 2025-03-04T22:17:50.2657085Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:17:50.2659162Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:17:50.2659557Z 2025-03-04T22:17:50.2660382Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:17:50.2661838Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:17:50.2662643Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:17:50.2663166Z 2025-03-04T22:17:50.2663424Z # No stacktrace found for following nodes 2025-03-04T22:17:50.2663898Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:17:50.2664328Z 2025-03-04T22:17:50.2665016Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:17:50.2665992Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:50.2666494Z 2025-03-04T22:17:50.2667201Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:17:50.2668812Z positions_1: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:50.2669928Z 2025-03-04T22:17:50.2670183Z # No stacktrace found for following nodes 2025-03-04T22:17:50.2670824Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:17:50.2671256Z 2025-03-04T22:17:50.2672133Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1002 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:17:50.2673187Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + positions_1; inputs_embeds = positions_1 = None 2025-03-04T22:17:50.2673721Z 2025-03-04T22:17:50.2674637Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1004 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:50.2676016Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:17:50.2676685Z 2025-03-04T22:17:50.2677404Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1032 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:50.2678256Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:50.2678600Z 2025-03-04T22:17:50.2679336Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1033 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:17:50.2680268Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:17:50.2680688Z 2025-03-04T22:17:51.1422426Z 2025-03-04T22:17:51.1423046Z class GraphModule(torch.nn.Module): 2025-03-04T22:17:51.1424290Z def forward(self, L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:17:51.1425420Z l_input_ids_ = L_input_ids_ 2025-03-04T22:17:51.1426159Z l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:17:51.1427309Z l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:17:51.1428048Z 2025-03-04T22:17:51.1430092Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:976 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:17:51.1431079Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T22:17:51.1431524Z 2025-03-04T22:17:51.1432403Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:986 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:17:51.1434120Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:17:51.1435375Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:17:51.1435829Z 2025-03-04T22:17:51.1436646Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:17:51.1438190Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:17:51.1438711Z 2025-03-04T22:17:51.1439643Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:17:51.1440615Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:17:51.1441060Z 2025-03-04T22:17:51.1441864Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:17:51.1442759Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:17:51.1443156Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:17:51.1443628Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:17:51.1444213Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:17:51.1444693Z 2025-03-04T22:17:51.1445346Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:17:51.1446167Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:17:51.1446560Z 2025-03-04T22:17:51.1447394Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:17:51.1448742Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:17:51.1449556Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:17:51.1450086Z 2025-03-04T22:17:51.1450346Z # No stacktrace found for following nodes 2025-03-04T22:17:51.1450816Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:17:51.1451263Z 2025-03-04T22:17:51.1451956Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:17:51.1452937Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:17:51.1453444Z 2025-03-04T22:17:51.1454158Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:17:51.1455801Z positions_1: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:17:51.1456934Z 2025-03-04T22:17:51.1457200Z # No stacktrace found for following nodes 2025-03-04T22:17:51.1457679Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:17:51.1458112Z 2025-03-04T22:17:51.1458853Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1002 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:17:51.1459918Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + positions_1; inputs_embeds = positions_1 = None 2025-03-04T22:17:51.1460456Z 2025-03-04T22:17:51.1461788Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1004 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:17:51.1463288Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:17:51.1463988Z 2025-03-04T22:17:51.1464744Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1032 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:17:51.1465598Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:17:51.1465944Z 2025-03-04T22:17:51.1466680Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1033 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:17:51.1467618Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:17:51.1468046Z 2025-03-04T22:18:03.1702614Z Compilation time (from dynamo_timed): 5.562049695 2025-03-04T22:18:03.1720295Z pass 2025-03-04T22:18:03.2500485Z TIMING: entire_frame_compile:4.21203 gc:0.00616 _recursive_pre_grad_passes:0.00613 _recursive_joint_graph_passes:0.45621 inductor_compile:2.69542 backend_compile:3.34424 async_compile.precompile:0.1005 async_compile.wait:0.78039 _recursive_post_grad_passes:0.07719 code_gen:1.80282 pad_mm_benchmark:0.22675 entire_backward_compile:1.35002 total_wall_time:5.56205 2025-03-04T22:18:03.2502256Z STATS: call_* op count: 58 | FakeTensorMode.__torch_dispatch__:4256 | ProxyTorchDispatchMode.__torch_dispatch__:1804 | FakeTensor.__torch_dispatch__:664 2025-03-04T22:18:03.2503065Z Dynamo produced 6 graphs covering 58 ops with 6 graph breaks (5 unique) 2025-03-04T22:18:08.9593519Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:18:08.9594722Z warnings.warn( 2025-03-04T22:18:09.3036364Z 2025-03-04T22:18:20.2745317Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:18:20.2745707Z loading model: 0it [00:10, ?it/s] 2025-03-04T22:18:20.2746071Z cuda train PegasusForConditionalGeneration 2025-03-04T22:18:20.3776829Z WARNING:common:fp64 golden ref were not generated for PegasusForConditionalGeneration. Setting accuracy check to cosine 2025-03-04T22:18:23.0094515Z 2025-03-04T22:18:23.0095174Z class GraphModule(torch.nn.Module): 2025-03-04T22:18:23.0096680Z def forward(self, L_cloned_inputs_labels_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:18:23.0098136Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:18:23.0098631Z l_cloned_inputs_decoder_input_ids_ = L_cloned_inputs_decoder_input_ids_ 2025-03-04T22:18:23.0099126Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:18:23.0099900Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:18:23.0101013Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:18:23.0101728Z 2025-03-04T22:18:23.0101994Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0102977Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:18:23.0103682Z 2025-03-04T22:18:23.0104468Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:739 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:18:23.0105520Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:18:23.0106028Z 2025-03-04T22:18:23.0106836Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:746 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:18:23.0108530Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:18:23.0110217Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:18:23.0110673Z 2025-03-04T22:18:23.0110933Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0111403Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:18:23.0111838Z 2025-03-04T22:18:23.0112529Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:18:23.0113509Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:18:23.0114015Z 2025-03-04T22:18:23.0114731Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:18:23.0116342Z embed_pos: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:18:23.0117463Z 2025-03-04T22:18:23.0117723Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0118199Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:18:23.0118635Z 2025-03-04T22:18:23.0119372Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:750 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:18:23.0120411Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:18:23.0120934Z 2025-03-04T22:18:23.0121849Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:752 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:18:23.0123226Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:18:23.0123902Z 2025-03-04T22:18:23.0124621Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:775 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:18:23.0125542Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:18:23.0126037Z 2025-03-04T22:18:23.0127001Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:776 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:18:23.0128294Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:18:23.0128703Z 2025-03-04T22:18:23.0128861Z 2025-03-04T22:18:23.0128986Z class GraphModule(torch.nn.Module): 2025-03-04T22:18:23.0130361Z def forward(self, L_cloned_inputs_labels_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:18:23.0131785Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:18:23.0132254Z l_cloned_inputs_decoder_input_ids_ = L_cloned_inputs_decoder_input_ids_ 2025-03-04T22:18:23.0132753Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:18:23.0133510Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:18:23.0134618Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:18:23.0135325Z 2025-03-04T22:18:23.0135585Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0136164Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:18:23.0136697Z 2025-03-04T22:18:23.0137459Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:739 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:18:23.0138514Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:18:23.0139023Z 2025-03-04T22:18:23.0139833Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:746 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:18:23.0141516Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:18:23.0142762Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:18:23.0143209Z 2025-03-04T22:18:23.0143469Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0143941Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:18:23.0144367Z 2025-03-04T22:18:23.0145060Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:18:23.0146037Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:18:23.0146539Z 2025-03-04T22:18:23.0147248Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:18:23.0148852Z embed_pos: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:18:23.0149959Z 2025-03-04T22:18:23.0150306Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0150970Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:18:23.0151406Z 2025-03-04T22:18:23.0152145Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:750 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:18:23.0153186Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:18:23.0153705Z 2025-03-04T22:18:23.0154620Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:752 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:18:23.0156254Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:18:23.0156934Z 2025-03-04T22:18:23.0157655Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:775 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:18:23.0158501Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:18:23.0158850Z 2025-03-04T22:18:23.0159643Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:776 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:18:23.0160619Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:18:23.0161325Z 2025-03-04T22:18:23.0161462Z 2025-03-04T22:18:23.0161587Z class GraphModule(torch.nn.Module): 2025-03-04T22:18:23.0162970Z def forward(self, L_cloned_inputs_labels_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:18:23.0164376Z l_cloned_inputs_labels_ = L_cloned_inputs_labels_ 2025-03-04T22:18:23.0164849Z l_cloned_inputs_decoder_input_ids_ = L_cloned_inputs_decoder_input_ids_ 2025-03-04T22:18:23.0165344Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:18:23.0166101Z l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:18:23.0167209Z l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:18:23.0168015Z 2025-03-04T22:18:23.0168280Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0168857Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:18:23.0169390Z 2025-03-04T22:18:23.0170153Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:739 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:18:23.0171201Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:18:23.0171701Z 2025-03-04T22:18:23.0172505Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:746 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:18:23.0174352Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:18:23.0175734Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:18:23.0176179Z 2025-03-04T22:18:23.0176440Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0176905Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:18:23.0177333Z 2025-03-04T22:18:23.0178018Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:18:23.0178995Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:18:23.0179506Z 2025-03-04T22:18:23.0180213Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:18:23.0181821Z embed_pos: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_mod_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:18:23.0182929Z 2025-03-04T22:18:23.0183186Z # No stacktrace found for following nodes 2025-03-04T22:18:23.0183662Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:18:23.0184093Z 2025-03-04T22:18:23.0184837Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:750 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:18:23.0185866Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:18:23.0186385Z 2025-03-04T22:18:23.0187300Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:752 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:18:23.0188683Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:18:23.0189354Z 2025-03-04T22:18:23.0190078Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:775 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:18:23.0190985Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:18:23.0191347Z 2025-03-04T22:18:23.0202927Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:776 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:18:23.0204159Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:18:23.0204577Z 2025-03-04T22:18:23.9009478Z 2025-03-04T22:18:23.9009910Z class GraphModule(torch.nn.Module): 2025-03-04T22:18:23.9011221Z def forward(self, L_labels_: "i64[1, 128][128, 1]cuda:0", L_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:18:23.9012513Z l_labels_ = L_labels_ 2025-03-04T22:18:23.9013276Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T22:18:23.9013813Z l_input_ids_ = L_input_ids_ 2025-03-04T22:18:23.9014519Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:18:23.9015634Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:18:23.9016343Z 2025-03-04T22:18:23.9017123Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:739 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:18:23.9018083Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T22:18:23.9018499Z 2025-03-04T22:18:23.9019316Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:746 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:18:23.9021969Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:18:23.9023250Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:18:23.9023706Z 2025-03-04T22:18:23.9023968Z # No stacktrace found for following nodes 2025-03-04T22:18:23.9024445Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:18:23.9024876Z 2025-03-04T22:18:23.9025609Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:18:23.9026599Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:18:23.9027107Z 2025-03-04T22:18:23.9027817Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:18:23.9029434Z embed_pos: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:18:23.9030548Z 2025-03-04T22:18:23.9030805Z # No stacktrace found for following nodes 2025-03-04T22:18:23.9031285Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:18:23.9031744Z 2025-03-04T22:18:23.9032477Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:750 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:18:23.9033512Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:18:23.9034025Z 2025-03-04T22:18:23.9034939Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:752 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:18:23.9036312Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:18:23.9036984Z 2025-03-04T22:18:23.9037818Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:775 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:18:23.9038736Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:18:23.9039083Z 2025-03-04T22:18:23.9039871Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:776 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:18:23.9040841Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:18:23.9041246Z 2025-03-04T22:18:23.9041368Z 2025-03-04T22:18:23.9041491Z class GraphModule(torch.nn.Module): 2025-03-04T22:18:23.9042742Z def forward(self, L_labels_: "i64[1, 128][128, 1]cuda:0", L_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:18:23.9043969Z l_labels_ = L_labels_ 2025-03-04T22:18:23.9044288Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T22:18:23.9044634Z l_input_ids_ = L_input_ids_ 2025-03-04T22:18:23.9045325Z l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:18:23.9046444Z l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:18:23.9047153Z 2025-03-04T22:18:23.9048051Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:739 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:18:23.9049024Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T22:18:23.9049441Z 2025-03-04T22:18:23.9050253Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:746 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:18:23.9051948Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_model_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:18:23.9053196Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:18:23.9053642Z 2025-03-04T22:18:23.9053904Z # No stacktrace found for following nodes 2025-03-04T22:18:23.9054369Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:18:23.9054803Z 2025-03-04T22:18:23.9055503Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:18:23.9056476Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:18:23.9056983Z 2025-03-04T22:18:23.9057696Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:18:23.9059313Z embed_pos: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_model_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:18:23.9060431Z 2025-03-04T22:18:23.9060785Z # No stacktrace found for following nodes 2025-03-04T22:18:23.9061627Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:18:23.9062064Z 2025-03-04T22:18:23.9062800Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:750 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:18:23.9063833Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:18:23.9064346Z 2025-03-04T22:18:23.9065263Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:752 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:18:23.9066685Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:18:23.9067376Z 2025-03-04T22:18:23.9068106Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:775 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:18:23.9068953Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:18:23.9069305Z 2025-03-04T22:18:23.9070093Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:776 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:18:23.9071069Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:18:23.9071522Z 2025-03-04T22:18:23.9854782Z 2025-03-04T22:18:23.9856016Z class GraphModule(torch.nn.Module): 2025-03-04T22:18:23.9857182Z def forward(self, L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_encoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_encoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:18:23.9858303Z l_input_ids_ = L_input_ids_ 2025-03-04T22:18:23.9858939Z l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_encoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:18:23.9859915Z l_self_modules_encoder_modules_embed_positions_parameters_weight_ = L_self_modules_encoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:18:23.9860559Z 2025-03-04T22:18:23.9861647Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:739 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:18:23.9862642Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T22:18:23.9863077Z 2025-03-04T22:18:23.9863909Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:746 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:18:23.9865551Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_encoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:18:23.9866744Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:18:23.9867196Z 2025-03-04T22:18:23.9867459Z # No stacktrace found for following nodes 2025-03-04T22:18:23.9867943Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:18:23.9868378Z 2025-03-04T22:18:23.9870184Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:18:23.9871355Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:18:23.9871865Z 2025-03-04T22:18:23.9872585Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:18:23.9874118Z embed_pos: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_encoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_encoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:18:23.9875151Z 2025-03-04T22:18:23.9875417Z # No stacktrace found for following nodes 2025-03-04T22:18:23.9875909Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:18:23.9876351Z 2025-03-04T22:18:23.9877091Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:750 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:18:23.9878133Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:18:23.9878653Z 2025-03-04T22:18:23.9879574Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:752 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:18:23.9880954Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:18:23.9881677Z 2025-03-04T22:18:23.9882403Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:775 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:18:23.9883261Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:18:23.9883614Z 2025-03-04T22:18:23.9884405Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:776 in forward, code: if dropout_probability < self.layerdrop: # skip the layer 2025-03-04T22:18:23.9885382Z lt: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt = None 2025-03-04T22:18:23.9885795Z 2025-03-04T22:18:26.2273064Z 2025-03-04T22:18:26.2273693Z class GraphModule(torch.nn.Module): 2025-03-04T22:18:26.2275079Z def forward(self, dict_getitem_L_stack0_list_dict_keys_L_stack0_0_: "f32[1, 128, 1024][131072, 1024, 1]cuda:0", L_decoder_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_decoder_modules_embed_positions_parameters_weight_: "f32[1024, 1024][1024, 1]cuda:0"): 2025-03-04T22:18:26.2276614Z dict_getitem_l_stack0_list_dict_keys_l_stack0_0_ = dict_getitem_L_stack0_list_dict_keys_L_stack0_0_ 2025-03-04T22:18:26.2277165Z l_decoder_input_ids_ = L_decoder_input_ids_ 2025-03-04T22:18:26.2277817Z l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:18:26.2278776Z l_self_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:18:26.2279406Z 2025-03-04T22:18:26.2280203Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:976 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:18:26.2282119Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_decoder_input_ids_.view(-1, 128); l_decoder_input_ids_ = None 2025-03-04T22:18:26.2282802Z 2025-03-04T22:18:26.2283622Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:986 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:18:26.2285249Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_decoder_modules_embed_tokens_parameters_weight_, 0, None, 2.0, False, False); input_ids = l_self_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:18:26.2286415Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:18:26.2286863Z 2025-03-04T22:18:26.2287685Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:18:26.2288942Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:18:26.2289451Z 2025-03-04T22:18:26.2290202Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:18:26.2291166Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:18:26.2291607Z 2025-03-04T22:18:26.2292410Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:18:26.2293304Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:18:26.2293707Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:18:26.2294182Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:18:26.2294743Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:18:26.2295192Z 2025-03-04T22:18:26.2295838Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:18:26.2296670Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:18:26.2297054Z 2025-03-04T22:18:26.2297890Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:18:26.2299078Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:18:26.2299880Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:18:26.2300400Z 2025-03-04T22:18:26.2300652Z # No stacktrace found for following nodes 2025-03-04T22:18:26.2301126Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:18:26.2301551Z 2025-03-04T22:18:26.2302257Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:105 in forward, code: positions = torch.arange( 2025-03-04T22:18:26.2303255Z positions: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:18:26.2303763Z 2025-03-04T22:18:26.2304565Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:108 in forward, code: return super().forward(positions) 2025-03-04T22:18:26.2306244Z positions_1: "f32[128, 1024][1024, 1]cuda:0" = torch.nn.functional.embedding(positions, l_self_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); positions = l_self_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:18:26.2307310Z 2025-03-04T22:18:26.2307571Z # No stacktrace found for following nodes 2025-03-04T22:18:26.2308059Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:18:26.2308496Z 2025-03-04T22:18:26.2309251Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1002 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:18:26.2310344Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + positions_1; inputs_embeds = positions_1 = None 2025-03-04T22:18:26.2310896Z 2025-03-04T22:18:26.2311841Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1004 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:18:26.2313265Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:18:26.2313950Z 2025-03-04T22:18:26.2314690Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1032 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:18:26.2315561Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:18:26.2315916Z 2025-03-04T22:18:26.2316675Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/pegasus/modeling_pegasus.py:1033 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:18:26.2317641Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:18:26.2318067Z 2025-03-04T22:18:49.2626458Z Compilation time (from dynamo_timed): 8.503703604 2025-03-04T22:18:49.2680799Z pass 2025-03-04T22:18:49.3990263Z TIMING: entire_frame_compile:6.43695 gc:0.01004 _recursive_pre_grad_passes:0.00734 _recursive_joint_graph_passes:0.40119 inductor_compile:4.08395 backend_compile:4.87322 async_compile.precompile:0.01319 async_compile.wait:0.5133 pad_mm_benchmark:0.04216 _recursive_post_grad_passes:0.16411 code_gen:2.32439 entire_backward_compile:2.06675 total_wall_time:8.5037 2025-03-04T22:18:49.3992083Z STATS: call_* op count: 121 | FakeTensorMode.__torch_dispatch__:9041 | ProxyTorchDispatchMode.__torch_dispatch__:4238 | FakeTensor.__torch_dispatch__:1439 2025-03-04T22:18:49.3992948Z Dynamo produced 7 graphs covering 121 ops with 7 graph breaks (4 unique) 2025-03-04T22:18:55.2842976Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:18:55.2844199Z warnings.warn( 2025-03-04T22:18:55.5671495Z 2025-03-04T22:18:55.5701455Z loading model: 0it [00:00, ?it/s]If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.` 2025-03-04T22:18:57.5875687Z We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked. 2025-03-04T22:18:57.5877649Z You may ignore this warning if your `pad_token_id` (0) is identical to the `bos_token_id` (0), `eos_token_id` (2), or the `sep_token_id` (None), and your input is not padded. 2025-03-04T22:18:57.9975923Z 2025-03-04T22:18:57.9976422Z loading model: 0it [00:02, ?it/s] 2025-03-04T22:18:57.9977028Z cuda train RobertaForCausalLM 2025-03-04T22:19:31.2809786Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:19:31.2810684Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:19:31.2811402Z pred = mod(**cloned_inputs) 2025-03-04T22:19:31.2812064Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/roberta/modeling_roberta.py", line 953, in forward 2025-03-04T22:19:31.2812731Z outputs = self.roberta( 2025-03-04T22:19:31.2813361Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/roberta/modeling_roberta.py", line 828, in forward 2025-03-04T22:19:31.2814035Z embedding_output = self.embeddings( 2025-03-04T22:19:31.2814732Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/roberta/modeling_roberta.py", line 125, in forward 2025-03-04T22:19:31.2815440Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T22:19:31.2815696Z 2025-03-04T22:19:31.4620820Z W0304 22:19:31.461000 21046 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:20:13.5702163Z Compilation time (from dynamo_timed): 68.289360918 2025-03-04T22:20:13.5729418Z pass 2025-03-04T22:20:13.6252345Z TIMING: entire_frame_compile:60.32094 gc:0.00421 _recursive_pre_grad_passes:0.03942 pad_mm_benchmark:0.19995 _recursive_joint_graph_passes:1.55083 _recursive_post_grad_passes:0.61262 async_compile.wait:3.06677 code_gen:18.52715 inductor_compile:33.37318 backend_compile:47.49929 entire_backward_compile:7.96842 total_wall_time:68.28936 2025-03-04T22:20:13.6254010Z STATS: call_* op count: 1413 | FakeTensorMode.__torch_dispatch__:63189 | FakeTensor.__torch_dispatch__:14290 | ProxyTorchDispatchMode.__torch_dispatch__:28657 2025-03-04T22:20:13.6254872Z Dynamo produced 2 graphs covering 1413 ops with 5 graph breaks (4 unique) 2025-03-04T22:20:22.1383646Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:20:22.1384856Z warnings.warn( 2025-03-04T22:20:22.4431958Z 2025-03-04T22:20:24.0879631Z loading model: 0it [00:00, ?it/s]We strongly recommend passing in an `attention_mask` since your input_ids may be padded. See https://huggingface.co/docs/transformers/troubleshooting#incorrect-output-when-padding-tokens-arent-masked. 2025-03-04T22:20:24.0882737Z You may ignore this warning if your `pad_token_id` (0) is identical to the `bos_token_id` (0), `eos_token_id` (2), or the `sep_token_id` (None), and your input is not padded. 2025-03-04T22:20:24.5303444Z 2025-03-04T22:20:24.5304155Z loading model: 0it [00:02, ?it/s] 2025-03-04T22:20:24.5304807Z cuda train RobertaForQuestionAnswering 2025-03-04T22:20:56.7305252Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:20:56.7306160Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:20:56.7306885Z pred = mod(**cloned_inputs) 2025-03-04T22:20:56.7307562Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/roberta/modeling_roberta.py", line 1500, in forward 2025-03-04T22:20:56.7308240Z outputs = self.roberta( 2025-03-04T22:20:56.7308880Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/roberta/modeling_roberta.py", line 828, in forward 2025-03-04T22:20:56.7309566Z embedding_output = self.embeddings( 2025-03-04T22:20:56.7310745Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/roberta/modeling_roberta.py", line 125, in forward 2025-03-04T22:20:56.7311462Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T22:20:56.7311930Z 2025-03-04T22:20:56.9031175Z W0304 22:20:56.902000 21299 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:21:35.7817513Z Compilation time (from dynamo_timed): 64.425091118 2025-03-04T22:21:35.7847020Z pass 2025-03-04T22:21:35.8515999Z TIMING: entire_frame_compile:56.78252 gc:0.00491 _recursive_pre_grad_passes:0.0414 pad_mm_benchmark:0.20102 _recursive_joint_graph_passes:1.54481 _recursive_post_grad_passes:0.62057 async_compile.wait:0.17468 code_gen:15.33344 inductor_compile:29.94901 backend_compile:44.12566 entire_backward_compile:7.64257 total_wall_time:64.42509 2025-03-04T22:21:35.8517744Z STATS: call_* op count: 1400 | FakeTensorMode.__torch_dispatch__:62548 | FakeTensor.__torch_dispatch__:14123 | ProxyTorchDispatchMode.__torch_dispatch__:28393 2025-03-04T22:21:35.8518610Z Dynamo produced 2 graphs covering 1400 ops with 5 graph breaks (4 unique) 2025-03-04T22:21:44.4348016Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:21:44.4349241Z warnings.warn( 2025-03-04T22:21:44.7886181Z 2025-03-04T22:21:45.5626303Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:21:45.5626757Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:21:45.5627135Z cuda train Speech2Text2ForCausalLM 2025-03-04T22:21:45.5799905Z WARNING:common:fp64 golden ref were not generated for Speech2Text2ForCausalLM. Setting accuracy check to cosine 2025-03-04T22:21:47.2434133Z 2025-03-04T22:21:47.2434723Z class GraphModule(torch.nn.Module): 2025-03-04T22:21:47.2436091Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[10000, 256][256, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_: "f32[1026, 256][256, 1]cuda:0"): 2025-03-04T22:21:47.2437800Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:21:47.2438644Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:21:47.2439834Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ 2025-03-04T22:21:47.2440598Z 2025-03-04T22:21:47.2440872Z # No stacktrace found for following nodes 2025-03-04T22:21:47.2441498Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:21:47.2442051Z 2025-03-04T22:21:47.2442899Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:559 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:21:47.2444018Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:21:47.2446004Z 2025-03-04T22:21:47.2446866Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:569 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:21:47.2448806Z embedding: "f32[1, 128, 256][32768, 256, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:21:47.2450420Z inputs_embeds: "f32[1, 128, 256][32768, 256, 1]cuda:0" = embedding * 16.0; embedding = None 2025-03-04T22:21:47.2451050Z 2025-03-04T22:21:47.2451892Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:21:47.2453028Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:21:47.2453545Z 2025-03-04T22:21:47.2454309Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:21:47.2455296Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:21:47.2455744Z 2025-03-04T22:21:47.2456558Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:21:47.2457487Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:21:47.2457894Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:21:47.2458371Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:21:47.2458939Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:21:47.2459399Z 2025-03-04T22:21:47.2469022Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:21:47.2470106Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:21:47.2470643Z 2025-03-04T22:21:47.2471518Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:21:47.2472720Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:21:47.2473538Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:21:47.2474063Z 2025-03-04T22:21:47.2474323Z # No stacktrace found for following nodes 2025-03-04T22:21:47.2474796Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:21:47.2475226Z 2025-03-04T22:21:47.2476098Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:112 in create_position_ids_from_input_ids, code: mask = input_ids.ne(padding_idx).int() 2025-03-04T22:21:47.2477102Z ne: "b8[1, 128][128, 1]cuda:0" = input_ids.ne(1); input_ids = None 2025-03-04T22:21:47.2477535Z mask_2: "i32[1, 128][128, 1]cuda:0" = ne.int(); ne = None 2025-03-04T22:21:47.2477881Z 2025-03-04T22:21:47.2478924Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:113 in create_position_ids_from_input_ids, code: incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask 2025-03-04T22:21:47.2480114Z cumsum: "i64[1, 128][128, 1]cuda:0" = torch.cumsum(mask_2, dim = 1) 2025-03-04T22:21:47.2480596Z type_as: "i32[1, 128][128, 1]cuda:0" = cumsum.type_as(mask_2); cumsum = None 2025-03-04T22:21:47.2481064Z add_1: "i32[1, 128][128, 1]cuda:0" = type_as + 0; type_as = None 2025-03-04T22:21:47.2481784Z incremental_indices: "i32[1, 128][128, 1]cuda:0" = add_1 * mask_2; add_1 = mask_2 = None 2025-03-04T22:21:47.2482352Z 2025-03-04T22:21:47.2483246Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:114 in create_position_ids_from_input_ids, code: return incremental_indices.long() + padding_idx 2025-03-04T22:21:47.2484350Z long: "i64[1, 128][128, 1]cuda:0" = incremental_indices.long(); incremental_indices = None 2025-03-04T22:21:47.2484861Z add_2: "i64[1, 128][128, 1]cuda:0" = long + 1; long = None 2025-03-04T22:21:47.2485209Z 2025-03-04T22:21:47.2486222Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:89 in forward, code: position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to( 2025-03-04T22:21:47.2487470Z position_ids: "i64[1, 128][128, 1]cuda:0" = add_2.to(device(type='cuda', index=0)); add_2 = None 2025-03-04T22:21:47.2488063Z 2025-03-04T22:21:47.2489012Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:98 in forward, code: return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach() 2025-03-04T22:21:47.2490108Z view_2: "i64[128][1]cuda:0" = position_ids.view(-1); position_ids = None 2025-03-04T22:21:47.2491150Z index_select: "f32[128, 256][256, 1]cuda:0" = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_.index_select(0, view_2); l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ = view_2 = None 2025-03-04T22:21:47.2492234Z view_3: "f32[1, 128, 256][32768, 256, 1]cuda:0" = index_select.view(1, 128, -1); index_select = None 2025-03-04T22:21:47.2492806Z positions: "f32[1, 128, 256][32768, 256, 1]cuda:0" = view_3.detach(); view_3 = None 2025-03-04T22:21:47.2493227Z 2025-03-04T22:21:47.2493490Z # No stacktrace found for following nodes 2025-03-04T22:21:47.2493971Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:21:47.2494398Z 2025-03-04T22:21:47.2495171Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:585 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:21:47.2496230Z hidden_states: "f32[1, 128, 256][32768, 256, 1]cuda:0" = inputs_embeds + positions; inputs_embeds = positions = None 2025-03-04T22:21:47.2496733Z 2025-03-04T22:21:47.2497682Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:586 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:21:47.2499079Z hidden_states_1: "f32[1, 128, 256][32768, 256, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:21:47.2499738Z 2025-03-04T22:21:47.2500494Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:614 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:21:47.2501383Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:21:47.2501729Z 2025-03-04T22:21:47.2502503Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:615 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:21:47.2503471Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:21:47.2503890Z 2025-03-04T22:21:47.2504045Z 2025-03-04T22:21:47.2504169Z class GraphModule(torch.nn.Module): 2025-03-04T22:21:47.2505339Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[10000, 256][256, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_: "f32[1026, 256][256, 1]cuda:0"): 2025-03-04T22:21:47.2506545Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:21:47.2507302Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:21:47.2508414Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ 2025-03-04T22:21:47.2509134Z 2025-03-04T22:21:47.2509393Z # No stacktrace found for following nodes 2025-03-04T22:21:47.2509982Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:21:47.2510520Z 2025-03-04T22:21:47.2511325Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:559 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:21:47.2512411Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:21:47.2512919Z 2025-03-04T22:21:47.2513759Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:569 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:21:47.2515442Z embedding: "f32[1, 128, 256][32768, 256, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:21:47.2516638Z inputs_embeds: "f32[1, 128, 256][32768, 256, 1]cuda:0" = embedding * 16.0; embedding = None 2025-03-04T22:21:47.2517079Z 2025-03-04T22:21:47.2517901Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:21:47.2519001Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:21:47.2519511Z 2025-03-04T22:21:47.2520261Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:21:47.2521222Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:21:47.2521667Z 2025-03-04T22:21:47.2522466Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:21:47.2523364Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:21:47.2523755Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:21:47.2524222Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:21:47.2524774Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:21:47.2525221Z 2025-03-04T22:21:47.2525871Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:21:47.2526775Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:21:47.2527168Z 2025-03-04T22:21:47.2528201Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:21:47.2529374Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:21:47.2530178Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:21:47.2530705Z 2025-03-04T22:21:47.2530969Z # No stacktrace found for following nodes 2025-03-04T22:21:47.2531439Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:21:47.2531873Z 2025-03-04T22:21:47.2532734Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:112 in create_position_ids_from_input_ids, code: mask = input_ids.ne(padding_idx).int() 2025-03-04T22:21:47.2533731Z ne: "b8[1, 128][128, 1]cuda:0" = input_ids.ne(1); input_ids = None 2025-03-04T22:21:47.2534164Z mask_2: "i32[1, 128][128, 1]cuda:0" = ne.int(); ne = None 2025-03-04T22:21:47.2534512Z 2025-03-04T22:21:47.2535542Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:113 in create_position_ids_from_input_ids, code: incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask 2025-03-04T22:21:47.2536729Z cumsum: "i64[1, 128][128, 1]cuda:0" = torch.cumsum(mask_2, dim = 1) 2025-03-04T22:21:47.2537212Z type_as: "i32[1, 128][128, 1]cuda:0" = cumsum.type_as(mask_2); cumsum = None 2025-03-04T22:21:47.2537684Z add_1: "i32[1, 128][128, 1]cuda:0" = type_as + 0; type_as = None 2025-03-04T22:21:47.2538193Z incremental_indices: "i32[1, 128][128, 1]cuda:0" = add_1 * mask_2; add_1 = mask_2 = None 2025-03-04T22:21:47.2538631Z 2025-03-04T22:21:47.2539510Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:114 in create_position_ids_from_input_ids, code: return incremental_indices.long() + padding_idx 2025-03-04T22:21:47.2540599Z long: "i64[1, 128][128, 1]cuda:0" = incremental_indices.long(); incremental_indices = None 2025-03-04T22:21:47.2541099Z add_2: "i64[1, 128][128, 1]cuda:0" = long + 1; long = None 2025-03-04T22:21:47.2541445Z 2025-03-04T22:21:47.2542441Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:89 in forward, code: position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to( 2025-03-04T22:21:47.2543660Z position_ids: "i64[1, 128][128, 1]cuda:0" = add_2.to(device(type='cuda', index=0)); add_2 = None 2025-03-04T22:21:47.2544111Z 2025-03-04T22:21:47.2545047Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:98 in forward, code: return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach() 2025-03-04T22:21:47.2546136Z view_2: "i64[128][1]cuda:0" = position_ids.view(-1); position_ids = None 2025-03-04T22:21:47.2547176Z index_select: "f32[128, 256][256, 1]cuda:0" = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_.index_select(0, view_2); l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ = view_2 = None 2025-03-04T22:21:47.2548271Z view_3: "f32[1, 128, 256][32768, 256, 1]cuda:0" = index_select.view(1, 128, -1); index_select = None 2025-03-04T22:21:47.2548935Z positions: "f32[1, 128, 256][32768, 256, 1]cuda:0" = view_3.detach(); view_3 = None 2025-03-04T22:21:47.2549449Z 2025-03-04T22:21:47.2549712Z # No stacktrace found for following nodes 2025-03-04T22:21:47.2550187Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:21:47.2550619Z 2025-03-04T22:21:47.2551396Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:585 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:21:47.2552459Z hidden_states: "f32[1, 128, 256][32768, 256, 1]cuda:0" = inputs_embeds + positions; inputs_embeds = positions = None 2025-03-04T22:21:47.2552962Z 2025-03-04T22:21:47.2553915Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:586 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:21:47.2555315Z hidden_states_1: "f32[1, 128, 256][32768, 256, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:21:47.2555976Z 2025-03-04T22:21:47.2556732Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:614 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:21:47.2557619Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:21:47.2557968Z 2025-03-04T22:21:47.2558734Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:615 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:21:47.2559701Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:21:47.2560118Z 2025-03-04T22:21:48.1357905Z 2025-03-04T22:21:48.1358529Z class GraphModule(torch.nn.Module): 2025-03-04T22:21:48.1361778Z def forward(self, L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[10000, 256][256, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weights_: "f32[1026, 256][256, 1]cuda:0"): 2025-03-04T22:21:48.1363048Z l_input_ids_ = L_input_ids_ 2025-03-04T22:21:48.1363887Z l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:21:48.1365325Z l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ = L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ 2025-03-04T22:21:48.1366215Z 2025-03-04T22:21:48.1367196Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:559 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:21:48.1368575Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T22:21:48.1369018Z 2025-03-04T22:21:48.1370016Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:569 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:21:48.1372081Z embedding: "f32[1, 128, 256][32768, 256, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:21:48.1373383Z inputs_embeds: "f32[1, 128, 256][32768, 256, 1]cuda:0" = embedding * 16.0; embedding = None 2025-03-04T22:21:48.1373897Z 2025-03-04T22:21:48.1375817Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:21:48.1377270Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:21:48.1377786Z 2025-03-04T22:21:48.1378619Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:21:48.1379722Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:21:48.1380179Z 2025-03-04T22:21:48.1381069Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:21:48.1382051Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:21:48.1382521Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:21:48.1383001Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:21:48.1383615Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:21:48.1384141Z 2025-03-04T22:21:48.1384853Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:21:48.1385760Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:21:48.1386162Z 2025-03-04T22:21:48.1387085Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:21:48.1388422Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:21:48.1389307Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:21:48.1389862Z 2025-03-04T22:21:48.1390173Z # No stacktrace found for following nodes 2025-03-04T22:21:48.1390645Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:21:48.1391159Z 2025-03-04T22:21:48.1392146Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:112 in create_position_ids_from_input_ids, code: mask = input_ids.ne(padding_idx).int() 2025-03-04T22:21:48.1393269Z ne: "b8[1, 128][128, 1]cuda:0" = input_ids.ne(1); input_ids = None 2025-03-04T22:21:48.1393773Z mask_2: "i32[1, 128][128, 1]cuda:0" = ne.int(); ne = None 2025-03-04T22:21:48.1394140Z 2025-03-04T22:21:48.1395295Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:113 in create_position_ids_from_input_ids, code: incremental_indices = (torch.cumsum(mask, dim=1).type_as(mask) + past_key_values_length) * mask 2025-03-04T22:21:48.1396564Z cumsum: "i64[1, 128][128, 1]cuda:0" = torch.cumsum(mask_2, dim = 1) 2025-03-04T22:21:48.1397123Z type_as: "i32[1, 128][128, 1]cuda:0" = cumsum.type_as(mask_2); cumsum = None 2025-03-04T22:21:48.1397636Z add_1: "i32[1, 128][128, 1]cuda:0" = type_as + 0; type_as = None 2025-03-04T22:21:48.1398172Z incremental_indices: "i32[1, 128][128, 1]cuda:0" = add_1 * mask_2; add_1 = mask_2 = None 2025-03-04T22:21:48.1398634Z 2025-03-04T22:21:48.1399671Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:114 in create_position_ids_from_input_ids, code: return incremental_indices.long() + padding_idx 2025-03-04T22:21:48.1400941Z long: "i64[1, 128][128, 1]cuda:0" = incremental_indices.long(); incremental_indices = None 2025-03-04T22:21:48.1401449Z add_2: "i64[1, 128][128, 1]cuda:0" = long + 1; long = None 2025-03-04T22:21:48.1401793Z 2025-03-04T22:21:48.1402877Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:89 in forward, code: position_ids = self.create_position_ids_from_input_ids(input_ids, self.padding_idx, past_key_values_length).to( 2025-03-04T22:21:48.1404102Z position_ids: "i64[1, 128][128, 1]cuda:0" = add_2.to(device(type='cuda', index=0)); add_2 = None 2025-03-04T22:21:48.1404545Z 2025-03-04T22:21:48.1405480Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:98 in forward, code: return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, -1).detach() 2025-03-04T22:21:48.1406661Z view_2: "i64[128][1]cuda:0" = position_ids.view(-1); position_ids = None 2025-03-04T22:21:48.1407829Z index_select: "f32[128, 256][256, 1]cuda:0" = l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weights_.index_select(0, view_2); l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weights_ = view_2 = None 2025-03-04T22:21:48.1408934Z view_3: "f32[1, 128, 256][32768, 256, 1]cuda:0" = index_select.view(1, 128, -1); index_select = None 2025-03-04T22:21:48.1409503Z positions: "f32[1, 128, 256][32768, 256, 1]cuda:0" = view_3.detach(); view_3 = None 2025-03-04T22:21:48.1409914Z 2025-03-04T22:21:48.1410170Z # No stacktrace found for following nodes 2025-03-04T22:21:48.1410643Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:21:48.1411076Z 2025-03-04T22:21:48.1411906Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:585 in forward, code: hidden_states = inputs_embeds + positions 2025-03-04T22:21:48.1412965Z hidden_states: "f32[1, 128, 256][32768, 256, 1]cuda:0" = inputs_embeds + positions; inputs_embeds = positions = None 2025-03-04T22:21:48.1413563Z 2025-03-04T22:21:48.1414521Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:586 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:21:48.1415920Z hidden_states_1: "f32[1, 128, 256][32768, 256, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:21:48.1416578Z 2025-03-04T22:21:48.1417337Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:614 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:21:48.1418220Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:21:48.1418567Z 2025-03-04T22:21:48.1419337Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/speech_to_text_2/modeling_speech_to_text_2.py:615 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:21:48.1420297Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:21:48.1420720Z 2025-03-04T22:21:56.4737890Z Compilation time (from dynamo_timed): 5.282863068 2025-03-04T22:21:56.4747744Z pass 2025-03-04T22:21:56.4800937Z TIMING: entire_frame_compile:4.16099 gc:0.00704 _recursive_pre_grad_passes:0.0066 _recursive_joint_graph_passes:0.24881 inductor_compile:2.56518 backend_compile:3.25422 async_compile.precompile:0.12114 async_compile.wait:0.66684 _recursive_post_grad_passes:0.0809 code_gen:1.68274 pad_mm_benchmark:0.01579 entire_backward_compile:1.12188 total_wall_time:5.28286 2025-03-04T22:21:56.4802898Z STATS: call_* op count: 68 | FakeTensorMode.__torch_dispatch__:4429 | ProxyTorchDispatchMode.__torch_dispatch__:1868 | FakeTensor.__torch_dispatch__:636 2025-03-04T22:21:56.4803709Z Dynamo produced 6 graphs covering 68 ops with 6 graph breaks (5 unique) 2025-03-04T22:22:02.1664260Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:22:02.1665460Z warnings.warn( 2025-03-04T22:22:02.4327035Z 2025-03-04T22:22:04.0042538Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:22:04.0042979Z loading model: 0it [00:01, ?it/s] 2025-03-04T22:22:04.0043362Z cuda train T5ForConditionalGeneration 2025-03-04T22:22:40.5760621Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:22:40.5761882Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:22:40.5762733Z pred = mod(**cloned_inputs) 2025-03-04T22:22:40.5763598Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1706, in forward 2025-03-04T22:22:40.5764432Z encoder_outputs = self.encoder( 2025-03-04T22:22:40.5765046Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1016, in forward 2025-03-04T22:22:40.5765691Z inputs_embeds = self.embed_tokens(input_ids) 2025-03-04T22:22:40.5765935Z 2025-03-04T22:22:40.7131867Z W0304 22:22:40.712000 21693 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:23:12.1710509Z Compilation time (from dynamo_timed): 60.408059717 2025-03-04T22:23:12.1738995Z pass 2025-03-04T22:23:12.2342030Z TIMING: entire_frame_compile:52.59131 gc:0.00354 _recursive_pre_grad_passes:0.03573 pad_mm_benchmark:0.23518 _recursive_joint_graph_passes:1.76593 _recursive_post_grad_passes:1.17546 async_compile.wait:4.02834 code_gen:15.68856 inductor_compile:28.42148 backend_compile:40.78012 entire_backward_compile:7.81675 total_wall_time:60.40806 2025-03-04T22:23:12.2344382Z STATS: call_* op count: 1487 | FakeTensorMode.__torch_dispatch__:67856 | ProxyTorchDispatchMode.__torch_dispatch__:31555 | FakeTensor.__torch_dispatch__:11585 2025-03-04T22:23:20.1510683Z Dynamo produced 2 graphs covering 1487 ops with 5 graph breaks (4 unique) 2025-03-04T22:23:20.1512155Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:23:20.1513349Z warnings.warn( 2025-03-04T22:23:21.1979957Z 2025-03-04T22:23:22.7700164Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:23:22.7700535Z loading model: 0it [00:01, ?it/s] 2025-03-04T22:23:22.7700872Z cuda train T5Small 2025-03-04T22:23:34.0435322Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:23:34.0436194Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:23:34.0436912Z pred = mod(**cloned_inputs) 2025-03-04T22:23:34.0437542Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1706, in forward 2025-03-04T22:23:34.0438178Z encoder_outputs = self.encoder( 2025-03-04T22:23:34.0439291Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1016, in forward 2025-03-04T22:23:34.0440183Z inputs_embeds = self.embed_tokens(input_ids) 2025-03-04T22:23:34.0440422Z 2025-03-04T22:23:35.3334601Z W0304 22:23:35.332000 22049 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:23:47.1665845Z Compilation time (from dynamo_timed): 17.83938229 2025-03-04T22:23:47.1699489Z pass 2025-03-04T22:23:47.2302056Z TIMING: entire_frame_compile:17.83938 gc:0.00439 _recursive_pre_grad_passes:0.03508 async_compile.wait:0.42877 backend_compile:6.65545 total_wall_time:17.83938 2025-03-04T22:23:47.2302997Z STATS: call_* op count: 1487 | FakeTensorMode.__torch_dispatch__:10190 | FakeTensor.__torch_dispatch__:1576 2025-03-04T22:23:47.2303654Z Dynamo produced 2 graphs covering 1487 ops with 5 graph breaks (4 unique) 2025-03-04T22:23:52.9611549Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:23:52.9612808Z warnings.warn( 2025-03-04T22:23:53.1989521Z 2025-03-04T22:23:57.2021636Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:23:57.2022131Z loading model: 0it [00:04, ?it/s] 2025-03-04T22:23:57.2022479Z cuda train TrOCRForCausalLM 2025-03-04T22:23:57.2332791Z WARNING:common:fp64 golden ref were not generated for TrOCRForCausalLM. Setting accuracy check to cosine 2025-03-04T22:23:59.1511688Z 2025-03-04T22:23:59.1512489Z class GraphModule(torch.nn.Module): 2025-03-04T22:23:59.1514570Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 256][256, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[514, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:23:59.1517196Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:23:59.1517984Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:23:59.1519115Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:23:59.1520291Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:23:59.1521489Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:23:59.1522279Z 2025-03-04T22:23:59.1522544Z # No stacktrace found for following nodes 2025-03-04T22:23:59.1523131Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:23:59.1523684Z 2025-03-04T22:23:59.1524454Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:578 in forward, code: input_ids = input_ids.view(-1, input.shape[-1]) 2025-03-04T22:23:59.1525495Z input_ids: "i64[1, 256][256, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 256); l_cloned_inputs_input_ids_ = None 2025-03-04T22:23:59.1526004Z 2025-03-04T22:23:59.1527314Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:589 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:23:59.1529212Z embedding: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:23:59.1530679Z inputs_embeds: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:23:59.1531133Z 2025-03-04T22:23:59.1531806Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:62 in forward, code: positions = torch.arange( 2025-03-04T22:23:59.1532800Z arange: "i64[256][1]cuda:0" = torch.arange(0, 256, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:23:59.1533293Z 2025-03-04T22:23:59.1533940Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:64 in forward, code: ).expand(bsz, -1) 2025-03-04T22:23:59.1534775Z positions: "i64[1, 256][256, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:23:59.1535185Z 2025-03-04T22:23:59.1535921Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:66 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:23:59.1536913Z add: "i64[1, 256][256, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:23:59.1538536Z embed_pos: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:23:59.1540037Z 2025-03-04T22:23:59.1540972Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:596 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:23:59.1542374Z hidden_states: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:23:59.1542890Z 2025-03-04T22:23:59.1543654Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:599 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:23:59.1545925Z hidden_states_1: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:23:59.1547641Z 2025-03-04T22:23:59.1548550Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:601 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:23:59.1549925Z hidden_states_2: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:23:59.1558980Z 2025-03-04T22:23:59.1559846Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:23:59.1560960Z mask: "f32[256, 256][256, 1]cuda:0" = torch.full((256, 256), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:23:59.1561780Z 2025-03-04T22:23:59.1562723Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:23:59.1563805Z mask_cond: "i64[256][1]cuda:0" = torch.arange(256, device = device(type='cuda', index=0)) 2025-03-04T22:23:59.1564245Z 2025-03-04T22:23:59.1565043Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:23:59.1565935Z add_2: "i64[256][1]cuda:0" = mask_cond + 1 2025-03-04T22:23:59.1566335Z view_1: "i64[256, 1][1, 1]cuda:0" = add_2.view(256, 1); add_2 = None 2025-03-04T22:23:59.1566810Z lt: "b8[256, 256][256, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:23:59.1567368Z masked_fill_: "f32[256, 256][256, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:23:59.1567939Z 2025-03-04T22:23:59.1568609Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:23:59.1569423Z mask_1: "f32[256, 256][256, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:23:59.1569815Z 2025-03-04T22:23:59.1570642Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:23:59.1571820Z getitem: "f32[1, 1, 256, 256][65536, 65536, 256, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:23:59.1572622Z causal_4d_mask: "f32[1, 1, 256, 256][65536, 65536, 256, 1]cuda:0" = getitem.expand(1, 1, 256, 256); getitem = causal_4d_mask = None 2025-03-04T22:23:59.1573143Z 2025-03-04T22:23:59.1573846Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:642 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:23:59.1574670Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:23:59.1575015Z 2025-03-04T22:23:59.1575726Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:643 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:23:59.1576628Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:23:59.1577045Z 2025-03-04T22:23:59.1577198Z 2025-03-04T22:23:59.1577320Z class GraphModule(torch.nn.Module): 2025-03-04T22:23:59.1579052Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 256][256, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[514, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:23:59.1580817Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:23:59.1581581Z l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:23:59.1582729Z l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:23:59.1583970Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:23:59.1585133Z l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:23:59.1585923Z 2025-03-04T22:23:59.1586180Z # No stacktrace found for following nodes 2025-03-04T22:23:59.1586755Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:23:59.1587285Z 2025-03-04T22:23:59.1588025Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:578 in forward, code: input_ids = input_ids.view(-1, input.shape[-1]) 2025-03-04T22:23:59.1589046Z input_ids: "i64[1, 256][256, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 256); l_cloned_inputs_input_ids_ = None 2025-03-04T22:23:59.1589546Z 2025-03-04T22:23:59.1590333Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:589 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:23:59.1591997Z embedding: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:23:59.1593238Z inputs_embeds: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:23:59.1593685Z 2025-03-04T22:23:59.1594350Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:62 in forward, code: positions = torch.arange( 2025-03-04T22:23:59.1595280Z arange: "i64[256][1]cuda:0" = torch.arange(0, 256, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:23:59.1595771Z 2025-03-04T22:23:59.1596405Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:64 in forward, code: ).expand(bsz, -1) 2025-03-04T22:23:59.1597233Z positions: "i64[1, 256][256, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:23:59.1597642Z 2025-03-04T22:23:59.1598369Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:66 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:23:59.1599255Z add: "i64[1, 256][256, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:23:59.1600430Z embed_pos: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_mod_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:23:59.1601507Z 2025-03-04T22:23:59.1602226Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:596 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:23:59.1603286Z hidden_states: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:23:59.1603796Z 2025-03-04T22:23:59.1604556Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:599 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:23:59.1606889Z hidden_states_1: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_mod_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:23:59.1608765Z 2025-03-04T22:23:59.1609663Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:601 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:23:59.1611029Z hidden_states_2: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:23:59.1611712Z 2025-03-04T22:23:59.1612527Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:23:59.1613632Z mask: "f32[256, 256][256, 1]cuda:0" = torch.full((256, 256), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:23:59.1614140Z 2025-03-04T22:23:59.1614890Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:23:59.1615849Z mask_cond: "i64[256][1]cuda:0" = torch.arange(256, device = device(type='cuda', index=0)) 2025-03-04T22:23:59.1616290Z 2025-03-04T22:23:59.1617086Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:23:59.1617984Z add_2: "i64[256][1]cuda:0" = mask_cond + 1 2025-03-04T22:23:59.1618381Z view_1: "i64[256, 1][1, 1]cuda:0" = add_2.view(256, 1); add_2 = None 2025-03-04T22:23:59.1618861Z lt: "b8[256, 256][256, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:23:59.1619426Z masked_fill_: "f32[256, 256][256, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:23:59.1619876Z 2025-03-04T22:23:59.1620524Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:23:59.1621335Z mask_1: "f32[256, 256][256, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:23:59.1621727Z 2025-03-04T22:23:59.1622557Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:23:59.1623723Z getitem: "f32[1, 1, 256, 256][65536, 65536, 256, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:23:59.1624523Z causal_4d_mask: "f32[1, 1, 256, 256][65536, 65536, 256, 1]cuda:0" = getitem.expand(1, 1, 256, 256); getitem = causal_4d_mask = None 2025-03-04T22:23:59.1625046Z 2025-03-04T22:23:59.1625741Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:642 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:23:59.1626567Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:23:59.1626914Z 2025-03-04T22:23:59.1627622Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:643 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:23:59.1628526Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:23:59.1628950Z 2025-03-04T22:24:00.0496914Z 2025-03-04T22:24:00.0497594Z class GraphModule(torch.nn.Module): 2025-03-04T22:24:00.0499862Z def forward(self, L_input_ids_: "i64[1, 256][256, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_: "f32[50265, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_: "f32[514, 1024][1024, 1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_: "f32[1024][1]cuda:0", L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_: "f32[1024][1]cuda:0"): 2025-03-04T22:24:00.0501931Z l_input_ids_ = L_input_ids_ 2025-03-04T22:24:00.0502646Z l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ 2025-03-04T22:24:00.0503784Z l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = L_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ 2025-03-04T22:24:00.0504981Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ 2025-03-04T22:24:00.0506199Z l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = L_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ 2025-03-04T22:24:00.0506928Z 2025-03-04T22:24:00.0507702Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:578 in forward, code: input_ids = input_ids.view(-1, input.shape[-1]) 2025-03-04T22:24:00.0508655Z input_ids: "i64[1, 256][256, 1]cuda:0" = l_input_ids_.view(-1, 256); l_input_ids_ = None 2025-03-04T22:24:00.0509079Z 2025-03-04T22:24:00.0509881Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:589 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:24:00.0511581Z embedding: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_decoder_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:24:00.0512858Z inputs_embeds: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = embedding * 1.0; embedding = None 2025-03-04T22:24:00.0513317Z 2025-03-04T22:24:00.0513994Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:62 in forward, code: positions = torch.arange( 2025-03-04T22:24:00.0514943Z arange: "i64[256][1]cuda:0" = torch.arange(0, 256, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:24:00.0515435Z 2025-03-04T22:24:00.0516075Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:64 in forward, code: ).expand(bsz, -1) 2025-03-04T22:24:00.0516907Z positions: "i64[1, 256][256, 1]cuda:0" = arange.expand(1, -1); arange = None 2025-03-04T22:24:00.0517321Z 2025-03-04T22:24:00.0518061Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:66 in forward, code: return super().forward(positions + self.offset) 2025-03-04T22:24:00.0518978Z add: "i64[1, 256][256, 1]cuda:0" = positions + 2; positions = None 2025-03-04T22:24:00.0520177Z embed_pos: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.embedding(add, l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_, None, None, 2.0, False, False); add = l_self_modules_model_modules_decoder_modules_embed_positions_parameters_weight_ = None 2025-03-04T22:24:00.0521268Z 2025-03-04T22:24:00.0521986Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:596 in forward, code: hidden_states = inputs_embeds + embed_pos 2025-03-04T22:24:00.0523090Z hidden_states: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = inputs_embeds + embed_pos; inputs_embeds = embed_pos = None 2025-03-04T22:24:00.0523698Z 2025-03-04T22:24:00.0524464Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:599 in forward, code: hidden_states = self.layernorm_embedding(hidden_states) 2025-03-04T22:24:00.0526736Z hidden_states_1: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.layer_norm(hidden_states, (1024,), l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_, l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_, 1e-05); hidden_states = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_weight_ = l_self_modules_model_modules_decoder_modules_layernorm_embedding_parameters_bias_ = None 2025-03-04T22:24:00.0528586Z 2025-03-04T22:24:00.0529489Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:601 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) 2025-03-04T22:24:00.0530858Z hidden_states_2: "f32[1, 256, 1024][262144, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_1, p = 0.1, training = True); hidden_states_1 = hidden_states_2 = None 2025-03-04T22:24:00.0531532Z 2025-03-04T22:24:00.0532346Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:24:00.0533499Z mask: "f32[256, 256][256, 1]cuda:0" = torch.full((256, 256), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:24:00.0534007Z 2025-03-04T22:24:00.0534758Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:24:00.0535718Z mask_cond: "i64[256][1]cuda:0" = torch.arange(256, device = device(type='cuda', index=0)) 2025-03-04T22:24:00.0536159Z 2025-03-04T22:24:00.0536955Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:24:00.0537849Z add_2: "i64[256][1]cuda:0" = mask_cond + 1 2025-03-04T22:24:00.0538251Z view_1: "i64[256, 1][1, 1]cuda:0" = add_2.view(256, 1); add_2 = None 2025-03-04T22:24:00.0538731Z lt: "b8[256, 256][256, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:24:00.0539296Z masked_fill_: "f32[256, 256][256, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:24:00.0539748Z 2025-03-04T22:24:00.0540408Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:24:00.0541236Z mask_1: "f32[256, 256][256, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:24:00.0541634Z 2025-03-04T22:24:00.0542462Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:24:00.0543652Z getitem: "f32[1, 1, 256, 256][65536, 65536, 256, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:24:00.0544450Z causal_4d_mask: "f32[1, 1, 256, 256][65536, 65536, 256, 1]cuda:0" = getitem.expand(1, 1, 256, 256); getitem = causal_4d_mask = None 2025-03-04T22:24:00.0544966Z 2025-03-04T22:24:00.0545759Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:642 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:24:00.0546664Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:24:00.0547001Z 2025-03-04T22:24:00.0547706Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py:643 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:24:00.0548618Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:24:00.0549034Z 2025-03-04T22:24:05.3554323Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:24:05.3555140Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/trocr/modeling_trocr.py", line 66, in forward 2025-03-04T22:24:05.3555843Z return super().forward(positions + self.offset) 2025-03-04T22:24:05.3556101Z 2025-03-04T22:24:12.5208668Z Compilation time (from dynamo_timed): 6.0357510009999995 2025-03-04T22:24:12.5230084Z pass 2025-03-04T22:24:12.6040047Z TIMING: entire_frame_compile:4.5899 gc:0.00742 _recursive_pre_grad_passes:0.00629 _recursive_joint_graph_passes:0.47588 inductor_compile:3.05725 backend_compile:3.67211 _recursive_post_grad_passes:0.08067 async_compile.precompile:0.23718 async_compile.wait:0.79196 code_gen:2.10924 pad_mm_benchmark:0.23224 entire_backward_compile:1.44585 total_wall_time:6.03575 2025-03-04T22:24:12.6041824Z STATS: call_* op count: 59 | FakeTensorMode.__torch_dispatch__:4517 | FakeTensor.__torch_dispatch__:678 | ProxyTorchDispatchMode.__torch_dispatch__:1916 2025-03-04T22:24:12.6042636Z Dynamo produced 6 graphs covering 59 ops with 6 graph breaks (5 unique) 2025-03-04T22:24:18.3033735Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:24:18.3034918Z warnings.warn( 2025-03-04T22:24:18.5833203Z 2025-03-04T22:24:28.4244471Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:24:28.4245267Z loading model: 0it [00:09, ?it/s] 2025-03-04T22:24:28.4245937Z cuda train XGLMForCausalLM 2025-03-04T22:24:28.4764032Z WARNING:common:fp64 golden ref were not generated for XGLMForCausalLM. Setting accuracy check to cosine 2025-03-04T22:24:30.9700283Z 2025-03-04T22:24:30.9700891Z class GraphModule(torch.nn.Module): 2025-03-04T22:24:30.9702170Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_embed_tokens_parameters_weight_: "f32[256008, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_embed_positions_buffers_weights_: "f32[2050, 1024][1024, 1]cuda:0"): 2025-03-04T22:24:30.9703792Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:24:30.9704935Z l_mod_modules_model_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_embed_tokens_parameters_weight_ 2025-03-04T22:24:30.9705846Z l_mod_modules_model_modules_embed_positions_buffers_weights_ = L_mod_modules_model_modules_embed_positions_buffers_weights_ 2025-03-04T22:24:30.9706440Z 2025-03-04T22:24:30.9706706Z # No stacktrace found for following nodes 2025-03-04T22:24:30.9707289Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:24:30.9707826Z 2025-03-04T22:24:30.9708577Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:555 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:24:30.9709601Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:24:30.9710111Z 2025-03-04T22:24:30.9711308Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:564 in forward, code: position_ids = torch.arange( 2025-03-04T22:24:30.9712511Z position_ids: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:24:30.9713033Z 2025-03-04T22:24:30.9713746Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:570 in forward, code: position_ids = position_ids.unsqueeze(0) 2025-03-04T22:24:30.9714690Z position_ids_1: "i64[1, 128][128, 1]cuda:0" = position_ids.unsqueeze(0); position_ids = None 2025-03-04T22:24:30.9715142Z 2025-03-04T22:24:30.9715932Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:573 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:24:30.9717501Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:24:30.9718654Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 32.0; embedding = None 2025-03-04T22:24:30.9719110Z 2025-03-04T22:24:30.9719992Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:24:30.9721268Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:24:30.9721920Z 2025-03-04T22:24:30.9722902Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:24:30.9724007Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:24:30.9724545Z 2025-03-04T22:24:30.9725357Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:24:30.9726256Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:24:30.9726656Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:24:30.9727130Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:24:30.9727864Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:24:30.9728322Z 2025-03-04T22:24:30.9728989Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:24:30.9729879Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:24:30.9730273Z 2025-03-04T22:24:30.9731117Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:24:30.9732311Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:24:30.9733133Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:24:30.9733666Z 2025-03-04T22:24:30.9733934Z # No stacktrace found for following nodes 2025-03-04T22:24:30.9734535Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:24:30.9734977Z 2025-03-04T22:24:30.9735718Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:174 in forward, code: position_ids += self.offset 2025-03-04T22:24:30.9736636Z position_ids_1 += 2; position_ids_2: "i64[1, 128][128, 1]cuda:0" = position_ids_1; position_ids_1 = None 2025-03-04T22:24:30.9737106Z 2025-03-04T22:24:30.9738056Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:181 in forward, code: return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach() 2025-03-04T22:24:30.9739173Z view_2: "i64[128][1]cuda:0" = position_ids_2.view(-1); position_ids_2 = None 2025-03-04T22:24:30.9740128Z index_select: "f32[128, 1024][1024, 1]cuda:0" = l_mod_modules_model_modules_embed_positions_buffers_weights_.index_select(0, view_2); l_mod_modules_model_modules_embed_positions_buffers_weights_ = view_2 = None 2025-03-04T22:24:30.9741130Z view_3: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = index_select.view(1, 128, 1024); index_select = None 2025-03-04T22:24:30.9741721Z detach: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = view_3.detach(); view_3 = None 2025-03-04T22:24:30.9742134Z 2025-03-04T22:24:30.9742393Z # No stacktrace found for following nodes 2025-03-04T22:24:30.9742871Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:24:30.9743302Z 2025-03-04T22:24:30.9744181Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:586 in forward, code: hidden_states = inputs_embeds + self.embed_positions(position_ids, past_key_values_length) 2025-03-04T22:24:30.9745337Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + detach; inputs_embeds = detach = None 2025-03-04T22:24:30.9745845Z 2025-03-04T22:24:30.9746762Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:587 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=float(self.dropout), training=self.training) 2025-03-04T22:24:30.9748149Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:24:30.9748823Z 2025-03-04T22:24:30.9749519Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:616 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:24:30.9750391Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:24:30.9750748Z 2025-03-04T22:24:30.9751463Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:617 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:24:30.9752373Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:24:30.9752801Z 2025-03-04T22:24:30.9752965Z 2025-03-04T22:24:30.9753093Z class GraphModule(torch.nn.Module): 2025-03-04T22:24:30.9754079Z def forward(self, L_cloned_inputs_input_ids_: "i64[1, 128][128, 1]cuda:0", L_mod_modules_model_modules_embed_tokens_parameters_weight_: "f32[256008, 1024][1024, 1]cuda:0", L_mod_modules_model_modules_embed_positions_buffers_weights_: "f32[2050, 1024][1024, 1]cuda:0"): 2025-03-04T22:24:30.9755114Z l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_ 2025-03-04T22:24:30.9755770Z l_mod_modules_model_modules_embed_tokens_parameters_weight_ = L_mod_modules_model_modules_embed_tokens_parameters_weight_ 2025-03-04T22:24:30.9756641Z l_mod_modules_model_modules_embed_positions_buffers_weights_ = L_mod_modules_model_modules_embed_positions_buffers_weights_ 2025-03-04T22:24:30.9757348Z 2025-03-04T22:24:30.9757609Z # No stacktrace found for following nodes 2025-03-04T22:24:30.9758270Z _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None); _enter_autocast = None 2025-03-04T22:24:30.9758808Z 2025-03-04T22:24:30.9759544Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:555 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:24:30.9760568Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_cloned_inputs_input_ids_.view(-1, 128); l_cloned_inputs_input_ids_ = None 2025-03-04T22:24:30.9761328Z 2025-03-04T22:24:30.9762019Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:564 in forward, code: position_ids = torch.arange( 2025-03-04T22:24:30.9762990Z position_ids: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:24:30.9763519Z 2025-03-04T22:24:30.9764227Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:570 in forward, code: position_ids = position_ids.unsqueeze(0) 2025-03-04T22:24:30.9765175Z position_ids_1: "i64[1, 128][128, 1]cuda:0" = position_ids.unsqueeze(0); position_ids = None 2025-03-04T22:24:30.9765627Z 2025-03-04T22:24:30.9766412Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:573 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:24:30.9768036Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_mod_modules_model_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_mod_modules_model_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:24:30.9769178Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 32.0; embedding = None 2025-03-04T22:24:30.9769633Z 2025-03-04T22:24:30.9770449Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:24:30.9771560Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:24:30.9772069Z 2025-03-04T22:24:30.9772819Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:24:30.9773779Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:24:30.9774214Z 2025-03-04T22:24:30.9775015Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:24:30.9775916Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:24:30.9776307Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:24:30.9776778Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:24:30.9777340Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:24:30.9777790Z 2025-03-04T22:24:30.9778442Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:24:30.9779290Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:24:30.9779709Z 2025-03-04T22:24:30.9780692Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:24:30.9781997Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:24:30.9782800Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:24:30.9783328Z 2025-03-04T22:24:30.9783585Z # No stacktrace found for following nodes 2025-03-04T22:24:30.9784061Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:24:30.9784499Z 2025-03-04T22:24:30.9785161Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:174 in forward, code: position_ids += self.offset 2025-03-04T22:24:30.9786082Z position_ids_1 += 2; position_ids_2: "i64[1, 128][128, 1]cuda:0" = position_ids_1; position_ids_1 = None 2025-03-04T22:24:30.9786554Z 2025-03-04T22:24:30.9787492Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:181 in forward, code: return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach() 2025-03-04T22:24:30.9788612Z view_2: "i64[128][1]cuda:0" = position_ids_2.view(-1); position_ids_2 = None 2025-03-04T22:24:30.9789552Z index_select: "f32[128, 1024][1024, 1]cuda:0" = l_mod_modules_model_modules_embed_positions_buffers_weights_.index_select(0, view_2); l_mod_modules_model_modules_embed_positions_buffers_weights_ = view_2 = None 2025-03-04T22:24:30.9790546Z view_3: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = index_select.view(1, 128, 1024); index_select = None 2025-03-04T22:24:30.9791142Z detach: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = view_3.detach(); view_3 = None 2025-03-04T22:24:30.9791563Z 2025-03-04T22:24:30.9791824Z # No stacktrace found for following nodes 2025-03-04T22:24:30.9792300Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:24:30.9792733Z 2025-03-04T22:24:30.9793609Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:586 in forward, code: hidden_states = inputs_embeds + self.embed_positions(position_ids, past_key_values_length) 2025-03-04T22:24:30.9794753Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + detach; inputs_embeds = detach = None 2025-03-04T22:24:30.9795250Z 2025-03-04T22:24:30.9796172Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:587 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=float(self.dropout), training=self.training) 2025-03-04T22:24:30.9797543Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:24:30.9798218Z 2025-03-04T22:24:30.9798911Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:616 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:24:30.9799728Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:24:30.9800067Z 2025-03-04T22:24:30.9800769Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:617 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:24:30.9801669Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:24:30.9802085Z 2025-03-04T22:24:31.8583489Z 2025-03-04T22:24:31.8584536Z class GraphModule(torch.nn.Module): 2025-03-04T22:24:31.8585558Z def forward(self, L_input_ids_: "i64[1, 128][128, 1]cuda:0", L_self_modules_model_modules_embed_tokens_parameters_weight_: "f32[256008, 1024][1024, 1]cuda:0", L_self_modules_model_modules_embed_positions_buffers_weights_: "f32[2050, 1024][1024, 1]cuda:0"): 2025-03-04T22:24:31.8586696Z l_input_ids_ = L_input_ids_ 2025-03-04T22:24:31.8587302Z l_self_modules_model_modules_embed_tokens_parameters_weight_ = L_self_modules_model_modules_embed_tokens_parameters_weight_ 2025-03-04T22:24:31.8588204Z l_self_modules_model_modules_embed_positions_buffers_weights_ = L_self_modules_model_modules_embed_positions_buffers_weights_ 2025-03-04T22:24:31.8588794Z 2025-03-04T22:24:31.8589547Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:555 in forward, code: input_ids = input_ids.view(-1, input_shape[-1]) 2025-03-04T22:24:31.8590491Z input_ids: "i64[1, 128][128, 1]cuda:0" = l_input_ids_.view(-1, 128); l_input_ids_ = None 2025-03-04T22:24:31.8590914Z 2025-03-04T22:24:31.8591572Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:564 in forward, code: position_ids = torch.arange( 2025-03-04T22:24:31.8592530Z position_ids: "i64[128][1]cuda:0" = torch.arange(0, 128, dtype = torch.int64, device = device(type='cuda', index=0)) 2025-03-04T22:24:31.8593037Z 2025-03-04T22:24:31.8601734Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:570 in forward, code: position_ids = position_ids.unsqueeze(0) 2025-03-04T22:24:31.8602741Z position_ids_1: "i64[1, 128][128, 1]cuda:0" = position_ids.unsqueeze(0); position_ids = None 2025-03-04T22:24:31.8603197Z 2025-03-04T22:24:31.8603992Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:573 in forward, code: inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale 2025-03-04T22:24:31.8605555Z embedding: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_model_modules_embed_tokens_parameters_weight_, 1, None, 2.0, False, False); input_ids = l_self_modules_model_modules_embed_tokens_parameters_weight_ = None 2025-03-04T22:24:31.8606706Z inputs_embeds: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = embedding * 32.0; embedding = None 2025-03-04T22:24:31.8607154Z 2025-03-04T22:24:31.8608153Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:156 in _make_causal_mask, code: mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device) 2025-03-04T22:24:31.8609254Z mask: "f32[128, 128][128, 1]cuda:0" = torch.full((128, 128), -3.4028234663852886e+38, device = device(type='cuda', index=0)) 2025-03-04T22:24:31.8609760Z 2025-03-04T22:24:31.8610515Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:157 in _make_causal_mask, code: mask_cond = torch.arange(mask.size(-1), device=device) 2025-03-04T22:24:31.8611481Z mask_cond: "i64[128][1]cuda:0" = torch.arange(128, device = device(type='cuda', index=0)) 2025-03-04T22:24:31.8611918Z 2025-03-04T22:24:31.8612710Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:158 in _make_causal_mask, code: mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0) 2025-03-04T22:24:31.8613600Z add: "i64[128][1]cuda:0" = mask_cond + 1 2025-03-04T22:24:31.8613992Z view_1: "i64[128, 1][1, 1]cuda:0" = add.view(128, 1); add = None 2025-03-04T22:24:31.8614461Z lt: "b8[128, 128][128, 1]cuda:0" = mask_cond < view_1; mask_cond = view_1 = None 2025-03-04T22:24:31.8615015Z masked_fill_: "f32[128, 128][128, 1]cuda:0" = mask.masked_fill_(lt, 0); lt = masked_fill_ = None 2025-03-04T22:24:31.8615584Z 2025-03-04T22:24:31.8616318Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:160 in _make_causal_mask, code: mask = mask.to(dtype) 2025-03-04T22:24:31.8617138Z mask_1: "f32[128, 128][128, 1]cuda:0" = mask.to(torch.float32); mask = None 2025-03-04T22:24:31.8617544Z 2025-03-04T22:24:31.8618377Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_attn_mask_utils.py:172 in _make_causal_mask, code: return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length) 2025-03-04T22:24:31.8619555Z getitem: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = mask_1[(None, None, slice(None, None, None), slice(None, None, None))]; mask_1 = None 2025-03-04T22:24:31.8620357Z causal_4d_mask: "f32[1, 1, 128, 128][16384, 16384, 128, 1]cuda:0" = getitem.expand(1, 1, 128, 128); getitem = causal_4d_mask = None 2025-03-04T22:24:31.8620886Z 2025-03-04T22:24:31.8621148Z # No stacktrace found for following nodes 2025-03-04T22:24:31.8621621Z _set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None 2025-03-04T22:24:31.8622051Z 2025-03-04T22:24:31.8622712Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:174 in forward, code: position_ids += self.offset 2025-03-04T22:24:31.8623626Z position_ids_1 += 2; position_ids_2: "i64[1, 128][128, 1]cuda:0" = position_ids_1; position_ids_1 = None 2025-03-04T22:24:31.8624092Z 2025-03-04T22:24:31.8625029Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:181 in forward, code: return self.weights.index_select(0, position_ids.view(-1)).view(bsz, seq_len, self.weights.shape[-1]).detach() 2025-03-04T22:24:31.8626136Z view_2: "i64[128][1]cuda:0" = position_ids_2.view(-1); position_ids_2 = None 2025-03-04T22:24:31.8627082Z index_select: "f32[128, 1024][1024, 1]cuda:0" = l_self_modules_model_modules_embed_positions_buffers_weights_.index_select(0, view_2); l_self_modules_model_modules_embed_positions_buffers_weights_ = view_2 = None 2025-03-04T22:24:31.8628075Z view_3: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = index_select.view(1, 128, 1024); index_select = None 2025-03-04T22:24:31.8628658Z detach: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = view_3.detach(); view_3 = None 2025-03-04T22:24:31.8629065Z 2025-03-04T22:24:31.8629321Z # No stacktrace found for following nodes 2025-03-04T22:24:31.8629789Z _set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None 2025-03-04T22:24:31.8630242Z 2025-03-04T22:24:31.8631131Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:586 in forward, code: hidden_states = inputs_embeds + self.embed_positions(position_ids, past_key_values_length) 2025-03-04T22:24:31.8632265Z hidden_states: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = inputs_embeds + detach; inputs_embeds = detach = None 2025-03-04T22:24:31.8632763Z 2025-03-04T22:24:31.8633666Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:587 in forward, code: hidden_states = nn.functional.dropout(hidden_states, p=float(self.dropout), training=self.training) 2025-03-04T22:24:31.8635027Z hidden_states_1: "f32[1, 128, 1024][131072, 1024, 1]cuda:0" = torch.nn.functional.dropout(hidden_states, p = 0.1, training = True); hidden_states = hidden_states_1 = None 2025-03-04T22:24:31.8635691Z 2025-03-04T22:24:31.8636377Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:616 in forward, code: dropout_probability = torch.rand([]) 2025-03-04T22:24:31.8637188Z dropout_probability: "f32[][]cpu" = torch.rand([]) 2025-03-04T22:24:31.8637613Z 2025-03-04T22:24:31.8638313Z # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py:617 in forward, code: if dropout_probability < self.layerdrop: 2025-03-04T22:24:31.8639283Z lt_1: "b8[][]cpu" = dropout_probability < 0.0; dropout_probability = lt_1 = None 2025-03-04T22:24:31.8639694Z 2025-03-04T22:24:32.6836138Z skipping cudagraphs due to mutated inputs (1 instances). Found from : 2025-03-04T22:24:32.6836951Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xglm/modeling_xglm.py", line 174, in forward 2025-03-04T22:24:32.6837596Z position_ids += self.offset 2025-03-04T22:24:32.6837797Z 2025-03-04T22:24:51.4211194Z Compilation time (from dynamo_timed): 5.647518096000001 2025-03-04T22:24:51.4245774Z pass 2025-03-04T22:24:51.5159876Z TIMING: entire_frame_compile:4.48495 gc:0.00739 _recursive_pre_grad_passes:0.00693 _recursive_joint_graph_passes:0.25012 inductor_compile:2.83643 backend_compile:3.55071 async_compile.precompile:0.18959 async_compile.wait:0.78893 _recursive_post_grad_passes:0.08684 code_gen:1.8772 pad_mm_benchmark:0.01572 entire_backward_compile:1.16257 total_wall_time:5.64752 2025-03-04T22:24:51.5163145Z STATS: call_* op count: 67 | FakeTensorMode.__torch_dispatch__:4601 | ProxyTorchDispatchMode.__torch_dispatch__:1946 | FakeTensor.__torch_dispatch__:717 2025-03-04T22:24:51.5164483Z Dynamo produced 6 graphs covering 67 ops with 6 graph breaks (5 unique) 2025-03-04T22:24:57.2459215Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:24:57.2461552Z warnings.warn( 2025-03-04T22:24:57.5046661Z 2025-03-04T22:25:02.1246296Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:25:02.1246900Z loading model: 0it [00:04, ?it/s] 2025-03-04T22:25:02.1247336Z cuda train XLNetLMHeadModel 2025-03-04T22:25:54.1239750Z W0304 22:25:54.123000 22661 site-packages/torch/_inductor/utils.py:1714] [2/0_1] DeviceCopy in input program 2025-03-04T22:26:09.1679225Z skipping cudagraphs due to skipping cudagraphs due to cpu device (cat). Found from : 2025-03-04T22:26:09.1681752Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:26:09.1682989Z pred = mod(**cloned_inputs) 2025-03-04T22:26:09.1684140Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xlnet/modeling_xlnet.py", line 1446, in forward 2025-03-04T22:26:09.1685022Z transformer_outputs = self.transformer( 2025-03-04T22:26:09.1685714Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xlnet/modeling_xlnet.py", line 1203, in forward 2025-03-04T22:26:09.1686472Z pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz) 2025-03-04T22:26:09.1687346Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xlnet/modeling_xlnet.py", line 1055, in relative_positional_encoding 2025-03-04T22:26:09.1688351Z pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz) 2025-03-04T22:26:09.1689156Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xlnet/modeling_xlnet.py", line 1014, in positional_embedding 2025-03-04T22:26:09.1689877Z pos_emb = pos_emb[:, None, :] 2025-03-04T22:26:09.1690082Z 2025-03-04T22:26:29.0210995Z W0304 22:26:29.020000 22661 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:27:53.1352394Z Compilation time (from dynamo_timed): 156.693013608 2025-03-04T22:27:53.1415664Z pass 2025-03-04T22:27:53.3496393Z TIMING: entire_frame_compile:138.82253 gc:0.0062 _recursive_pre_grad_passes:0.06795 pad_mm_benchmark:0.40613 _recursive_joint_graph_passes:4.03785 _recursive_post_grad_passes:1.59205 async_compile.wait:7.0627 code_gen:43.23833 inductor_compile:75.55433 backend_compile:109.50266 entire_backward_compile:17.87048 total_wall_time:156.69301 2025-03-04T22:27:53.3499313Z STATS: call_* op count: 2599 | FakeTensorMode.__torch_dispatch__:163632 | FakeTensor.__torch_dispatch__:29054 | ProxyTorchDispatchMode.__torch_dispatch__:69620 2025-03-04T22:27:53.3500284Z Dynamo produced 2 graphs covering 2599 ops with 5 graph breaks (4 unique) 2025-03-04T22:28:05.4910074Z /opt/conda/envs/py_3.10/lib/python3.10/site-packages/huggingface_hub/file_download.py:797: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`. 2025-03-04T22:28:05.4911404Z warnings.warn( 2025-03-04T22:28:05.7563181Z 2025-03-04T22:28:08.1198671Z loading model: 0it [00:00, ?it/s] 2025-03-04T22:28:08.1199106Z loading model: 0it [00:02, ?it/s] 2025-03-04T22:28:08.1199537Z cuda train YituTechConvBert 2025-03-04T22:28:56.8362255Z skipping cudagraphs due to deterministic index put. Found from : 2025-03-04T22:28:56.8363131Z File "/var/lib/jenkins/workspace/benchmarks/dynamo/huggingface.py", line 528, in torch_dynamo_resume_in_forward_and_backward_pass_at_526 2025-03-04T22:28:56.8363849Z pred = mod(**cloned_inputs) 2025-03-04T22:28:56.8364535Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/convbert/modeling_convbert.py", line 918, in forward 2025-03-04T22:28:56.8365244Z generator_hidden_states = self.convbert( 2025-03-04T22:28:56.8365947Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/convbert/modeling_convbert.py", line 834, in forward 2025-03-04T22:28:56.8366633Z hidden_states = self.embeddings( 2025-03-04T22:28:56.8367315Z File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/convbert/modeling_convbert.py", line 230, in forward 2025-03-04T22:28:56.8368216Z inputs_embeds = self.word_embeddings(input_ids) 2025-03-04T22:28:56.8368480Z 2025-03-04T22:28:57.0805858Z W0304 22:28:57.079000 23194 site-packages/torch/_logging/_internal.py:1134] [4/0] Profiler function will be ignored 2025-03-04T22:29:59.8804400Z Compilation time (from dynamo_timed): 103.096458364 2025-03-04T22:29:59.8839450Z pass 2025-03-04T22:29:59.9382791Z TIMING: entire_frame_compile:91.04318 gc:0.00565 _recursive_pre_grad_passes:0.05478 pad_mm_benchmark:0.62302 _recursive_joint_graph_passes:2.51705 _recursive_post_grad_passes:1.7673 async_compile.wait:5.32213 code_gen:29.61451 inductor_compile:50.88122 backend_compile:71.81217 entire_backward_compile:12.05328 total_wall_time:103.09646 2025-03-04T22:29:59.9385178Z STATS: call_* op count: 2085 | FakeTensorMode.__torch_dispatch__:93096 | FakeTensor.__torch_dispatch__:20357 | ProxyTorchDispatchMode.__torch_dispatch__:42000 2025-03-04T22:29:59.9386082Z Dynamo produced 2 graphs covering 2085 ops with 5 graph breaks (4 unique) 2025-03-04T22:30:06.5690090Z accuracy pass_rate=93.48% 2025-03-04T22:30:06.5693482Z calls_captured gmean=0.00x mean=951.261x 2025-03-04T22:30:06.5695842Z unique_graphs gmean=0.00x mean=3.217x 2025-03-04T22:30:06.5699314Z graph_breaks gmean=0.00x mean=5.109x 2025-03-04T22:30:06.5703018Z unique_graph_breaks gmean=0.00x mean=4.000x 2025-03-04T22:30:06.5706563Z autograd_captures gmean=0.00x mean=0.000x 2025-03-04T22:30:06.5710057Z autograd_compiles gmean=0.00x mean=0.000x 2025-03-04T22:30:06.5713483Z cudagraph_skips gmean=0.00x mean=0.891x 2025-03-04T22:30:06.5714224Z compilation_latency mean=46.394 seconds 2025-03-04T22:30:08.2721630Z + python benchmarks/dynamo/check_accuracy.py --actual /var/lib/jenkins/workspace/test/test-reports/training_huggingface.csv --expected benchmarks/dynamo/ci_expected_accuracy/inductor_huggingface_training.csv 2025-03-04T22:30:08.5415744Z AlbertForMaskedLM PASS 2025-03-04T22:30:08.5422660Z AlbertForQuestionAnswering PASS 2025-03-04T22:30:08.5426808Z AllenaiLongformerBase PASS 2025-03-04T22:30:08.5432200Z BartForCausalLM PASS 2025-03-04T22:30:08.5437738Z BartForConditionalGeneration PASS 2025-03-04T22:30:08.5443307Z BertForMaskedLM PASS 2025-03-04T22:30:08.5448792Z BertForQuestionAnswering PASS 2025-03-04T22:30:08.5454444Z BlenderbotForCausalLM XFAIL 2025-03-04T22:30:08.5459606Z BlenderbotSmallForCausalLM PASS 2025-03-04T22:30:08.5465617Z BlenderbotSmallForConditionalGeneration PASS 2025-03-04T22:30:08.5470766Z CamemBert PASS 2025-03-04T22:30:08.5476310Z DebertaForMaskedLM PASS 2025-03-04T22:30:08.5481649Z DebertaForQuestionAnswering PASS 2025-03-04T22:30:08.5486875Z DebertaV2ForMaskedLM XFAIL 2025-03-04T22:30:08.5492650Z DebertaV2ForQuestionAnswering XFAIL 2025-03-04T22:30:08.5497863Z DistilBertForMaskedLM PASS 2025-03-04T22:30:08.5503330Z DistilBertForQuestionAnswering PASS 2025-03-04T22:30:08.5508504Z DistillGPT2 PASS 2025-03-04T22:30:08.5513890Z ElectraForCausalLM PASS 2025-03-04T22:30:08.5519086Z ElectraForQuestionAnswering PASS 2025-03-04T22:30:08.5524434Z GPT2ForSequenceClassification PASS 2025-03-04T22:30:08.5530024Z GoogleFnet PASS 2025-03-04T22:30:08.5535280Z LayoutLMForMaskedLM PASS 2025-03-04T22:30:08.5540610Z LayoutLMForSequenceClassification PASS 2025-03-04T22:30:08.5545819Z M2M100ForConditionalGeneration PASS 2025-03-04T22:30:08.5551093Z MBartForCausalLM PASS 2025-03-04T22:30:08.5556433Z MBartForConditionalGeneration PASS 2025-03-04T22:30:08.5562188Z MT5ForConditionalGeneration PASS 2025-03-04T22:30:08.5567460Z MegatronBertForCausalLM PASS 2025-03-04T22:30:08.5573069Z MegatronBertForQuestionAnswering PASS 2025-03-04T22:30:08.5578275Z MobileBertForMaskedLM PASS 2025-03-04T22:30:08.5583529Z MobileBertForQuestionAnswering PASS 2025-03-04T22:30:08.5588708Z OPTForCausalLM PASS 2025-03-04T22:30:08.5594009Z PLBartForCausalLM PASS 2025-03-04T22:30:08.5599254Z PLBartForConditionalGeneration PASS 2025-03-04T22:30:08.5604462Z PegasusForCausalLM PASS 2025-03-04T22:30:08.5609914Z PegasusForConditionalGeneration PASS 2025-03-04T22:30:08.5615191Z RobertaForCausalLM PASS 2025-03-04T22:30:08.5620363Z RobertaForQuestionAnswering PASS 2025-03-04T22:30:08.5625670Z Speech2Text2ForCausalLM PASS 2025-03-04T22:30:08.5630839Z T5ForConditionalGeneration PASS 2025-03-04T22:30:08.5637837Z T5Small PASS 2025-03-04T22:30:08.5642452Z TrOCRForCausalLM PASS 2025-03-04T22:30:08.5648047Z XGLMForCausalLM PASS 2025-03-04T22:30:08.5653311Z XLNetLMHeadModel PASS 2025-03-04T22:30:08.5658512Z YituTechConvBert PASS 2025-03-04T22:30:08.6128973Z + python benchmarks/dynamo/check_graph_breaks.py --actual /var/lib/jenkins/workspace/test/test-reports/training_huggingface.csv --expected benchmarks/dynamo/ci_expected_accuracy/inductor_huggingface_training.csv 2025-03-04T22:30:08.8822390Z AlbertForMaskedLM PASS 2025-03-04T22:30:08.8824571Z AlbertForQuestionAnswering PASS 2025-03-04T22:30:08.8829352Z AllenaiLongformerBase PASS 2025-03-04T22:30:08.8834223Z BartForCausalLM PASS 2025-03-04T22:30:08.8839124Z BartForConditionalGeneration PASS 2025-03-04T22:30:08.8844038Z BertForMaskedLM PASS 2025-03-04T22:30:08.8848969Z BertForQuestionAnswering PASS 2025-03-04T22:30:08.8853912Z BlenderbotForCausalLM PASS 2025-03-04T22:30:08.8858632Z BlenderbotSmallForCausalLM PASS 2025-03-04T22:30:08.8864129Z BlenderbotSmallForConditionalGeneration PASS 2025-03-04T22:30:08.8870024Z CamemBert PASS 2025-03-04T22:30:08.8873757Z DebertaForMaskedLM PASS 2025-03-04T22:30:08.8878582Z DebertaForQuestionAnswering PASS 2025-03-04T22:30:08.8883340Z DebertaV2ForMaskedLM PASS 2025-03-04T22:30:08.8888297Z DebertaV2ForQuestionAnswering PASS 2025-03-04T22:30:08.8893302Z DistilBertForMaskedLM PASS 2025-03-04T22:30:08.8898068Z DistilBertForQuestionAnswering PASS 2025-03-04T22:30:08.8902728Z DistillGPT2 PASS 2025-03-04T22:30:08.8907482Z ElectraForCausalLM PASS 2025-03-04T22:30:08.8912191Z ElectraForQuestionAnswering PASS 2025-03-04T22:30:08.8917062Z GPT2ForSequenceClassification PASS 2025-03-04T22:30:08.8921772Z GoogleFnet PASS 2025-03-04T22:30:08.8926552Z LayoutLMForMaskedLM PASS 2025-03-04T22:30:08.8931585Z LayoutLMForSequenceClassification PASS 2025-03-04T22:30:08.8936294Z M2M100ForConditionalGeneration PASS 2025-03-04T22:30:08.8941032Z MBartForCausalLM PASS 2025-03-04T22:30:08.8945819Z MBartForConditionalGeneration PASS 2025-03-04T22:30:08.8950569Z MT5ForConditionalGeneration PASS 2025-03-04T22:30:08.8955328Z MegatronBertForCausalLM PASS 2025-03-04T22:30:08.8960122Z MegatronBertForQuestionAnswering PASS 2025-03-04T22:30:08.8966690Z MobileBertForMaskedLM PASS 2025-03-04T22:30:08.8972170Z MobileBertForQuestionAnswering PASS 2025-03-04T22:30:08.8976738Z OPTForCausalLM PASS 2025-03-04T22:30:08.8981369Z PLBartForCausalLM PASS 2025-03-04T22:30:08.8986118Z PLBartForConditionalGeneration PASS 2025-03-04T22:30:08.8991014Z PegasusForCausalLM PASS 2025-03-04T22:30:08.8995665Z PegasusForConditionalGeneration PASS 2025-03-04T22:30:08.9000436Z RobertaForCausalLM PASS 2025-03-04T22:30:08.9005187Z RobertaForQuestionAnswering PASS 2025-03-04T22:30:08.9010292Z Speech2Text2ForCausalLM PASS 2025-03-04T22:30:08.9015013Z T5ForConditionalGeneration PASS 2025-03-04T22:30:08.9019806Z T5Small PASS 2025-03-04T22:30:08.9024530Z TrOCRForCausalLM PASS 2025-03-04T22:30:08.9029345Z XGLMForCausalLM PASS_BUT_FLAKY 2025-03-04T22:30:08.9034099Z XLNetLMHeadModel PASS 2025-03-04T22:30:08.9038836Z YituTechConvBert PASS 2025-03-04T22:30:08.9511574Z + cleanup_workspace 2025-03-04T22:30:08.9512379Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.' 2025-03-04T22:30:08.9514648Z sudo may print the following warning message that can be ignored. The chown command will still run. 2025-03-04T22:30:08.9515297Z + echo ' sudo: setrlimit(RLIMIT_STACK): Operation not permitted' 2025-03-04T22:30:08.9515776Z sudo: setrlimit(RLIMIT_STACK): Operation not permitted 2025-03-04T22:30:08.9516324Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42' 2025-03-04T22:30:08.9516908Z For more details refer to https://github.com/sudo-project/sudo/issues/42 2025-03-04T22:30:08.9517379Z + sudo chown -R 1000 /var/lib/jenkins/workspace 2025-03-04T22:30:09.7934242Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main 2025-03-04T22:30:09.7934713Z with: 2025-03-04T22:30:09.7934978Z benchmark-results-dir: test/test-reports 2025-03-04T22:30:09.7935308Z dry-run: false 2025-03-04T22:30:09.7935560Z schema-version: v3 2025-03-04T22:30:09.7936010Z github-token: *** 2025-03-04T22:30:09.7936257Z env: 2025-03-04T22:30:09.7936483Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:09.7936825Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:09.7937379Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:09.7937870Z ##[endgroup] 2025-03-04T22:30:09.7965442Z ##[group]Run set -eux 2025-03-04T22:30:09.7965707Z set -eux 2025-03-04T22:30:09.7965983Z python3 -mpip install boto3==1.35.33 2025-03-04T22:30:09.7980051Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:09.7980427Z env: 2025-03-04T22:30:09.7980688Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:09.7981024Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:09.7981575Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:09.7982067Z ##[endgroup] 2025-03-04T22:30:09.8018301Z + python3 -mpip install boto3==1.35.33 2025-03-04T22:30:10.1251415Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T22:30:11.2837632Z Collecting boto3==1.35.33 2025-03-04T22:30:11.3078806Z Downloading boto3-1.35.33-py3-none-any.whl (139 kB) 2025-03-04T22:30:12.5644304Z Collecting botocore<1.36.0,>=1.35.33 2025-03-04T22:30:12.5677534Z Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB) 2025-03-04T22:30:12.7154288Z 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) 2025-03-04T22:30:12.7549082Z Collecting s3transfer<0.11.0,>=0.10.0 2025-03-04T22:30:12.7581058Z Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB) 2025-03-04T22:30:12.7677958Z 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) 2025-03-04T22:30:12.7687889Z 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) 2025-03-04T22:30:12.8941068Z 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) 2025-03-04T22:30:12.9831538Z Installing collected packages: botocore, s3transfer, boto3 2025-03-04T22:30:13.5799854Z Successfully installed boto3-1.35.33 botocore-1.35.99 s3transfer-0.10.4 2025-03-04T22:30:13.7078766Z ##[group]Run set -eux 2025-03-04T22:30:13.7079059Z set -eux 2025-03-04T22:30:13.7079316Z  2025-03-04T22:30:13.7079582Z if [[ -z "${GITHUB_TOKEN}" ]]; then 2025-03-04T22:30:13.7079969Z  echo "Missing github-token input" 2025-03-04T22:30:13.7080299Z  exit 1 2025-03-04T22:30:13.7080536Z fi 2025-03-04T22:30:13.7089669Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:13.7090045Z env: 2025-03-04T22:30:13.7090269Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:13.7090614Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:13.7091169Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:13.7092047Z GITHUB_TOKEN: *** 2025-03-04T22:30:13.7092291Z ##[endgroup] 2025-03-04T22:30:13.7126584Z + [[ -z *** ]] 2025-03-04T22:30:13.7196498Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main 2025-03-04T22:30:13.7196939Z with: 2025-03-04T22:30:13.7197310Z github-token: *** 2025-03-04T22:30:13.7197558Z env: 2025-03-04T22:30:13.7197810Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:13.7198152Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:13.7198917Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:13.7199417Z ##[endgroup] 2025-03-04T22:30:13.7224453Z ##[group]Run set -eux 2025-03-04T22:30:13.7224728Z set -eux 2025-03-04T22:30:13.7224967Z  2025-03-04T22:30:13.7225437Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}" 2025-03-04T22:30:13.7234525Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:13.7234892Z env: 2025-03-04T22:30:13.7235112Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:13.7235449Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:13.7236049Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:13.7236903Z GITHUB_TOKEN: *** 2025-03-04T22:30:13.7237353Z ##[endgroup] 2025-03-04T22:30:13.7267366Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/get-workflow-job-id/../../scripts/get_workflow_job_id.py 13661696663 i-01112049b7e156298 2025-03-04T22:30:14.9634075Z setting job-id=38197724420 2025-03-04T22:30:14.9634689Z setting job-name=cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T22:30:14.9745451Z ##[group]Run set -eux 2025-03-04T22:30:14.9745779Z set -eux 2025-03-04T22:30:14.9746020Z  2025-03-04T22:30:14.9746413Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \ 2025-03-04T22:30:14.9746931Z  --schema-version "${SCHEMA_VERSION}" \ 2025-03-04T22:30:14.9747284Z  --repo "${REPO}" \ 2025-03-04T22:30:14.9747604Z  --head-branch "${HEAD_BRANCH}" \ 2025-03-04T22:30:14.9747951Z  --head-sha "${HEAD_SHA}" \ 2025-03-04T22:30:14.9748296Z  --workflow-id "${WORKFLOW_RUN_ID}" \ 2025-03-04T22:30:14.9748674Z  --run-attempt "${RUN_ATTEMPT}" \ 2025-03-04T22:30:14.9749018Z  --job-id "${JOB_ID}" \ 2025-03-04T22:30:14.9749330Z  --job-name "${JOB_NAME}" 2025-03-04T22:30:14.9758079Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:14.9758460Z env: 2025-03-04T22:30:14.9758695Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:14.9759046Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:14.9759604Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:14.9760112Z SCHEMA_VERSION: v3 2025-03-04T22:30:14.9760375Z REPO: pytorch/pytorch 2025-03-04T22:30:14.9760687Z HEAD_BRANCH: refs/tags/ciflow/inductor/148205 2025-03-04T22:30:14.9761391Z HEAD_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911 2025-03-04T22:30:14.9761755Z WORKFLOW_RUN_ID: 13661696663 2025-03-04T22:30:14.9762057Z RUN_ATTEMPT: 1 2025-03-04T22:30:14.9762309Z JOB_ID: 38197724420 2025-03-04T22:30:14.9762796Z JOB_NAME: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T22:30:14.9763321Z ##[endgroup] 2025-03-04T22:30:14.9792317Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/tags/ciflow/inductor/148205 --head-sha 1b7498080987913ecb3aff6253c5e88f3540d911 --workflow-id 13661696663 --run-attempt 1 --job-id 38197724420 --job-name 'cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu)' 2025-03-04T22:30:15.0116533Z ##[group]Run set -eux 2025-03-04T22:30:15.0116823Z set -eux 2025-03-04T22:30:15.0117064Z  2025-03-04T22:30:15.0117328Z # TODO (huydhn): Implement this part 2025-03-04T22:30:15.0117699Z echo "runners=[]" >> "${GITHUB_OUTPUT}" 2025-03-04T22:30:15.0126464Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:15.0126860Z env: 2025-03-04T22:30:15.0127096Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:15.0127814Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:15.0128385Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:15.0128882Z ##[endgroup] 2025-03-04T22:30:15.0157942Z + echo 'runners=[]' 2025-03-04T22:30:15.0189862Z ##[group]Run set -eux 2025-03-04T22:30:15.0190143Z set -eux 2025-03-04T22:30:15.0190377Z  2025-03-04T22:30:15.0190636Z # TODO (huydhn): Implement this part 2025-03-04T22:30:15.0191016Z echo "dependencies={}" >> "${GITHUB_OUTPUT}" 2025-03-04T22:30:15.0199362Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:15.0199736Z env: 2025-03-04T22:30:15.0199967Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:15.0200310Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:15.0200862Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:15.0201546Z ##[endgroup] 2025-03-04T22:30:15.0227766Z + echo 'dependencies={}' 2025-03-04T22:30:15.0259498Z ##[group]Run set -eux 2025-03-04T22:30:15.0259805Z set -eux 2025-03-04T22:30:15.0260044Z  2025-03-04T22:30:15.0260325Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then 2025-03-04T22:30:15.0260766Z  echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping" 2025-03-04T22:30:15.0261571Z  # We don't want the job to fail if the directory doesn't exist 2025-03-04T22:30:15.0262082Z  exit 0 2025-03-04T22:30:15.0262575Z fi 2025-03-04T22:30:15.0262899Z  2025-03-04T22:30:15.0263200Z if [[ "${DRY_RUN}" == "true" ]]; then 2025-03-04T22:30:15.0263914Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-03-04T22:30:15.0264675Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-03-04T22:30:15.0265300Z  --metadata "${BENCHMARK_METADATA}" \ 2025-03-04T22:30:15.0265838Z  --runners "${RUNNER_INFO}" \ 2025-03-04T22:30:15.0266313Z  --dependencies "${DEPENDENCIES}" \ 2025-03-04T22:30:15.0278248Z  --dry-run 2025-03-04T22:30:15.0278523Z else 2025-03-04T22:30:15.0278910Z  python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \ 2025-03-04T22:30:15.0279456Z  --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \ 2025-03-04T22:30:15.0279873Z  --metadata "${BENCHMARK_METADATA}" \ 2025-03-04T22:30:15.0280232Z  --runners "${RUNNER_INFO}" \ 2025-03-04T22:30:15.0280628Z  --dependencies "${DEPENDENCIES}" 2025-03-04T22:30:15.0280983Z fi 2025-03-04T22:30:15.0289539Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:15.0289911Z env: 2025-03-04T22:30:15.0290142Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:15.0290505Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:15.0291068Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:15.0291597Z BENCHMARK_RESULTS_DIR: test/test-reports 2025-03-04T22:30:15.0291919Z DRY_RUN: false 2025-03-04T22:30:15.0293235Z BENCHMARK_METADATA: {"timestamp": 1741127415, "schema_version": "v3", "name": "cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu)", "repo": "pytorch/pytorch", "head_branch": "refs/tags/ciflow/inductor/148205", "head_sha": "1b7498080987913ecb3aff6253c5e88f3540d911", "workflow_id": 13661696663, "run_attempt": 1, "job_id": 38197724420} 2025-03-04T22:30:15.0294599Z RUNNER_INFO: [] 2025-03-04T22:30:15.0294847Z DEPENDENCIES: {} 2025-03-04T22:30:15.0295091Z ##[endgroup] 2025-03-04T22:30:15.0322322Z + [[ ! -d test/test-reports ]] 2025-03-04T22:30:15.0322651Z + [[ false == \t\r\u\e ]] 2025-03-04T22:30:15.0325111Z + 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": 1741127415, "schema_version": "v3", "name": "cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu)", "repo": "pytorch/pytorch", "head_branch": "refs/tags/ciflow/inductor/148205", "head_sha": "1b7498080987913ecb3aff6253c5e88f3540d911", "workflow_id": 13661696663, "run_attempt": 1, "job_id": 38197724420}' --runners '[]' --dependencies '{}' 2025-03-04T22:30:15.1905364Z INFO:root:Upload test/test-reports/inference_huggingface.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38197724420/inference_huggingface.json 2025-03-04T22:30:15.2337699Z INFO:botocore.credentials:Found credentials from IAM Role: gh-ci-github-action-runners-runner-role 2025-03-04T22:30:15.4408090Z INFO:root:Upload test/test-reports/inference_huggingface_graph_breaks.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38197724420/inference_huggingface_graph_breaks.json 2025-03-04T22:30:15.5305769Z INFO:root:Upload test/test-reports/training_huggingface.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38197724420/training_huggingface.json 2025-03-04T22:30:15.6817083Z INFO:root:Upload test/test-reports/training_huggingface_graph_breaks.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38197724420/training_huggingface_graph_breaks.json 2025-03-04T22:30:15.8675423Z ##[group]Run cat test/**/*_toprint.log || true 2025-03-04T22:30:15.8675818Z cat test/**/*_toprint.log || true 2025-03-04T22:30:15.8683887Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:15.8684260Z env: 2025-03-04T22:30:15.8684491Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:15.8684838Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:15.8685397Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:15.8685896Z ##[endgroup] 2025-03-04T22:30:15.8775957Z cat: 'test/**/*_toprint.log': No such file or directory 2025-03-04T22:30:15.8812793Z ##[group]Run kill "$MONITOR_SCRIPT_PID" 2025-03-04T22:30:15.8813160Z kill "$MONITOR_SCRIPT_PID" 2025-03-04T22:30:15.8821072Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:15.8821444Z env: 2025-03-04T22:30:15.8821674Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:15.8822016Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:15.8822571Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:15.8823079Z MONITOR_SCRIPT_PID: 53765 2025-03-04T22:30:15.8823354Z ##[endgroup] 2025-03-04T22:30:15.8969967Z Prepare all required actions 2025-03-04T22:30:15.8970383Z Getting action download info 2025-03-04T22:30:16.0164163Z Download action repository 'actions/upload-artifact@v4' (SHA:4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1) 2025-03-04T22:30:16.4952272Z ##[group]Run ./.github/actions/upload-test-artifacts 2025-03-04T22:30:16.4952650Z with: 2025-03-04T22:30:16.4953070Z file-suffix: test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420 2025-03-04T22:30:16.4953583Z s3-bucket: gha-artifacts 2025-03-04T22:30:16.4953854Z env: 2025-03-04T22:30:16.4954078Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:16.4954416Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:16.4954966Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:16.4955453Z ##[endgroup] 2025-03-04T22:30:16.5000302Z ##[group]Run # Remove any previous test jsons if they exist 2025-03-04T22:30:16.5000794Z # Remove any previous test jsons if they exist 2025-03-04T22:30:16.5001165Z rm -f test-jsons-*.zip 2025-03-04T22:30:16.5001590Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json' 2025-03-04T22:30:16.5010732Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:16.5011113Z env: 2025-03-04T22:30:16.5011339Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:16.5011677Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:16.5012225Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:16.5012896Z FILE_SUFFIX: test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420 2025-03-04T22:30:16.5013377Z ##[endgroup] 2025-03-04T22:30:16.5202980Z adding: test/test-reports/inference_huggingface.json (deflated 99%) 2025-03-04T22:30:16.5205871Z adding: test/test-reports/inference_huggingface_graph_breaks.json (deflated 96%) 2025-03-04T22:30:16.5273076Z adding: test/test-reports/training_huggingface.json (deflated 99%) 2025-03-04T22:30:16.5414186Z adding: test/test-reports/training_huggingface_graph_breaks.json (deflated 98%) 2025-03-04T22:30:16.5447841Z ##[group]Run # Remove any previous test reports if they exist 2025-03-04T22:30:16.5448312Z # Remove any previous test reports if they exist 2025-03-04T22:30:16.5448875Z rm -f test-reports-*.zip 2025-03-04T22:30:16.5449341Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv' 2025-03-04T22:30:16.5458206Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:16.5458585Z env: 2025-03-04T22:30:16.5458818Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:16.5459159Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:16.5459716Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:16.5460399Z FILE_SUFFIX: test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420 2025-03-04T22:30:16.5460901Z ##[endgroup] 2025-03-04T22:30:16.5518604Z adding: test/test-reports/inference_huggingface.csv (deflated 70%) 2025-03-04T22:30:16.5519206Z adding: test/test-reports/inference_huggingface_graph_breaks.csv (deflated 85%) 2025-03-04T22:30:16.5519861Z adding: test/test-reports/inference_huggingface_graph_break_deduped.csv (deflated 63%) 2025-03-04T22:30:16.5520487Z adding: test/test-reports/training_huggingface.csv (deflated 68%) 2025-03-04T22:30:16.5530781Z adding: test/test-reports/training_huggingface_graph_breaks.csv (deflated 97%) 2025-03-04T22:30:16.5531634Z adding: test/test-reports/training_huggingface_graph_break_deduped.csv (deflated 72%) 2025-03-04T22:30:16.5562734Z ##[group]Run # Remove any previous usage logs if they exist 2025-03-04T22:30:16.5563190Z # Remove any previous usage logs if they exist 2025-03-04T22:30:16.5563557Z rm -f logs-*.zip 2025-03-04T22:30:16.5564013Z # this workflow is also run in bazel build test, but we dont generate usage reports for it 2025-03-04T22:30:16.5564734Z # so check to see if the file exists first 2025-03-04T22:30:16.5565106Z if [ -f 'usage_log.txt' ]; then 2025-03-04T22:30:16.5565481Z  zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt' 2025-03-04T22:30:16.5565821Z fi 2025-03-04T22:30:16.5566205Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then 2025-03-04T22:30:16.5566742Z  zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log' 2025-03-04T22:30:16.5567132Z fi 2025-03-04T22:30:16.5575150Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:16.5575526Z env: 2025-03-04T22:30:16.5575754Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:16.5576092Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:16.5576636Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:16.5577315Z FILE_SUFFIX: test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420 2025-03-04T22:30:16.5577798Z ##[endgroup] 2025-03-04T22:30:16.5670382Z adding: usage_log.txt (deflated 97%) 2025-03-04T22:30:16.5722230Z ##[group]Run # Remove any previous debugging artifacts if they exist 2025-03-04T22:30:16.5722738Z # Remove any previous debugging artifacts if they exist 2025-03-04T22:30:16.5723152Z rm -f debug-*.zip 2025-03-04T22:30:16.5723446Z if [ -d 'test/debug' ]; then 2025-03-04T22:30:16.5723806Z  zip -r "debug-${FILE_SUFFIX}.zip" test/debug 2025-03-04T22:30:16.5724146Z fi 2025-03-04T22:30:16.5733022Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:16.5733395Z env: 2025-03-04T22:30:16.5733618Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:16.5733957Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:16.5734502Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:16.5735187Z FILE_SUFFIX: test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420 2025-03-04T22:30:16.5735667Z ##[endgroup] 2025-03-04T22:30:16.5842946Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T22:30:16.5843289Z with: 2025-03-04T22:30:16.5843540Z s3-bucket: gha-artifacts 2025-03-04T22:30:16.5843881Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T22:30:16.5844454Z retention-days: 14 2025-03-04T22:30:16.5844732Z if-no-files-found: warn 2025-03-04T22:30:16.5845037Z path: test-jsons-*.zip 2025-03-04T22:30:16.5845319Z name: artifact 2025-03-04T22:30:16.5845564Z region: us-east-1 2025-03-04T22:30:16.5845807Z env: 2025-03-04T22:30:16.5846029Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:16.5846372Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:16.5846934Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:16.5847431Z ##[endgroup] 2025-03-04T22:30:16.9489203Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T22:30:16.9489677Z With the provided path, there will be 1 file uploaded 2025-03-04T22:30:16.9490143Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T22:30:16.9545406Z Starting upload of test-jsons-test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420.zip 2025-03-04T22:30:17.0689988Z Finished upload of test-jsons-test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420.zip 2025-03-04T22:30:17.0985816Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T22:30:17.0986152Z with: 2025-03-04T22:30:17.0986389Z s3-bucket: gha-artifacts 2025-03-04T22:30:17.0986716Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T22:30:17.0987094Z retention-days: 14 2025-03-04T22:30:17.0987388Z if-no-files-found: error 2025-03-04T22:30:17.0987679Z path: test-reports-*.zip 2025-03-04T22:30:17.0987951Z name: artifact 2025-03-04T22:30:17.0988188Z region: us-east-1 2025-03-04T22:30:17.0988426Z env: 2025-03-04T22:30:17.0988652Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:17.0989200Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:17.0989755Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:17.0990251Z ##[endgroup] 2025-03-04T22:30:17.4331881Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T22:30:17.4332358Z With the provided path, there will be 1 file uploaded 2025-03-04T22:30:17.4332823Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T22:30:17.4389001Z Starting upload of test-reports-test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420.zip 2025-03-04T22:30:17.6173642Z Finished upload of test-reports-test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420.zip 2025-03-04T22:30:17.6469484Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T22:30:17.6469830Z with: 2025-03-04T22:30:17.6470071Z s3-bucket: gha-artifacts 2025-03-04T22:30:17.6470408Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T22:30:17.6470774Z retention-days: 14 2025-03-04T22:30:17.6471052Z if-no-files-found: ignore 2025-03-04T22:30:17.6471338Z path: logs-*.zip 2025-03-04T22:30:17.6471591Z name: artifact 2025-03-04T22:30:17.6471828Z region: us-east-1 2025-03-04T22:30:17.6472070Z env: 2025-03-04T22:30:17.6472308Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:17.6472653Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:17.6473208Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:17.6473696Z ##[endgroup] 2025-03-04T22:30:17.9832519Z NOTE: s3-prefix specified, ignoring name parameter 2025-03-04T22:30:17.9833162Z With the provided path, there will be 1 file uploaded 2025-03-04T22:30:17.9833784Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T22:30:17.9889680Z Starting upload of logs-test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420.zip 2025-03-04T22:30:18.1115735Z Finished upload of logs-test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420.zip 2025-03-04T22:30:18.1425933Z ##[group]Run seemethere/upload-artifact-s3@v5 2025-03-04T22:30:18.1426279Z with: 2025-03-04T22:30:18.1426516Z s3-bucket: gha-artifacts 2025-03-04T22:30:18.1426849Z s3-prefix: pytorch/pytorch/13661696663/1/artifact 2025-03-04T22:30:18.1427414Z retention-days: 14 2025-03-04T22:30:18.1427721Z if-no-files-found: ignore 2025-03-04T22:30:18.1428042Z path: debug-*.zip 2025-03-04T22:30:18.1428304Z name: artifact 2025-03-04T22:30:18.1428561Z region: us-east-1 2025-03-04T22:30:18.1428812Z env: 2025-03-04T22:30:18.1429041Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:18.1429406Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:18.1429974Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:18.1430481Z ##[endgroup] 2025-03-04T22:30:18.4692770Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded. 2025-03-04T22:30:18.5003908Z ##[group]Run # shellcheck disable=SC2156 2025-03-04T22:30:18.5004294Z # shellcheck disable=SC2156 2025-03-04T22:30:18.5004856Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \; 2025-03-04T22:30:18.5015678Z shell: /usr/bin/bash -e {0} 2025-03-04T22:30:18.5015960Z env: 2025-03-04T22:30:18.5016191Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:18.5016527Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:18.5017084Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:18.5017580Z ##[endgroup] 2025-03-04T22:30:18.7851425Z Prepare all required actions 2025-03-04T22:30:18.7851834Z Getting action download info 2025-03-04T22:30:18.8966387Z ##[group]Run ./.github/actions/upload-utilization-stats 2025-03-04T22:30:18.8966748Z with: 2025-03-04T22:30:18.8966975Z job_id: 38197724420 2025-03-04T22:30:18.8967443Z job_name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T22:30:18.8968115Z workflow_name: inductor 2025-03-04T22:30:18.8968425Z workflow_run_id: 13661696663 2025-03-04T22:30:18.8968704Z workflow_attempt: 1 2025-03-04T22:30:18.8968949Z env: 2025-03-04T22:30:18.8969169Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:18.8969515Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:18.8970059Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:18.8970547Z ##[endgroup] 2025-03-04T22:30:18.8993144Z ##[group]Run echo "workflow_id: 13661696663" 2025-03-04T22:30:18.8993534Z echo "workflow_id: 13661696663" 2025-03-04T22:30:18.8993871Z echo "workflow_attempt: 1" 2025-03-04T22:30:18.8994195Z echo "workflow_Name: inductor" 2025-03-04T22:30:18.8994513Z echo "job_id: 38197724420" 2025-03-04T22:30:18.8995070Z echo "job_name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu)" 2025-03-04T22:30:18.9004872Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:18.9005240Z env: 2025-03-04T22:30:18.9005457Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:18.9005796Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:18.9006346Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:18.9006839Z ##[endgroup] 2025-03-04T22:30:18.9035075Z workflow_id: 13661696663 2025-03-04T22:30:18.9035360Z workflow_attempt: 1 2025-03-04T22:30:18.9035679Z workflow_Name: inductor 2025-03-04T22:30:18.9036034Z job_id: 38197724420 2025-03-04T22:30:18.9036547Z job_name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu) 2025-03-04T22:30:18.9077051Z ##[group]Run nick-fields/retry@v3.0.0 2025-03-04T22:30:18.9077350Z with: 2025-03-04T22:30:18.9077567Z shell: bash 2025-03-04T22:30:18.9077797Z timeout_minutes: 5 2025-03-04T22:30:18.9078050Z max_attempts: 5 2025-03-04T22:30:18.9078297Z retry_wait_seconds: 30 2025-03-04T22:30:18.9078747Z command: set -eu python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3 2025-03-04T22:30:18.9079248Z polling_interval_seconds: 1 2025-03-04T22:30:18.9079538Z warning_on_retry: true 2025-03-04T22:30:18.9079990Z continue_on_error: false 2025-03-04T22:30:18.9080253Z env: 2025-03-04T22:30:18.9080475Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:18.9080806Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:18.9081354Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:18.9081854Z ##[endgroup] 2025-03-04T22:30:19.2401633Z Defaulting to user installation because normal site-packages is not writeable 2025-03-04T22:30:19.3286341Z Collecting python-dateutil==2.8.2 2025-03-04T22:30:19.3510185Z Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB) 2025-03-04T22:30:20.3858473Z Collecting boto3==1.35.42 2025-03-04T22:30:20.3891625Z Downloading boto3-1.35.42-py3-none-any.whl (139 kB) 2025-03-04T22:30:20.9413610Z Collecting pandas==2.1.3 2025-03-04T22:30:20.9479415Z Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB) 2025-03-04T22:30:21.0922852Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0) 2025-03-04T22:30:21.0967762Z 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) 2025-03-04T22:30:21.0971554Z 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) 2025-03-04T22:30:21.0975946Z 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) 2025-03-04T22:30:21.1602109Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1) 2025-03-04T22:30:21.1914464Z Collecting tzdata>=2022.1 2025-03-04T22:30:21.1949897Z Downloading tzdata-2025.1-py2.py3-none-any.whl (346 kB) 2025-03-04T22:30:22.0743321Z Collecting numpy<2,>=1.22.4 2025-03-04T22:30:22.0782971Z Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB) 2025-03-04T22:30:22.2934594Z 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) 2025-03-04T22:30:22.4586217Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3 2025-03-04T22:30:27.4019196Z Attempting uninstall: boto3 2025-03-04T22:30:27.4019575Z Found existing installation: boto3 1.35.33 2025-03-04T22:30:27.4144103Z Uninstalling boto3-1.35.33: 2025-03-04T22:30:27.4159698Z Successfully uninstalled boto3-1.35.33 2025-03-04T22:30:27.4730831Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.1 2025-03-04T22:30:27.9951592Z Command completed after 1 attempt(s). 2025-03-04T22:30:28.0022659Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-03-04T22:30:28.0023387Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \ 2025-03-04T22:30:28.0023908Z  --workflow-run-id "13661696663" \ 2025-03-04T22:30:28.0024271Z  --workflow-name "inductor" \ 2025-03-04T22:30:28.0024625Z  --workflow-run-attempt "1" \ 2025-03-04T22:30:28.0024960Z  --job-id "38197724420" \ 2025-03-04T22:30:28.0025525Z  --job-name "cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu)" 2025-03-04T22:30:28.0034591Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:28.0034966Z env: 2025-03-04T22:30:28.0035197Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:28.0035536Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:28.0036089Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:28.0036581Z ##[endgroup] 2025-03-04T22:30:29.6685869Z repo: pytorch/pytorch 2025-03-04T22:30:29.6687229Z Downloading logs-test-inductor_huggingface-1-1-linux.g5.4xlarge.nvidia.gpu_38197724420.zip 2025-03-04T22:30:29.6689037Z Converted Log Model: UtilizationMetadata: 2025-03-04T22:30:29.6691779Z UtilizationMetadata(level='metadata', workflow_id='13661696663', job_id='38197724420', workflow_name='inductor', job_name='cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1741122703, gpu_count=1, cpu_count=16, gpu_type='pynvml', error=None) 2025-03-04T22:30:29.6694269Z [Db Segments] detected pytest cmd: 12, generated segments: 12 2025-03-04T22:30:29.6694689Z [db model] Peek db timeseries 2025-03-04T22:30:29.6694982Z :{ 2025-03-04T22:30:29.6695208Z "created_at": 1741127429, 2025-03-04T22:30:29.6695502Z "type": "utilization", 2025-03-04T22:30:29.6695787Z "tags": [ 2025-03-04T22:30:29.6696021Z "record" 2025-03-04T22:30:29.6696254Z ], 2025-03-04T22:30:29.6696483Z "time_stamp": 1741122703, 2025-03-04T22:30:29.6696776Z "repo": "pytorch/pytorch", 2025-03-04T22:30:29.6697071Z "workflow_id": 13661696663, 2025-03-04T22:30:29.6697366Z "run_attempt": 1, 2025-03-04T22:30:29.6697628Z "job_id": 38197724420, 2025-03-04T22:30:29.6697914Z "workflow_name": "inductor", 2025-03-04T22:30:29.6698444Z "job_name": "cuda12.4-py3.10-gcc9-sm86 / test (inductor_huggingface, 1, 1, linux.g5.4xlarge.nvidia.gpu)", 2025-03-04T22:30:29.6714642Z "json_data": "{}" 2025-03-04T22:30:29.6714949Z } 2025-03-04T22:30:29.6715453Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661696663/1/38197724420/metadata 2025-03-04T22:30:29.6716329Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661696663/1/38197724420/metadata 2025-03-04T22:30:29.6717225Z Writing 933 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661696663/1/38197724420/time_series 2025-03-04T22:30:29.6718139Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661696663/1/38197724420/time_series 2025-03-04T22:30:29.7963204Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main 2025-03-04T22:30:29.7963649Z with: 2025-03-04T22:30:29.7963871Z env: 2025-03-04T22:30:29.7964135Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:29.7964508Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:29.7965061Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:29.7965561Z ##[endgroup] 2025-03-04T22:30:29.7992575Z ##[group]Run set -eou pipefail 2025-03-04T22:30:29.7992918Z set -eou pipefail 2025-03-04T22:30:29.7993197Z  2025-03-04T22:30:29.7993568Z echo "Holding runner for 2 hours until all ssh sessions have logged out" 2025-03-04T22:30:29.7994024Z for _ in $(seq 1440); do 2025-03-04T22:30:29.7994374Z  # Break if no ssh session exists anymore 2025-03-04T22:30:29.7994731Z  if [ "$(who)" = "" ]; then 2025-03-04T22:30:29.7995044Z  break 2025-03-04T22:30:29.7995339Z  fi 2025-03-04T22:30:29.7995597Z  echo "." 2025-03-04T22:30:29.7995863Z  sleep 5 2025-03-04T22:30:29.7996117Z done 2025-03-04T22:30:29.8005453Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:29.8005846Z env: 2025-03-04T22:30:29.8006089Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:29.8006436Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:29.8006990Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:29.8007492Z ##[endgroup] 2025-03-04T22:30:29.8035274Z Holding runner for 2 hours until all ssh sessions have logged out 2025-03-04T22:30:29.8138204Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T22:30:29.8138741Z # ignore expansion of "docker ps -q" since it could be empty 2025-03-04T22:30:29.8139153Z # shellcheck disable=SC2046 2025-03-04T22:30:29.8139505Z docker stop $(docker ps -q) || true 2025-03-04T22:30:29.8140019Z # Prune all of the docker images 2025-03-04T22:30:29.8140349Z docker system prune -af 2025-03-04T22:30:29.8149014Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:29.8149389Z env: 2025-03-04T22:30:29.8149616Z GIT_DEFAULT_BRANCH: main 2025-03-04T22:30:29.8149959Z GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all 2025-03-04T22:30:29.8150507Z DOCKER_CONTAINER_ID: bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:29.8151003Z ##[endgroup] 2025-03-04T22:30:30.7639226Z bc0ac25a1209 2025-03-04T22:30:31.6800525Z Deleted Containers: 2025-03-04T22:30:31.6800976Z bc0ac25a12098d03bfac15860acfde7edbb048ccb356b1535e4ba343abda5236 2025-03-04T22:30:31.6801312Z 2025-03-04T22:30:43.7734499Z Deleted Images: 2025-03-04T22:30:43.7735581Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e 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HOME='/home/ec2-user/actions-runner/_work/_temp/836baab0-1ab7-48cb-ab4f-fe6b877d1747' before making global git config changes 2025-03-04T22:30:43.9088835Z Adding repository directory to the temporary git global config as a safe directory 2025-03-04T22:30:43.9093641Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch 2025-03-04T22:30:43.9138979Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand 2025-03-04T22:30:43.9184791Z [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' || :" 2025-03-04T22:30:43.9560526Z Entering 'android/libs/fbjni' 2025-03-04T22:30:43.9630659Z Entering 'third_party/FP16' 2025-03-04T22:30:43.9700903Z Entering 'third_party/FXdiv' 2025-03-04T22:30:43.9771488Z Entering 'third_party/NNPACK' 2025-03-04T22:30:43.9839889Z Entering 'third_party/NVTX' 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2025-03-04T22:30:44.4336904Z Entering 'third_party/python-peachpy' 2025-03-04T22:30:44.4407104Z Entering 'third_party/sleef' 2025-03-04T22:30:44.4477662Z Entering 'third_party/tensorpipe' 2025-03-04T22:30:44.4545386Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T22:30:44.4612739Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T22:30:44.4679771Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T22:30:44.4747091Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T22:30:44.4814982Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T22:30:44.4917503Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader 2025-03-04T22:30:44.4943077Z http.https://github.com/.extraheader 2025-03-04T22:30:44.4955585Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader 2025-03-04T22:30:44.4991992Z [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' || :" 2025-03-04T22:30:44.5354269Z Entering 'android/libs/fbjni' 2025-03-04T22:30:44.5400698Z http.https://github.com/.extraheader 2025-03-04T22:30:44.5443759Z Entering 'third_party/FP16' 2025-03-04T22:30:44.5488719Z http.https://github.com/.extraheader 2025-03-04T22:30:44.5532123Z Entering 'third_party/FXdiv' 2025-03-04T22:30:44.5576421Z http.https://github.com/.extraheader 2025-03-04T22:30:44.5619474Z Entering 'third_party/NNPACK' 2025-03-04T22:30:44.5664064Z http.https://github.com/.extraheader 2025-03-04T22:30:44.5708215Z Entering 'third_party/NVTX' 2025-03-04T22:30:44.5752455Z http.https://github.com/.extraheader 2025-03-04T22:30:44.5797398Z Entering 'third_party/VulkanMemoryAllocator' 2025-03-04T22:30:44.5844093Z http.https://github.com/.extraheader 2025-03-04T22:30:44.5888257Z Entering 'third_party/XNNPACK' 2025-03-04T22:30:44.5933544Z http.https://github.com/.extraheader 2025-03-04T22:30:44.5993060Z Entering 'third_party/benchmark' 2025-03-04T22:30:44.6037630Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6081814Z Entering 'third_party/composable_kernel' 2025-03-04T22:30:44.6125998Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6177408Z Entering 'third_party/cpp-httplib' 2025-03-04T22:30:44.6224101Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6268060Z Entering 'third_party/cpuinfo' 2025-03-04T22:30:44.6314009Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6361033Z Entering 'third_party/cudnn_frontend' 2025-03-04T22:30:44.6405397Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6450042Z Entering 'third_party/cutlass' 2025-03-04T22:30:44.6504900Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6546789Z Entering 'third_party/eigen' 2025-03-04T22:30:44.6592201Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6637706Z Entering 'third_party/fbgemm' 2025-03-04T22:30:44.6682005Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6724626Z Entering 'third_party/fbgemm/third_party/asmjit' 2025-03-04T22:30:44.6772254Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6815625Z Entering 'third_party/fbgemm/third_party/cpuinfo' 2025-03-04T22:30:44.6858622Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6902917Z Entering 'third_party/fbgemm/third_party/cutlass' 2025-03-04T22:30:44.6946584Z http.https://github.com/.extraheader 2025-03-04T22:30:44.6998096Z Entering 'third_party/fbgemm/third_party/googletest' 2025-03-04T22:30:44.7041957Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7086621Z Entering 'third_party/fbgemm/third_party/hipify_torch' 2025-03-04T22:30:44.7129829Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7177132Z Entering 'third_party/flash-attention' 2025-03-04T22:30:44.7224256Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7268229Z Entering 'third_party/flash-attention/csrc/composable_kernel' 2025-03-04T22:30:44.7312449Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7366916Z Entering 'third_party/flash-attention/csrc/cutlass' 2025-03-04T22:30:44.7412105Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7467912Z Entering 'third_party/flatbuffers' 2025-03-04T22:30:44.7512709Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7560669Z Entering 'third_party/fmt' 2025-03-04T22:30:44.7606481Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7650911Z Entering 'third_party/gemmlowp/gemmlowp' 2025-03-04T22:30:44.7695166Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7738393Z Entering 'third_party/gloo' 2025-03-04T22:30:44.7783845Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7827070Z Entering 'third_party/googletest' 2025-03-04T22:30:44.7872140Z http.https://github.com/.extraheader 2025-03-04T22:30:44.7915903Z Entering 'third_party/ideep' 2025-03-04T22:30:44.7959123Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8000843Z Entering 'third_party/ideep/mkl-dnn' 2025-03-04T22:30:44.8043126Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8095495Z Entering 'third_party/ittapi' 2025-03-04T22:30:44.8143042Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8189851Z Entering 'third_party/kineto' 2025-03-04T22:30:44.8234118Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8275915Z Entering 'third_party/kineto/libkineto/third_party/dynolog' 2025-03-04T22:30:44.8319296Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8362835Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM' 2025-03-04T22:30:44.8406055Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8451955Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr' 2025-03-04T22:30:44.8497168Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8543652Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt' 2025-03-04T22:30:44.8587846Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8632221Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags' 2025-03-04T22:30:44.8682295Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8726031Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc' 2025-03-04T22:30:44.8771568Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8820450Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog' 2025-03-04T22:30:44.8864771Z http.https://github.com/.extraheader 2025-03-04T22:30:44.8910453Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest' 2025-03-04T22:30:44.8954809Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9001304Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json' 2025-03-04T22:30:44.9044943Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9093089Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs' 2025-03-04T22:30:44.9141124Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9190722Z Entering 'third_party/kineto/libkineto/third_party/fmt' 2025-03-04T22:30:44.9233294Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9276439Z Entering 'third_party/kineto/libkineto/third_party/googletest' 2025-03-04T22:30:44.9318777Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9365852Z Entering 'third_party/kleidiai' 2025-03-04T22:30:44.9413670Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9457704Z Entering 'third_party/mimalloc' 2025-03-04T22:30:44.9502676Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9546649Z Entering 'third_party/nlohmann' 2025-03-04T22:30:44.9590946Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9639956Z Entering 'third_party/onnx' 2025-03-04T22:30:44.9683917Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9744157Z Entering 'third_party/onnx/third_party/pybind11' 2025-03-04T22:30:44.9792743Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9841764Z Entering 'third_party/opentelemetry-cpp' 2025-03-04T22:30:44.9885640Z http.https://github.com/.extraheader 2025-03-04T22:30:44.9929039Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark' 2025-03-04T22:30:44.9971692Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0014548Z Entering 'third_party/opentelemetry-cpp/third_party/googletest' 2025-03-04T22:30:45.0056813Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0100997Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl' 2025-03-04T22:30:45.0143175Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0185972Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json' 2025-03-04T22:30:45.0227655Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0271818Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto' 2025-03-04T22:30:45.0314202Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0357294Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp' 2025-03-04T22:30:45.0399952Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0443233Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp' 2025-03-04T22:30:45.0485903Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0527221Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb' 2025-03-04T22:30:45.0571936Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0618068Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest' 2025-03-04T22:30:45.0662079Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0709933Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg' 2025-03-04T22:30:45.0754587Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0823081Z Entering 'third_party/pocketfft' 2025-03-04T22:30:45.0867323Z http.https://github.com/.extraheader 2025-03-04T22:30:45.0912706Z Entering 'third_party/protobuf' 2025-03-04T22:30:45.0962183Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1011127Z Entering 'third_party/protobuf/third_party/benchmark' 2025-03-04T22:30:45.1053761Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1097187Z Entering 'third_party/protobuf/third_party/googletest' 2025-03-04T22:30:45.1139478Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1187290Z Entering 'third_party/psimd' 2025-03-04T22:30:45.1231376Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1277798Z Entering 'third_party/pthreadpool' 2025-03-04T22:30:45.1321220Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1366606Z Entering 'third_party/pybind11' 2025-03-04T22:30:45.1409799Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1452761Z Entering 'third_party/python-peachpy' 2025-03-04T22:30:45.1497110Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1540287Z Entering 'third_party/sleef' 2025-03-04T22:30:45.1585847Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1629492Z Entering 'third_party/tensorpipe' 2025-03-04T22:30:45.1674049Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1716731Z Entering 'third_party/tensorpipe/third_party/googletest' 2025-03-04T22:30:45.1759069Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1804787Z Entering 'third_party/tensorpipe/third_party/libnop' 2025-03-04T22:30:45.1846514Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1889720Z Entering 'third_party/tensorpipe/third_party/libuv' 2025-03-04T22:30:45.1933554Z http.https://github.com/.extraheader 2025-03-04T22:30:45.1979027Z Entering 'third_party/tensorpipe/third_party/pybind11' 2025-03-04T22:30:45.2022529Z http.https://github.com/.extraheader 2025-03-04T22:30:45.2066255Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang' 2025-03-04T22:30:45.2109957Z http.https://github.com/.extraheader 2025-03-04T22:30:45.2280509Z A job completed hook has been configured by the self-hosted runner administrator 2025-03-04T22:30:45.2311744Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh' 2025-03-04T22:30:45.2319651Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0} 2025-03-04T22:30:45.2320045Z ##[endgroup] 2025-03-04T22:30:51.8991883Z Cleaning up orphan processes