2025-03-04T21:01:22.4081287Z Current runner version: '2.322.0'
2025-03-04T21:01:22.4088125Z Runner name: 'i-05d5204902c63dc95'
2025-03-04T21:01:22.4089028Z Runner group name: 'Default'
2025-03-04T21:01:22.4090069Z Machine name: 'ip-10-0-36-142'
2025-03-04T21:01:22.4095123Z ##[group]GITHUB_TOKEN Permissions
2025-03-04T21:01:22.4097742Z Actions: read
2025-03-04T21:01:22.4098410Z Attestations: read
2025-03-04T21:01:22.4099067Z Checks: read
2025-03-04T21:01:22.4099691Z Contents: read
2025-03-04T21:01:22.4100323Z Deployments: read
2025-03-04T21:01:22.4100974Z Discussions: read
2025-03-04T21:01:22.4101614Z Issues: read
2025-03-04T21:01:22.4102234Z Metadata: read
2025-03-04T21:01:22.4102860Z Packages: read
2025-03-04T21:01:22.4103491Z Pages: read
2025-03-04T21:01:22.4104118Z PullRequests: read
2025-03-04T21:01:22.4104792Z RepositoryProjects: read
2025-03-04T21:01:22.4105522Z SecurityEvents: read
2025-03-04T21:01:22.4106196Z Statuses: read
2025-03-04T21:01:22.4106825Z ##[endgroup]
2025-03-04T21:01:22.4109761Z Secret source: Actions
2025-03-04T21:01:22.4110669Z Prepare workflow directory
2025-03-04T21:01:22.4590862Z Prepare all required actions
2025-03-04T21:01:22.4627677Z Getting action download info
2025-03-04T21:01:22.6408614Z Download action repository 'pytorch/test-infra@main' (SHA:79438512a0632583899938d3b0277da78f5569e0)
2025-03-04T21:01:24.1547328Z Download action repository 'pytorch/pytorch@main' (SHA:f30776c37a52cc70cc0494eb8eefd0e9544cd748)
2025-03-04T21:01:38.2663857Z Download action repository 'aws-actions/configure-aws-credentials@v3' (SHA:50ac8dd1e1b10d09dac7b8727528b91bed831ac0)
2025-03-04T21:01:38.4554427Z Download action repository 'seemethere/upload-artifact-s3@v5' (SHA:baba72d0712b404f646cebe0730933554ebce96a)
2025-03-04T21:01:38.9281963Z Getting action download info
2025-03-04T21:01:39.0330187Z Download action repository 'actions/checkout@v4' (SHA:11bd71901bbe5b1630ceea73d27597364c9af683)
2025-03-04T21:01:39.2877385Z Getting action download info
2025-03-04T21:01:39.3815350Z Download action repository 'nick-fields/retry@v3.0.0' (SHA:7152eba30c6575329ac0576536151aca5a72780e)
2025-03-04T21:01:39.5629090Z Getting action download info
2025-03-04T21:01:39.6617534Z Download action repository 'nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482' (SHA:3e91a01664abd3c5cd539100d10d33b9c5b68482)
2025-03-04T21:01:39.8121394Z Getting action download info
2025-03-04T21:01:39.9305813Z Uses: pytorch/pytorch/.github/workflows/_linux-test.yml@refs/tags/ciflow/inductor/148205 (1b7498080987913ecb3aff6253c5e88f3540d911)
2025-03-04T21:01:39.9307970Z ##[group] Inputs
2025-03-04T21:01:39.9308369Z   build-environment: linux-focal-cuda12.4-py3.10-gcc9-sm86
2025-03-04T21:01:39.9310324Z   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"}]}
2025-03-04T21:01:39.9312699Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:01:39.9313659Z   sync-tag: 
2025-03-04T21:01:39.9314408Z   timeout-minutes: 240
2025-03-04T21:01:39.9314690Z   use-gha: 
2025-03-04T21:01:39.9314931Z   dashboard-tag: 
2025-03-04T21:01:39.9315198Z   s3-bucket: gha-artifacts
2025-03-04T21:01:39.9315491Z   aws-role-to-assume: 
2025-03-04T21:01:39.9316030Z   disable-monitor: false
2025-03-04T21:01:39.9316329Z ##[endgroup]
2025-03-04T21:01:39.9316830Z Complete job name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:01:39.9927228Z A job started hook has been configured by the self-hosted runner administrator
2025-03-04T21:01:40.0026809Z ##[group]Run '/home/ec2-user/runner-scripts/before_job.sh'
2025-03-04T21:01:40.0038150Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:01:40.0038770Z ##[endgroup]
2025-03-04T21:01:41.1917872Z Runner Type: linux.g5.4xlarge.nvidia.gpu
2025-03-04T21:01:41.1918392Z Instance Type: g5.4xlarge
2025-03-04T21:01:41.1918709Z AMI Name: unknown
2025-03-04T21:01:41.1955943Z AMI ID: ami-05b10e08d247fb927
2025-03-04T21:01:46.5872221Z ##[group]Run pytorch/test-infra/.github/actions/setup-ssh@main
2025-03-04T21:01:46.5872656Z with:
2025-03-04T21:01:46.5873273Z   github-secret: ***
2025-03-04T21:01:46.5873980Z   instructions: All testing is done inside the container, to start an interactive session run:
  docker exec -it $(docker container ps --format '{{.ID}}') bash

2025-03-04T21:01:46.5874729Z   activate-with-label: false
2025-03-04T21:01:46.5875032Z   label: with-ssh
2025-03-04T21:01:46.5875335Z   remove-existing-keys: true
2025-03-04T21:01:46.5875866Z   fail-silently: true
2025-03-04T21:01:46.5876150Z env:
2025-03-04T21:01:46.5876382Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:01:46.5876666Z ##[endgroup]
2025-03-04T21:01:46.7090544Z Please see https://github.com/pytorch/pytorch/wiki/Debugging-using-with-ssh-for-Github-Actions for more info.
2025-03-04T21:01:46.7091809Z ciflow reference detected, attempting to extract PR number
2025-03-04T21:01:47.0677455Z Grabbing public ssh keys from https://github.com/pytorch-bot[bot].keys
2025-03-04T21:01:47.2149565Z No SSH keys found for user pytorch-bot[bot]
2025-03-04T21:01:47.2150142Z Grabbing public ssh keys from https://github.com/williamwen42.keys
2025-03-04T21:01:47.2618144Z ~/.ssh/authorized_keys file found on node, removing ~/.ssh and starting fresh
2025-03-04T21:01:47.2633068Z Public keys pulled and installed to /home/ec2-user/.ssh/authorized_keys
2025-03-04T21:01:47.2666637Z Login using: ssh ec2-user@ec2-18-212-10-128.compute-1.amazonaws.com
2025-03-04T21:01:47.2667240Z All testing is done inside the container, to start an interactive session run:
2025-03-04T21:01:47.2667892Z   docker exec -it $(docker container ps --format '{{.ID}}') bash
2025-03-04T21:01:47.2819851Z ##[group]Run pytorch/pytorch/.github/actions/checkout-pytorch@main
2025-03-04T21:01:47.2820294Z with:
2025-03-04T21:01:47.2820525Z   no-sudo: true
2025-03-04T21:01:47.2820778Z   submodules: recursive
2025-03-04T21:01:47.2821045Z   fetch-depth: 0
2025-03-04T21:01:47.2821280Z env:
2025-03-04T21:01:47.2821499Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:01:47.2821770Z ##[endgroup]
2025-03-04T21:01:47.2901579Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T21:01:47.2902565Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T21:01:47.2915960Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:01:47.2916348Z env:
2025-03-04T21:01:47.2916584Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:01:47.2916874Z ##[endgroup]
2025-03-04T21:01:47.3010939Z ##[group]Run retry () {
2025-03-04T21:01:47.3028770Z retry () {
2025-03-04T21:01:47.3029187Z   $* || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
2025-03-04T21:01:47.3029581Z }
2025-03-04T21:01:47.3029833Z echo "${GITHUB_WORKSPACE}"
2025-03-04T21:01:47.3030163Z if [ -z "${NO_SUDO}" ]; then
2025-03-04T21:01:47.3030529Z   retry sudo rm -rf "${GITHUB_WORKSPACE}"
2025-03-04T21:01:47.3030865Z else
2025-03-04T21:01:47.3031137Z   retry rm -rf "${GITHUB_WORKSPACE}"
2025-03-04T21:01:47.3031451Z fi
2025-03-04T21:01:47.3031702Z mkdir "${GITHUB_WORKSPACE}"
2025-03-04T21:01:47.3032006Z 
2025-03-04T21:01:47.3032326Z # Use all available CPUs for fetching
2025-03-04T21:01:47.3032692Z cd "${GITHUB_WORKSPACE}"
2025-03-04T21:01:47.3033036Z git config --global fetch.parallel 0
2025-03-04T21:01:47.3033694Z git config --global submodule.fetchJobs 0
2025-03-04T21:01:47.3043043Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:01:47.3043421Z env:
2025-03-04T21:01:47.3043660Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:01:47.3043932Z   NO_SUDO: true
2025-03-04T21:01:47.3044174Z ##[endgroup]
2025-03-04T21:01:47.3071701Z /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T21:01:47.3211323Z ##[group]Run actions/checkout@v4
2025-03-04T21:01:47.3211638Z with:
2025-03-04T21:01:47.3211911Z   ref: 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:01:47.3212260Z   fetch-depth: 0
2025-03-04T21:01:47.3212524Z   submodules: recursive
2025-03-04T21:01:47.3212826Z   show-progress: false
2025-03-04T21:01:47.3213122Z   repository: pytorch/pytorch
2025-03-04T21:01:47.3213519Z   token: ***
2025-03-04T21:01:47.3213771Z   ssh-strict: true
2025-03-04T21:01:47.3214025Z   ssh-user: git
2025-03-04T21:01:47.3214290Z   persist-credentials: true
2025-03-04T21:01:47.3214596Z   clean: true
2025-03-04T21:01:47.3214864Z   sparse-checkout-cone-mode: true
2025-03-04T21:01:47.3215203Z   fetch-tags: false
2025-03-04T21:01:47.3215462Z   lfs: false
2025-03-04T21:01:47.3215721Z   set-safe-directory: true
2025-03-04T21:01:47.3216002Z env:
2025-03-04T21:01:47.3216240Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:01:47.3216521Z ##[endgroup]
2025-03-04T21:01:47.4351493Z Syncing repository: pytorch/pytorch
2025-03-04T21:01:47.4352765Z ##[group]Getting Git version info
2025-03-04T21:01:47.4353245Z Working directory is '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2025-03-04T21:01:47.4353936Z [command]/usr/bin/git version
2025-03-04T21:01:47.4354256Z git version 2.47.1
2025-03-04T21:01:47.4379654Z ##[endgroup]
2025-03-04T21:01:47.4390644Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/cb7d85d9-862f-4241-9167-e3343db0c882/.gitconfig'
2025-03-04T21:01:47.4410182Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/cb7d85d9-862f-4241-9167-e3343db0c882' before making global git config changes
2025-03-04T21:01:47.4411401Z Adding repository directory to the temporary git global config as a safe directory
2025-03-04T21:01:47.4415386Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T21:01:47.4454789Z Deleting the contents of '/home/ec2-user/actions-runner/_work/pytorch/pytorch'
2025-03-04T21:01:47.4457659Z ##[group]Initializing the repository
2025-03-04T21:01:47.4461888Z [command]/usr/bin/git init /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T21:01:47.4504142Z hint: Using 'master' as the name for the initial branch. This default branch name
2025-03-04T21:01:47.4504983Z hint: is subject to change. To configure the initial branch name to use in all
2025-03-04T21:01:47.4505752Z hint: of your new repositories, which will suppress this warning, call:
2025-03-04T21:01:47.4506376Z hint:
2025-03-04T21:01:47.4506788Z hint: 	git config --global init.defaultBranch <name>
2025-03-04T21:01:47.4507189Z hint:
2025-03-04T21:01:47.4507586Z hint: Names commonly chosen instead of 'master' are 'main', 'trunk' and
2025-03-04T21:01:47.4508164Z hint: 'development'. The just-created branch can be renamed via this command:
2025-03-04T21:01:47.4508615Z hint:
2025-03-04T21:01:47.4508864Z hint: 	git branch -m <name>
2025-03-04T21:01:47.4509365Z Initialized empty Git repository in /home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/
2025-03-04T21:01:47.4516290Z [command]/usr/bin/git remote add origin https://github.com/pytorch/pytorch
2025-03-04T21:01:47.4551273Z ##[endgroup]
2025-03-04T21:01:47.4551878Z ##[group]Disabling automatic garbage collection
2025-03-04T21:01:47.4554821Z [command]/usr/bin/git config --local gc.auto 0
2025-03-04T21:01:47.4585669Z ##[endgroup]
2025-03-04T21:01:47.4586237Z ##[group]Setting up auth
2025-03-04T21:01:47.4591627Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-04T21:01:47.4623829Z [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' || :"
2025-03-04T21:01:47.4987373Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-04T21:01:47.5018384Z [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' || :"
2025-03-04T21:01:47.5364093Z [command]/usr/bin/git config --local http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-04T21:01:47.5413430Z ##[endgroup]
2025-03-04T21:01:47.5413923Z ##[group]Fetching the repository
2025-03-04T21:01:47.5420774Z [command]/usr/bin/git -c protocol.version=2 fetch --prune --no-recurse-submodules origin +refs/heads/*:refs/remotes/origin/* +refs/tags/*:refs/tags/*
2025-03-04T21:02:35.7808380Z From https://github.com/pytorch/pytorch
2025-03-04T21:02:35.7809088Z  * [new branch]              2.1-dynamic-doc             -> origin/2.1-dynamic-doc
2025-03-04T21:02:35.7809627Z  * [new branch]              2.6.0.dev20241004+          -> origin/2.6.0.dev20241004+
2025-03-04T21:02:35.7810198Z  * [new branch]              20250219_e8m0_intermediate  -> origin/20250219_e8m0_intermediate
2025-03-04T21:02:35.7810829Z  * [new branch]              20250219_test               -> origin/20250219_test
2025-03-04T21:02:35.7811923Z  * [new branch]              Adjust-Description-for-linux-binary-test-Workflow -> origin/Adjust-Description-for-linux-binary-test-Workflow
2025-03-04T21:02:35.7813069Z  * [new branch]              Chillee-patch-5             -> origin/Chillee-patch-5
2025-03-04T21:02:35.7814209Z  * [new branch]              Flamefire-patch-1           -> origin/Flamefire-patch-1
2025-03-04T21:02:35.7816824Z  * [new branch]              HDCharles-2.6.0-release-notes -> origin/HDCharles-2.6.0-release-notes
2025-03-04T21:02:35.7819373Z  * [new branch]              JackCaoG/add_new_lazy_counter_macro -> origin/JackCaoG/add_new_lazy_counter_macro
2025-03-04T21:02:35.7821713Z  * [new branch]              JackCaoG/dynamo_make_fx_non_core_aten_ops -> origin/JackCaoG/dynamo_make_fx_non_core_aten_ops
2025-03-04T21:02:35.7823146Z  * [new branch]              JackCaoG/fix_xla_torchbench -> origin/JackCaoG/fix_xla_torchbench
2025-03-04T21:02:35.7825615Z  * [new branch]              JackCaoG/update_dynamo_doc  -> origin/JackCaoG/update_dynamo_doc
2025-03-04T21:02:35.7828030Z  * [new branch]              JackCaoG/update_xla_pin_to_skip_test -> origin/JackCaoG/update_xla_pin_to_skip_test
2025-03-04T21:02:35.7830355Z  * [new branch]              JackCaoG/update_xla_pin_to_skip_test2 -> origin/JackCaoG/update_xla_pin_to_skip_test2
2025-03-04T21:02:35.7832335Z  * [new branch]              NicolasHug-patch-2          -> origin/NicolasHug-patch-2
2025-03-04T21:02:35.7834326Z  * [new branch]              PR-AOTInductorNoneBug       -> origin/PR-AOTInductorNoneBug
2025-03-04T21:02:35.7837066Z  * [new branch]              PR-AOTInductorNoneBugFix    -> origin/PR-AOTInductorNoneBugFix
2025-03-04T21:02:35.7838989Z  * [new branch]              PR-FixConfigsIssue          -> origin/PR-FixConfigsIssue
2025-03-04T21:02:35.7840815Z  * [new branch]              PR-NoneBugFix-viable        -> origin/PR-NoneBugFix-viable
2025-03-04T21:02:35.7842844Z  * [new branch]              PR-ResetToZero              -> origin/PR-ResetToZero
2025-03-04T21:02:35.7844919Z  * [new branch]              Remove-linux_t4g_2xlarge-Usage -> origin/Remove-linux_t4g_2xlarge-Usage
2025-03-04T21:02:35.7846837Z  * [new branch]              Revert-PR-110949            -> origin/Revert-PR-110949
2025-03-04T21:02:35.7849099Z  * [new branch]              Update-Flash-Packaging      -> origin/Update-Flash-Packaging
2025-03-04T21:02:35.7851959Z  * [new branch]              Valentine/flash_attention_bf16 -> origin/Valentine/flash_attention_bf16
2025-03-04T21:02:35.7855489Z  * [new branch]              _tmp-orig/release/2.6       -> origin/_tmp-orig/release/2.6
2025-03-04T21:02:35.7857898Z  * [new branch]              _tmp-release/2.6            -> origin/_tmp-release/2.6
2025-03-04T21:02:35.7860651Z  * [new branch]              abock/onnx-1.15.0-validation -> origin/abock/onnx-1.15.0-validation
2025-03-04T21:02:35.7863083Z  * [new branch]              abock/ort-nightly==1.16.0.dev20230908001 -> origin/abock/ort-nightly==1.16.0.dev20230908001
2025-03-04T21:02:35.7864943Z  * [new branch]              add-android-build-workflow  -> origin/add-android-build-workflow
2025-03-04T21:02:35.7866735Z  * [new branch]              add-assign                  -> origin/add-assign
2025-03-04T21:02:35.7869505Z  * [new branch]              add_broadcast_functional_collective -> origin/add_broadcast_functional_collective
2025-03-04T21:02:35.7871814Z  * [new branch]              add_from_group_doc_and_test -> origin/add_from_group_doc_and_test
2025-03-04T21:02:35.7872999Z  * [new branch]              add_mha_to_autocast_policy  -> origin/add_mha_to_autocast_policy
2025-03-04T21:02:35.7875300Z  * [new branch]              add_non_parallel_model_comparison -> origin/add_non_parallel_model_comparison
2025-03-04T21:02:35.7877200Z  * [new branch]              add_test_to_show_view_gap   -> origin/add_test_to_show_view_gap
2025-03-04T21:02:35.7879287Z  * [new branch]              add_windows_testing_back    -> origin/add_windows_testing_back
2025-03-04T21:02:35.7881305Z  * [new branch]              addmm-heuristic             -> origin/addmm-heuristic
2025-03-04T21:02:35.7883419Z  * [new branch]              addsimde                    -> origin/addsimde
2025-03-04T21:02:35.7886737Z  * [new branch]              adi/gemm_bf16f32            -> origin/adi/gemm_bf16f32
2025-03-04T21:02:35.7888555Z  * [new branch]              ah-globalfeedback-hook      -> origin/ah-globalfeedback-hook
2025-03-04T21:02:35.7909378Z  * [new branch]              alanwaketan/pin2            -> origin/alanwaketan/pin2
2025-03-04T21:02:35.7910442Z  * [new branch]              albanD-patch-1              -> origin/albanD-patch-1
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2025-03-04T21:02:36.6004487Z  * [new branch]              mlazos/vary-beta2           -> origin/mlazos/vary-beta2
2025-03-04T21:02:36.6006114Z  * [new branch]              mlazos/weird-perf1          -> origin/mlazos/weird-perf1
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2025-03-04T21:02:36.6015646Z  * [new branch]              mradmila/host_stats         -> origin/mradmila/host_stats
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2025-03-04T21:02:36.6760683Z  * [new tag]                 ciflow/binaries/148173      -> ciflow/binaries/148173
2025-03-04T21:02:36.6762097Z  * [new tag]                 ciflow/binaries/148343      -> ciflow/binaries/148343
2025-03-04T21:02:36.6763565Z  * [new tag]                 ciflow/binaries_wheel/138834 -> ciflow/binaries_wheel/138834
2025-03-04T21:02:36.6764616Z  * [new tag]                 ciflow/binaries_wheel/142279 -> ciflow/binaries_wheel/142279
2025-03-04T21:02:36.6765858Z  * [new tag]                 ciflow/binaries_wheel/143388 -> ciflow/binaries_wheel/143388
2025-03-04T21:02:36.6767056Z  * [new tag]                 ciflow/binaries_wheel/144049 -> ciflow/binaries_wheel/144049
2025-03-04T21:02:36.6768065Z  * [new tag]                 ciflow/binaries_wheel/146055 -> ciflow/binaries_wheel/146055
2025-03-04T21:02:36.6769525Z  * [new tag]                 ciflow/binaries_wheel/146573 -> ciflow/binaries_wheel/146573
2025-03-04T21:02:36.6770782Z  * [new tag]                 ciflow/binaries_wheel/147074 -> ciflow/binaries_wheel/147074
2025-03-04T21:02:36.6771666Z  * [new tag]                 ciflow/binaries_wheel/147448 -> ciflow/binaries_wheel/147448
2025-03-04T21:02:36.6773079Z  * [new tag]                 ciflow/binaries_wheel/147455 -> ciflow/binaries_wheel/147455
2025-03-04T21:02:36.6774369Z  * [new tag]                 ciflow/binaries_wheel/148313 -> ciflow/binaries_wheel/148313
2025-03-04T21:02:36.6775701Z  * [new tag]                 ciflow/binaries_wheel/148319 -> ciflow/binaries_wheel/148319
2025-03-04T21:02:36.6776883Z  * [new tag]                 ciflow/cuda/70978           -> ciflow/cuda/70978
2025-03-04T21:02:36.6777763Z  * [new tag]                 ciflow/cuda/70979           -> ciflow/cuda/70979
2025-03-04T21:02:36.6779041Z  * [new tag]                 ciflow/cuda/70989           -> ciflow/cuda/70989
2025-03-04T21:02:36.6780514Z  * [new tag]                 ciflow/inductor-cu126/140793 -> ciflow/inductor-cu126/140793
2025-03-04T21:02:36.6782448Z  * [new tag]                 ciflow/inductor-micro-benchmark/141910 -> ciflow/inductor-micro-benchmark/141910
2025-03-04T21:02:36.6783738Z  * [new tag]                 ciflow/inductor-perf-compare/140195 -> ciflow/inductor-perf-compare/140195
2025-03-04T21:02:36.6785091Z  * [new tag]                 ciflow/inductor-perf-test-nightly/140195 -> ciflow/inductor-perf-test-nightly/140195
2025-03-04T21:02:36.6786589Z  * [new tag]                 ciflow/inductor-periodic/140793 -> ciflow/inductor-periodic/140793
2025-03-04T21:02:36.6787468Z  * [new tag]                 ciflow/inductor-periodic/145612 -> ciflow/inductor-periodic/145612
2025-03-04T21:02:36.6788913Z  * [new tag]                 ciflow/inductor-periodic/147315 -> ciflow/inductor-periodic/147315
2025-03-04T21:02:36.6790462Z  * [new tag]                 ciflow/inductor-rocm/140989 -> ciflow/inductor-rocm/140989
2025-03-04T21:02:36.6791808Z  * [new tag]                 ciflow/inductor-rocm/141309 -> ciflow/inductor-rocm/141309
2025-03-04T21:02:36.6793129Z  * [new tag]                 ciflow/inductor-rocm/141355 -> ciflow/inductor-rocm/141355
2025-03-04T21:02:36.6794317Z  * [new tag]                 ciflow/inductor-rocm/146264 -> ciflow/inductor-rocm/146264
2025-03-04T21:02:36.6795598Z  * [new tag]                 ciflow/inductor-rocm/146903 -> ciflow/inductor-rocm/146903
2025-03-04T21:02:36.6796528Z  * [new tag]                 ciflow/inductor-rocm/147315 -> ciflow/inductor-rocm/147315
2025-03-04T21:02:36.6797911Z  * [new tag]                 ciflow/inductor-rocm/147320 -> ciflow/inductor-rocm/147320
2025-03-04T21:02:36.6799171Z  * [new tag]                 ciflow/inductor-rocm/147452 -> ciflow/inductor-rocm/147452
2025-03-04T21:02:36.6800560Z  * [new tag]                 ciflow/inductor-rocm/147583 -> ciflow/inductor-rocm/147583
2025-03-04T21:02:36.6801843Z  * [new tag]                 ciflow/inductor-rocm/147619 -> ciflow/inductor-rocm/147619
2025-03-04T21:02:36.6803263Z  * [new tag]                 ciflow/inductor-rocm/148305 -> ciflow/inductor-rocm/148305
2025-03-04T21:02:36.6804891Z  * [new tag]                 ciflow/inductor-rocm/148437 -> ciflow/inductor-rocm/148437
2025-03-04T21:02:36.6806311Z  * [new tag]                 ciflow/inductor/110155      -> ciflow/inductor/110155
2025-03-04T21:02:36.6807211Z  * [new tag]                 ciflow/inductor/113257      -> ciflow/inductor/113257
2025-03-04T21:02:36.6808684Z  * [new tag]                 ciflow/inductor/119496      -> ciflow/inductor/119496
2025-03-04T21:02:36.6809844Z  * [new tag]                 ciflow/inductor/119977      -> ciflow/inductor/119977
2025-03-04T21:02:36.6810849Z  * [new tag]                 ciflow/inductor/120076      -> ciflow/inductor/120076
2025-03-04T21:02:36.6812049Z  * [new tag]                 ciflow/inductor/121445      -> ciflow/inductor/121445
2025-03-04T21:02:36.6812935Z  * [new tag]                 ciflow/inductor/124490      -> ciflow/inductor/124490
2025-03-04T21:02:36.6814241Z  * [new tag]                 ciflow/inductor/125270      -> ciflow/inductor/125270
2025-03-04T21:02:36.6815362Z  * [new tag]                 ciflow/inductor/125326      -> ciflow/inductor/125326
2025-03-04T21:02:36.6816963Z  * [new tag]                 ciflow/inductor/125428      -> ciflow/inductor/125428
2025-03-04T21:02:36.6817876Z  * [new tag]                 ciflow/inductor/125469      -> ciflow/inductor/125469
2025-03-04T21:02:36.6819504Z  * [new tag]                 ciflow/inductor/125806      -> ciflow/inductor/125806
2025-03-04T21:02:36.6821121Z  * [new tag]                 ciflow/inductor/125888      -> ciflow/inductor/125888
2025-03-04T21:02:36.6822534Z  * [new tag]                 ciflow/inductor/125995      -> ciflow/inductor/125995
2025-03-04T21:02:36.6823688Z  * [new tag]                 ciflow/inductor/126348      -> ciflow/inductor/126348
2025-03-04T21:02:36.6825148Z  * [new tag]                 ciflow/inductor/127011      -> ciflow/inductor/127011
2025-03-04T21:02:36.6826725Z  * [new tag]                 ciflow/inductor/127171      -> ciflow/inductor/127171
2025-03-04T21:02:36.6827417Z  * [new tag]                 ciflow/inductor/127293      -> ciflow/inductor/127293
2025-03-04T21:02:36.6828781Z  * [new tag]                 ciflow/inductor/127294      -> ciflow/inductor/127294
2025-03-04T21:02:36.6830074Z  * [new tag]                 ciflow/inductor/129352      -> ciflow/inductor/129352
2025-03-04T21:02:36.6831270Z  * [new tag]                 ciflow/inductor/129420      -> ciflow/inductor/129420
2025-03-04T21:02:36.6832562Z  * [new tag]                 ciflow/inductor/130141      -> ciflow/inductor/130141
2025-03-04T21:02:36.6833815Z  * [new tag]                 ciflow/inductor/130499      -> ciflow/inductor/130499
2025-03-04T21:02:36.6834970Z  * [new tag]                 ciflow/inductor/130887      -> ciflow/inductor/130887
2025-03-04T21:02:36.6836145Z  * [new tag]                 ciflow/inductor/131354      -> ciflow/inductor/131354
2025-03-04T21:02:36.6837313Z  * [new tag]                 ciflow/inductor/132021      -> ciflow/inductor/132021
2025-03-04T21:02:36.6838491Z  * [new tag]                 ciflow/inductor/132414      -> ciflow/inductor/132414
2025-03-04T21:02:36.6839672Z  * [new tag]                 ciflow/inductor/133044      -> ciflow/inductor/133044
2025-03-04T21:02:36.6840851Z  * [new tag]                 ciflow/inductor/133121      -> ciflow/inductor/133121
2025-03-04T21:02:36.6842023Z  * [new tag]                 ciflow/inductor/133287      -> ciflow/inductor/133287
2025-03-04T21:02:36.6843051Z  * [new tag]                 ciflow/inductor/133289      -> ciflow/inductor/133289
2025-03-04T21:02:36.6844410Z  * [new tag]                 ciflow/inductor/133296      -> ciflow/inductor/133296
2025-03-04T21:02:36.6845590Z  * [new tag]                 ciflow/inductor/133297      -> ciflow/inductor/133297
2025-03-04T21:02:36.6846746Z  * [new tag]                 ciflow/inductor/133315      -> ciflow/inductor/133315
2025-03-04T21:02:36.6848047Z  * [new tag]                 ciflow/inductor/133392      -> ciflow/inductor/133392
2025-03-04T21:02:36.6849238Z  * [new tag]                 ciflow/inductor/133419      -> ciflow/inductor/133419
2025-03-04T21:02:36.6850399Z  * [new tag]                 ciflow/inductor/133423      -> ciflow/inductor/133423
2025-03-04T21:02:36.6851530Z  * [new tag]                 ciflow/inductor/133667      -> ciflow/inductor/133667
2025-03-04T21:02:36.6852709Z  * [new tag]                 ciflow/inductor/133753      -> ciflow/inductor/133753
2025-03-04T21:02:36.6854090Z  * [new tag]                 ciflow/inductor/134592      -> ciflow/inductor/134592
2025-03-04T21:02:36.6855273Z  * [new tag]                 ciflow/inductor/134681      -> ciflow/inductor/134681
2025-03-04T21:02:36.6856501Z  * [new tag]                 ciflow/inductor/135708      -> ciflow/inductor/135708
2025-03-04T21:02:36.6857701Z  * [new tag]                 ciflow/inductor/135792      -> ciflow/inductor/135792
2025-03-04T21:02:36.6858860Z  * [new tag]                 ciflow/inductor/136355      -> ciflow/inductor/136355
2025-03-04T21:02:36.6860009Z  * [new tag]                 ciflow/inductor/136702      -> ciflow/inductor/136702
2025-03-04T21:02:36.6861323Z  * [new tag]                 ciflow/inductor/137400      -> ciflow/inductor/137400
2025-03-04T21:02:36.6862298Z  * [new tag]                 ciflow/inductor/137568      -> ciflow/inductor/137568
2025-03-04T21:02:36.6868037Z  * [new tag]                 ciflow/inductor/137583      -> ciflow/inductor/137583
2025-03-04T21:02:36.6868419Z  * [new tag]                 ciflow/inductor/137846      -> ciflow/inductor/137846
2025-03-04T21:02:36.6868615Z  * [new tag]                 ciflow/inductor/137884      -> ciflow/inductor/137884
2025-03-04T21:02:36.6868801Z  * [new tag]                 ciflow/inductor/138185      -> ciflow/inductor/138185
2025-03-04T21:02:36.6868990Z  * [new tag]                 ciflow/inductor/138202      -> ciflow/inductor/138202
2025-03-04T21:02:36.6869524Z  * [new tag]                 ciflow/inductor/138213      -> ciflow/inductor/138213
2025-03-04T21:02:36.6870875Z  * [new tag]                 ciflow/inductor/138214      -> ciflow/inductor/138214
2025-03-04T21:02:36.6872335Z  * [new tag]                 ciflow/inductor/138388      -> ciflow/inductor/138388
2025-03-04T21:02:36.6873497Z  * [new tag]                 ciflow/inductor/138513      -> ciflow/inductor/138513
2025-03-04T21:02:36.6874628Z  * [new tag]                 ciflow/inductor/138519      -> ciflow/inductor/138519
2025-03-04T21:02:36.6875914Z  * [new tag]                 ciflow/inductor/138555      -> ciflow/inductor/138555
2025-03-04T21:02:36.6877327Z  * [new tag]                 ciflow/inductor/138626      -> ciflow/inductor/138626
2025-03-04T21:02:36.6878506Z  * [new tag]                 ciflow/inductor/138889      -> ciflow/inductor/138889
2025-03-04T21:02:36.6881259Z  * [new tag]                 ciflow/inductor/138930      -> ciflow/inductor/138930
2025-03-04T21:02:36.6881493Z  * [new tag]                 ciflow/inductor/139094      -> ciflow/inductor/139094
2025-03-04T21:02:36.6882087Z  * [new tag]                 ciflow/inductor/139271      -> ciflow/inductor/139271
2025-03-04T21:02:36.6882922Z  * [new tag]                 ciflow/inductor/139561      -> ciflow/inductor/139561
2025-03-04T21:02:36.6884324Z  * [new tag]                 ciflow/inductor/139975      -> ciflow/inductor/139975
2025-03-04T21:02:36.6885199Z  * [new tag]                 ciflow/inductor/140032      -> ciflow/inductor/140032
2025-03-04T21:02:36.6886608Z  * [new tag]                 ciflow/inductor/140084      -> ciflow/inductor/140084
2025-03-04T21:02:36.6888004Z  * [new tag]                 ciflow/inductor/140159      -> ciflow/inductor/140159
2025-03-04T21:02:36.6889268Z  * [new tag]                 ciflow/inductor/140195      -> ciflow/inductor/140195
2025-03-04T21:02:36.6890547Z  * [new tag]                 ciflow/inductor/140746      -> ciflow/inductor/140746
2025-03-04T21:02:36.6891714Z  * [new tag]                 ciflow/inductor/140756      -> ciflow/inductor/140756
2025-03-04T21:02:36.6892993Z  * [new tag]                 ciflow/inductor/140979      -> ciflow/inductor/140979
2025-03-04T21:02:36.6894680Z  * [new tag]                 ciflow/inductor/141082      -> ciflow/inductor/141082
2025-03-04T21:02:36.6896291Z  * [new tag]                 ciflow/inductor/141096      -> ciflow/inductor/141096
2025-03-04T21:02:36.6897512Z  * [new tag]                 ciflow/inductor/141097      -> ciflow/inductor/141097
2025-03-04T21:02:36.6898835Z  * [new tag]                 ciflow/inductor/141213      -> ciflow/inductor/141213
2025-03-04T21:02:36.6900038Z  * [new tag]                 ciflow/inductor/141309      -> ciflow/inductor/141309
2025-03-04T21:02:36.6901193Z  * [new tag]                 ciflow/inductor/141393      -> ciflow/inductor/141393
2025-03-04T21:02:36.6902336Z  * [new tag]                 ciflow/inductor/141641      -> ciflow/inductor/141641
2025-03-04T21:02:36.6903581Z  * [new tag]                 ciflow/inductor/141684      -> ciflow/inductor/141684
2025-03-04T21:02:36.6904855Z  * [new tag]                 ciflow/inductor/141700      -> ciflow/inductor/141700
2025-03-04T21:02:36.6906131Z  * [new tag]                 ciflow/inductor/141730      -> ciflow/inductor/141730
2025-03-04T21:02:36.6907303Z  * [new tag]                 ciflow/inductor/141842      -> ciflow/inductor/141842
2025-03-04T21:02:36.6908468Z  * [new tag]                 ciflow/inductor/141940      -> ciflow/inductor/141940
2025-03-04T21:02:36.6909658Z  * [new tag]                 ciflow/inductor/141944      -> ciflow/inductor/141944
2025-03-04T21:02:36.6911102Z  * [new tag]                 ciflow/inductor/141961      -> ciflow/inductor/141961
2025-03-04T21:02:36.6911966Z  * [new tag]                 ciflow/inductor/142091      -> ciflow/inductor/142091
2025-03-04T21:02:36.6913318Z  * [new tag]                 ciflow/inductor/142092      -> ciflow/inductor/142092
2025-03-04T21:02:36.6914698Z  * [new tag]                 ciflow/inductor/142163      -> ciflow/inductor/142163
2025-03-04T21:02:36.6916169Z  * [new tag]                 ciflow/inductor/142272      -> ciflow/inductor/142272
2025-03-04T21:02:36.6917582Z  * [new tag]                 ciflow/inductor/142273      -> ciflow/inductor/142273
2025-03-04T21:02:36.6918769Z  * [new tag]                 ciflow/inductor/142295      -> ciflow/inductor/142295
2025-03-04T21:02:36.6919978Z  * [new tag]                 ciflow/inductor/142296      -> ciflow/inductor/142296
2025-03-04T21:02:36.6921283Z  * [new tag]                 ciflow/inductor/142309      -> ciflow/inductor/142309
2025-03-04T21:02:36.6922465Z  * [new tag]                 ciflow/inductor/142350      -> ciflow/inductor/142350
2025-03-04T21:02:36.6923642Z  * [new tag]                 ciflow/inductor/142372      -> ciflow/inductor/142372
2025-03-04T21:02:36.6924961Z  * [new tag]                 ciflow/inductor/142483      -> ciflow/inductor/142483
2025-03-04T21:02:36.6928837Z  * [new tag]                 ciflow/inductor/142851      -> ciflow/inductor/142851
2025-03-04T21:02:36.6929987Z  * [new tag]                 ciflow/inductor/143044      -> ciflow/inductor/143044
2025-03-04T21:02:36.6931165Z  * [new tag]                 ciflow/inductor/143103      -> ciflow/inductor/143103
2025-03-04T21:02:36.6932348Z  * [new tag]                 ciflow/inductor/143220      -> ciflow/inductor/143220
2025-03-04T21:02:36.6933600Z  * [new tag]                 ciflow/inductor/143256      -> ciflow/inductor/143256
2025-03-04T21:02:36.6934792Z  * [new tag]                 ciflow/inductor/143275      -> ciflow/inductor/143275
2025-03-04T21:02:36.6935808Z  * [new tag]                 ciflow/inductor/143313      -> ciflow/inductor/143313
2025-03-04T21:02:36.6937250Z  * [new tag]                 ciflow/inductor/143411      -> ciflow/inductor/143411
2025-03-04T21:02:36.6938393Z  * [new tag]                 ciflow/inductor/143457      -> ciflow/inductor/143457
2025-03-04T21:02:36.6939842Z  * [new tag]                 ciflow/inductor/143464      -> ciflow/inductor/143464
2025-03-04T21:02:36.6941181Z  * [new tag]                 ciflow/inductor/143475      -> ciflow/inductor/143475
2025-03-04T21:02:36.6942375Z  * [new tag]                 ciflow/inductor/143525      -> ciflow/inductor/143525
2025-03-04T21:02:36.6943794Z  * [new tag]                 ciflow/inductor/143527      -> ciflow/inductor/143527
2025-03-04T21:02:36.6944981Z  * [new tag]                 ciflow/inductor/143533      -> ciflow/inductor/143533
2025-03-04T21:02:36.6946148Z  * [new tag]                 ciflow/inductor/143534      -> ciflow/inductor/143534
2025-03-04T21:02:36.6947442Z  * [new tag]                 ciflow/inductor/143544      -> ciflow/inductor/143544
2025-03-04T21:02:36.6948730Z  * [new tag]                 ciflow/inductor/143666      -> ciflow/inductor/143666
2025-03-04T21:02:36.6950219Z  * [new tag]                 ciflow/inductor/143671      -> ciflow/inductor/143671
2025-03-04T21:02:36.6951385Z  * [new tag]                 ciflow/inductor/143712      -> ciflow/inductor/143712
2025-03-04T21:02:36.6952737Z  * [new tag]                 ciflow/inductor/143812      -> ciflow/inductor/143812
2025-03-04T21:02:36.6954134Z  * [new tag]                 ciflow/inductor/143833      -> ciflow/inductor/143833
2025-03-04T21:02:36.6955303Z  * [new tag]                 ciflow/inductor/143961      -> ciflow/inductor/143961
2025-03-04T21:02:36.6956603Z  * [new tag]                 ciflow/inductor/143987      -> ciflow/inductor/143987
2025-03-04T21:02:36.6957793Z  * [new tag]                 ciflow/inductor/144008      -> ciflow/inductor/144008
2025-03-04T21:02:36.6959250Z  * [new tag]                 ciflow/inductor/144017      -> ciflow/inductor/144017
2025-03-04T21:02:36.6960101Z  * [new tag]                 ciflow/inductor/144073      -> ciflow/inductor/144073
2025-03-04T21:02:36.6961512Z  * [new tag]                 ciflow/inductor/144097      -> ciflow/inductor/144097
2025-03-04T21:02:36.6962526Z  * [new tag]                 ciflow/inductor/144120      -> ciflow/inductor/144120
2025-03-04T21:02:36.6964049Z  * [new tag]                 ciflow/inductor/144172      -> ciflow/inductor/144172
2025-03-04T21:02:36.6965333Z  * [new tag]                 ciflow/inductor/144234      -> ciflow/inductor/144234
2025-03-04T21:02:36.6966751Z  * [new tag]                 ciflow/inductor/144272      -> ciflow/inductor/144272
2025-03-04T21:02:36.6968030Z  * [new tag]                 ciflow/inductor/144288      -> ciflow/inductor/144288
2025-03-04T21:02:36.6969279Z  * [new tag]                 ciflow/inductor/144293      -> ciflow/inductor/144293
2025-03-04T21:02:36.6970838Z  * [new tag]                 ciflow/inductor/144294      -> ciflow/inductor/144294
2025-03-04T21:02:36.6972055Z  * [new tag]                 ciflow/inductor/144332      -> ciflow/inductor/144332
2025-03-04T21:02:36.6973218Z  * [new tag]                 ciflow/inductor/144333      -> ciflow/inductor/144333
2025-03-04T21:02:36.6974388Z  * [new tag]                 ciflow/inductor/144349      -> ciflow/inductor/144349
2025-03-04T21:02:36.6975589Z  * [new tag]                 ciflow/inductor/144353      -> ciflow/inductor/144353
2025-03-04T21:02:36.6976816Z  * [new tag]                 ciflow/inductor/144365      -> ciflow/inductor/144365
2025-03-04T21:02:36.6977828Z  * [new tag]                 ciflow/inductor/144366      -> ciflow/inductor/144366
2025-03-04T21:02:36.6979629Z  * [new tag]                 ciflow/inductor/144405      -> ciflow/inductor/144405
2025-03-04T21:02:36.6980817Z  * [new tag]                 ciflow/inductor/144413      -> ciflow/inductor/144413
2025-03-04T21:02:36.6982077Z  * [new tag]                 ciflow/inductor/144414      -> ciflow/inductor/144414
2025-03-04T21:02:36.6983193Z  * [new tag]                 ciflow/inductor/144438      -> ciflow/inductor/144438
2025-03-04T21:02:36.6984368Z  * [new tag]                 ciflow/inductor/144452      -> ciflow/inductor/144452
2025-03-04T21:02:36.6985564Z  * [new tag]                 ciflow/inductor/144458      -> ciflow/inductor/144458
2025-03-04T21:02:36.6986745Z  * [new tag]                 ciflow/inductor/144501      -> ciflow/inductor/144501
2025-03-04T21:02:36.6987934Z  * [new tag]                 ciflow/inductor/144505      -> ciflow/inductor/144505
2025-03-04T21:02:36.6989156Z  * [new tag]                 ciflow/inductor/144507      -> ciflow/inductor/144507
2025-03-04T21:02:36.6990214Z  * [new tag]                 ciflow/inductor/144516      -> ciflow/inductor/144516
2025-03-04T21:02:36.6991514Z  * [new tag]                 ciflow/inductor/144542      -> ciflow/inductor/144542
2025-03-04T21:02:36.6992682Z  * [new tag]                 ciflow/inductor/144548      -> ciflow/inductor/144548
2025-03-04T21:02:36.6993994Z  * [new tag]                 ciflow/inductor/144551      -> ciflow/inductor/144551
2025-03-04T21:02:36.6995180Z  * [new tag]                 ciflow/inductor/144553      -> ciflow/inductor/144553
2025-03-04T21:02:36.6996357Z  * [new tag]                 ciflow/inductor/144555      -> ciflow/inductor/144555
2025-03-04T21:02:36.6997540Z  * [new tag]                 ciflow/inductor/144556      -> ciflow/inductor/144556
2025-03-04T21:02:36.6998703Z  * [new tag]                 ciflow/inductor/144579      -> ciflow/inductor/144579
2025-03-04T21:02:36.7000125Z  * [new tag]                 ciflow/inductor/144598      -> ciflow/inductor/144598
2025-03-04T21:02:36.7001318Z  * [new tag]                 ciflow/inductor/144712      -> ciflow/inductor/144712
2025-03-04T21:02:36.7002492Z  * [new tag]                 ciflow/inductor/144721      -> ciflow/inductor/144721
2025-03-04T21:02:36.7003918Z  * [new tag]                 ciflow/inductor/144724      -> ciflow/inductor/144724
2025-03-04T21:02:36.7005184Z  * [new tag]                 ciflow/inductor/144733      -> ciflow/inductor/144733
2025-03-04T21:02:36.7006074Z  * [new tag]                 ciflow/inductor/144741      -> ciflow/inductor/144741
2025-03-04T21:02:36.7007446Z  * [new tag]                 ciflow/inductor/144765      -> ciflow/inductor/144765
2025-03-04T21:02:36.7008911Z  * [new tag]                 ciflow/inductor/144771      -> ciflow/inductor/144771
2025-03-04T21:02:36.7010263Z  * [new tag]                 ciflow/inductor/144880      -> ciflow/inductor/144880
2025-03-04T21:02:36.7011457Z  * [new tag]                 ciflow/inductor/144905      -> ciflow/inductor/144905
2025-03-04T21:02:36.7012632Z  * [new tag]                 ciflow/inductor/144925      -> ciflow/inductor/144925
2025-03-04T21:02:36.7013804Z  * [new tag]                 ciflow/inductor/144943      -> ciflow/inductor/144943
2025-03-04T21:02:36.7015100Z  * [new tag]                 ciflow/inductor/144953      -> ciflow/inductor/144953
2025-03-04T21:02:36.7016289Z  * [new tag]                 ciflow/inductor/144975      -> ciflow/inductor/144975
2025-03-04T21:02:36.7017491Z  * [new tag]                 ciflow/inductor/144979      -> ciflow/inductor/144979
2025-03-04T21:02:36.7018685Z  * [new tag]                 ciflow/inductor/144986      -> ciflow/inductor/144986
2025-03-04T21:02:36.7020038Z  * [new tag]                 ciflow/inductor/144992      -> ciflow/inductor/144992
2025-03-04T21:02:36.7021198Z  * [new tag]                 ciflow/inductor/145024      -> ciflow/inductor/145024
2025-03-04T21:02:36.7022399Z  * [new tag]                 ciflow/inductor/145061      -> ciflow/inductor/145061
2025-03-04T21:02:36.7023581Z  * [new tag]                 ciflow/inductor/145117      -> ciflow/inductor/145117
2025-03-04T21:02:36.7024753Z  * [new tag]                 ciflow/inductor/145119      -> ciflow/inductor/145119
2025-03-04T21:02:36.7026256Z  * [new tag]                 ciflow/inductor/145150      -> ciflow/inductor/145150
2025-03-04T21:02:36.7027596Z  * [new tag]                 ciflow/inductor/145153      -> ciflow/inductor/145153
2025-03-04T21:02:36.7028772Z  * [new tag]                 ciflow/inductor/145254      -> ciflow/inductor/145254
2025-03-04T21:02:36.7029974Z  * [new tag]                 ciflow/inductor/145331      -> ciflow/inductor/145331
2025-03-04T21:02:36.7031143Z  * [new tag]                 ciflow/inductor/145353      -> ciflow/inductor/145353
2025-03-04T21:02:36.7032299Z  * [new tag]                 ciflow/inductor/145475      -> ciflow/inductor/145475
2025-03-04T21:02:36.7033598Z  * [new tag]                 ciflow/inductor/145523      -> ciflow/inductor/145523
2025-03-04T21:02:36.7034774Z  * [new tag]                 ciflow/inductor/145540      -> ciflow/inductor/145540
2025-03-04T21:02:36.7035956Z  * [new tag]                 ciflow/inductor/145559      -> ciflow/inductor/145559
2025-03-04T21:02:36.7037129Z  * [new tag]                 ciflow/inductor/145562      -> ciflow/inductor/145562
2025-03-04T21:02:36.7038330Z  * [new tag]                 ciflow/inductor/145594      -> ciflow/inductor/145594
2025-03-04T21:02:36.7039553Z  * [new tag]                 ciflow/inductor/145595      -> ciflow/inductor/145595
2025-03-04T21:02:36.7040817Z  * [new tag]                 ciflow/inductor/145605      -> ciflow/inductor/145605
2025-03-04T21:02:36.7041979Z  * [new tag]                 ciflow/inductor/145612      -> ciflow/inductor/145612
2025-03-04T21:02:36.7043179Z  * [new tag]                 ciflow/inductor/145636      -> ciflow/inductor/145636
2025-03-04T21:02:36.7044401Z  * [new tag]                 ciflow/inductor/145647      -> ciflow/inductor/145647
2025-03-04T21:02:36.7045646Z  * [new tag]                 ciflow/inductor/145681      -> ciflow/inductor/145681
2025-03-04T21:02:36.7046864Z  * [new tag]                 ciflow/inductor/145865      -> ciflow/inductor/145865
2025-03-04T21:02:36.7048120Z  * [new tag]                 ciflow/inductor/145885      -> ciflow/inductor/145885
2025-03-04T21:02:36.7049539Z  * [new tag]                 ciflow/inductor/145911      -> ciflow/inductor/145911
2025-03-04T21:02:36.7050364Z  * [new tag]                 ciflow/inductor/145922      -> ciflow/inductor/145922
2025-03-04T21:02:36.7051766Z  * [new tag]                 ciflow/inductor/145936      -> ciflow/inductor/145936
2025-03-04T21:02:36.7052919Z  * [new tag]                 ciflow/inductor/145969      -> ciflow/inductor/145969
2025-03-04T21:02:36.7054118Z  * [new tag]                 ciflow/inductor/145979      -> ciflow/inductor/145979
2025-03-04T21:02:36.7055307Z  * [new tag]                 ciflow/inductor/145992      -> ciflow/inductor/145992
2025-03-04T21:02:36.7056638Z  * [new tag]                 ciflow/inductor/146051      -> ciflow/inductor/146051
2025-03-04T21:02:36.7058303Z  * [new tag]                 ciflow/inductor/146063      -> ciflow/inductor/146063
2025-03-04T21:02:36.7059524Z  * [new tag]                 ciflow/inductor/146101      -> ciflow/inductor/146101
2025-03-04T21:02:36.7060982Z  * [new tag]                 ciflow/inductor/146115      -> ciflow/inductor/146115
2025-03-04T21:02:36.7062178Z  * [new tag]                 ciflow/inductor/146135      -> ciflow/inductor/146135
2025-03-04T21:02:36.7063361Z  * [new tag]                 ciflow/inductor/146171      -> ciflow/inductor/146171
2025-03-04T21:02:36.7064570Z  * [new tag]                 ciflow/inductor/146172      -> ciflow/inductor/146172
2025-03-04T21:02:36.7065734Z  * [new tag]                 ciflow/inductor/146176      -> ciflow/inductor/146176
2025-03-04T21:02:36.7066915Z  * [new tag]                 ciflow/inductor/146180      -> ciflow/inductor/146180
2025-03-04T21:02:36.7068103Z  * [new tag]                 ciflow/inductor/146218      -> ciflow/inductor/146218
2025-03-04T21:02:36.7069656Z  * [new tag]                 ciflow/inductor/146228      -> ciflow/inductor/146228
2025-03-04T21:02:36.7070803Z  * [new tag]                 ciflow/inductor/146264      -> ciflow/inductor/146264
2025-03-04T21:02:36.7072145Z  * [new tag]                 ciflow/inductor/146267      -> ciflow/inductor/146267
2025-03-04T21:02:36.7073443Z  * [new tag]                 ciflow/inductor/146275      -> ciflow/inductor/146275
2025-03-04T21:02:36.7074669Z  * [new tag]                 ciflow/inductor/146280      -> ciflow/inductor/146280
2025-03-04T21:02:36.7075849Z  * [new tag]                 ciflow/inductor/146288      -> ciflow/inductor/146288
2025-03-04T21:02:36.7077038Z  * [new tag]                 ciflow/inductor/146319      -> ciflow/inductor/146319
2025-03-04T21:02:36.7078210Z  * [new tag]                 ciflow/inductor/146335      -> ciflow/inductor/146335
2025-03-04T21:02:36.7079400Z  * [new tag]                 ciflow/inductor/146341      -> ciflow/inductor/146341
2025-03-04T21:02:36.7080577Z  * [new tag]                 ciflow/inductor/146393      -> ciflow/inductor/146393
2025-03-04T21:02:36.7081746Z  * [new tag]                 ciflow/inductor/146395      -> ciflow/inductor/146395
2025-03-04T21:02:36.7082949Z  * [new tag]                 ciflow/inductor/146415      -> ciflow/inductor/146415
2025-03-04T21:02:36.7084145Z  * [new tag]                 ciflow/inductor/146421      -> ciflow/inductor/146421
2025-03-04T21:02:36.7085338Z  * [new tag]                 ciflow/inductor/146436      -> ciflow/inductor/146436
2025-03-04T21:02:36.7086519Z  * [new tag]                 ciflow/inductor/146455      -> ciflow/inductor/146455
2025-03-04T21:02:36.7087829Z  * [new tag]                 ciflow/inductor/146499      -> ciflow/inductor/146499
2025-03-04T21:02:36.7089071Z  * [new tag]                 ciflow/inductor/146500      -> ciflow/inductor/146500
2025-03-04T21:02:36.7090264Z  * [new tag]                 ciflow/inductor/146501      -> ciflow/inductor/146501
2025-03-04T21:02:36.7091447Z  * [new tag]                 ciflow/inductor/146502      -> ciflow/inductor/146502
2025-03-04T21:02:36.7092701Z  * [new tag]                 ciflow/inductor/146504      -> ciflow/inductor/146504
2025-03-04T21:02:36.7093887Z  * [new tag]                 ciflow/inductor/146505      -> ciflow/inductor/146505
2025-03-04T21:02:36.7095225Z  * [new tag]                 ciflow/inductor/146506      -> ciflow/inductor/146506
2025-03-04T21:02:36.7096102Z  * [new tag]                 ciflow/inductor/146526      -> ciflow/inductor/146526
2025-03-04T21:02:36.7097831Z  * [new tag]                 ciflow/inductor/146530      -> ciflow/inductor/146530
2025-03-04T21:02:36.7098998Z  * [new tag]                 ciflow/inductor/146535      -> ciflow/inductor/146535
2025-03-04T21:02:36.7100222Z  * [new tag]                 ciflow/inductor/146558      -> ciflow/inductor/146558
2025-03-04T21:02:36.7101398Z  * [new tag]                 ciflow/inductor/146561      -> ciflow/inductor/146561
2025-03-04T21:02:36.7102592Z  * [new tag]                 ciflow/inductor/146562      -> ciflow/inductor/146562
2025-03-04T21:02:36.7103788Z  * [new tag]                 ciflow/inductor/146636      -> ciflow/inductor/146636
2025-03-04T21:02:36.7104967Z  * [new tag]                 ciflow/inductor/146661      -> ciflow/inductor/146661
2025-03-04T21:02:36.7106174Z  * [new tag]                 ciflow/inductor/146678      -> ciflow/inductor/146678
2025-03-04T21:02:36.7107395Z  * [new tag]                 ciflow/inductor/146706      -> ciflow/inductor/146706
2025-03-04T21:02:36.7108583Z  * [new tag]                 ciflow/inductor/146718      -> ciflow/inductor/146718
2025-03-04T21:02:36.7109802Z  * [new tag]                 ciflow/inductor/146779      -> ciflow/inductor/146779
2025-03-04T21:02:36.7113039Z  * [new tag]                 ciflow/inductor/146781      -> ciflow/inductor/146781
2025-03-04T21:02:36.7113905Z  * [new tag]                 ciflow/inductor/146823      -> ciflow/inductor/146823
2025-03-04T21:02:36.7115304Z  * [new tag]                 ciflow/inductor/146826      -> ciflow/inductor/146826
2025-03-04T21:02:36.7116496Z  * [new tag]                 ciflow/inductor/146827      -> ciflow/inductor/146827
2025-03-04T21:02:36.7117829Z  * [new tag]                 ciflow/inductor/146844      -> ciflow/inductor/146844
2025-03-04T21:02:36.7119098Z  * [new tag]                 ciflow/inductor/146845      -> ciflow/inductor/146845
2025-03-04T21:02:36.7120029Z  * [new tag]                 ciflow/inductor/146850      -> ciflow/inductor/146850
2025-03-04T21:02:36.7121449Z  * [new tag]                 ciflow/inductor/146864      -> ciflow/inductor/146864
2025-03-04T21:02:36.7122639Z  * [new tag]                 ciflow/inductor/146870      -> ciflow/inductor/146870
2025-03-04T21:02:36.7123858Z  * [new tag]                 ciflow/inductor/146873      -> ciflow/inductor/146873
2025-03-04T21:02:36.7125474Z  * [new tag]                 ciflow/inductor/146874      -> ciflow/inductor/146874
2025-03-04T21:02:36.7126702Z  * [new tag]                 ciflow/inductor/146894      -> ciflow/inductor/146894
2025-03-04T21:02:36.7128091Z  * [new tag]                 ciflow/inductor/146895      -> ciflow/inductor/146895
2025-03-04T21:02:36.7129494Z  * [new tag]                 ciflow/inductor/146919      -> ciflow/inductor/146919
2025-03-04T21:02:36.7130646Z  * [new tag]                 ciflow/inductor/146921      -> ciflow/inductor/146921
2025-03-04T21:02:36.7131850Z  * [new tag]                 ciflow/inductor/146928      -> ciflow/inductor/146928
2025-03-04T21:02:36.7133062Z  * [new tag]                 ciflow/inductor/146935      -> ciflow/inductor/146935
2025-03-04T21:02:36.7134269Z  * [new tag]                 ciflow/inductor/146942      -> ciflow/inductor/146942
2025-03-04T21:02:36.7135582Z  * [new tag]                 ciflow/inductor/146962      -> ciflow/inductor/146962
2025-03-04T21:02:36.7136858Z  * [new tag]                 ciflow/inductor/146983      -> ciflow/inductor/146983
2025-03-04T21:02:36.7138152Z  * [new tag]                 ciflow/inductor/146989      -> ciflow/inductor/146989
2025-03-04T21:02:36.7140213Z  * [new tag]                 ciflow/inductor/147007      -> ciflow/inductor/147007
2025-03-04T21:02:36.7141396Z  * [new tag]                 ciflow/inductor/147014      -> ciflow/inductor/147014
2025-03-04T21:02:36.7142713Z  * [new tag]                 ciflow/inductor/147021      -> ciflow/inductor/147021
2025-03-04T21:02:36.7144049Z  * [new tag]                 ciflow/inductor/147036      -> ciflow/inductor/147036
2025-03-04T21:02:36.7144983Z  * [new tag]                 ciflow/inductor/147049      -> ciflow/inductor/147049
2025-03-04T21:02:36.7146344Z  * [new tag]                 ciflow/inductor/147105      -> ciflow/inductor/147105
2025-03-04T21:02:36.7147525Z  * [new tag]                 ciflow/inductor/147146      -> ciflow/inductor/147146
2025-03-04T21:02:36.7148718Z  * [new tag]                 ciflow/inductor/147149      -> ciflow/inductor/147149
2025-03-04T21:02:36.7149925Z  * [new tag]                 ciflow/inductor/147155      -> ciflow/inductor/147155
2025-03-04T21:02:36.7151084Z  * [new tag]                 ciflow/inductor/147178      -> ciflow/inductor/147178
2025-03-04T21:02:36.7152289Z  * [new tag]                 ciflow/inductor/147205      -> ciflow/inductor/147205
2025-03-04T21:02:36.7153476Z  * [new tag]                 ciflow/inductor/147225      -> ciflow/inductor/147225
2025-03-04T21:02:36.7154802Z  * [new tag]                 ciflow/inductor/147229      -> ciflow/inductor/147229
2025-03-04T21:02:36.7156004Z  * [new tag]                 ciflow/inductor/147269      -> ciflow/inductor/147269
2025-03-04T21:02:36.7157189Z  * [new tag]                 ciflow/inductor/147272      -> ciflow/inductor/147272
2025-03-04T21:02:36.7158503Z  * [new tag]                 ciflow/inductor/147314      -> ciflow/inductor/147314
2025-03-04T21:02:36.7159691Z  * [new tag]                 ciflow/inductor/147315      -> ciflow/inductor/147315
2025-03-04T21:02:36.7160924Z  * [new tag]                 ciflow/inductor/147320      -> ciflow/inductor/147320
2025-03-04T21:02:36.7162111Z  * [new tag]                 ciflow/inductor/147341      -> ciflow/inductor/147341
2025-03-04T21:02:36.7163330Z  * [new tag]                 ciflow/inductor/147360      -> ciflow/inductor/147360
2025-03-04T21:02:36.7164657Z  * [new tag]                 ciflow/inductor/147368      -> ciflow/inductor/147368
2025-03-04T21:02:36.7165690Z  * [new tag]                 ciflow/inductor/147403      -> ciflow/inductor/147403
2025-03-04T21:02:36.7167075Z  * [new tag]                 ciflow/inductor/147410      -> ciflow/inductor/147410
2025-03-04T21:02:36.7168347Z  * [new tag]                 ciflow/inductor/147414      -> ciflow/inductor/147414
2025-03-04T21:02:36.7169582Z  * [new tag]                 ciflow/inductor/147415      -> ciflow/inductor/147415
2025-03-04T21:02:36.7170811Z  * [new tag]                 ciflow/inductor/147422      -> ciflow/inductor/147422
2025-03-04T21:02:36.7172205Z  * [new tag]                 ciflow/inductor/147445      -> ciflow/inductor/147445
2025-03-04T21:02:36.7173645Z  * [new tag]                 ciflow/inductor/147452      -> ciflow/inductor/147452
2025-03-04T21:02:36.7174974Z  * [new tag]                 ciflow/inductor/147481      -> ciflow/inductor/147481
2025-03-04T21:02:36.7176204Z  * [new tag]                 ciflow/inductor/147485      -> ciflow/inductor/147485
2025-03-04T21:02:36.7177447Z  * [new tag]                 ciflow/inductor/147498      -> ciflow/inductor/147498
2025-03-04T21:02:36.7178706Z  * [new tag]                 ciflow/inductor/147514      -> ciflow/inductor/147514
2025-03-04T21:02:36.7179926Z  * [new tag]                 ciflow/inductor/147528      -> ciflow/inductor/147528
2025-03-04T21:02:36.7181116Z  * [new tag]                 ciflow/inductor/147552      -> ciflow/inductor/147552
2025-03-04T21:02:36.7182376Z  * [new tag]                 ciflow/inductor/147557      -> ciflow/inductor/147557
2025-03-04T21:02:36.7183626Z  * [new tag]                 ciflow/inductor/147561      -> ciflow/inductor/147561
2025-03-04T21:02:36.7184835Z  * [new tag]                 ciflow/inductor/147562      -> ciflow/inductor/147562
2025-03-04T21:02:36.7186107Z  * [new tag]                 ciflow/inductor/147574      -> ciflow/inductor/147574
2025-03-04T21:02:36.7187340Z  * [new tag]                 ciflow/inductor/147583      -> ciflow/inductor/147583
2025-03-04T21:02:36.7188794Z  * [new tag]                 ciflow/inductor/147592      -> ciflow/inductor/147592
2025-03-04T21:02:36.7189679Z  * [new tag]                 ciflow/inductor/147603      -> ciflow/inductor/147603
2025-03-04T21:02:36.7191115Z  * [new tag]                 ciflow/inductor/147619      -> ciflow/inductor/147619
2025-03-04T21:02:36.7192371Z  * [new tag]                 ciflow/inductor/147648      -> ciflow/inductor/147648
2025-03-04T21:02:36.7193631Z  * [new tag]                 ciflow/inductor/147660      -> ciflow/inductor/147660
2025-03-04T21:02:36.7194928Z  * [new tag]                 ciflow/inductor/147727      -> ciflow/inductor/147727
2025-03-04T21:02:36.7196144Z  * [new tag]                 ciflow/inductor/147741      -> ciflow/inductor/147741
2025-03-04T21:02:36.7197382Z  * [new tag]                 ciflow/inductor/147745      -> ciflow/inductor/147745
2025-03-04T21:02:36.7198772Z  * [new tag]                 ciflow/inductor/147768      -> ciflow/inductor/147768
2025-03-04T21:02:36.7200015Z  * [new tag]                 ciflow/inductor/147790      -> ciflow/inductor/147790
2025-03-04T21:02:36.7201229Z  * [new tag]                 ciflow/inductor/147797      -> ciflow/inductor/147797
2025-03-04T21:02:36.7202458Z  * [new tag]                 ciflow/inductor/147798      -> ciflow/inductor/147798
2025-03-04T21:02:36.7203700Z  * [new tag]                 ciflow/inductor/147800      -> ciflow/inductor/147800
2025-03-04T21:02:36.7204953Z  * [new tag]                 ciflow/inductor/147817      -> ciflow/inductor/147817
2025-03-04T21:02:36.7206307Z  * [new tag]                 ciflow/inductor/147821      -> ciflow/inductor/147821
2025-03-04T21:02:36.7207530Z  * [new tag]                 ciflow/inductor/147836      -> ciflow/inductor/147836
2025-03-04T21:02:36.7208885Z  * [new tag]                 ciflow/inductor/147863      -> ciflow/inductor/147863
2025-03-04T21:02:36.7210259Z  * [new tag]                 ciflow/inductor/147870      -> ciflow/inductor/147870
2025-03-04T21:02:36.7211517Z  * [new tag]                 ciflow/inductor/147881      -> ciflow/inductor/147881
2025-03-04T21:02:36.7212899Z  * [new tag]                 ciflow/inductor/147899      -> ciflow/inductor/147899
2025-03-04T21:02:36.7214122Z  * [new tag]                 ciflow/inductor/147902      -> ciflow/inductor/147902
2025-03-04T21:02:36.7215349Z  * [new tag]                 ciflow/inductor/147903      -> ciflow/inductor/147903
2025-03-04T21:02:36.7216852Z  * [new tag]                 ciflow/inductor/147908      -> ciflow/inductor/147908
2025-03-04T21:02:36.7218105Z  * [new tag]                 ciflow/inductor/147910      -> ciflow/inductor/147910
2025-03-04T21:02:36.7219359Z  * [new tag]                 ciflow/inductor/147915      -> ciflow/inductor/147915
2025-03-04T21:02:36.7221173Z  * [new tag]                 ciflow/inductor/147917      -> ciflow/inductor/147917
2025-03-04T21:02:36.7222499Z  * [new tag]                 ciflow/inductor/147927      -> ciflow/inductor/147927
2025-03-04T21:02:36.7223691Z  * [new tag]                 ciflow/inductor/147945      -> ciflow/inductor/147945
2025-03-04T21:02:36.7225183Z  * [new tag]                 ciflow/inductor/147955      -> ciflow/inductor/147955
2025-03-04T21:02:36.7226636Z  * [new tag]                 ciflow/inductor/147956      -> ciflow/inductor/147956
2025-03-04T21:02:36.7227916Z  * [new tag]                 ciflow/inductor/147957      -> ciflow/inductor/147957
2025-03-04T21:02:36.7229296Z  * [new tag]                 ciflow/inductor/147958      -> ciflow/inductor/147958
2025-03-04T21:02:36.7230646Z  * [new tag]                 ciflow/inductor/147959      -> ciflow/inductor/147959
2025-03-04T21:02:36.7232016Z  * [new tag]                 ciflow/inductor/147960      -> ciflow/inductor/147960
2025-03-04T21:02:36.7233272Z  * [new tag]                 ciflow/inductor/147962      -> ciflow/inductor/147962
2025-03-04T21:02:36.7234506Z  * [new tag]                 ciflow/inductor/147990      -> ciflow/inductor/147990
2025-03-04T21:02:36.7235746Z  * [new tag]                 ciflow/inductor/148002      -> ciflow/inductor/148002
2025-03-04T21:02:36.7237378Z  * [new tag]                 ciflow/inductor/148007      -> ciflow/inductor/148007
2025-03-04T21:02:36.7238688Z  * [new tag]                 ciflow/inductor/148008      -> ciflow/inductor/148008
2025-03-04T21:02:36.7240031Z  * [new tag]                 ciflow/inductor/148010      -> ciflow/inductor/148010
2025-03-04T21:02:36.7241402Z  * [new tag]                 ciflow/inductor/148042      -> ciflow/inductor/148042
2025-03-04T21:02:36.7242758Z  * [new tag]                 ciflow/inductor/148046      -> ciflow/inductor/148046
2025-03-04T21:02:36.7244117Z  * [new tag]                 ciflow/inductor/148063      -> ciflow/inductor/148063
2025-03-04T21:02:36.7245505Z  * [new tag]                 ciflow/inductor/148083      -> ciflow/inductor/148083
2025-03-04T21:02:36.7246886Z  * [new tag]                 ciflow/inductor/148091      -> ciflow/inductor/148091
2025-03-04T21:02:36.7248421Z  * [new tag]                 ciflow/inductor/148092      -> ciflow/inductor/148092
2025-03-04T21:02:36.7249783Z  * [new tag]                 ciflow/inductor/148104      -> ciflow/inductor/148104
2025-03-04T21:02:36.7251120Z  * [new tag]                 ciflow/inductor/148130      -> ciflow/inductor/148130
2025-03-04T21:02:36.7252531Z  * [new tag]                 ciflow/inductor/148131      -> ciflow/inductor/148131
2025-03-04T21:02:36.7253891Z  * [new tag]                 ciflow/inductor/148132      -> ciflow/inductor/148132
2025-03-04T21:02:36.7255251Z  * [new tag]                 ciflow/inductor/148138      -> ciflow/inductor/148138
2025-03-04T21:02:36.7256700Z  * [new tag]                 ciflow/inductor/148139      -> ciflow/inductor/148139
2025-03-04T21:02:36.7258026Z  * [new tag]                 ciflow/inductor/148160      -> ciflow/inductor/148160
2025-03-04T21:02:36.7259405Z  * [new tag]                 ciflow/inductor/148163      -> ciflow/inductor/148163
2025-03-04T21:02:36.7260733Z  * [new tag]                 ciflow/inductor/148173      -> ciflow/inductor/148173
2025-03-04T21:02:36.7262180Z  * [new tag]                 ciflow/inductor/148174      -> ciflow/inductor/148174
2025-03-04T21:02:36.7263474Z  * [new tag]                 ciflow/inductor/148176      -> ciflow/inductor/148176
2025-03-04T21:02:36.7264831Z  * [new tag]                 ciflow/inductor/148186      -> ciflow/inductor/148186
2025-03-04T21:02:36.7266366Z  * [new tag]                 ciflow/inductor/148190      -> ciflow/inductor/148190
2025-03-04T21:02:36.7267750Z  * [new tag]                 ciflow/inductor/148202      -> ciflow/inductor/148202
2025-03-04T21:02:36.7269097Z  * [new tag]                 ciflow/inductor/148205      -> ciflow/inductor/148205
2025-03-04T21:02:36.7270419Z  * [new tag]                 ciflow/inductor/148206      -> ciflow/inductor/148206
2025-03-04T21:02:36.7271778Z  * [new tag]                 ciflow/inductor/148209      -> ciflow/inductor/148209
2025-03-04T21:02:36.7273176Z  * [new tag]                 ciflow/inductor/148210      -> ciflow/inductor/148210
2025-03-04T21:02:36.7274522Z  * [new tag]                 ciflow/inductor/148212      -> ciflow/inductor/148212
2025-03-04T21:02:36.7275891Z  * [new tag]                 ciflow/inductor/148220      -> ciflow/inductor/148220
2025-03-04T21:02:36.7277453Z  * [new tag]                 ciflow/inductor/148223      -> ciflow/inductor/148223
2025-03-04T21:02:36.7278862Z  * [new tag]                 ciflow/inductor/148233      -> ciflow/inductor/148233
2025-03-04T21:02:36.7280175Z  * [new tag]                 ciflow/inductor/148234      -> ciflow/inductor/148234
2025-03-04T21:02:36.7281646Z  * [new tag]                 ciflow/inductor/148235      -> ciflow/inductor/148235
2025-03-04T21:02:36.7282994Z  * [new tag]                 ciflow/inductor/148236      -> ciflow/inductor/148236
2025-03-04T21:02:36.7284380Z  * [new tag]                 ciflow/inductor/148243      -> ciflow/inductor/148243
2025-03-04T21:02:36.7285702Z  * [new tag]                 ciflow/inductor/148260      -> ciflow/inductor/148260
2025-03-04T21:02:36.7287160Z  * [new tag]                 ciflow/inductor/148261      -> ciflow/inductor/148261
2025-03-04T21:02:36.7288509Z  * [new tag]                 ciflow/inductor/148279      -> ciflow/inductor/148279
2025-03-04T21:02:36.7289914Z  * [new tag]                 ciflow/inductor/148288      -> ciflow/inductor/148288
2025-03-04T21:02:36.7291396Z  * [new tag]                 ciflow/inductor/148290      -> ciflow/inductor/148290
2025-03-04T21:02:36.7292752Z  * [new tag]                 ciflow/inductor/148292      -> ciflow/inductor/148292
2025-03-04T21:02:36.7294229Z  * [new tag]                 ciflow/inductor/148294      -> ciflow/inductor/148294
2025-03-04T21:02:36.7295621Z  * [new tag]                 ciflow/inductor/148303      -> ciflow/inductor/148303
2025-03-04T21:02:36.7296982Z  * [new tag]                 ciflow/inductor/148305      -> ciflow/inductor/148305
2025-03-04T21:02:36.7298348Z  * [new tag]                 ciflow/inductor/148323      -> ciflow/inductor/148323
2025-03-04T21:02:36.7299700Z  * [new tag]                 ciflow/inductor/148328      -> ciflow/inductor/148328
2025-03-04T21:02:36.7301083Z  * [new tag]                 ciflow/inductor/148357      -> ciflow/inductor/148357
2025-03-04T21:02:36.7302434Z  * [new tag]                 ciflow/inductor/148358      -> ciflow/inductor/148358
2025-03-04T21:02:36.7303767Z  * [new tag]                 ciflow/inductor/148359      -> ciflow/inductor/148359
2025-03-04T21:02:36.7305313Z  * [new tag]                 ciflow/inductor/148363      -> ciflow/inductor/148363
2025-03-04T21:02:36.7306717Z  * [new tag]                 ciflow/inductor/148364      -> ciflow/inductor/148364
2025-03-04T21:02:36.7308055Z  * [new tag]                 ciflow/inductor/148366      -> ciflow/inductor/148366
2025-03-04T21:02:36.7309876Z  * [new tag]                 ciflow/inductor/148367      -> ciflow/inductor/148367
2025-03-04T21:02:36.7311244Z  * [new tag]                 ciflow/inductor/148376      -> ciflow/inductor/148376
2025-03-04T21:02:36.7312618Z  * [new tag]                 ciflow/inductor/148377      -> ciflow/inductor/148377
2025-03-04T21:02:36.7314225Z  * [new tag]                 ciflow/inductor/148380      -> ciflow/inductor/148380
2025-03-04T21:02:36.7315596Z  * [new tag]                 ciflow/inductor/148381      -> ciflow/inductor/148381
2025-03-04T21:02:36.7316958Z  * [new tag]                 ciflow/inductor/148385      -> ciflow/inductor/148385
2025-03-04T21:02:36.7318325Z  * [new tag]                 ciflow/inductor/148386      -> ciflow/inductor/148386
2025-03-04T21:02:36.7319667Z  * [new tag]                 ciflow/inductor/148401      -> ciflow/inductor/148401
2025-03-04T21:02:36.7321137Z  * [new tag]                 ciflow/inductor/148407      -> ciflow/inductor/148407
2025-03-04T21:02:36.7322577Z  * [new tag]                 ciflow/inductor/148413      -> ciflow/inductor/148413
2025-03-04T21:02:36.7323892Z  * [new tag]                 ciflow/inductor/148414      -> ciflow/inductor/148414
2025-03-04T21:02:36.7325338Z  * [new tag]                 ciflow/inductor/148415      -> ciflow/inductor/148415
2025-03-04T21:02:36.7329705Z  * [new tag]                 ciflow/inductor/148418      -> ciflow/inductor/148418
2025-03-04T21:02:36.7330962Z  * [new tag]                 ciflow/inductor/148423      -> ciflow/inductor/148423
2025-03-04T21:02:36.7332429Z  * [new tag]                 ciflow/inductor/148424      -> ciflow/inductor/148424
2025-03-04T21:02:36.7333791Z  * [new tag]                 ciflow/inductor/148430      -> ciflow/inductor/148430
2025-03-04T21:02:36.7335320Z  * [new tag]                 ciflow/inductor/148432      -> ciflow/inductor/148432
2025-03-04T21:02:36.7336797Z  * [new tag]                 ciflow/inductor/148445      -> ciflow/inductor/148445
2025-03-04T21:02:36.7338148Z  * [new tag]                 ciflow/inductor/148450      -> ciflow/inductor/148450
2025-03-04T21:02:36.7339588Z  * [new tag]                 ciflow/inductor/148454      -> ciflow/inductor/148454
2025-03-04T21:02:36.7340933Z  * [new tag]                 ciflow/inductor/148459      -> ciflow/inductor/148459
2025-03-04T21:02:36.7342426Z  * [new tag]                 ciflow/inductor/148470      -> ciflow/inductor/148470
2025-03-04T21:02:36.7343651Z  * [new tag]                 ciflow/inductor/148480      -> ciflow/inductor/148480
2025-03-04T21:02:36.7345164Z  * [new tag]                 ciflow/inductor/148484      -> ciflow/inductor/148484
2025-03-04T21:02:36.7346621Z  * [new tag]                 ciflow/inductor/148485      -> ciflow/inductor/148485
2025-03-04T21:02:36.7348255Z  * [new tag]                 ciflow/inductor/3b9a386     -> ciflow/inductor/3b9a386
2025-03-04T21:02:36.7349818Z  * [new tag]                 ciflow/inductor/3d4b92b     -> ciflow/inductor/3d4b92b
2025-03-04T21:02:36.7351286Z  * [new tag]                 ciflow/inductor/88106       -> ciflow/inductor/88106
2025-03-04T21:02:36.7352839Z  * [new tag]                 ciflow/inductor/88196       -> ciflow/inductor/88196
2025-03-04T21:02:36.7354509Z  * [new tag]                 ciflow/inductor/88998       -> ciflow/inductor/88998
2025-03-04T21:02:36.7355991Z  * [new tag]                 ciflow/inductor/d224ac7     -> ciflow/inductor/d224ac7
2025-03-04T21:02:36.7357392Z  * [new tag]                 ciflow/linux-aarch64/125888 -> ciflow/linux-aarch64/125888
2025-03-04T21:02:36.7358330Z  * [new tag]                 ciflow/linux-aarch64/126050 -> ciflow/linux-aarch64/126050
2025-03-04T21:02:36.7359687Z  * [new tag]                 ciflow/linux-aarch64/126054 -> ciflow/linux-aarch64/126054
2025-03-04T21:02:36.7360548Z  * [new tag]                 ciflow/linux-aarch64/133297 -> ciflow/linux-aarch64/133297
2025-03-04T21:02:36.7361852Z  * [new tag]                 ciflow/linux-aarch64/133315 -> ciflow/linux-aarch64/133315
2025-03-04T21:02:36.7362735Z  * [new tag]                 ciflow/linux-aarch64/133392 -> ciflow/linux-aarch64/133392
2025-03-04T21:02:36.7364065Z  * [new tag]                 ciflow/linux-aarch64/133419 -> ciflow/linux-aarch64/133419
2025-03-04T21:02:36.7364937Z  * [new tag]                 ciflow/linux-aarch64/133423 -> ciflow/linux-aarch64/133423
2025-03-04T21:02:36.7366017Z  * [new tag]                 ciflow/linux-aarch64/133667 -> ciflow/linux-aarch64/133667
2025-03-04T21:02:36.7367314Z  * [new tag]                 ciflow/linux-aarch64/133753 -> ciflow/linux-aarch64/133753
2025-03-04T21:02:36.7368718Z  * [new tag]                 ciflow/linux-aarch64/135058 -> ciflow/linux-aarch64/135058
2025-03-04T21:02:36.7370162Z  * [new tag]                 ciflow/linux-aarch64/135333 -> ciflow/linux-aarch64/135333
2025-03-04T21:02:36.7371745Z  * [new tag]                 ciflow/linux-aarch64/135792 -> ciflow/linux-aarch64/135792
2025-03-04T21:02:36.7373151Z  * [new tag]                 ciflow/linux-aarch64/136355 -> ciflow/linux-aarch64/136355
2025-03-04T21:02:36.7374334Z  * [new tag]                 ciflow/linux-aarch64/137568 -> ciflow/linux-aarch64/137568
2025-03-04T21:02:36.7375485Z  * [new tag]                 ciflow/linux-aarch64/138388 -> ciflow/linux-aarch64/138388
2025-03-04T21:02:36.7376680Z  * [new tag]                 ciflow/linux-aarch64/138889 -> ciflow/linux-aarch64/138889
2025-03-04T21:02:36.7377848Z  * [new tag]                 ciflow/linux-aarch64/140159 -> ciflow/linux-aarch64/140159
2025-03-04T21:02:36.7379175Z  * [new tag]                 ciflow/linux-aarch64/143741 -> ciflow/linux-aarch64/143741
2025-03-04T21:02:36.7380636Z  * [new tag]                 ciflow/linux-aarch64/145942 -> ciflow/linux-aarch64/145942
2025-03-04T21:02:36.7381807Z  * [new tag]                 ciflow/linux-aarch64/146823 -> ciflow/linux-aarch64/146823
2025-03-04T21:02:36.7382982Z  * [new tag]                 ciflow/linux-aarch64/146826 -> ciflow/linux-aarch64/146826
2025-03-04T21:02:36.7384155Z  * [new tag]                 ciflow/linux-aarch64/146895 -> ciflow/linux-aarch64/146895
2025-03-04T21:02:36.7385322Z  * [new tag]                 ciflow/linux-aarch64/147073 -> ciflow/linux-aarch64/147073
2025-03-04T21:02:36.7386665Z  * [new tag]                 ciflow/linux-aarch64/147337 -> ciflow/linux-aarch64/147337
2025-03-04T21:02:36.7387941Z  * [new tag]                 ciflow/linux-aarch64/147341 -> ciflow/linux-aarch64/147341
2025-03-04T21:02:36.7388836Z  * [new tag]                 ciflow/linux-aarch64/147359 -> ciflow/linux-aarch64/147359
2025-03-04T21:02:36.7390182Z  * [new tag]                 ciflow/linux-aarch64/147498 -> ciflow/linux-aarch64/147498
2025-03-04T21:02:36.7391491Z  * [new tag]                 ciflow/linux-aarch64/147763 -> ciflow/linux-aarch64/147763
2025-03-04T21:02:36.7392715Z  * [new tag]                 ciflow/linux-aarch64/147817 -> ciflow/linux-aarch64/147817
2025-03-04T21:02:36.7393892Z  * [new tag]                 ciflow/linux-aarch64/147855 -> ciflow/linux-aarch64/147855
2025-03-04T21:02:36.7395068Z  * [new tag]                 ciflow/linux-aarch64/147917 -> ciflow/linux-aarch64/147917
2025-03-04T21:02:36.7396232Z  * [new tag]                 ciflow/linux-aarch64/147945 -> ciflow/linux-aarch64/147945
2025-03-04T21:02:36.7397417Z  * [new tag]                 ciflow/linux-aarch64/147955 -> ciflow/linux-aarch64/147955
2025-03-04T21:02:36.7398465Z  * [new tag]                 ciflow/linux-aarch64/147956 -> ciflow/linux-aarch64/147956
2025-03-04T21:02:36.7399762Z  * [new tag]                 ciflow/linux-aarch64/147957 -> ciflow/linux-aarch64/147957
2025-03-04T21:02:36.7400920Z  * [new tag]                 ciflow/linux-aarch64/147958 -> ciflow/linux-aarch64/147958
2025-03-04T21:02:36.7402091Z  * [new tag]                 ciflow/linux-aarch64/147959 -> ciflow/linux-aarch64/147959
2025-03-04T21:02:36.7403269Z  * [new tag]                 ciflow/linux-aarch64/147964 -> ciflow/linux-aarch64/147964
2025-03-04T21:02:36.7404560Z  * [new tag]                 ciflow/linux-aarch64/148076 -> ciflow/linux-aarch64/148076
2025-03-04T21:02:36.7405733Z  * [new tag]                 ciflow/linux-aarch64/148163 -> ciflow/linux-aarch64/148163
2025-03-04T21:02:36.7406889Z  * [new tag]                 ciflow/linux-aarch64/148173 -> ciflow/linux-aarch64/148173
2025-03-04T21:02:36.7408173Z  * [new tag]                 ciflow/linux-aarch64/148403 -> ciflow/linux-aarch64/148403
2025-03-04T21:02:36.7409906Z  * [new tag]                 ciflow/mps/102148           -> ciflow/mps/102148
2025-03-04T21:02:36.7410808Z  * [new tag]                 ciflow/mps/119496           -> ciflow/mps/119496
2025-03-04T21:02:36.7412092Z  * [new tag]                 ciflow/mps/120076           -> ciflow/mps/120076
2025-03-04T21:02:36.7412983Z  * [new tag]                 ciflow/mps/133423           -> ciflow/mps/133423
2025-03-04T21:02:36.7414317Z  * [new tag]                 ciflow/mps/133667           -> ciflow/mps/133667
2025-03-04T21:02:36.7415721Z  * [new tag]                 ciflow/mps/138640           -> ciflow/mps/138640
2025-03-04T21:02:36.7416828Z  * [new tag]                 ciflow/mps/139469           -> ciflow/mps/139469
2025-03-04T21:02:36.7417727Z  * [new tag]                 ciflow/mps/140159           -> ciflow/mps/140159
2025-03-04T21:02:36.7419144Z  * [new tag]                 ciflow/mps/140211           -> ciflow/mps/140211
2025-03-04T21:02:36.7420661Z  * [new tag]                 ciflow/mps/140725           -> ciflow/mps/140725
2025-03-04T21:02:36.7421889Z  * [new tag]                 ciflow/mps/142097           -> ciflow/mps/142097
2025-03-04T21:02:36.7423544Z  * [new tag]                 ciflow/mps/142202           -> ciflow/mps/142202
2025-03-04T21:02:36.7425288Z  * [new tag]                 ciflow/mps/142477           -> ciflow/mps/142477
2025-03-04T21:02:36.7426878Z  * [new tag]                 ciflow/mps/143630           -> ciflow/mps/143630
2025-03-04T21:02:36.7427875Z  * [new tag]                 ciflow/mps/143666           -> ciflow/mps/143666
2025-03-04T21:02:36.7429176Z  * [new tag]                 ciflow/mps/143911           -> ciflow/mps/143911
2025-03-04T21:02:36.7430339Z  * [new tag]                 ciflow/mps/143966           -> ciflow/mps/143966
2025-03-04T21:02:36.7431491Z  * [new tag]                 ciflow/mps/144405           -> ciflow/mps/144405
2025-03-04T21:02:36.7432663Z  * [new tag]                 ciflow/mps/144664           -> ciflow/mps/144664
2025-03-04T21:02:36.7434460Z  * [new tag]                 ciflow/mps/145955           -> ciflow/mps/145955
2025-03-04T21:02:36.7435691Z  * [new tag]                 ciflow/mps/146098           -> ciflow/mps/146098
2025-03-04T21:02:36.7436856Z  * [new tag]                 ciflow/mps/146436           -> ciflow/mps/146436
2025-03-04T21:02:36.7438130Z  * [new tag]                 ciflow/mps/146754           -> ciflow/mps/146754
2025-03-04T21:02:36.7439295Z  * [new tag]                 ciflow/mps/146989           -> ciflow/mps/146989
2025-03-04T21:02:36.7440519Z  * [new tag]                 ciflow/mps/147205           -> ciflow/mps/147205
2025-03-04T21:02:36.7441714Z  * [new tag]                 ciflow/mps/147583           -> ciflow/mps/147583
2025-03-04T21:02:36.7443174Z  * [new tag]                 ciflow/mps/147644           -> ciflow/mps/147644
2025-03-04T21:02:36.7444357Z  * [new tag]                 ciflow/mps/147893           -> ciflow/mps/147893
2025-03-04T21:02:36.7445528Z  * [new tag]                 ciflow/mps/148305           -> ciflow/mps/148305
2025-03-04T21:02:36.7446843Z  * [new tag]                 ciflow/mps/148350           -> ciflow/mps/148350
2025-03-04T21:02:36.7448091Z  * [new tag]                 ciflow/mps/148415           -> ciflow/mps/148415
2025-03-04T21:02:36.7449275Z  * [new tag]                 ciflow/mps/148449           -> ciflow/mps/148449
2025-03-04T21:02:36.7450536Z  * [new tag]                 ciflow/mps/148468           -> ciflow/mps/148468
2025-03-04T21:02:36.7451705Z  * [new tag]                 ciflow/mps/148471           -> ciflow/mps/148471
2025-03-04T21:02:36.7453278Z  * [new tag]                 ciflow/op-benchmark/143733  -> ciflow/op-benchmark/143733
2025-03-04T21:02:36.7454833Z  * [new tag]                 ciflow/periodic/054a2fd     -> ciflow/periodic/054a2fd
2025-03-04T21:02:36.7456189Z  * [new tag]                 ciflow/periodic/123020      -> ciflow/periodic/123020
2025-03-04T21:02:36.7457400Z  * [new tag]                 ciflow/periodic/134817      -> ciflow/periodic/134817
2025-03-04T21:02:36.7458384Z  * [new tag]                 ciflow/periodic/140989      -> ciflow/periodic/140989
2025-03-04T21:02:36.7459648Z  * [new tag]                 ciflow/periodic/141309      -> ciflow/periodic/141309
2025-03-04T21:02:36.7460507Z  * [new tag]                 ciflow/periodic/141355      -> ciflow/periodic/141355
2025-03-04T21:02:36.7461830Z  * [new tag]                 ciflow/periodic/141730      -> ciflow/periodic/141730
2025-03-04T21:02:36.7462743Z  * [new tag]                 ciflow/periodic/142179      -> ciflow/periodic/142179
2025-03-04T21:02:36.7464138Z  * [new tag]                 ciflow/periodic/143959      -> ciflow/periodic/143959
2025-03-04T21:02:36.7465028Z  * [new tag]                 ciflow/periodic/144953      -> ciflow/periodic/144953
2025-03-04T21:02:36.7466343Z  * [new tag]                 ciflow/periodic/146264      -> ciflow/periodic/146264
2025-03-04T21:02:36.7467973Z  * [new tag]                 ciflow/periodic/146403      -> ciflow/periodic/146403
2025-03-04T21:02:36.7469458Z  * [new tag]                 ciflow/periodic/146823      -> ciflow/periodic/146823
2025-03-04T21:02:36.7470846Z  * [new tag]                 ciflow/periodic/146903      -> ciflow/periodic/146903
2025-03-04T21:02:36.7472148Z  * [new tag]                 ciflow/periodic/147459      -> ciflow/periodic/147459
2025-03-04T21:02:36.7473380Z  * [new tag]                 ciflow/periodic/147870      -> ciflow/periodic/147870
2025-03-04T21:02:36.7474544Z  * [new tag]                 ciflow/periodic/148351      -> ciflow/periodic/148351
2025-03-04T21:02:36.7475888Z  * [new tag]                 ciflow/periodic/2a6d37d     -> ciflow/periodic/2a6d37d
2025-03-04T21:02:36.7477227Z  * [new tag]                 ciflow/periodic/317eeb8     -> ciflow/periodic/317eeb8
2025-03-04T21:02:36.7478565Z  * [new tag]                 ciflow/periodic/3c32        -> ciflow/periodic/3c32
2025-03-04T21:02:36.7479924Z  * [new tag]                 ciflow/periodic/3e98831     -> ciflow/periodic/3e98831
2025-03-04T21:02:36.7481499Z  * [new tag]                 ciflow/periodic/94512-point -> ciflow/periodic/94512-point
2025-03-04T21:02:36.7483141Z  * [new tag]                 ciflow/periodic/csl/test87519 -> ciflow/periodic/csl/test87519
2025-03-04T21:02:36.7484464Z  * [new tag]                 ciflow/periodic/csltest88275 -> ciflow/periodic/csltest88275
2025-03-04T21:02:36.7485774Z  * [new tag]                 ciflow/periodic/csltest88761 -> ciflow/periodic/csltest88761
2025-03-04T21:02:36.7487166Z  * [new tag]                 ciflow/periodic/release_1.12 -> ciflow/periodic/release_1.12
2025-03-04T21:02:36.7488912Z  * [new tag]                 ciflow/periodic/release_1.12.0 -> ciflow/periodic/release_1.12.0
2025-03-04T21:02:36.7490405Z  * [new tag]                 ciflow/periodic/sha-ec5b83  -> ciflow/periodic/sha-ec5b83
2025-03-04T21:02:36.7491857Z  * [new tag]                 ciflow/riscv64/143979       -> ciflow/riscv64/143979
2025-03-04T21:02:36.7493385Z  * [new tag]                 ciflow/rocm/124424          -> ciflow/rocm/124424
2025-03-04T21:02:36.7494305Z  * [new tag]                 ciflow/rocm/134817          -> ciflow/rocm/134817
2025-03-04T21:02:36.7495832Z  * [new tag]                 ciflow/rocm/137136          -> ciflow/rocm/137136
2025-03-04T21:02:36.7496718Z  * [new tag]                 ciflow/rocm/139469          -> ciflow/rocm/139469
2025-03-04T21:02:36.7498021Z  * [new tag]                 ciflow/rocm/139975          -> ciflow/rocm/139975
2025-03-04T21:02:36.7499121Z  * [new tag]                 ciflow/rocm/140989          -> ciflow/rocm/140989
2025-03-04T21:02:36.7500395Z  * [new tag]                 ciflow/rocm/141309          -> ciflow/rocm/141309
2025-03-04T21:02:36.7501119Z  * [new tag]                 ciflow/rocm/141355          -> ciflow/rocm/141355
2025-03-04T21:02:36.7502480Z  * [new tag]                 ciflow/rocm/142097          -> ciflow/rocm/142097
2025-03-04T21:02:36.7504100Z  * [new tag]                 ciflow/rocm/142859          -> ciflow/rocm/142859
2025-03-04T21:02:36.7505037Z  * [new tag]                 ciflow/rocm/143416          -> ciflow/rocm/143416
2025-03-04T21:02:36.7506775Z  * [new tag]                 ciflow/rocm/143971          -> ciflow/rocm/143971
2025-03-04T21:02:36.7508242Z  * [new tag]                 ciflow/rocm/144120          -> ciflow/rocm/144120
2025-03-04T21:02:36.7509753Z  * [new tag]                 ciflow/rocm/144572          -> ciflow/rocm/144572
2025-03-04T21:02:36.7510948Z  * [new tag]                 ciflow/rocm/144664          -> ciflow/rocm/144664
2025-03-04T21:02:36.7512125Z  * [new tag]                 ciflow/rocm/145475          -> ciflow/rocm/145475
2025-03-04T21:02:36.7513454Z  * [new tag]                 ciflow/rocm/145584          -> ciflow/rocm/145584
2025-03-04T21:02:36.7514657Z  * [new tag]                 ciflow/rocm/145685          -> ciflow/rocm/145685
2025-03-04T21:02:36.7515944Z  * [new tag]                 ciflow/rocm/145946          -> ciflow/rocm/145946
2025-03-04T21:02:36.7517219Z  * [new tag]                 ciflow/rocm/146227          -> ciflow/rocm/146227
2025-03-04T21:02:36.7518396Z  * [new tag]                 ciflow/rocm/146264          -> ciflow/rocm/146264
2025-03-04T21:02:36.7519751Z  * [new tag]                 ciflow/rocm/146448          -> ciflow/rocm/146448
2025-03-04T21:02:36.7520875Z  * [new tag]                 ciflow/rocm/146903          -> ciflow/rocm/146903
2025-03-04T21:02:36.7522210Z  * [new tag]                 ciflow/rocm/147034          -> ciflow/rocm/147034
2025-03-04T21:02:36.7523654Z  * [new tag]                 ciflow/rocm/147243          -> ciflow/rocm/147243
2025-03-04T21:02:36.7525463Z  * [new tag]                 ciflow/rocm/147315          -> ciflow/rocm/147315
2025-03-04T21:02:36.7526370Z  * [new tag]                 ciflow/rocm/147320          -> ciflow/rocm/147320
2025-03-04T21:02:36.7528263Z  * [new tag]                 ciflow/rocm/147382          -> ciflow/rocm/147382
2025-03-04T21:02:36.7529508Z  * [new tag]                 ciflow/rocm/147403          -> ciflow/rocm/147403
2025-03-04T21:02:36.7530930Z  * [new tag]                 ciflow/rocm/147452          -> ciflow/rocm/147452
2025-03-04T21:02:36.7531718Z  * [new tag]                 ciflow/rocm/147459          -> ciflow/rocm/147459
2025-03-04T21:02:36.7533359Z  * [new tag]                 ciflow/rocm/147527          -> ciflow/rocm/147527
2025-03-04T21:02:36.7534489Z  * [new tag]                 ciflow/rocm/147619          -> ciflow/rocm/147619
2025-03-04T21:02:36.7536062Z  * [new tag]                 ciflow/rocm/147630          -> ciflow/rocm/147630
2025-03-04T21:02:36.7537265Z  * [new tag]                 ciflow/rocm/147821          -> ciflow/rocm/147821
2025-03-04T21:02:36.7538451Z  * [new tag]                 ciflow/rocm/147904          -> ciflow/rocm/147904
2025-03-04T21:02:36.7539739Z  * [new tag]                 ciflow/rocm/147993          -> ciflow/rocm/147993
2025-03-04T21:02:36.7541020Z  * [new tag]                 ciflow/rocm/148223          -> ciflow/rocm/148223
2025-03-04T21:02:36.7542334Z  * [new tag]                 ciflow/rocm/148228          -> ciflow/rocm/148228
2025-03-04T21:02:36.7543689Z  * [new tag]                 ciflow/rocm/148355          -> ciflow/rocm/148355
2025-03-04T21:02:36.7544997Z  * [new tag]                 ciflow/rocm/148371          -> ciflow/rocm/148371
2025-03-04T21:02:36.7546232Z  * [new tag]                 ciflow/rocm/148394          -> ciflow/rocm/148394
2025-03-04T21:02:36.7547380Z  * [new tag]                 ciflow/rocm/148432          -> ciflow/rocm/148432
2025-03-04T21:02:36.7548661Z  * [new tag]                 ciflow/rocm/148433          -> ciflow/rocm/148433
2025-03-04T21:02:36.7549843Z  * [new tag]                 ciflow/rocm/148437          -> ciflow/rocm/148437
2025-03-04T21:02:36.7551339Z  * [new tag]                 ciflow/s390/142346          -> ciflow/s390/142346
2025-03-04T21:02:36.7552221Z  * [new tag]                 ciflow/s390/143959          -> ciflow/s390/143959
2025-03-04T21:02:36.7553626Z  * [new tag]                 ciflow/s390/148452          -> ciflow/s390/148452
2025-03-04T21:02:36.7555199Z  * [new tag]                 ciflow/slow/01c7106         -> ciflow/slow/01c7106
2025-03-04T21:02:36.7556541Z  * [new tag]                 ciflow/slow/0577043         -> ciflow/slow/0577043
2025-03-04T21:02:36.7558162Z  * [new tag]                 ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym -> ciflow/slow/0d5b74da0cab798fbfdb9caa53fad816999c8386-sdym
2025-03-04T21:02:36.7558911Z  * [new tag]                 ciflow/slow/0e81104         -> ciflow/slow/0e81104
2025-03-04T21:02:36.7560195Z  * [new tag]                 ciflow/slow/139975          -> ciflow/slow/139975
2025-03-04T21:02:36.7561344Z  * [new tag]                 ciflow/slow/146256          -> ciflow/slow/146256
2025-03-04T21:02:36.7562182Z  * [new tag]                 ciflow/slow/146903          -> ciflow/slow/146903
2025-03-04T21:02:36.7563682Z  * [new tag]                 ciflow/slow/1732077         -> ciflow/slow/1732077
2025-03-04T21:02:36.7565398Z  * [new tag]                 ciflow/slow/187eb7c         -> ciflow/slow/187eb7c
2025-03-04T21:02:36.7567148Z  * [new tag]                 ciflow/slow/1faef89         -> ciflow/slow/1faef89
2025-03-04T21:02:36.7568772Z  * [new tag]                 ciflow/slow/3920ec1         -> ciflow/slow/3920ec1
2025-03-04T21:02:36.7570098Z  * [new tag]                 ciflow/slow/3b7c6b2         -> ciflow/slow/3b7c6b2
2025-03-04T21:02:36.7571439Z  * [new tag]                 ciflow/slow/59a3759         -> ciflow/slow/59a3759
2025-03-04T21:02:36.7572724Z  * [new tag]                 ciflow/slow/70ef0bb         -> ciflow/slow/70ef0bb
2025-03-04T21:02:36.7574035Z  * [new tag]                 ciflow/slow/788ff06         -> ciflow/slow/788ff06
2025-03-04T21:02:36.7575817Z  * [new tag]                 ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym -> ciflow/slow/8751002215790a3a88750faa8f4366933e296693-sdym
2025-03-04T21:02:36.7576630Z  * [new tag]                 ciflow/slow/9d85864         -> ciflow/slow/9d85864
2025-03-04T21:02:36.7578130Z  * [new tag]                 ciflow/slow/9ffad5b         -> ciflow/slow/9ffad5b
2025-03-04T21:02:36.7579547Z  * [new tag]                 ciflow/slow/a206e8b         -> ciflow/slow/a206e8b
2025-03-04T21:02:36.7580873Z  * [new tag]                 ciflow/slow/a837609         -> ciflow/slow/a837609
2025-03-04T21:02:36.7583736Z  * [new tag]                 ciflow/slow/af841f3         -> ciflow/slow/af841f3
2025-03-04T21:02:36.7585510Z  * [new tag]                 ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym -> ciflow/slow/da3aba1e46157c4df504b067477cdf2b3c96b194-sdym
2025-03-04T21:02:36.7586405Z  * [new tag]                 ciflow/trunk/108303         -> ciflow/trunk/108303
2025-03-04T21:02:36.7587734Z  * [new tag]                 ciflow/trunk/113257         -> ciflow/trunk/113257
2025-03-04T21:02:36.7588631Z  * [new tag]                 ciflow/trunk/113258         -> ciflow/trunk/113258
2025-03-04T21:02:36.7589923Z  * [new tag]                 ciflow/trunk/120076         -> ciflow/trunk/120076
2025-03-04T21:02:36.7591074Z  * [new tag]                 ciflow/trunk/121445         -> ciflow/trunk/121445
2025-03-04T21:02:36.7591957Z  * [new tag]                 ciflow/trunk/123020         -> ciflow/trunk/123020
2025-03-04T21:02:36.7593236Z  * [new tag]                 ciflow/trunk/124424         -> ciflow/trunk/124424
2025-03-04T21:02:36.7594141Z  * [new tag]                 ciflow/trunk/124490         -> ciflow/trunk/124490
2025-03-04T21:02:36.7595427Z  * [new tag]                 ciflow/trunk/125469         -> ciflow/trunk/125469
2025-03-04T21:02:36.7596322Z  * [new tag]                 ciflow/trunk/125806         -> ciflow/trunk/125806
2025-03-04T21:02:36.7598122Z  * [new tag]                 ciflow/trunk/125888         -> ciflow/trunk/125888
2025-03-04T21:02:36.7599609Z  * [new tag]                 ciflow/trunk/125995         -> ciflow/trunk/125995
2025-03-04T21:02:36.7601082Z  * [new tag]                 ciflow/trunk/126050         -> ciflow/trunk/126050
2025-03-04T21:02:36.7602462Z  * [new tag]                 ciflow/trunk/126054         -> ciflow/trunk/126054
2025-03-04T21:02:36.7603899Z  * [new tag]                 ciflow/trunk/126635         -> ciflow/trunk/126635
2025-03-04T21:02:36.7605163Z  * [new tag]                 ciflow/trunk/127171         -> ciflow/trunk/127171
2025-03-04T21:02:36.7606461Z  * [new tag]                 ciflow/trunk/127919         -> ciflow/trunk/127919
2025-03-04T21:02:36.7607678Z  * [new tag]                 ciflow/trunk/129352         -> ciflow/trunk/129352
2025-03-04T21:02:36.7609012Z  * [new tag]                 ciflow/trunk/129420         -> ciflow/trunk/129420
2025-03-04T21:02:36.7610242Z  * [new tag]                 ciflow/trunk/130141         -> ciflow/trunk/130141
2025-03-04T21:02:36.7611401Z  * [new tag]                 ciflow/trunk/130752         -> ciflow/trunk/130752
2025-03-04T21:02:36.7612576Z  * [new tag]                 ciflow/trunk/131354         -> ciflow/trunk/131354
2025-03-04T21:02:36.7613775Z  * [new tag]                 ciflow/trunk/131507         -> ciflow/trunk/131507
2025-03-04T21:02:36.7614954Z  * [new tag]                 ciflow/trunk/132021         -> ciflow/trunk/132021
2025-03-04T21:02:36.7616111Z  * [new tag]                 ciflow/trunk/133044         -> ciflow/trunk/133044
2025-03-04T21:02:36.7617286Z  * [new tag]                 ciflow/trunk/133289         -> ciflow/trunk/133289
2025-03-04T21:02:36.7618468Z  * [new tag]                 ciflow/trunk/133296         -> ciflow/trunk/133296
2025-03-04T21:02:36.7619646Z  * [new tag]                 ciflow/trunk/133297         -> ciflow/trunk/133297
2025-03-04T21:02:36.7620788Z  * [new tag]                 ciflow/trunk/133315         -> ciflow/trunk/133315
2025-03-04T21:02:36.7621991Z  * [new tag]                 ciflow/trunk/133392         -> ciflow/trunk/133392
2025-03-04T21:02:36.7623151Z  * [new tag]                 ciflow/trunk/133419         -> ciflow/trunk/133419
2025-03-04T21:02:36.7624320Z  * [new tag]                 ciflow/trunk/133423         -> ciflow/trunk/133423
2025-03-04T21:02:36.7625648Z  * [new tag]                 ciflow/trunk/133667         -> ciflow/trunk/133667
2025-03-04T21:02:36.7627027Z  * [new tag]                 ciflow/trunk/133753         -> ciflow/trunk/133753
2025-03-04T21:02:36.7628284Z  * [new tag]                 ciflow/trunk/134219         -> ciflow/trunk/134219
2025-03-04T21:02:36.7629600Z  * [new tag]                 ciflow/trunk/134515         -> ciflow/trunk/134515
2025-03-04T21:02:36.7630786Z  * [new tag]                 ciflow/trunk/135058         -> ciflow/trunk/135058
2025-03-04T21:02:36.7632077Z  * [new tag]                 ciflow/trunk/135631         -> ciflow/trunk/135631
2025-03-04T21:02:36.7633364Z  * [new tag]                 ciflow/trunk/136780         -> ciflow/trunk/136780
2025-03-04T21:02:36.7634693Z  * [new tag]                 ciflow/trunk/136824         -> ciflow/trunk/136824
2025-03-04T21:02:36.7635975Z  * [new tag]                 ciflow/trunk/136835         -> ciflow/trunk/136835
2025-03-04T21:02:36.7637348Z  * [new tag]                 ciflow/trunk/136993         -> ciflow/trunk/136993
2025-03-04T21:02:36.7638534Z  * [new tag]                 ciflow/trunk/137400         -> ciflow/trunk/137400
2025-03-04T21:02:36.7639825Z  * [new tag]                 ciflow/trunk/137580         -> ciflow/trunk/137580
2025-03-04T21:02:36.7640984Z  * [new tag]                 ciflow/trunk/138213         -> ciflow/trunk/138213
2025-03-04T21:02:36.7642151Z  * [new tag]                 ciflow/trunk/138436         -> ciflow/trunk/138436
2025-03-04T21:02:36.7643352Z  * [new tag]                 ciflow/trunk/138626         -> ciflow/trunk/138626
2025-03-04T21:02:36.7644496Z  * [new tag]                 ciflow/trunk/138834         -> ciflow/trunk/138834
2025-03-04T21:02:36.7645683Z  * [new tag]                 ciflow/trunk/138889         -> ciflow/trunk/138889
2025-03-04T21:02:36.7646825Z  * [new tag]                 ciflow/trunk/138996         -> ciflow/trunk/138996
2025-03-04T21:02:36.7648332Z  * [new tag]                 ciflow/trunk/139070         -> ciflow/trunk/139070
2025-03-04T21:02:36.7649365Z  * [new tag]                 ciflow/trunk/139094         -> ciflow/trunk/139094
2025-03-04T21:02:36.7650724Z  * [new tag]                 ciflow/trunk/139971         -> ciflow/trunk/139971
2025-03-04T21:02:36.7651880Z  * [new tag]                 ciflow/trunk/139975         -> ciflow/trunk/139975
2025-03-04T21:02:36.7653086Z  * [new tag]                 ciflow/trunk/140084         -> ciflow/trunk/140084
2025-03-04T21:02:36.7654234Z  * [new tag]                 ciflow/trunk/140159         -> ciflow/trunk/140159
2025-03-04T21:02:36.7655462Z  * [new tag]                 ciflow/trunk/140211         -> ciflow/trunk/140211
2025-03-04T21:02:36.7656627Z  * [new tag]                 ciflow/trunk/140298         -> ciflow/trunk/140298
2025-03-04T21:02:36.7657895Z  * [new tag]                 ciflow/trunk/140323         -> ciflow/trunk/140323
2025-03-04T21:02:36.7658864Z  * [new tag]                 ciflow/trunk/140365         -> ciflow/trunk/140365
2025-03-04T21:02:36.7660328Z  * [new tag]                 ciflow/trunk/140399         -> ciflow/trunk/140399
2025-03-04T21:02:36.7661499Z  * [new tag]                 ciflow/trunk/140793         -> ciflow/trunk/140793
2025-03-04T21:02:36.7662698Z  * [new tag]                 ciflow/trunk/140979         -> ciflow/trunk/140979
2025-03-04T21:02:36.7663839Z  * [new tag]                 ciflow/trunk/140989         -> ciflow/trunk/140989
2025-03-04T21:02:36.7665001Z  * [new tag]                 ciflow/trunk/141178         -> ciflow/trunk/141178
2025-03-04T21:02:36.7666382Z  * [new tag]                 ciflow/trunk/141257         -> ciflow/trunk/141257
2025-03-04T21:02:36.7667593Z  * [new tag]                 ciflow/trunk/141309         -> ciflow/trunk/141309
2025-03-04T21:02:36.7668729Z  * [new tag]                 ciflow/trunk/141730         -> ciflow/trunk/141730
2025-03-04T21:02:36.7670048Z  * [new tag]                 ciflow/trunk/141796         -> ciflow/trunk/141796
2025-03-04T21:02:36.7671234Z  * [new tag]                 ciflow/trunk/141842         -> ciflow/trunk/141842
2025-03-04T21:02:36.7672572Z  * [new tag]                 ciflow/trunk/141910         -> ciflow/trunk/141910
2025-03-04T21:02:36.7673883Z  * [new tag]                 ciflow/trunk/141914         -> ciflow/trunk/141914
2025-03-04T21:02:36.7675035Z  * [new tag]                 ciflow/trunk/141961         -> ciflow/trunk/141961
2025-03-04T21:02:36.7676223Z  * [new tag]                 ciflow/trunk/142091         -> ciflow/trunk/142091
2025-03-04T21:02:36.7677919Z  * [new tag]                 ciflow/trunk/142092         -> ciflow/trunk/142092
2025-03-04T21:02:36.7679089Z  * [new tag]                 ciflow/trunk/142097         -> ciflow/trunk/142097
2025-03-04T21:02:36.7680280Z  * [new tag]                 ciflow/trunk/142179         -> ciflow/trunk/142179
2025-03-04T21:02:36.7681480Z  * [new tag]                 ciflow/trunk/142272         -> ciflow/trunk/142272
2025-03-04T21:02:36.7682677Z  * [new tag]                 ciflow/trunk/142273         -> ciflow/trunk/142273
2025-03-04T21:02:36.7683944Z  * [new tag]                 ciflow/trunk/142326         -> ciflow/trunk/142326
2025-03-04T21:02:36.7685138Z  * [new tag]                 ciflow/trunk/142346         -> ciflow/trunk/142346
2025-03-04T21:02:36.7686334Z  * [new tag]                 ciflow/trunk/142350         -> ciflow/trunk/142350
2025-03-04T21:02:36.7687793Z  * [new tag]                 ciflow/trunk/142372         -> ciflow/trunk/142372
2025-03-04T21:02:36.7689003Z  * [new tag]                 ciflow/trunk/142477         -> ciflow/trunk/142477
2025-03-04T21:02:36.7690362Z  * [new tag]                 ciflow/trunk/142821         -> ciflow/trunk/142821
2025-03-04T21:02:36.7695963Z  * [new tag]                 ciflow/trunk/142859         -> ciflow/trunk/142859
2025-03-04T21:02:36.7696159Z  * [new tag]                 ciflow/trunk/142865         -> ciflow/trunk/142865
2025-03-04T21:02:36.7696338Z  * [new tag]                 ciflow/trunk/143082         -> ciflow/trunk/143082
2025-03-04T21:02:36.7696505Z  * [new tag]                 ciflow/trunk/143093         -> ciflow/trunk/143093
2025-03-04T21:02:36.7696808Z  * [new tag]                 ciflow/trunk/143220         -> ciflow/trunk/143220
2025-03-04T21:02:36.7697744Z  * [new tag]                 ciflow/trunk/143261         -> ciflow/trunk/143261
2025-03-04T21:02:36.7699133Z  * [new tag]                 ciflow/trunk/143303         -> ciflow/trunk/143303
2025-03-04T21:02:36.7700268Z  * [new tag]                 ciflow/trunk/143313         -> ciflow/trunk/143313
2025-03-04T21:02:36.7701667Z  * [new tag]                 ciflow/trunk/143347         -> ciflow/trunk/143347
2025-03-04T21:02:36.7703034Z  * [new tag]                 ciflow/trunk/143402         -> ciflow/trunk/143402
2025-03-04T21:02:36.7704262Z  * [new tag]                 ciflow/trunk/143416         -> ciflow/trunk/143416
2025-03-04T21:02:36.7705632Z  * [new tag]                 ciflow/trunk/143451         -> ciflow/trunk/143451
2025-03-04T21:02:36.7706818Z  * [new tag]                 ciflow/trunk/143475         -> ciflow/trunk/143475
2025-03-04T21:02:36.7708010Z  * [new tag]                 ciflow/trunk/143630         -> ciflow/trunk/143630
2025-03-04T21:02:36.7709164Z  * [new tag]                 ciflow/trunk/143666         -> ciflow/trunk/143666
2025-03-04T21:02:36.7710206Z  * [new tag]                 ciflow/trunk/143671         -> ciflow/trunk/143671
2025-03-04T21:02:36.7711735Z  * [new tag]                 ciflow/trunk/143689         -> ciflow/trunk/143689
2025-03-04T21:02:36.7712909Z  * [new tag]                 ciflow/trunk/143712         -> ciflow/trunk/143712
2025-03-04T21:02:36.7714212Z  * [new tag]                 ciflow/trunk/143733         -> ciflow/trunk/143733
2025-03-04T21:02:36.7715516Z  * [new tag]                 ciflow/trunk/143822         -> ciflow/trunk/143822
2025-03-04T21:02:36.7716836Z  * [new tag]                 ciflow/trunk/143833         -> ciflow/trunk/143833
2025-03-04T21:02:36.7718269Z  * [new tag]                 ciflow/trunk/143894         -> ciflow/trunk/143894
2025-03-04T21:02:36.7719497Z  * [new tag]                 ciflow/trunk/143896         -> ciflow/trunk/143896
2025-03-04T21:02:36.7720805Z  * [new tag]                 ciflow/trunk/143961         -> ciflow/trunk/143961
2025-03-04T21:02:36.7721669Z  * [new tag]                 ciflow/trunk/143966         -> ciflow/trunk/143966
2025-03-04T21:02:36.7723038Z  * [new tag]                 ciflow/trunk/144017         -> ciflow/trunk/144017
2025-03-04T21:02:36.7724308Z  * [new tag]                 ciflow/trunk/144019         -> ciflow/trunk/144019
2025-03-04T21:02:36.7725832Z  * [new tag]                 ciflow/trunk/144120         -> ciflow/trunk/144120
2025-03-04T21:02:36.7726980Z  * [new tag]                 ciflow/trunk/144138         -> ciflow/trunk/144138
2025-03-04T21:02:36.7728415Z  * [new tag]                 ciflow/trunk/144172         -> ciflow/trunk/144172
2025-03-04T21:02:36.7729709Z  * [new tag]                 ciflow/trunk/144177         -> ciflow/trunk/144177
2025-03-04T21:02:36.7731050Z  * [new tag]                 ciflow/trunk/144268         -> ciflow/trunk/144268
2025-03-04T21:02:36.7732523Z  * [new tag]                 ciflow/trunk/144272         -> ciflow/trunk/144272
2025-03-04T21:02:36.7733728Z  * [new tag]                 ciflow/trunk/144293         -> ciflow/trunk/144293
2025-03-04T21:02:36.7734894Z  * [new tag]                 ciflow/trunk/144452         -> ciflow/trunk/144452
2025-03-04T21:02:36.7736285Z  * [new tag]                 ciflow/trunk/144468         -> ciflow/trunk/144468
2025-03-04T21:02:36.7737464Z  * [new tag]                 ciflow/trunk/144557         -> ciflow/trunk/144557
2025-03-04T21:02:36.7738680Z  * [new tag]                 ciflow/trunk/144572         -> ciflow/trunk/144572
2025-03-04T21:02:36.7740085Z  * [new tag]                 ciflow/trunk/144590         -> ciflow/trunk/144590
2025-03-04T21:02:36.7741537Z  * [new tag]                 ciflow/trunk/144616         -> ciflow/trunk/144616
2025-03-04T21:02:36.7742693Z  * [new tag]                 ciflow/trunk/144620         -> ciflow/trunk/144620
2025-03-04T21:02:36.7743866Z  * [new tag]                 ciflow/trunk/144664         -> ciflow/trunk/144664
2025-03-04T21:02:36.7745240Z  * [new tag]                 ciflow/trunk/144708         -> ciflow/trunk/144708
2025-03-04T21:02:36.7746381Z  * [new tag]                 ciflow/trunk/144721         -> ciflow/trunk/144721
2025-03-04T21:02:36.7747577Z  * [new tag]                 ciflow/trunk/144733         -> ciflow/trunk/144733
2025-03-04T21:02:36.7748915Z  * [new tag]                 ciflow/trunk/144763         -> ciflow/trunk/144763
2025-03-04T21:02:36.7750088Z  * [new tag]                 ciflow/trunk/144771         -> ciflow/trunk/144771
2025-03-04T21:02:36.7751393Z  * [new tag]                 ciflow/trunk/144844         -> ciflow/trunk/144844
2025-03-04T21:02:36.7752575Z  * [new tag]                 ciflow/trunk/144880         -> ciflow/trunk/144880
2025-03-04T21:02:36.7753777Z  * [new tag]                 ciflow/trunk/144925         -> ciflow/trunk/144925
2025-03-04T21:02:36.7754965Z  * [new tag]                 ciflow/trunk/144953         -> ciflow/trunk/144953
2025-03-04T21:02:36.7756179Z  * [new tag]                 ciflow/trunk/144975         -> ciflow/trunk/144975
2025-03-04T21:02:36.7757356Z  * [new tag]                 ciflow/trunk/144992         -> ciflow/trunk/144992
2025-03-04T21:02:36.7759031Z  * [new tag]                 ciflow/trunk/145061         -> ciflow/trunk/145061
2025-03-04T21:02:36.7760253Z  * [new tag]                 ciflow/trunk/145116         -> ciflow/trunk/145116
2025-03-04T21:02:36.7761441Z  * [new tag]                 ciflow/trunk/145119         -> ciflow/trunk/145119
2025-03-04T21:02:36.7762750Z  * [new tag]                 ciflow/trunk/145136         -> ciflow/trunk/145136
2025-03-04T21:02:36.7763936Z  * [new tag]                 ciflow/trunk/145153         -> ciflow/trunk/145153
2025-03-04T21:02:36.7765106Z  * [new tag]                 ciflow/trunk/145224         -> ciflow/trunk/145224
2025-03-04T21:02:36.7766305Z  * [new tag]                 ciflow/trunk/145241         -> ciflow/trunk/145241
2025-03-04T21:02:36.7767714Z  * [new tag]                 ciflow/trunk/145254         -> ciflow/trunk/145254
2025-03-04T21:02:36.7768936Z  * [new tag]                 ciflow/trunk/145331         -> ciflow/trunk/145331
2025-03-04T21:02:36.7770235Z  * [new tag]                 ciflow/trunk/145406         -> ciflow/trunk/145406
2025-03-04T21:02:36.7771402Z  * [new tag]                 ciflow/trunk/145523         -> ciflow/trunk/145523
2025-03-04T21:02:36.7772585Z  * [new tag]                 ciflow/trunk/145559         -> ciflow/trunk/145559
2025-03-04T21:02:36.7773944Z  * [new tag]                 ciflow/trunk/145677         -> ciflow/trunk/145677
2025-03-04T21:02:36.7775157Z  * [new tag]                 ciflow/trunk/145717         -> ciflow/trunk/145717
2025-03-04T21:02:36.7776321Z  * [new tag]                 ciflow/trunk/145936         -> ciflow/trunk/145936
2025-03-04T21:02:36.7777550Z  * [new tag]                 ciflow/trunk/145946         -> ciflow/trunk/145946
2025-03-04T21:02:36.7778743Z  * [new tag]                 ciflow/trunk/145979         -> ciflow/trunk/145979
2025-03-04T21:02:36.7779919Z  * [new tag]                 ciflow/trunk/146051         -> ciflow/trunk/146051
2025-03-04T21:02:36.7781255Z  * [new tag]                 ciflow/trunk/146069         -> ciflow/trunk/146069
2025-03-04T21:02:36.7782572Z  * [new tag]                 ciflow/trunk/146090         -> ciflow/trunk/146090
2025-03-04T21:02:36.7783758Z  * [new tag]                 ciflow/trunk/146098         -> ciflow/trunk/146098
2025-03-04T21:02:36.7784973Z  * [new tag]                 ciflow/trunk/146115         -> ciflow/trunk/146115
2025-03-04T21:02:36.7786159Z  * [new tag]                 ciflow/trunk/146176         -> ciflow/trunk/146176
2025-03-04T21:02:36.7787526Z  * [new tag]                 ciflow/trunk/146182         -> ciflow/trunk/146182
2025-03-04T21:02:36.7788736Z  * [new tag]                 ciflow/trunk/146256         -> ciflow/trunk/146256
2025-03-04T21:02:36.7789949Z  * [new tag]                 ciflow/trunk/146275         -> ciflow/trunk/146275
2025-03-04T21:02:36.7791248Z  * [new tag]                 ciflow/trunk/146289         -> ciflow/trunk/146289
2025-03-04T21:02:36.7792479Z  * [new tag]                 ciflow/trunk/146335         -> ciflow/trunk/146335
2025-03-04T21:02:36.7793669Z  * [new tag]                 ciflow/trunk/146421         -> ciflow/trunk/146421
2025-03-04T21:02:36.7795076Z  * [new tag]                 ciflow/trunk/146489         -> ciflow/trunk/146489
2025-03-04T21:02:36.7796372Z  * [new tag]                 ciflow/trunk/146517         -> ciflow/trunk/146517
2025-03-04T21:02:36.7797575Z  * [new tag]                 ciflow/trunk/146530         -> ciflow/trunk/146530
2025-03-04T21:02:36.7798767Z  * [new tag]                 ciflow/trunk/146561         -> ciflow/trunk/146561
2025-03-04T21:02:36.7799961Z  * [new tag]                 ciflow/trunk/146573         -> ciflow/trunk/146573
2025-03-04T21:02:36.7801254Z  * [new tag]                 ciflow/trunk/146582         -> ciflow/trunk/146582
2025-03-04T21:02:36.7802486Z  * [new tag]                 ciflow/trunk/146661         -> ciflow/trunk/146661
2025-03-04T21:02:36.7803649Z  * [new tag]                 ciflow/trunk/146718         -> ciflow/trunk/146718
2025-03-04T21:02:36.7804817Z  * [new tag]                 ciflow/trunk/146777         -> ciflow/trunk/146777
2025-03-04T21:02:36.7806216Z  * [new tag]                 ciflow/trunk/146807         -> ciflow/trunk/146807
2025-03-04T21:02:36.7807431Z  * [new tag]                 ciflow/trunk/146823         -> ciflow/trunk/146823
2025-03-04T21:02:36.7808858Z  * [new tag]                 ciflow/trunk/146826         -> ciflow/trunk/146826
2025-03-04T21:02:36.7810025Z  * [new tag]                 ciflow/trunk/146827         -> ciflow/trunk/146827
2025-03-04T21:02:36.7811207Z  * [new tag]                 ciflow/trunk/146845         -> ciflow/trunk/146845
2025-03-04T21:02:36.7812471Z  * [new tag]                 ciflow/trunk/146870         -> ciflow/trunk/146870
2025-03-04T21:02:36.7813581Z  * [new tag]                 ciflow/trunk/146873         -> ciflow/trunk/146873
2025-03-04T21:02:36.7814863Z  * [new tag]                 ciflow/trunk/146874         -> ciflow/trunk/146874
2025-03-04T21:02:36.7815754Z  * [new tag]                 ciflow/trunk/146903         -> ciflow/trunk/146903
2025-03-04T21:02:36.7817144Z  * [new tag]                 ciflow/trunk/146928         -> ciflow/trunk/146928
2025-03-04T21:02:36.7818541Z  * [new tag]                 ciflow/trunk/146970         -> ciflow/trunk/146970
2025-03-04T21:02:36.7819736Z  * [new tag]                 ciflow/trunk/147014         -> ciflow/trunk/147014
2025-03-04T21:02:36.7820937Z  * [new tag]                 ciflow/trunk/147072         -> ciflow/trunk/147072
2025-03-04T21:02:36.7822117Z  * [new tag]                 ciflow/trunk/147105         -> ciflow/trunk/147105
2025-03-04T21:02:36.7823288Z  * [new tag]                 ciflow/trunk/147155         -> ciflow/trunk/147155
2025-03-04T21:02:36.7824474Z  * [new tag]                 ciflow/trunk/147272         -> ciflow/trunk/147272
2025-03-04T21:02:36.7828575Z  * [new tag]                 ciflow/trunk/147314         -> ciflow/trunk/147314
2025-03-04T21:02:36.7829772Z  * [new tag]                 ciflow/trunk/147320         -> ciflow/trunk/147320
2025-03-04T21:02:36.7831077Z  * [new tag]                 ciflow/trunk/147334         -> ciflow/trunk/147334
2025-03-04T21:02:36.7832279Z  * [new tag]                 ciflow/trunk/147349         -> ciflow/trunk/147349
2025-03-04T21:02:36.7833455Z  * [new tag]                 ciflow/trunk/147368         -> ciflow/trunk/147368
2025-03-04T21:02:36.7834636Z  * [new tag]                 ciflow/trunk/147403         -> ciflow/trunk/147403
2025-03-04T21:02:36.7835874Z  * [new tag]                 ciflow/trunk/147422         -> ciflow/trunk/147422
2025-03-04T21:02:36.7837052Z  * [new tag]                 ciflow/trunk/147448         -> ciflow/trunk/147448
2025-03-04T21:02:36.7838242Z  * [new tag]                 ciflow/trunk/147452         -> ciflow/trunk/147452
2025-03-04T21:02:36.7839433Z  * [new tag]                 ciflow/trunk/147481         -> ciflow/trunk/147481
2025-03-04T21:02:36.7841081Z  * [new tag]                 ciflow/trunk/147498         -> ciflow/trunk/147498
2025-03-04T21:02:36.7842388Z  * [new tag]                 ciflow/trunk/147518         -> ciflow/trunk/147518
2025-03-04T21:02:36.7843572Z  * [new tag]                 ciflow/trunk/147574         -> ciflow/trunk/147574
2025-03-04T21:02:36.7844745Z  * [new tag]                 ciflow/trunk/147583         -> ciflow/trunk/147583
2025-03-04T21:02:36.7845959Z  * [new tag]                 ciflow/trunk/147660         -> ciflow/trunk/147660
2025-03-04T21:02:36.7847186Z  * [new tag]                 ciflow/trunk/147664         -> ciflow/trunk/147664
2025-03-04T21:02:36.7848506Z  * [new tag]                 ciflow/trunk/147741         -> ciflow/trunk/147741
2025-03-04T21:02:36.7849849Z  * [new tag]                 ciflow/trunk/147742         -> ciflow/trunk/147742
2025-03-04T21:02:36.7851076Z  * [new tag]                 ciflow/trunk/147752         -> ciflow/trunk/147752
2025-03-04T21:02:36.7852264Z  * [new tag]                 ciflow/trunk/147797         -> ciflow/trunk/147797
2025-03-04T21:02:36.7853514Z  * [new tag]                 ciflow/trunk/147798         -> ciflow/trunk/147798
2025-03-04T21:02:36.7854861Z  * [new tag]                 ciflow/trunk/147808         -> ciflow/trunk/147808
2025-03-04T21:02:36.7856039Z  * [new tag]                 ciflow/trunk/147817         -> ciflow/trunk/147817
2025-03-04T21:02:36.7857658Z  * [new tag]                 ciflow/trunk/147820         -> ciflow/trunk/147820
2025-03-04T21:02:36.7858877Z  * [new tag]                 ciflow/trunk/147821         -> ciflow/trunk/147821
2025-03-04T21:02:36.7860043Z  * [new tag]                 ciflow/trunk/147836         -> ciflow/trunk/147836
2025-03-04T21:02:36.7861232Z  * [new tag]                 ciflow/trunk/147862         -> ciflow/trunk/147862
2025-03-04T21:02:36.7862397Z  * [new tag]                 ciflow/trunk/147870         -> ciflow/trunk/147870
2025-03-04T21:02:36.7863628Z  * [new tag]                 ciflow/trunk/147881         -> ciflow/trunk/147881
2025-03-04T21:02:36.7864984Z  * [new tag]                 ciflow/trunk/147897         -> ciflow/trunk/147897
2025-03-04T21:02:36.7865853Z  * [new tag]                 ciflow/trunk/147910         -> ciflow/trunk/147910
2025-03-04T21:02:36.7867226Z  * [new tag]                 ciflow/trunk/147917         -> ciflow/trunk/147917
2025-03-04T21:02:36.7868403Z  * [new tag]                 ciflow/trunk/147945         -> ciflow/trunk/147945
2025-03-04T21:02:36.7869627Z  * [new tag]                 ciflow/trunk/147955         -> ciflow/trunk/147955
2025-03-04T21:02:36.7870755Z  * [new tag]                 ciflow/trunk/147956         -> ciflow/trunk/147956
2025-03-04T21:02:36.7871930Z  * [new tag]                 ciflow/trunk/147957         -> ciflow/trunk/147957
2025-03-04T21:02:36.7873102Z  * [new tag]                 ciflow/trunk/147958         -> ciflow/trunk/147958
2025-03-04T21:02:36.7874314Z  * [new tag]                 ciflow/trunk/147959         -> ciflow/trunk/147959
2025-03-04T21:02:36.7875506Z  * [new tag]                 ciflow/trunk/147962         -> ciflow/trunk/147962
2025-03-04T21:02:36.7876680Z  * [new tag]                 ciflow/trunk/147964         -> ciflow/trunk/147964
2025-03-04T21:02:36.7877866Z  * [new tag]                 ciflow/trunk/147994         -> ciflow/trunk/147994
2025-03-04T21:02:36.7879079Z  * [new tag]                 ciflow/trunk/147997         -> ciflow/trunk/147997
2025-03-04T21:02:36.7880387Z  * [new tag]                 ciflow/trunk/148049         -> ciflow/trunk/148049
2025-03-04T21:02:36.7881621Z  * [new tag]                 ciflow/trunk/148076         -> ciflow/trunk/148076
2025-03-04T21:02:36.7882838Z  * [new tag]                 ciflow/trunk/148083         -> ciflow/trunk/148083
2025-03-04T21:02:36.7884117Z  * [new tag]                 ciflow/trunk/148131         -> ciflow/trunk/148131
2025-03-04T21:02:36.7886446Z  * [new tag]                 ciflow/trunk/148163         -> ciflow/trunk/148163
2025-03-04T21:02:36.7886776Z  * [new tag]                 ciflow/trunk/148173         -> ciflow/trunk/148173
2025-03-04T21:02:36.7887754Z  * [new tag]                 ciflow/trunk/148180         -> ciflow/trunk/148180
2025-03-04T21:02:36.7888778Z  * [new tag]                 ciflow/trunk/148188         -> ciflow/trunk/148188
2025-03-04T21:02:36.7890268Z  * [new tag]                 ciflow/trunk/148261         -> ciflow/trunk/148261
2025-03-04T21:02:36.7891394Z  * [new tag]                 ciflow/trunk/148266         -> ciflow/trunk/148266
2025-03-04T21:02:36.7892571Z  * [new tag]                 ciflow/trunk/148279         -> ciflow/trunk/148279
2025-03-04T21:02:36.7893882Z  * [new tag]                 ciflow/trunk/148281         -> ciflow/trunk/148281
2025-03-04T21:02:36.7895093Z  * [new tag]                 ciflow/trunk/148290         -> ciflow/trunk/148290
2025-03-04T21:02:36.7896257Z  * [new tag]                 ciflow/trunk/148292         -> ciflow/trunk/148292
2025-03-04T21:02:36.7897452Z  * [new tag]                 ciflow/trunk/148305         -> ciflow/trunk/148305
2025-03-04T21:02:36.7898655Z  * [new tag]                 ciflow/trunk/148343         -> ciflow/trunk/148343
2025-03-04T21:02:36.7899864Z  * [new tag]                 ciflow/trunk/148350         -> ciflow/trunk/148350
2025-03-04T21:02:36.7901058Z  * [new tag]                 ciflow/trunk/148364         -> ciflow/trunk/148364
2025-03-04T21:02:36.7902224Z  * [new tag]                 ciflow/trunk/148366         -> ciflow/trunk/148366
2025-03-04T21:02:36.7903414Z  * [new tag]                 ciflow/trunk/148371         -> ciflow/trunk/148371
2025-03-04T21:02:36.7904650Z  * [new tag]                 ciflow/trunk/148388         -> ciflow/trunk/148388
2025-03-04T21:02:36.7905913Z  * [new tag]                 ciflow/trunk/148421         -> ciflow/trunk/148421
2025-03-04T21:02:36.7907141Z  * [new tag]                 ciflow/trunk/148423         -> ciflow/trunk/148423
2025-03-04T21:02:36.7908296Z  * [new tag]                 ciflow/trunk/148450         -> ciflow/trunk/148450
2025-03-04T21:02:36.7909639Z  * [new tag]                 ciflow/trunk/148480         -> ciflow/trunk/148480
2025-03-04T21:02:36.7910520Z  * [new tag]                 ciflow/trunk/148483         -> ciflow/trunk/148483
2025-03-04T21:02:36.7912009Z  * [new tag]                 ciflow/trunk/70978          -> ciflow/trunk/70978
2025-03-04T21:02:36.7913284Z  * [new tag]                 ciflow/trunk/70979          -> ciflow/trunk/70979
2025-03-04T21:02:36.7914980Z  * [new tag]                 ciflow/unstable/123         -> ciflow/unstable/123
2025-03-04T21:02:36.7916193Z  * [new tag]                 ciflow/unstable/146104      -> ciflow/unstable/146104
2025-03-04T21:02:36.7917103Z  * [new tag]                 ciflow/unstable/146264      -> ciflow/unstable/146264
2025-03-04T21:02:36.7918344Z  * [new tag]                 ciflow/unstable/147320      -> ciflow/unstable/147320
2025-03-04T21:02:36.7919789Z  * [new tag]                 ciflow/xpu/137566           -> ciflow/xpu/137566
2025-03-04T21:02:36.7920652Z  * [new tag]                 ciflow/xpu/137580           -> ciflow/xpu/137580
2025-03-04T21:02:36.7921899Z  * [new tag]                 ciflow/xpu/138889           -> ciflow/xpu/138889
2025-03-04T21:02:36.7922990Z  * [new tag]                 ciflow/xpu/138996           -> ciflow/xpu/138996
2025-03-04T21:02:36.7923978Z  * [new tag]                 ciflow/xpu/139469           -> ciflow/xpu/139469
2025-03-04T21:02:36.7925430Z  * [new tag]                 ciflow/xpu/139971           -> ciflow/xpu/139971
2025-03-04T21:02:36.7926748Z  * [new tag]                 ciflow/xpu/140365           -> ciflow/xpu/140365
2025-03-04T21:02:36.7927664Z  * [new tag]                 ciflow/xpu/140372           -> ciflow/xpu/140372
2025-03-04T21:02:36.7928970Z  * [new tag]                 ciflow/xpu/140686           -> ciflow/xpu/140686
2025-03-04T21:02:36.7930257Z  * [new tag]                 ciflow/xpu/140972           -> ciflow/xpu/140972
2025-03-04T21:02:36.7931357Z  * [new tag]                 ciflow/xpu/142040           -> ciflow/xpu/142040
2025-03-04T21:02:36.7932945Z  * [new tag]                 ciflow/xpu/142097           -> ciflow/xpu/142097
2025-03-04T21:02:36.7934398Z  * [new tag]                 ciflow/xpu/143597           -> ciflow/xpu/143597
2025-03-04T21:02:36.7935758Z  * [new tag]                 ciflow/xpu/143833           -> ciflow/xpu/143833
2025-03-04T21:02:36.7937067Z  * [new tag]                 ciflow/xpu/144240           -> ciflow/xpu/144240
2025-03-04T21:02:36.7938254Z  * [new tag]                 ciflow/xpu/144452           -> ciflow/xpu/144452
2025-03-04T21:02:36.7939411Z  * [new tag]                 ciflow/xpu/144664           -> ciflow/xpu/144664
2025-03-04T21:02:36.7941041Z  * [new tag]                 ciflow/xpu/146098           -> ciflow/xpu/146098
2025-03-04T21:02:36.7942350Z  * [new tag]                 ciflow/xpu/147161           -> ciflow/xpu/147161
2025-03-04T21:02:36.7943523Z  * [new tag]                 ciflow/xpu/147349           -> ciflow/xpu/147349
2025-03-04T21:02:36.7944853Z  * [new tag]                 ciflow/xpu/147355           -> ciflow/xpu/147355
2025-03-04T21:02:36.7946048Z  * [new tag]                 ciflow/xpu/147403           -> ciflow/xpu/147403
2025-03-04T21:02:36.7947454Z  * [new tag]                 ciflow/xpu/147448           -> ciflow/xpu/147448
2025-03-04T21:02:36.7948180Z  * [new tag]                 ciflow/xpu/147498           -> ciflow/xpu/147498
2025-03-04T21:02:36.7949519Z  * [new tag]                 ciflow/xpu/147507           -> ciflow/xpu/147507
2025-03-04T21:02:36.7950718Z  * [new tag]                 ciflow/xpu/147583           -> ciflow/xpu/147583
2025-03-04T21:02:36.7952021Z  * [new tag]                 ciflow/xpu/147593           -> ciflow/xpu/147593
2025-03-04T21:02:36.7953155Z  * [new tag]                 ciflow/xpu/147664           -> ciflow/xpu/147664
2025-03-04T21:02:36.7954344Z  * [new tag]                 ciflow/xpu/147727           -> ciflow/xpu/147727
2025-03-04T21:02:36.7955503Z  * [new tag]                 ciflow/xpu/147821           -> ciflow/xpu/147821
2025-03-04T21:02:36.7956816Z  * [new tag]                 ciflow/xpu/147945           -> ciflow/xpu/147945
2025-03-04T21:02:36.7957636Z  * [new tag]                 ciflow/xpu/147955           -> ciflow/xpu/147955
2025-03-04T21:02:36.7958975Z  * [new tag]                 ciflow/xpu/147956           -> ciflow/xpu/147956
2025-03-04T21:02:36.7960144Z  * [new tag]                 ciflow/xpu/147957           -> ciflow/xpu/147957
2025-03-04T21:02:36.7961307Z  * [new tag]                 ciflow/xpu/147958           -> ciflow/xpu/147958
2025-03-04T21:02:36.7962467Z  * [new tag]                 ciflow/xpu/147959           -> ciflow/xpu/147959
2025-03-04T21:02:36.7963618Z  * [new tag]                 ciflow/xpu/147962           -> ciflow/xpu/147962
2025-03-04T21:02:36.7964778Z  * [new tag]                 ciflow/xpu/148076           -> ciflow/xpu/148076
2025-03-04T21:02:36.7966237Z  * [new tag]                 ciflow/xpu/148081           -> ciflow/xpu/148081
2025-03-04T21:02:36.7967438Z  * [new tag]                 ciflow/xpu/148305           -> ciflow/xpu/148305
2025-03-04T21:02:36.7968827Z  * [new tag]                 ciflow/xpu/148313           -> ciflow/xpu/148313
2025-03-04T21:02:36.7969976Z  * [new tag]                 ciflow/xpu/148366           -> ciflow/xpu/148366
2025-03-04T21:02:36.7971120Z  * [new tag]                 ciflow/xpu/148403           -> ciflow/xpu/148403
2025-03-04T21:02:36.7972284Z  * [new tag]                 ciflow/xpu/148423           -> ciflow/xpu/148423
2025-03-04T21:02:36.7973498Z  * [new tag]                 cslpull75                   -> cslpull75
2025-03-04T21:02:36.7974641Z  * [new tag]                 cslpull76                   -> cslpull76
2025-03-04T21:02:36.7975797Z  * [new tag]                 cslpull77                   -> cslpull77
2025-03-04T21:02:36.7976960Z  * [new tag]                 cslpull78                   -> cslpull78
2025-03-04T21:02:36.7978428Z  * [new tag]                 cslpull79                   -> cslpull79
2025-03-04T21:02:36.7979966Z  * [new tag]                 cslpull80                   -> cslpull80
2025-03-04T21:02:36.7981354Z  * [new tag]                 cslpull81                   -> cslpull81
2025-03-04T21:02:36.7982532Z  * [new tag]                 cslpull82                   -> cslpull82
2025-03-04T21:02:36.7983808Z  * [new tag]                 cslpull83                   -> cslpull83
2025-03-04T21:02:36.7985011Z  * [new tag]                 cslpull84                   -> cslpull84
2025-03-04T21:02:36.7986201Z  * [new tag]                 cslpull85                   -> cslpull85
2025-03-04T21:02:36.7987485Z  * [new tag]                 cslpull86                   -> cslpull86
2025-03-04T21:02:36.7988782Z  * [new tag]                 cslpull87                   -> cslpull87
2025-03-04T21:02:36.7990026Z  * [new tag]                 cslpull88                   -> cslpull88
2025-03-04T21:02:36.7991253Z  * [new tag]                 cslpull89                   -> cslpull89
2025-03-04T21:02:36.7992115Z  * [new tag]                 cslpull90                   -> cslpull90
2025-03-04T21:02:36.7993932Z  * [new tag]                 cslpull91                   -> cslpull91
2025-03-04T21:02:36.7995228Z  * [new tag]                 cslpull92                   -> cslpull92
2025-03-04T21:02:36.7996468Z  * [new tag]                 flight_5                    -> flight_5
2025-03-04T21:02:36.7997785Z  * [new tag]                 flight_5.1                  -> flight_5.1
2025-03-04T21:02:36.7999051Z  * [new tag]                 flight_5.2                  -> flight_5.2
2025-03-04T21:02:36.8000223Z  * [new tag]                 flight_5.3                  -> flight_5.3
2025-03-04T21:02:36.8001434Z  * [new tag]                 forpull1                    -> forpull1
2025-03-04T21:02:36.8002959Z  * [new tag]                 malfet/tag-2ef5611          -> malfet/tag-2ef5611
2025-03-04T21:02:36.8004235Z  * [new tag]                 malfet/tag-317b1a0          -> malfet/tag-317b1a0
2025-03-04T21:02:36.8005520Z  * [new tag]                 malfet/tag-ec6f767          -> malfet/tag-ec6f767
2025-03-04T21:02:36.8006959Z  * [new tag]                 nightly-binary              -> nightly-binary
2025-03-04T21:02:36.8007833Z  * [new tag]                 sqzhang_flight4_plus        -> sqzhang_flight4_plus
2025-03-04T21:02:36.8009373Z  * [new tag]                 sqzhang_flight_3            -> sqzhang_flight_3
2025-03-04T21:02:36.8010620Z  * [new tag]                 v0.1.1                      -> v0.1.1
2025-03-04T21:02:36.8011864Z  * [new tag]                 v0.1.10                     -> v0.1.10
2025-03-04T21:02:36.8013128Z  * [new tag]                 v0.1.11                     -> v0.1.11
2025-03-04T21:02:36.8014364Z  * [new tag]                 v0.1.12                     -> v0.1.12
2025-03-04T21:02:36.8015537Z  * [new tag]                 v0.1.2                      -> v0.1.2
2025-03-04T21:02:36.8016782Z  * [new tag]                 v0.1.3                      -> v0.1.3
2025-03-04T21:02:36.8018033Z  * [new tag]                 v0.1.4                      -> v0.1.4
2025-03-04T21:02:36.8019269Z  * [new tag]                 v0.1.5                      -> v0.1.5
2025-03-04T21:02:36.8020560Z  * [new tag]                 v0.1.6                      -> v0.1.6
2025-03-04T21:02:36.8021723Z  * [new tag]                 v0.1.7                      -> v0.1.7
2025-03-04T21:02:36.8022978Z  * [new tag]                 v0.1.8                      -> v0.1.8
2025-03-04T21:02:36.8024715Z  * [new tag]                 v0.1.9                      -> v0.1.9
2025-03-04T21:02:36.8026200Z  * [new tag]                 v0.2.0                      -> v0.2.0
2025-03-04T21:02:36.8027453Z  * [new tag]                 v0.3.0                      -> v0.3.0
2025-03-04T21:02:36.8028839Z  * [new tag]                 v0.3.1                      -> v0.3.1
2025-03-04T21:02:36.8030110Z  * [new tag]                 v0.4.0                      -> v0.4.0
2025-03-04T21:02:36.8031339Z  * [new tag]                 v0.4.1                      -> v0.4.1
2025-03-04T21:02:36.8032569Z  * [new tag]                 v1.0.0                      -> v1.0.0
2025-03-04T21:02:36.8033896Z  * [new tag]                 v1.0.0a0                    -> v1.0.0a0
2025-03-04T21:02:36.8035134Z  * [new tag]                 v1.0.1                      -> v1.0.1
2025-03-04T21:02:36.8036429Z  * [new tag]                 v1.0rc0                     -> v1.0rc0
2025-03-04T21:02:36.8037511Z  * [new tag]                 v1.0rc1                     -> v1.0rc1
2025-03-04T21:02:36.8038767Z  * [new tag]                 v1.1.0                      -> v1.1.0
2025-03-04T21:02:36.8040070Z  * [new tag]                 v1.1.0a0                    -> v1.1.0a0
2025-03-04T21:02:36.8041512Z  * [new tag]                 v1.10.0                     -> v1.10.0
2025-03-04T21:02:36.8042849Z  * [new tag]                 v1.10.0-rc1                 -> v1.10.0-rc1
2025-03-04T21:02:36.8044192Z  * [new tag]                 v1.10.0-rc2                 -> v1.10.0-rc2
2025-03-04T21:02:36.8045063Z  * [new tag]                 v1.10.0-rc3                 -> v1.10.0-rc3
2025-03-04T21:02:36.8046677Z  * [new tag]                 v1.10.1                     -> v1.10.1
2025-03-04T21:02:36.8048069Z  * [new tag]                 v1.10.1-rc1                 -> v1.10.1-rc1
2025-03-04T21:02:36.8048749Z  * [new tag]                 v1.10.2                     -> v1.10.2
2025-03-04T21:02:36.8050057Z  * [new tag]                 v1.10.2-rc1                 -> v1.10.2-rc1
2025-03-04T21:02:36.8051396Z  * [new tag]                 v1.11.0                     -> v1.11.0
2025-03-04T21:02:36.8052766Z  * [new tag]                 v1.11.0-rc1                 -> v1.11.0-rc1
2025-03-04T21:02:36.8054184Z  * [new tag]                 v1.11.0-rc2                 -> v1.11.0-rc2
2025-03-04T21:02:36.8055527Z  * [new tag]                 v1.11.0-rc3                 -> v1.11.0-rc3
2025-03-04T21:02:36.8056851Z  * [new tag]                 v1.11.0-rc4                 -> v1.11.0-rc4
2025-03-04T21:02:36.8058132Z  * [new tag]                 v1.11.0-rc5                 -> v1.11.0-rc5
2025-03-04T21:02:36.8059331Z  * [new tag]                 v1.11.0-rc6                 -> v1.11.0-rc6
2025-03-04T21:02:36.8060374Z  * [new tag]                 v1.11.0-rc7                 -> v1.11.0-rc7
2025-03-04T21:02:36.8061754Z  * [new tag]                 v1.12.0                     -> v1.12.0
2025-03-04T21:02:36.8063009Z  * [new tag]                 v1.12.0-rc1                 -> v1.12.0-rc1
2025-03-04T21:02:36.8064337Z  * [new tag]                 v1.12.0-rc2                 -> v1.12.0-rc2
2025-03-04T21:02:36.8065651Z  * [new tag]                 v1.12.0-rc3                 -> v1.12.0-rc3
2025-03-04T21:02:36.8066947Z  * [new tag]                 v1.12.0-rc4                 -> v1.12.0-rc4
2025-03-04T21:02:36.8068261Z  * [new tag]                 v1.12.0-rc5                 -> v1.12.0-rc5
2025-03-04T21:02:36.8069615Z  * [new tag]                 v1.12.0-rc6                 -> v1.12.0-rc6
2025-03-04T21:02:36.8070715Z  * [new tag]                 v1.12.0-rc7                 -> v1.12.0-rc7
2025-03-04T21:02:36.8071585Z  * [new tag]                 v1.12.0-rc8                 -> v1.12.0-rc8
2025-03-04T21:02:36.8072841Z  * [new tag]                 v1.12.1                     -> v1.12.1
2025-03-04T21:02:36.8074293Z  * [new tag]                 v1.12.1-rc1                 -> v1.12.1-rc1
2025-03-04T21:02:36.8075543Z  * [new tag]                 v1.12.1-rc2                 -> v1.12.1-rc2
2025-03-04T21:02:36.8076903Z  * [new tag]                 v1.12.1-rc3                 -> v1.12.1-rc3
2025-03-04T21:02:36.8078276Z  * [new tag]                 v1.12.1-rc4                 -> v1.12.1-rc4
2025-03-04T21:02:36.8079133Z  * [new tag]                 v1.12.1-rc5                 -> v1.12.1-rc5
2025-03-04T21:02:36.8080663Z  * [new tag]                 v1.13.0                     -> v1.13.0
2025-03-04T21:02:36.8081939Z  * [new tag]                 v1.13.0-rc1                 -> v1.13.0-rc1
2025-03-04T21:02:36.8083209Z  * [new tag]                 v1.13.0-rc2                 -> v1.13.0-rc2
2025-03-04T21:02:36.8084537Z  * [new tag]                 v1.13.0-rc3                 -> v1.13.0-rc3
2025-03-04T21:02:36.8085886Z  * [new tag]                 v1.13.0-rc4                 -> v1.13.0-rc4
2025-03-04T21:02:36.8087041Z  * [new tag]                 v1.13.0-rc5                 -> v1.13.0-rc5
2025-03-04T21:02:36.8088282Z  * [new tag]                 v1.13.0-rc6                 -> v1.13.0-rc6
2025-03-04T21:02:36.8089628Z  * [new tag]                 v1.13.1                     -> v1.13.1
2025-03-04T21:02:36.8090602Z  * [new tag]                 v1.13.1-rc1                 -> v1.13.1-rc1
2025-03-04T21:02:36.8091967Z  * [new tag]                 v1.2.0                      -> v1.2.0
2025-03-04T21:02:36.8093277Z  * [new tag]                 v1.2.0a0                    -> v1.2.0a0
2025-03-04T21:02:36.8094554Z  * [new tag]                 v1.3.0                      -> v1.3.0
2025-03-04T21:02:36.8095780Z  * [new tag]                 v1.3.0a0                    -> v1.3.0a0
2025-03-04T21:02:36.8096853Z  * [new tag]                 v1.3.1                      -> v1.3.1
2025-03-04T21:02:36.8098193Z  * [new tag]                 v1.4.0                      -> v1.4.0
2025-03-04T21:02:36.8099477Z  * [new tag]                 v1.4.0a0                    -> v1.4.0a0
2025-03-04T21:02:36.8100577Z  * [new tag]                 v1.4.1                      -> v1.4.1
2025-03-04T21:02:36.8101957Z  * [new tag]                 v1.5.0                      -> v1.5.0
2025-03-04T21:02:36.8103348Z  * [new tag]                 v1.5.0-rc1                  -> v1.5.0-rc1
2025-03-04T21:02:36.8104665Z  * [new tag]                 v1.5.0-rc2                  -> v1.5.0-rc2
2025-03-04T21:02:36.8106662Z  * [new tag]                 v1.5.0-rc3                  -> v1.5.0-rc3
2025-03-04T21:02:36.8107872Z  * [new tag]                 v1.5.0-rc4                  -> v1.5.0-rc4
2025-03-04T21:02:36.8108745Z  * [new tag]                 v1.5.0-rc5                  -> v1.5.0-rc5
2025-03-04T21:02:36.8110259Z  * [new tag]                 v1.5.1                      -> v1.5.1
2025-03-04T21:02:36.8111366Z  * [new tag]                 v1.5.1-rc1                  -> v1.5.1-rc1
2025-03-04T21:02:36.8112818Z  * [new tag]                 v1.6.0                      -> v1.6.0
2025-03-04T21:02:36.8114040Z  * [new tag]                 v1.6.0-rc1                  -> v1.6.0-rc1
2025-03-04T21:02:36.8115397Z  * [new tag]                 v1.6.0-rc2                  -> v1.6.0-rc2
2025-03-04T21:02:36.8116728Z  * [new tag]                 v1.6.0-rc3                  -> v1.6.0-rc3
2025-03-04T21:02:36.8118001Z  * [new tag]                 v1.6.0-rc4                  -> v1.6.0-rc4
2025-03-04T21:02:36.8119290Z  * [new tag]                 v1.6.0-rc5                  -> v1.6.0-rc5
2025-03-04T21:02:36.8120616Z  * [new tag]                 v1.6.0-rc6                  -> v1.6.0-rc6
2025-03-04T21:02:36.8121725Z  * [new tag]                 v1.6.0-rc7                  -> v1.6.0-rc7
2025-03-04T21:02:36.8122998Z  * [new tag]                 v1.7.0                      -> v1.7.0
2025-03-04T21:02:36.8124376Z  * [new tag]                 v1.7.0-rc1                  -> v1.7.0-rc1
2025-03-04T21:02:36.8126048Z  * [new tag]                 v1.7.0-rc2                  -> v1.7.0-rc2
2025-03-04T21:02:36.8127282Z  * [new tag]                 v1.7.0-rc3                  -> v1.7.0-rc3
2025-03-04T21:02:36.8128600Z  * [new tag]                 v1.7.0-rc4                  -> v1.7.0-rc4
2025-03-04T21:02:36.8129901Z  * [new tag]                 v1.7.1                      -> v1.7.1
2025-03-04T21:02:36.8131340Z  * [new tag]                 v1.7.1-rc1                  -> v1.7.1-rc1
2025-03-04T21:02:36.8132710Z  * [new tag]                 v1.7.1-rc2                  -> v1.7.1-rc2
2025-03-04T21:02:36.8133585Z  * [new tag]                 v1.7.1-rc3                  -> v1.7.1-rc3
2025-03-04T21:02:36.8135047Z  * [new tag]                 v1.8.0                      -> v1.8.0
2025-03-04T21:02:36.8136160Z  * [new tag]                 v1.8.0-rc1                  -> v1.8.0-rc1
2025-03-04T21:02:36.8137509Z  * [new tag]                 v1.8.0-rc2                  -> v1.8.0-rc2
2025-03-04T21:02:36.8138830Z  * [new tag]                 v1.8.0-rc3                  -> v1.8.0-rc3
2025-03-04T21:02:36.8139957Z  * [new tag]                 v1.8.0-rc4                  -> v1.8.0-rc4
2025-03-04T21:02:36.8141201Z  * [new tag]                 v1.8.0-rc5                  -> v1.8.0-rc5
2025-03-04T21:02:36.8142176Z  * [new tag]                 v1.8.1                      -> v1.8.1
2025-03-04T21:02:36.8143680Z  * [new tag]                 v1.8.1-rc1                  -> v1.8.1-rc1
2025-03-04T21:02:36.8144793Z  * [new tag]                 v1.8.1-rc2                  -> v1.8.1-rc2
2025-03-04T21:02:36.8145661Z  * [new tag]                 v1.8.1-rc3                  -> v1.8.1-rc3
2025-03-04T21:02:36.8147611Z  * [new tag]                 v1.8.2                      -> v1.8.2
2025-03-04T21:02:36.8148722Z  * [new tag]                 v1.8.2-rc1                  -> v1.8.2-rc1
2025-03-04T21:02:36.8150051Z  * [new tag]                 v1.9.0                      -> v1.9.0
2025-03-04T21:02:36.8151321Z  * [new tag]                 v1.9.0-rc1                  -> v1.9.0-rc1
2025-03-04T21:02:36.8152672Z  * [new tag]                 v1.9.0-rc2                  -> v1.9.0-rc2
2025-03-04T21:02:36.8154025Z  * [new tag]                 v1.9.0-rc3                  -> v1.9.0-rc3
2025-03-04T21:02:36.8155136Z  * [new tag]                 v1.9.0-rc4                  -> v1.9.0-rc4
2025-03-04T21:02:36.8156467Z  * [new tag]                 v1.9.1                      -> v1.9.1
2025-03-04T21:02:36.8157958Z  * [new tag]                 v1.9.1-rc1                  -> v1.9.1-rc1
2025-03-04T21:02:36.8159046Z  * [new tag]                 v1.9.1-rc2                  -> v1.9.1-rc2
2025-03-04T21:02:36.8160386Z  * [new tag]                 v2.0.0                      -> v2.0.0
2025-03-04T21:02:36.8161629Z  * [new tag]                 v2.0.0-rc1                  -> v2.0.0-rc1
2025-03-04T21:02:36.8162965Z  * [new tag]                 v2.0.0-rc2                  -> v2.0.0-rc2
2025-03-04T21:02:36.8164340Z  * [new tag]                 v2.0.0-rc3                  -> v2.0.0-rc3
2025-03-04T21:02:36.8165744Z  * [new tag]                 v2.0.0-rc4                  -> v2.0.0-rc4
2025-03-04T21:02:36.8166994Z  * [new tag]                 v2.0.0-rc5                  -> v2.0.0-rc5
2025-03-04T21:02:36.8168043Z  * [new tag]                 v2.0.0-rc6                  -> v2.0.0-rc6
2025-03-04T21:02:36.8169628Z  * [new tag]                 v2.0.1                      -> v2.0.1
2025-03-04T21:02:36.8170964Z  * [new tag]                 v2.0.1-rc1                  -> v2.0.1-rc1
2025-03-04T21:02:36.8172052Z  * [new tag]                 v2.0.1-rc2                  -> v2.0.1-rc2
2025-03-04T21:02:36.8173334Z  * [new tag]                 v2.0.1-rc3                  -> v2.0.1-rc3
2025-03-04T21:02:36.8174195Z  * [new tag]                 v2.0.1-rc4                  -> v2.0.1-rc4
2025-03-04T21:02:36.8176081Z  * [new tag]                 v2.1.0                      -> v2.1.0
2025-03-04T21:02:36.8177378Z  * [new tag]                 v2.1.0-rc1                  -> v2.1.0-rc1
2025-03-04T21:02:36.8178689Z  * [new tag]                 v2.1.0-rc2                  -> v2.1.0-rc2
2025-03-04T21:02:36.8180035Z  * [new tag]                 v2.1.0-rc3                  -> v2.1.0-rc3
2025-03-04T21:02:36.8181381Z  * [new tag]                 v2.1.0-rc4                  -> v2.1.0-rc4
2025-03-04T21:02:36.8182682Z  * [new tag]                 v2.1.0-rc5                  -> v2.1.0-rc5
2025-03-04T21:02:36.8183761Z  * [new tag]                 v2.1.0-rc6                  -> v2.1.0-rc6
2025-03-04T21:02:36.8185022Z  * [new tag]                 v2.1.1                      -> v2.1.1
2025-03-04T21:02:36.8186434Z  * [new tag]                 v2.1.1-rc1                  -> v2.1.1-rc1
2025-03-04T21:02:36.8187677Z  * [new tag]                 v2.1.1-rc2                  -> v2.1.1-rc2
2025-03-04T21:02:36.8189518Z  * [new tag]                 v2.1.1-rc3                  -> v2.1.1-rc3
2025-03-04T21:02:36.8190809Z  * [new tag]                 v2.1.1-rc4                  -> v2.1.1-rc4
2025-03-04T21:02:36.8192066Z  * [new tag]                 v2.1.1-rc5                  -> v2.1.1-rc5
2025-03-04T21:02:36.8192976Z  * [new tag]                 v2.1.1-rc6                  -> v2.1.1-rc6
2025-03-04T21:02:36.8194355Z  * [new tag]                 v2.1.2                      -> v2.1.2
2025-03-04T21:02:36.8195673Z  * [new tag]                 v2.1.2-rc1                  -> v2.1.2-rc1
2025-03-04T21:02:36.8197015Z  * [new tag]                 v2.1.2-rc2                  -> v2.1.2-rc2
2025-03-04T21:02:36.8198160Z  * [new tag]                 v2.1.2-rc3                  -> v2.1.2-rc3
2025-03-04T21:02:36.8199505Z  * [new tag]                 v2.2.0                      -> v2.2.0
2025-03-04T21:02:36.8200774Z  * [new tag]                 v2.2.0-rc1                  -> v2.2.0-rc1
2025-03-04T21:02:36.8202018Z  * [new tag]                 v2.2.0-rc2                  -> v2.2.0-rc2
2025-03-04T21:02:36.8203283Z  * [new tag]                 v2.2.0-rc3                  -> v2.2.0-rc3
2025-03-04T21:02:36.8204522Z  * [new tag]                 v2.2.0-rc4                  -> v2.2.0-rc4
2025-03-04T21:02:36.8205794Z  * [new tag]                 v2.2.0-rc5                  -> v2.2.0-rc5
2025-03-04T21:02:36.8207032Z  * [new tag]                 v2.2.0-rc6                  -> v2.2.0-rc6
2025-03-04T21:02:36.8208293Z  * [new tag]                 v2.2.0-rc7                  -> v2.2.0-rc7
2025-03-04T21:02:36.8209374Z  * [new tag]                 v2.2.0-rc8                  -> v2.2.0-rc8
2025-03-04T21:02:36.8210754Z  * [new tag]                 v2.2.1                      -> v2.2.1
2025-03-04T21:02:36.8212118Z  * [new tag]                 v2.2.1-rc1                  -> v2.2.1-rc1
2025-03-04T21:02:36.8213224Z  * [new tag]                 v2.2.1-rc2                  -> v2.2.1-rc2
2025-03-04T21:02:36.8214100Z  * [new tag]                 v2.2.1-rc3                  -> v2.2.1-rc3
2025-03-04T21:02:36.8215354Z  * [new tag]                 v2.2.2                      -> v2.2.2
2025-03-04T21:02:36.8216773Z  * [new tag]                 v2.2.2-rc1                  -> v2.2.2-rc1
2025-03-04T21:02:36.8217883Z  * [new tag]                 v2.2.2-rc2                  -> v2.2.2-rc2
2025-03-04T21:02:36.8218847Z  * [new tag]                 v2.2.2-rc3                  -> v2.2.2-rc3
2025-03-04T21:02:36.8220270Z  * [new tag]                 v2.3.0                      -> v2.3.0
2025-03-04T21:02:36.8221526Z  * [new tag]                 v2.3.0-rc1                  -> v2.3.0-rc1
2025-03-04T21:02:36.8222864Z  * [new tag]                 v2.3.0-rc10                 -> v2.3.0-rc10
2025-03-04T21:02:36.8224218Z  * [new tag]                 v2.3.0-rc11                 -> v2.3.0-rc11
2025-03-04T21:02:36.8225254Z  * [new tag]                 v2.3.0-rc12                 -> v2.3.0-rc12
2025-03-04T21:02:36.8230033Z  * [new tag]                 v2.3.0-rc2                  -> v2.3.0-rc2
2025-03-04T21:02:36.8231307Z  * [new tag]                 v2.3.0-rc3                  -> v2.3.0-rc3
2025-03-04T21:02:36.8232588Z  * [new tag]                 v2.3.0-rc4                  -> v2.3.0-rc4
2025-03-04T21:02:36.8233874Z  * [new tag]                 v2.3.0-rc5                  -> v2.3.0-rc5
2025-03-04T21:02:36.8234995Z  * [new tag]                 v2.3.0-rc6                  -> v2.3.0-rc6
2025-03-04T21:02:36.8236289Z  * [new tag]                 v2.3.0-rc7                  -> v2.3.0-rc7
2025-03-04T21:02:36.8237577Z  * [new tag]                 v2.3.0-rc8                  -> v2.3.0-rc8
2025-03-04T21:02:36.8238669Z  * [new tag]                 v2.3.0-rc9                  -> v2.3.0-rc9
2025-03-04T21:02:36.8239593Z  * [new tag]                 v2.3.1                      -> v2.3.1
2025-03-04T21:02:36.8241050Z  * [new tag]                 v2.3.1-rc1                  -> v2.3.1-rc1
2025-03-04T21:02:36.8242340Z  * [new tag]                 v2.3.1-rc2                  -> v2.3.1-rc2
2025-03-04T21:02:36.8243659Z  * [new tag]                 v2.3.1-rc3                  -> v2.3.1-rc3
2025-03-04T21:02:36.8245018Z  * [new tag]                 v2.4.0                      -> v2.4.0
2025-03-04T21:02:36.8246377Z  * [new tag]                 v2.4.0-rc1                  -> v2.4.0-rc1
2025-03-04T21:02:36.8247436Z  * [new tag]                 v2.4.0-rc2                  -> v2.4.0-rc2
2025-03-04T21:02:36.8249103Z  * [new tag]                 v2.4.0-rc3                  -> v2.4.0-rc3
2025-03-04T21:02:36.8250462Z  * [new tag]                 v2.4.0-rc4                  -> v2.4.0-rc4
2025-03-04T21:02:36.8251925Z  * [new tag]                 v2.4.0-rc5                  -> v2.4.0-rc5
2025-03-04T21:02:36.8253193Z  * [new tag]                 v2.4.0-rc6                  -> v2.4.0-rc6
2025-03-04T21:02:36.8254613Z  * [new tag]                 v2.4.0-rc7                  -> v2.4.0-rc7
2025-03-04T21:02:36.8256025Z  * [new tag]                 v2.4.0-rc8                  -> v2.4.0-rc8
2025-03-04T21:02:36.8257426Z  * [new tag]                 v2.4.0-rc9                  -> v2.4.0-rc9
2025-03-04T21:02:36.8258279Z  * [new tag]                 v2.4.1                      -> v2.4.1
2025-03-04T21:02:36.8259834Z  * [new tag]                 v2.4.1-rc1                  -> v2.4.1-rc1
2025-03-04T21:02:36.8261194Z  * [new tag]                 v2.4.1-rc2                  -> v2.4.1-rc2
2025-03-04T21:02:36.8262614Z  * [new tag]                 v2.4.1-rc3                  -> v2.4.1-rc3
2025-03-04T21:02:36.8263887Z  * [new tag]                 v2.5.0                      -> v2.5.0
2025-03-04T21:02:36.8265229Z  * [new tag]                 v2.5.0-rc1                  -> v2.5.0-rc1
2025-03-04T21:02:36.8266216Z  * [new tag]                 v2.5.0-rc10                 -> v2.5.0-rc10
2025-03-04T21:02:36.8267688Z  * [new tag]                 v2.5.0-rc2                  -> v2.5.0-rc2
2025-03-04T21:02:36.8268951Z  * [new tag]                 v2.5.0-rc3                  -> v2.5.0-rc3
2025-03-04T21:02:36.8270257Z  * [new tag]                 v2.5.0-rc4                  -> v2.5.0-rc4
2025-03-04T21:02:36.8271885Z  * [new tag]                 v2.5.0-rc5                  -> v2.5.0-rc5
2025-03-04T21:02:36.8272977Z  * [new tag]                 v2.5.0-rc6                  -> v2.5.0-rc6
2025-03-04T21:02:36.8274880Z  * [new tag]                 v2.5.0-rc7                  -> v2.5.0-rc7
2025-03-04T21:02:36.8276340Z  * [new tag]                 v2.5.0-rc8                  -> v2.5.0-rc8
2025-03-04T21:02:36.8277462Z  * [new tag]                 v2.5.0-rc9                  -> v2.5.0-rc9
2025-03-04T21:02:36.8278553Z  * [new tag]                 v2.5.1                      -> v2.5.1
2025-03-04T21:02:36.8279416Z  * [new tag]                 v2.5.1-rc1                  -> v2.5.1-rc1
2025-03-04T21:02:36.8280643Z  * [new tag]                 v2.6.0                      -> v2.6.0
2025-03-04T21:02:36.8282182Z  * [new tag]                 v2.6.0-rc1                  -> v2.6.0-rc1
2025-03-04T21:02:36.8283512Z  * [new tag]                 v2.6.0-rc2                  -> v2.6.0-rc2
2025-03-04T21:02:36.8284835Z  * [new tag]                 v2.6.0-rc3                  -> v2.6.0-rc3
2025-03-04T21:02:36.8286152Z  * [new tag]                 v2.6.0-rc4                  -> v2.6.0-rc4
2025-03-04T21:02:36.8287805Z  * [new tag]                 v2.6.0-rc5                  -> v2.6.0-rc5
2025-03-04T21:02:36.8289319Z  * [new tag]                 v2.6.0-rc6                  -> v2.6.0-rc6
2025-03-04T21:02:36.8290582Z  * [new tag]                 v2.6.0-rc7                  -> v2.6.0-rc7
2025-03-04T21:02:36.8291979Z  * [new tag]                 v2.6.0-rc8                  -> v2.6.0-rc8
2025-03-04T21:02:36.8293356Z  * [new tag]                 v2.6.0-rc9                  -> v2.6.0-rc9
2025-03-04T21:02:36.8294641Z  * [new tag]                 whc_flight_1                -> whc_flight_1
2025-03-04T21:02:36.8295739Z  * [new tag]                 whc_flight_2                -> whc_flight_2
2025-03-04T21:02:36.8297016Z  * [new tag]                 whc_flight_4                -> whc_flight_4
2025-03-04T21:02:36.9131947Z [command]/usr/bin/git rev-parse --verify --quiet 1b7498080987913ecb3aff6253c5e88f3540d911^{object}
2025-03-04T21:02:36.9161990Z 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:02:36.9167183Z ##[endgroup]
2025-03-04T21:02:36.9167755Z ##[group]Determining the checkout info
2025-03-04T21:02:36.9169038Z ##[endgroup]
2025-03-04T21:02:36.9174175Z [command]/usr/bin/git sparse-checkout disable
2025-03-04T21:02:36.9215826Z [command]/usr/bin/git config --local --unset-all extensions.worktreeConfig
2025-03-04T21:02:36.9247003Z ##[group]Checking out the ref
2025-03-04T21:02:36.9250707Z [command]/usr/bin/git checkout --progress --force 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:02:37.9546863Z Updating files:  78% (13010/16541)
2025-03-04T21:02:37.9762746Z Updating files:  79% (13068/16541)
2025-03-04T21:02:37.9973723Z Updating files:  80% (13233/16541)
2025-03-04T21:02:38.0099401Z Updating files:  81% (13399/16541)
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2025-03-04T21:02:38.3075751Z Note: switching to '1b7498080987913ecb3aff6253c5e88f3540d911'.
2025-03-04T21:02:38.3076126Z 
2025-03-04T21:02:38.3076633Z You are in 'detached HEAD' state. You can look around, make experimental
2025-03-04T21:02:38.3077192Z changes and commit them, and you can discard any commits you make in this
2025-03-04T21:02:38.3077741Z state without impacting any branches by switching back to a branch.
2025-03-04T21:02:38.3078061Z 
2025-03-04T21:02:38.3078281Z If you want to create a new branch to retain commits you create, you may
2025-03-04T21:02:38.3078796Z do so (now or later) by using -c with the switch command. Example:
2025-03-04T21:02:38.3079083Z 
2025-03-04T21:02:38.3079218Z   git switch -c <new-branch-name>
2025-03-04T21:02:38.3079423Z 
2025-03-04T21:02:38.3079547Z Or undo this operation with:
2025-03-04T21:02:38.3079736Z 
2025-03-04T21:02:38.3079843Z   git switch -
2025-03-04T21:02:38.3079985Z 
2025-03-04T21:02:38.3080235Z Turn off this advice by setting config variable advice.detachedHead to false
2025-03-04T21:02:38.3080578Z 
2025-03-04T21:02:38.3080922Z HEAD is now at 1b749808098 Update on "[dynamo] remove internal stack trace for fullgraph=True graph breaks"
2025-03-04T21:02:38.3199620Z ##[endgroup]
2025-03-04T21:02:38.3200083Z ##[group]Setting up auth for fetching submodules
2025-03-04T21:02:38.3205969Z [command]/usr/bin/git config --global http.https://github.com/.extraheader AUTHORIZATION: basic ***
2025-03-04T21:02:38.3261441Z [command]/usr/bin/git config --global --unset-all url.https://github.com/.insteadOf
2025-03-04T21:02:38.3295336Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf git@github.com:
2025-03-04T21:02:38.3328571Z [command]/usr/bin/git config --global --add url.https://github.com/.insteadOf org-21003710@github.com:
2025-03-04T21:02:38.3358500Z ##[endgroup]
2025-03-04T21:02:38.3358927Z ##[group]Fetching submodules
2025-03-04T21:02:38.3362042Z [command]/usr/bin/git submodule sync --recursive
2025-03-04T21:02:38.3740170Z [command]/usr/bin/git -c protocol.version=2 submodule update --init --force --recursive
2025-03-04T21:02:38.4103350Z Submodule 'android/libs/fbjni' (https://github.com/facebookincubator/fbjni.git) registered for path 'android/libs/fbjni'
2025-03-04T21:02:38.4106644Z Submodule 'third_party/NNPACK_deps/FP16' (https://github.com/Maratyszcza/FP16.git) registered for path 'third_party/FP16'
2025-03-04T21:02:38.4375670Z Submodule 'third_party/NNPACK_deps/FXdiv' (https://github.com/Maratyszcza/FXdiv.git) registered for path 'third_party/FXdiv'
2025-03-04T21:02:38.4379561Z Submodule 'third_party/NNPACK' (https://github.com/Maratyszcza/NNPACK.git) registered for path 'third_party/NNPACK'
2025-03-04T21:02:38.4383986Z Submodule 'third_party/NVTX' (https://github.com/NVIDIA/NVTX.git) registered for path 'third_party/NVTX'
2025-03-04T21:02:38.4388891Z Submodule 'third_party/VulkanMemoryAllocator' (https://github.com/GPUOpen-LibrariesAndSDKs/VulkanMemoryAllocator.git) registered for path 'third_party/VulkanMemoryAllocator'
2025-03-04T21:02:38.4393379Z Submodule 'third_party/XNNPACK' (https://github.com/google/XNNPACK.git) registered for path 'third_party/XNNPACK'
2025-03-04T21:02:38.4398085Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/benchmark'
2025-03-04T21:02:38.4403030Z Submodule 'third_party/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/composable_kernel'
2025-03-04T21:02:38.4419622Z Submodule 'third_party/cpp-httplib' (https://github.com/yhirose/cpp-httplib.git) registered for path 'third_party/cpp-httplib'
2025-03-04T21:02:38.4424585Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo.git) registered for path 'third_party/cpuinfo'
2025-03-04T21:02:38.4430209Z Submodule 'third_party/cudnn_frontend' (https://github.com/NVIDIA/cudnn-frontend.git) registered for path 'third_party/cudnn_frontend'
2025-03-04T21:02:38.4435200Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/cutlass'
2025-03-04T21:02:38.4440604Z Submodule 'third_party/eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'third_party/eigen'
2025-03-04T21:02:38.4446073Z Submodule 'third_party/fbgemm' (https://github.com/pytorch/fbgemm) registered for path 'third_party/fbgemm'
2025-03-04T21:02:38.4451849Z Submodule 'third_party/flash-attention' (https://github.com/Dao-AILab/flash-attention.git) registered for path 'third_party/flash-attention'
2025-03-04T21:02:38.4469298Z Submodule 'third_party/flatbuffers' (https://github.com/google/flatbuffers.git) registered for path 'third_party/flatbuffers'
2025-03-04T21:02:38.4474750Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/fmt'
2025-03-04T21:02:38.4480937Z Submodule 'third_party/gemmlowp/gemmlowp' (https://github.com/google/gemmlowp.git) registered for path 'third_party/gemmlowp/gemmlowp'
2025-03-04T21:02:38.4492127Z Submodule 'third_party/gloo' (https://github.com/facebookincubator/gloo) registered for path 'third_party/gloo'
2025-03-04T21:02:38.4497987Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/googletest'
2025-03-04T21:02:38.4503641Z Submodule 'third_party/ideep' (https://github.com/intel/ideep) registered for path 'third_party/ideep'
2025-03-04T21:02:38.4509586Z Submodule 'third_party/ittapi' (https://github.com/intel/ittapi.git) registered for path 'third_party/ittapi'
2025-03-04T21:02:38.4528406Z Submodule 'third_party/kineto' (https://github.com/pytorch/kineto) registered for path 'third_party/kineto'
2025-03-04T21:02:38.4534569Z Submodule 'third_party/kleidiai' (https://github.com/ARM-software/kleidiai.git) registered for path 'third_party/kleidiai'
2025-03-04T21:02:38.4540674Z Submodule 'third_party/mimalloc' (https://github.com/microsoft/mimalloc.git) registered for path 'third_party/mimalloc'
2025-03-04T21:02:38.4546833Z Submodule 'third_party/nlohmann' (https://github.com/nlohmann/json.git) registered for path 'third_party/nlohmann'
2025-03-04T21:02:38.4553042Z Submodule 'third_party/onnx' (https://github.com/onnx/onnx.git) registered for path 'third_party/onnx'
2025-03-04T21:02:38.4559595Z Submodule 'third_party/opentelemetry-cpp' (https://github.com/open-telemetry/opentelemetry-cpp.git) registered for path 'third_party/opentelemetry-cpp'
2025-03-04T21:02:38.4565757Z Submodule 'third_party/pocketfft' (https://github.com/mreineck/pocketfft) registered for path 'third_party/pocketfft'
2025-03-04T21:02:38.4585942Z Submodule 'third_party/protobuf' (https://github.com/protocolbuffers/protobuf.git) registered for path 'third_party/protobuf'
2025-03-04T21:02:38.4592567Z Submodule 'third_party/NNPACK_deps/psimd' (https://github.com/Maratyszcza/psimd.git) registered for path 'third_party/psimd'
2025-03-04T21:02:38.4599279Z Submodule 'third_party/NNPACK_deps/pthreadpool' (https://github.com/Maratyszcza/pthreadpool.git) registered for path 'third_party/pthreadpool'
2025-03-04T21:02:38.4605817Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/pybind11'
2025-03-04T21:02:38.4612902Z Submodule 'third_party/python-peachpy' (https://github.com/malfet/PeachPy.git) registered for path 'third_party/python-peachpy'
2025-03-04T21:02:38.4619632Z Submodule 'third_party/sleef' (https://github.com/shibatch/sleef) registered for path 'third_party/sleef'
2025-03-04T21:02:38.4630595Z Submodule 'third_party/tensorpipe' (https://github.com/pytorch/tensorpipe.git) registered for path 'third_party/tensorpipe'
2025-03-04T21:02:38.4666666Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/android/libs/fbjni'...
2025-03-04T21:02:38.7320731Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FXdiv'...
2025-03-04T21:02:38.7321641Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/FP16'...
2025-03-04T21:02:38.7360445Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fmt'...
2025-03-04T21:02:41.7291831Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NNPACK'...
2025-03-04T21:02:41.7293539Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/NVTX'...
2025-03-04T21:02:41.7294895Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/benchmark'...
2025-03-04T21:02:41.7296175Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpp-httplib'...
2025-03-04T21:02:41.7297519Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gloo'...
2025-03-04T21:02:41.7298742Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention'...
2025-03-04T21:02:41.7300008Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cpuinfo'...
2025-03-04T21:02:41.7301288Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/gemmlowp/gemmlowp'...
2025-03-04T21:02:41.7302554Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ittapi'...
2025-03-04T21:02:41.7303780Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep'...
2025-03-04T21:02:41.7305081Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kleidiai'...
2025-03-04T21:02:41.7306299Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pocketfft'...
2025-03-04T21:02:41.7307676Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cudnn_frontend'...
2025-03-04T21:02:41.7309015Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/psimd'...
2025-03-04T21:02:41.7310267Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/mimalloc'...
2025-03-04T21:02:41.7311585Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/googletest'...
2025-03-04T21:02:41.7312839Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pthreadpool'...
2025-03-04T21:02:41.7314125Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/python-peachpy'...
2025-03-04T21:02:41.8293429Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flatbuffers'...
2025-03-04T21:02:41.9363082Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto'...
2025-03-04T21:02:42.2596189Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/VulkanMemoryAllocator'...
2025-03-04T21:02:42.3279218Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe'...
2025-03-04T21:02:42.4280622Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/XNNPACK'...
2025-03-04T21:02:55.5741695Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm'...
2025-03-04T21:02:55.5742435Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/pybind11'...
2025-03-04T21:02:55.5743128Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/sleef'...
2025-03-04T21:02:55.5743892Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/composable_kernel'...
2025-03-04T21:02:55.5744643Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/cutlass'...
2025-03-04T21:02:55.5745317Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx'...
2025-03-04T21:02:55.5746040Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp'...
2025-03-04T21:02:55.5746767Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/eigen'...
2025-03-04T21:02:55.5747445Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/nlohmann'...
2025-03-04T21:02:55.5748134Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf'...
2025-03-04T21:02:55.5935331Z Submodule path 'android/libs/fbjni': checked out '7e1e1fe3858c63c251c637ae41a20de425dde96f'
2025-03-04T21:02:55.6085879Z Submodule path 'third_party/FP16': checked out '4dfe081cf6bcd15db339cf2680b9281b8451eeb3'
2025-03-04T21:02:55.6210653Z Submodule path 'third_party/FXdiv': checked out 'b408327ac2a15ec3e43352421954f5b1967701d1'
2025-03-04T21:02:55.6521988Z Submodule path 'third_party/NNPACK': checked out 'c07e3a0400713d546e0dea2d5466dd22ea389c73'
2025-03-04T21:02:55.6957545Z Submodule path 'third_party/NVTX': checked out 'e170594ac7cf1dac584da473d4ca9301087090c1'
2025-03-04T21:02:55.7415445Z Submodule path 'third_party/VulkanMemoryAllocator': checked out 'a6bfc237255a6bac1513f7c1ebde6d8aed6b5191'
2025-03-04T21:02:56.6638262Z Submodule path 'third_party/XNNPACK': checked out '51a0103656eff6fc9bfd39a4597923c4b542c883'
2025-03-04T21:02:56.6937725Z Submodule path 'third_party/benchmark': checked out '0d98dba29d66e93259db7daa53a9327df767a415'
2025-03-04T21:02:57.0003603Z Submodule path 'third_party/composable_kernel': checked out '8086bbe3a78d931eb96fe12fdc014082e18d18d3'
2025-03-04T21:02:57.0569626Z Submodule path 'third_party/cpp-httplib': checked out '3b6597bba913d51161383657829b7e644e59c006'
2025-03-04T21:02:57.1707344Z Submodule path 'third_party/cpuinfo': checked out '1e83a2fdd3102f65c6f1fb602c1b320486218a99'
2025-03-04T21:02:57.2130261Z Submodule path 'third_party/cudnn_frontend': checked out '91b7532f3386768bba4f444ee7672b497f34da8a'
2025-03-04T21:02:57.9205356Z Submodule path 'third_party/cutlass': checked out 'afa1772203677c5118fcd82537a9c8fefbcc7008'
2025-03-04T21:02:58.2142649Z Submodule path 'third_party/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c'
2025-03-04T21:02:58.3285283Z Submodule path 'third_party/fbgemm': checked out 'dbc3157bf256f1339b3fa1fef2be89ac4078be0e'
2025-03-04T21:02:58.3306714Z Submodule 'third_party/asmjit' (https://github.com/asmjit/asmjit.git) registered for path 'third_party/fbgemm/third_party/asmjit'
2025-03-04T21:02:58.3309964Z Submodule 'third_party/cpuinfo' (https://github.com/pytorch/cpuinfo) registered for path 'third_party/fbgemm/third_party/cpuinfo'
2025-03-04T21:02:58.3313528Z Submodule 'third_party/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/fbgemm/third_party/cutlass'
2025-03-04T21:02:58.3317034Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/fbgemm/third_party/googletest'
2025-03-04T21:02:58.3320675Z Submodule 'third_party/hipify_torch' (https://github.com/ROCmSoftwarePlatform/hipify_torch.git) registered for path 'third_party/fbgemm/third_party/hipify_torch'
2025-03-04T21:02:58.3351277Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/asmjit'...
2025-03-04T21:02:59.1543070Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/hipify_torch'...
2025-03-04T21:02:59.1543980Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cpuinfo'...
2025-03-04T21:02:59.2544414Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/cutlass'...
2025-03-04T21:03:00.6207906Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/fbgemm/third_party/googletest'...
2025-03-04T21:03:00.6728038Z Submodule path 'third_party/fbgemm/third_party/asmjit': checked out 'd3fbf7c9bc7c1d1365a94a45614b91c5a3706b81'
2025-03-04T21:03:00.7833092Z Submodule path 'third_party/fbgemm/third_party/cpuinfo': checked out 'ed8b86a253800bafdb7b25c5c399f91bff9cb1f3'
2025-03-04T21:03:01.2937613Z Submodule path 'third_party/fbgemm/third_party/cutlass': checked out 'fc9ebc645b63f3a6bc80aaefde5c063fb72110d6'
2025-03-04T21:03:01.3642724Z Submodule path 'third_party/fbgemm/third_party/googletest': checked out 'cbf019de22c8dd37b2108da35b2748fd702d1796'
2025-03-04T21:03:01.3796715Z Submodule path 'third_party/fbgemm/third_party/hipify_torch': checked out '23f53b025b466d8ec3c45d52290d3442f7fbe6b1'
2025-03-04T21:03:01.4692171Z Submodule path 'third_party/flash-attention': checked out '979702c87a8713a8e0a5e9fee122b90d2ef13be5'
2025-03-04T21:03:01.4715441Z Submodule 'csrc/composable_kernel' (https://github.com/ROCm/composable_kernel.git) registered for path 'third_party/flash-attention/csrc/composable_kernel'
2025-03-04T21:03:01.4718514Z Submodule 'csrc/cutlass' (https://github.com/NVIDIA/cutlass.git) registered for path 'third_party/flash-attention/csrc/cutlass'
2025-03-04T21:03:01.4749982Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/composable_kernel'...
2025-03-04T21:03:03.8124414Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/flash-attention/csrc/cutlass'...
2025-03-04T21:03:04.1308344Z Submodule path 'third_party/flash-attention/csrc/composable_kernel': checked out '888317e698e9803c62bd38568abc9e05d7709f33'
2025-03-04T21:03:04.8217877Z Submodule path 'third_party/flash-attention/csrc/cutlass': checked out 'c506e16788cb08416a4a57e11a9067beeee29420'
2025-03-04T21:03:04.9921428Z Submodule path 'third_party/flatbuffers': checked out '01834de25e4bf3975a9a00e816292b1ad0fe184b'
2025-03-04T21:03:05.0309252Z Submodule path 'third_party/fmt': checked out '123913715afeb8a437e6388b4473fcc4753e1c9a'
2025-03-04T21:03:05.0775919Z Submodule path 'third_party/gemmlowp/gemmlowp': checked out '3fb5c176c17c765a3492cd2f0321b0dab712f350'
2025-03-04T21:03:05.1105454Z Submodule path 'third_party/gloo': checked out '5354032ea08eadd7fc4456477f7f7c6308818509'
2025-03-04T21:03:05.1620324Z Submodule path 'third_party/googletest': checked out 'b514bdc898e2951020cbdca1304b75f5950d1f59'
2025-03-04T21:03:05.1784330Z Submodule path 'third_party/ideep': checked out 'e026f3b0318087fe19e2b062e8edf55bfe7a522c'
2025-03-04T21:03:05.1804331Z Submodule 'mkl-dnn' (https://github.com/intel/mkl-dnn.git) registered for path 'third_party/ideep/mkl-dnn'
2025-03-04T21:03:05.1835184Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/ideep/mkl-dnn'...
2025-03-04T21:03:19.0564150Z Submodule path 'third_party/ideep/mkl-dnn': checked out '66f0cb9eb66affd2da3bf5f8d897376f04aae6af'
2025-03-04T21:03:19.0793530Z Submodule path 'third_party/ittapi': checked out '5b8a7d7422611c3a0d799fb5fc5dd4abfae35b42'
2025-03-04T21:03:19.1791142Z Submodule path 'third_party/kineto': checked out 'a054a4be0db117c579a21747debf19c863631f26'
2025-03-04T21:03:19.1816886Z Submodule 'libkineto/third_party/dynolog' (https://github.com/facebookincubator/dynolog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-04T21:03:19.1819998Z Submodule 'libkineto/third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/fmt'
2025-03-04T21:03:19.1823590Z Submodule 'libkineto/third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/googletest'
2025-03-04T21:03:19.1854995Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog'...
2025-03-04T21:03:20.1251178Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/fmt'...
2025-03-04T21:03:20.6553126Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/googletest'...
2025-03-04T21:03:20.7518286Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog': checked out '7d04a0053a845370ae06ce317a22a48e9edcc74e'
2025-03-04T21:03:20.7538087Z Submodule 'third_party/DCGM' (https://github.com/NVIDIA/DCGM.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-04T21:03:20.7541735Z Submodule 'third_party/cpr' (https://github.com/libcpr/cpr.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-04T21:03:20.7545632Z Submodule 'third_party/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-04T21:03:20.7549664Z Submodule 'third_party/gflags' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-04T21:03:20.7553991Z Submodule 'third_party/glog' (https://github.com/google/glog.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-04T21:03:20.7557899Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-04T21:03:20.7561988Z Submodule 'third_party/json' (https://github.com/nlohmann/json.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-04T21:03:20.7566243Z Submodule 'third_party/pfs' (https://github.com/dtrugman/pfs.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-04T21:03:20.7598575Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'...
2025-03-04T21:03:21.9965217Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'...
2025-03-04T21:03:21.9966810Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'...
2025-03-04T21:03:21.9968175Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'...
2025-03-04T21:03:21.9969268Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/glog'...
2025-03-04T21:03:22.0452198Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'...
2025-03-04T21:03:22.3249848Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'...
2025-03-04T21:03:22.4249794Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/json'...
2025-03-04T21:03:27.9870412Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM': checked out 'ffde4e54bc7249a6039a5e6b45b395141e1217f9'
2025-03-04T21:03:28.0108087Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr': checked out '871ed52d350214a034f6ef8a3b8f51c5ce1bd400'
2025-03-04T21:03:28.0539799Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt': checked out 'cd4af11efc9c622896a3e4cb599fa28668ca3d05'
2025-03-04T21:03:28.0721358Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags': checked out 'e171aa2d15ed9eb17054558e0b3a6a413bb01067'
2025-03-04T21:03:28.0743044Z Submodule 'doc' (https://github.com/gflags/gflags.git) registered for path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-04T21:03:28.0773778Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'...
2025-03-04T21:03:28.4149118Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc': checked out '8411df715cf522606e3b1aca386ddfc0b63d34b4'
2025-03-04T21:03:28.4381427Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog': checked out 'b33e3bad4c46c8a6345525fd822af355e5ef9446'
2025-03-04T21:03:28.4865345Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest': checked out '58d77fa8070e8cec2dc1ed015d66b454c8d78850'
2025-03-04T21:03:28.6049295Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/json': checked out '4f8fba14066156b73f1189a2b8bd568bde5284c5'
2025-03-04T21:03:28.6252785Z Submodule path 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs': checked out 'f68a2fa8ea36c783bdd760371411fcb495aa3150'
2025-03-04T21:03:28.6663954Z Submodule path 'third_party/kineto/libkineto/third_party/fmt': checked out '0041a40c1350ba702d475b9c4ad62da77caea164'
2025-03-04T21:03:28.7342220Z Submodule path 'third_party/kineto/libkineto/third_party/googletest': checked out '7aca84427f224eeed3144123d5230d5871e93347'
2025-03-04T21:03:28.7793111Z Submodule path 'third_party/kleidiai': checked out 'ef685a13cfbe8d418aa2ed34350e21e4938358b6'
2025-03-04T21:03:28.8251500Z Submodule path 'third_party/mimalloc': checked out 'b66e3214d8a104669c2ec05ae91ebc26a8f5ab78'
2025-03-04T21:03:28.9568673Z Submodule path 'third_party/nlohmann': checked out '87cda1d6646592ac5866dc703c8e1839046a6806'
2025-03-04T21:03:29.4766476Z Submodule path 'third_party/onnx': checked out 'b8baa8446686496da4cc8fda09f2b6fe65c2a02c'
2025-03-04T21:03:29.4804484Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/onnx/third_party/pybind11'
2025-03-04T21:03:29.4837085Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/onnx/third_party/pybind11'...
2025-03-04T21:03:30.7796268Z Submodule path 'third_party/onnx/third_party/pybind11': checked out '3e9dfa2866941655c56877882565e7577de6fc7b'
2025-03-04T21:03:30.8695614Z Submodule path 'third_party/opentelemetry-cpp': checked out 'a799f4aed9c94b765dcdaabaeab7d5e7e2310878'
2025-03-04T21:03:30.8718704Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark) registered for path 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-04T21:03:30.8721650Z Submodule 'third_party/googletest' (https://github.com/google/googletest) registered for path 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-04T21:03:30.8725353Z Submodule 'third_party/ms-gsl' (https://github.com/microsoft/GSL) registered for path 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-04T21:03:30.8729367Z Submodule 'third_party/nlohmann-json' (https://github.com/nlohmann/json) registered for path 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-04T21:03:30.8733213Z Submodule 'third_party/opentelemetry-proto' (https://github.com/open-telemetry/opentelemetry-proto) registered for path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-04T21:03:30.8737228Z Submodule 'third_party/opentracing-cpp' (https://github.com/opentracing/opentracing-cpp.git) registered for path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-04T21:03:30.8741050Z Submodule 'third_party/prometheus-cpp' (https://github.com/jupp0r/prometheus-cpp) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-04T21:03:30.8744861Z Submodule 'tools/vcpkg' (https://github.com/Microsoft/vcpkg) registered for path 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-04T21:03:30.8779499Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/benchmark'...
2025-03-04T21:03:31.5012316Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentracing-cpp'...
2025-03-04T21:03:31.5013424Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/opentelemetry-proto'...
2025-03-04T21:03:31.5014525Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp'...
2025-03-04T21:03:31.5015514Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/ms-gsl'...
2025-03-04T21:03:31.6013629Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/googletest'...
2025-03-04T21:03:32.4274424Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/nlohmann-json'...
2025-03-04T21:03:38.8097715Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/tools/vcpkg'...
2025-03-04T21:03:39.3712472Z Submodule path 'third_party/opentelemetry-cpp/third_party/benchmark': checked out 'd572f4777349d43653b21d6c2fc63020ab326db2'
2025-03-04T21:03:39.4181291Z Submodule path 'third_party/opentelemetry-cpp/third_party/googletest': checked out 'b796f7d44681514f58a683a3a71ff17c94edb0c1'
2025-03-04T21:03:39.4379960Z Submodule path 'third_party/opentelemetry-cpp/third_party/ms-gsl': checked out '6f4529395c5b7c2d661812257cd6780c67e54afa'
2025-03-04T21:03:39.5664696Z Submodule path 'third_party/opentelemetry-cpp/third_party/nlohmann-json': checked out 'bc889afb4c5bf1c0d8ee29ef35eaaf4c8bef8a5d'
2025-03-04T21:03:39.5831121Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto': checked out '4ca4f0335c63cda7ab31ea7ed70d6553aee14dce'
2025-03-04T21:03:39.6019770Z Submodule path 'third_party/opentelemetry-cpp/third_party/opentracing-cpp': checked out '06b57f48ded1fa3bdd3d4346f6ef29e40e08eaf5'
2025-03-04T21:03:39.6223957Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp': checked out 'c9ffcdda9086ffd9e1283ea7a0276d831f3c8a8d'
2025-03-04T21:03:39.6243902Z Submodule 'civetweb' (https://github.com/civetweb/civetweb.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-04T21:03:39.6245942Z Submodule 'googletest' (https://github.com/google/googletest.git) registered for path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-04T21:03:39.6277317Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'...
2025-03-04T21:03:41.6274540Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'...
2025-03-04T21:03:41.9078861Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb': checked out 'eefb26f82b233268fc98577d265352720d477ba4'
2025-03-04T21:03:41.9633299Z Submodule path 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest': checked out 'e2239ee6043f73722e7aa812a459f54a28552929'
2025-03-04T21:03:42.6302432Z Submodule path 'third_party/opentelemetry-cpp/tools/vcpkg': checked out '8eb57355a4ffb410a2e94c07b4dca2dffbee8e50'
2025-03-04T21:03:42.6452138Z Submodule path 'third_party/pocketfft': checked out '9d3ab05a7fffbc71a492bc6a17be034e83e8f0fe'
2025-03-04T21:03:42.9637206Z Submodule path 'third_party/protobuf': checked out 'd1eca4e4b421cd2997495c4b4e65cea6be4e9b8a'
2025-03-04T21:03:42.9665374Z Submodule 'third_party/benchmark' (https://github.com/google/benchmark.git) registered for path 'third_party/protobuf/third_party/benchmark'
2025-03-04T21:03:42.9667738Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/protobuf/third_party/googletest'
2025-03-04T21:03:42.9698503Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/benchmark'...
2025-03-04T21:03:43.5505842Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/protobuf/third_party/googletest'...
2025-03-04T21:03:44.1071896Z Submodule path 'third_party/protobuf/third_party/benchmark': checked out '5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8'
2025-03-04T21:03:44.1897919Z Submodule path 'third_party/protobuf/third_party/googletest': checked out '5ec7f0c4a113e2f18ac2c6cc7df51ad6afc24081'
2025-03-04T21:03:44.2023116Z Submodule path 'third_party/psimd': checked out '072586a71b55b7f8c584153d223e95687148a900'
2025-03-04T21:03:44.2178989Z Submodule path 'third_party/pthreadpool': checked out '4fe0e1e183925bf8cfa6aae24237e724a96479b8'
2025-03-04T21:03:44.2625470Z Submodule path 'third_party/pybind11': checked out 'a2e59f0e7065404b44dfe92a28aca47ba1378dc4'
2025-03-04T21:03:44.2973149Z Submodule path 'third_party/python-peachpy': checked out 'f45429b087dd7d5bc78bb40dc7cf06425c252d67'
2025-03-04T21:03:44.3477527Z Submodule path 'third_party/sleef': checked out '56e1f79cb140fb9326d612d0be06b5250565cade'
2025-03-04T21:03:44.3824492Z Submodule path 'third_party/tensorpipe': checked out '52791a2fd214b2a9dc5759d36725909c1daa7f2e'
2025-03-04T21:03:44.3846595Z Submodule 'third_party/googletest' (https://github.com/google/googletest.git) registered for path 'third_party/tensorpipe/third_party/googletest'
2025-03-04T21:03:44.3848516Z Submodule 'third_party/libnop' (https://github.com/google/libnop.git) registered for path 'third_party/tensorpipe/third_party/libnop'
2025-03-04T21:03:44.3852587Z Submodule 'third_party/libuv' (https://github.com/libuv/libuv.git) registered for path 'third_party/tensorpipe/third_party/libuv'
2025-03-04T21:03:44.3855916Z Submodule 'third_party/pybind11' (https://github.com/pybind/pybind11.git) registered for path 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T21:03:44.3886533Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/googletest'...
2025-03-04T21:03:45.5742947Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libnop'...
2025-03-04T21:03:45.5743870Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11'...
2025-03-04T21:03:45.6743556Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/libuv'...
2025-03-04T21:03:46.5996598Z Submodule path 'third_party/tensorpipe/third_party/googletest': checked out 'aee0f9d9b5b87796ee8a0ab26b7587ec30e8858e'
2025-03-04T21:03:46.6193350Z Submodule path 'third_party/tensorpipe/third_party/libnop': checked out '910b55815be16109f04f4180e9adee14fb4ce281'
2025-03-04T21:03:46.6929581Z Submodule path 'third_party/tensorpipe/third_party/libuv': checked out '1dff88e5161cba5c59276d2070d2e304e4dcb242'
2025-03-04T21:03:46.7283028Z Submodule path 'third_party/tensorpipe/third_party/pybind11': checked out 'a23996fce38ff6ccfbcdc09f1e63f2c4be5ea2ef'
2025-03-04T21:03:46.7305326Z Submodule 'tools/clang' (https://github.com/wjakob/clang-cindex-python3) registered for path 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T21:03:46.7340723Z Cloning into '/home/ec2-user/actions-runner/_work/pytorch/pytorch/third_party/tensorpipe/third_party/pybind11/tools/clang'...
2025-03-04T21:03:46.9264045Z Submodule path 'third_party/tensorpipe/third_party/pybind11/tools/clang': checked out '6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5'
2025-03-04T21:03:46.9312743Z [command]/usr/bin/git submodule foreach --recursive git config --local gc.auto 0
2025-03-04T21:03:46.9678822Z Entering 'android/libs/fbjni'
2025-03-04T21:03:46.9732615Z Entering 'third_party/FP16'
2025-03-04T21:03:46.9786308Z Entering 'third_party/FXdiv'
2025-03-04T21:03:46.9840022Z Entering 'third_party/NNPACK'
2025-03-04T21:03:46.9893199Z Entering 'third_party/NVTX'
2025-03-04T21:03:46.9948149Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-04T21:03:47.0001241Z Entering 'third_party/XNNPACK'
2025-03-04T21:03:47.0071215Z Entering 'third_party/benchmark'
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2025-03-04T21:03:47.3759803Z ##[endgroup]
2025-03-04T21:03:47.3761325Z ##[group]Persisting credentials for submodules
2025-03-04T21:03:47.3767014Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local --name-only --get-regexp 'url\.https\:\/\/github\.com\/\.insteadOf' && git config --local --unset-all 'url.https://github.com/.insteadOf' || :"
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2025-03-04T21:03:47.9260168Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T21:03:47.9324312Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T21:03:47.9419359Z [command]/usr/bin/git submodule foreach --recursive sh -c "git config --local 'http.https://github.com/.extraheader' 'AUTHORIZATION: basic ***' && git config --local --show-origin --name-only --get-regexp remote.origin.url"
2025-03-04T21:03:47.9778363Z Entering 'android/libs/fbjni'
2025-03-04T21:03:47.9842700Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/android/libs/fbjni/config	remote.origin.url
2025-03-04T21:03:47.9864795Z Entering 'third_party/FP16'
2025-03-04T21:03:47.9926945Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FP16/config	remote.origin.url
2025-03-04T21:03:47.9948352Z Entering 'third_party/FXdiv'
2025-03-04T21:03:48.0009992Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK_deps/FXdiv/config	remote.origin.url
2025-03-04T21:03:48.0033315Z Entering 'third_party/NNPACK'
2025-03-04T21:03:48.0093840Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NNPACK/config	remote.origin.url
2025-03-04T21:03:48.0115149Z Entering 'third_party/NVTX'
2025-03-04T21:03:48.0178482Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/NVTX/config	remote.origin.url
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2025-03-04T21:03:48.0261926Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/VulkanMemoryAllocator/config	remote.origin.url
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2025-03-04T21:03:48.0347722Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/XNNPACK/config	remote.origin.url
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2025-03-04T21:03:48.0448988Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/benchmark/config	remote.origin.url
2025-03-04T21:03:48.0470634Z Entering 'third_party/composable_kernel'
2025-03-04T21:03:48.0533389Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/composable_kernel/config	remote.origin.url
2025-03-04T21:03:48.0560955Z Entering 'third_party/cpp-httplib'
2025-03-04T21:03:48.0622057Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpp-httplib/config	remote.origin.url
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2025-03-04T21:03:48.0704349Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cpuinfo/config	remote.origin.url
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2025-03-04T21:03:48.0790700Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cudnn_frontend/config	remote.origin.url
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2025-03-04T21:03:48.0879115Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/cutlass/config	remote.origin.url
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2025-03-04T21:03:48.0970389Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/eigen/config	remote.origin.url
2025-03-04T21:03:48.0994008Z Entering 'third_party/fbgemm'
2025-03-04T21:03:48.1056814Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/config	remote.origin.url
2025-03-04T21:03:48.1077716Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-04T21:03:48.1139271Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/asmjit/config	remote.origin.url
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2025-03-04T21:03:48.1226091Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cpuinfo/config	remote.origin.url
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2025-03-04T21:03:48.1309110Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/cutlass/config	remote.origin.url
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2025-03-04T21:03:48.1397619Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/googletest/config	remote.origin.url
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2025-03-04T21:03:48.1480687Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fbgemm/modules/third_party/hipify_torch/config	remote.origin.url
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2025-03-04T21:03:48.1569581Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/config	remote.origin.url
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2025-03-04T21:03:48.1652186Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/composable_kernel/config	remote.origin.url
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2025-03-04T21:03:48.1739771Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flash-attention/modules/csrc/cutlass/config	remote.origin.url
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2025-03-04T21:03:48.1834869Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/flatbuffers/config	remote.origin.url
2025-03-04T21:03:48.1859275Z Entering 'third_party/fmt'
2025-03-04T21:03:48.1921722Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/fmt/config	remote.origin.url
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2025-03-04T21:03:48.2007130Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gemmlowp/gemmlowp/config	remote.origin.url
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2025-03-04T21:03:48.2093000Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/gloo/config	remote.origin.url
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2025-03-04T21:03:48.2174351Z file:/home/ec2-user/actions-runner/_work/pytorch/pytorch/.git/modules/third_party/googletest/config	remote.origin.url
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2025-03-04T21:03:49.1694746Z Entering 'third_party/FP16'
2025-03-04T21:03:49.1750176Z Entering 'third_party/FXdiv'
2025-03-04T21:03:49.1802401Z Entering 'third_party/NNPACK'
2025-03-04T21:03:49.1856095Z Entering 'third_party/NVTX'
2025-03-04T21:03:49.1908890Z Entering 'third_party/VulkanMemoryAllocator'
2025-03-04T21:03:49.1962025Z Entering 'third_party/XNNPACK'
2025-03-04T21:03:49.2034033Z Entering 'third_party/benchmark'
2025-03-04T21:03:49.2086741Z Entering 'third_party/composable_kernel'
2025-03-04T21:03:49.2151532Z Entering 'third_party/cpp-httplib'
2025-03-04T21:03:49.2203954Z Entering 'third_party/cpuinfo'
2025-03-04T21:03:49.2257537Z Entering 'third_party/cudnn_frontend'
2025-03-04T21:03:49.2310125Z Entering 'third_party/cutlass'
2025-03-04T21:03:49.2375998Z Entering 'third_party/eigen'
2025-03-04T21:03:49.2431636Z Entering 'third_party/fbgemm'
2025-03-04T21:03:49.2511437Z Entering 'third_party/fbgemm/third_party/asmjit'
2025-03-04T21:03:49.2561512Z Entering 'third_party/fbgemm/third_party/cpuinfo'
2025-03-04T21:03:49.2611620Z Entering 'third_party/fbgemm/third_party/cutlass'
2025-03-04T21:03:49.2672432Z Entering 'third_party/fbgemm/third_party/googletest'
2025-03-04T21:03:49.2722574Z Entering 'third_party/fbgemm/third_party/hipify_torch'
2025-03-04T21:03:49.2776641Z Entering 'third_party/flash-attention'
2025-03-04T21:03:49.2828472Z Entering 'third_party/flash-attention/csrc/composable_kernel'
2025-03-04T21:03:49.2885039Z Entering 'third_party/flash-attention/csrc/cutlass'
2025-03-04T21:03:49.2945096Z Entering 'third_party/flatbuffers'
2025-03-04T21:03:49.3001396Z Entering 'third_party/fmt'
2025-03-04T21:03:49.3058753Z Entering 'third_party/gemmlowp/gemmlowp'
2025-03-04T21:03:49.3114948Z Entering 'third_party/gloo'
2025-03-04T21:03:49.3167725Z Entering 'third_party/googletest'
2025-03-04T21:03:49.3220237Z Entering 'third_party/ideep'
2025-03-04T21:03:49.3270314Z Entering 'third_party/ideep/mkl-dnn'
2025-03-04T21:03:49.3328972Z Entering 'third_party/ittapi'
2025-03-04T21:03:49.3385990Z Entering 'third_party/kineto'
2025-03-04T21:03:49.3436762Z Entering 'third_party/kineto/libkineto/third_party/dynolog'
2025-03-04T21:03:49.3486178Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-04T21:03:49.3540050Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-04T21:03:49.3595456Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/fmt'
2025-03-04T21:03:49.3647950Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-04T21:03:49.3701929Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-04T21:03:49.3757818Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-04T21:03:49.3809137Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
2025-03-04T21:03:49.3861685Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-04T21:03:49.3914751Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/pfs'
2025-03-04T21:03:49.3970121Z Entering 'third_party/kineto/libkineto/third_party/fmt'
2025-03-04T21:03:49.4020360Z Entering 'third_party/kineto/libkineto/third_party/googletest'
2025-03-04T21:03:49.4075722Z Entering 'third_party/kleidiai'
2025-03-04T21:03:49.4130656Z Entering 'third_party/mimalloc'
2025-03-04T21:03:49.4182890Z Entering 'third_party/nlohmann'
2025-03-04T21:03:49.4238334Z Entering 'third_party/onnx'
2025-03-04T21:03:49.4304383Z Entering 'third_party/onnx/third_party/pybind11'
2025-03-04T21:03:49.4361988Z Entering 'third_party/opentelemetry-cpp'
2025-03-04T21:03:49.4416120Z Entering 'third_party/opentelemetry-cpp/third_party/benchmark'
2025-03-04T21:03:49.4466937Z Entering 'third_party/opentelemetry-cpp/third_party/googletest'
2025-03-04T21:03:49.4516511Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-04T21:03:49.4565610Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-04T21:03:49.4616062Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
2025-03-04T21:03:49.4666836Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-04T21:03:49.4715976Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-04T21:03:49.4764404Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-04T21:03:49.4819364Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-04T21:03:49.4873293Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-04T21:03:49.4944382Z Entering 'third_party/pocketfft'
2025-03-04T21:03:49.4996366Z Entering 'third_party/protobuf'
2025-03-04T21:03:49.5050545Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-04T21:03:49.5101593Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-04T21:03:49.5155522Z Entering 'third_party/psimd'
2025-03-04T21:03:49.5207656Z Entering 'third_party/pthreadpool'
2025-03-04T21:03:49.5259430Z Entering 'third_party/pybind11'
2025-03-04T21:03:49.5310921Z Entering 'third_party/python-peachpy'
2025-03-04T21:03:49.5364210Z Entering 'third_party/sleef'
2025-03-04T21:03:49.5417024Z Entering 'third_party/tensorpipe'
2025-03-04T21:03:49.5468475Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-04T21:03:49.5518595Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-04T21:03:49.5568394Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-04T21:03:49.5617057Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T21:03:49.5666384Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T21:03:49.5741451Z ##[endgroup]
2025-03-04T21:03:49.5787785Z [command]/usr/bin/git log -1 --format=%H
2025-03-04T21:03:49.5817613Z 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:03:49.6025864Z Prepare all required actions
2025-03-04T21:03:49.6026419Z Getting action download info
2025-03-04T21:03:49.7287676Z ##[group]Run ./.github/actions/setup-linux
2025-03-04T21:03:49.7288003Z env:
2025-03-04T21:03:49.7288231Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:49.7288499Z ##[endgroup]
2025-03-04T21:03:49.7345118Z ##[group]Run set -euo pipefail
2025-03-04T21:03:49.7345463Z set -euo pipefail
2025-03-04T21:03:49.7345771Z function get_ec2_metadata() {
2025-03-04T21:03:49.7346159Z   # Pulled from instance metadata endpoint for EC2
2025-03-04T21:03:49.7346790Z   # see https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/instancedata-data-retrieval.html
2025-03-04T21:03:49.7347348Z   category=$1
2025-03-04T21:03:49.7347718Z   # If it is GCP runner (runner name contains gcp), do not run this
2025-03-04T21:03:49.7348155Z   runner_name_str=i-05d5204902c63dc95
2025-03-04T21:03:49.7348548Z   if [[ -f /.inarc ]]; then
2025-03-04T21:03:49.7348905Z     echo "ARC Runner, no info on ec2 metadata"
2025-03-04T21:03:49.7349308Z   elif [[ $runner_name_str == *"gcp"* ]]; then
2025-03-04T21:03:49.7349781Z     echo "Runner is from Google Cloud Platform, No info on ec2 metadata"
2025-03-04T21:03:49.7350218Z   else
2025-03-04T21:03:49.7351052Z     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}"
2025-03-04T21:03:49.7351943Z   fi
2025-03-04T21:03:49.7352186Z }
2025-03-04T21:03:49.7352476Z echo "ami-id: $(get_ec2_metadata ami-id)"
2025-03-04T21:03:49.7352923Z echo "instance-id: $(get_ec2_metadata instance-id)"
2025-03-04T21:03:49.7353407Z echo "instance-type: $(get_ec2_metadata instance-type)"
2025-03-04T21:03:49.7353842Z echo "system info $(uname -a)"
2025-03-04T21:03:49.7365152Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:49.7365527Z env:
2025-03-04T21:03:49.7365753Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:49.7366011Z ##[endgroup]
2025-03-04T21:03:49.7537974Z ami-id: ami-05b10e08d247fb927
2025-03-04T21:03:49.7654987Z instance-id: i-05d5204902c63dc95
2025-03-04T21:03:49.7771560Z instance-type: g5.4xlarge
2025-03-04T21:03:49.7784238Z system info Linux ip-10-0-36-142.ec2.internal 6.1.128-136.201.amzn2023.x86_64 #1 SMP PREEMPT_DYNAMIC Mon Feb 10 16:18:01 UTC 2025 x86_64 x86_64 x86_64 GNU/Linux
2025-03-04T21:03:49.7813864Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T21:03:49.7814789Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T21:03:49.7824581Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:49.7825327Z env:
2025-03-04T21:03:49.7825562Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:49.7825838Z ##[endgroup]
2025-03-04T21:03:49.7891063Z ##[group]Run if systemctl is-active --quiet docker; then
2025-03-04T21:03:49.7891505Z if systemctl is-active --quiet docker; then
2025-03-04T21:03:49.7891901Z     echo "Docker daemon is running...";
2025-03-04T21:03:49.7892236Z else
2025-03-04T21:03:49.7892603Z     echo "Starting docker deamon..." && sudo systemctl start docker;
2025-03-04T21:03:49.7893028Z fi
2025-03-04T21:03:49.7900948Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:49.7901330Z env:
2025-03-04T21:03:49.7901559Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:49.7901832Z ##[endgroup]
2025-03-04T21:03:49.7993267Z Docker daemon is running...
2025-03-04T21:03:49.8038830Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-04T21:03:49.8039136Z with:
2025-03-04T21:03:49.8039354Z   shell: bash
2025-03-04T21:03:49.8039809Z   timeout_minutes: 5
2025-03-04T21:03:49.8040069Z   max_attempts: 3
2025-03-04T21:03:49.8040339Z   retry_wait_seconds: 30
2025-03-04T21:03:49.8042533Z   command: AWS_ACCOUNT_ID=$(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

2025-03-04T21:03:49.8044716Z   polling_interval_seconds: 1
2025-03-04T21:03:49.8045008Z   warning_on_retry: true
2025-03-04T21:03:49.8045285Z   continue_on_error: false
2025-03-04T21:03:49.8045542Z env:
2025-03-04T21:03:49.8045778Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:49.8046054Z   AWS_RETRY_MODE: standard
2025-03-04T21:03:49.8046327Z   AWS_MAX_ATTEMPTS: 5
2025-03-04T21:03:49.8046598Z   AWS_DEFAULT_REGION: us-east-1
2025-03-04T21:03:49.8046880Z ##[endgroup]
2025-03-04T21:03:50.9147753Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T21:03:50.9148600Z Configure a credential helper to remove this warning. See
2025-03-04T21:03:50.9149387Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T21:03:50.9149920Z 
2025-03-04T21:03:50.9150064Z Login Succeeded
2025-03-04T21:03:51.8912050Z Command completed after 1 attempt(s).
2025-03-04T21:03:51.9006597Z ##[group]Run env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T21:03:51.9007403Z env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T21:03:51.9008305Z env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T21:03:51.9020608Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:51.9021212Z env:
2025-03-04T21:03:51.9021573Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:51.9022021Z ##[endgroup]
2025-03-04T21:03:51.9128965Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-04T21:03:51.9129541Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-04T21:03:51.9129979Z # shellcheck disable=SC2046
2025-03-04T21:03:51.9130331Z docker stop $(docker ps -q) || true
2025-03-04T21:03:51.9130691Z # Prune all of the docker images
2025-03-04T21:03:51.9131033Z docker system prune -af
2025-03-04T21:03:51.9140185Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:51.9140568Z env:
2025-03-04T21:03:51.9140806Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:51.9141074Z ##[endgroup]
2025-03-04T21:03:51.9426945Z "docker stop" requires at least 1 argument.
2025-03-04T21:03:51.9427343Z See 'docker stop --help'.
2025-03-04T21:03:51.9427567Z 
2025-03-04T21:03:51.9427754Z Usage:  docker stop [OPTIONS] CONTAINER [CONTAINER...]
2025-03-04T21:03:51.9428031Z 
2025-03-04T21:03:51.9428156Z Stop one or more running containers
2025-03-04T21:03:51.9768112Z Total reclaimed space: 0B
2025-03-04T21:03:51.9821551Z ##[group]Run set +e
2025-03-04T21:03:51.9822000Z set +e
2025-03-04T21:03:51.9822278Z set -x
2025-03-04T21:03:51.9822515Z 
2025-03-04T21:03:51.9822780Z PT_DOMAIN=download.pytorch.org
2025-03-04T21:03:51.9823366Z # TODO: Flaky access to download.pytorch.org https://github.com/pytorch/pytorch/issues/100400,
2025-03-04T21:03:51.9824117Z # cleaning this up once the issue is fixed. There are more than one resolved IP here, the last
2025-03-04T21:03:51.9824656Z # one is returned at random
2025-03-04T21:03:51.9825402Z RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" | tail -n1)
2025-03-04T21:03:51.9825962Z 
2025-03-04T21:03:51.9826450Z if [ -z "${RESOLVED_IP}" ]; then
2025-03-04T21:03:51.9826894Z   echo "Couldn't resolve ${PT_DOMAIN}, retrying with Google DNS..."
2025-03-04T21:03:51.9827417Z   RESOLVED_IP=$(dig -4 +short "${PT_DOMAIN}" @8.8.8.8 | tail -n1)
2025-03-04T21:03:51.9827829Z 
2025-03-04T21:03:51.9828088Z   if [ -z "${RESOLVED_IP}" ]; then
2025-03-04T21:03:51.9828486Z     echo "Couldn't resolve ${PT_DOMAIN}, exiting..."
2025-03-04T21:03:51.9828857Z     exit 1
2025-03-04T21:03:51.9829106Z   fi
2025-03-04T21:03:51.9829340Z fi
2025-03-04T21:03:51.9829567Z 
2025-03-04T21:03:51.9829842Z if grep -r "${PT_DOMAIN}" /etc/hosts; then
2025-03-04T21:03:51.9830222Z   # Clean up any old records first
2025-03-04T21:03:51.9830597Z   sudo sed -i "/${PT_DOMAIN}/d" /etc/hosts
2025-03-04T21:03:51.9830931Z fi
2025-03-04T21:03:51.9831158Z 
2025-03-04T21:03:51.9831492Z echo "${RESOLVED_IP} ${PT_DOMAIN}" | sudo tee -a /etc/hosts
2025-03-04T21:03:51.9831904Z cat /etc/hosts
2025-03-04T21:03:51.9840779Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:51.9841166Z env:
2025-03-04T21:03:51.9841401Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:51.9841720Z ##[endgroup]
2025-03-04T21:03:51.9873940Z + PT_DOMAIN=download.pytorch.org
2025-03-04T21:03:51.9880096Z ++ dig -4 +short download.pytorch.org
2025-03-04T21:03:51.9880657Z ++ tail -n1
2025-03-04T21:03:52.0388216Z + RESOLVED_IP=18.160.10.28
2025-03-04T21:03:52.0388524Z + '[' -z 18.160.10.28 ']'
2025-03-04T21:03:52.0388830Z + grep -r download.pytorch.org /etc/hosts
2025-03-04T21:03:52.0411512Z + echo '18.160.10.28 download.pytorch.org'
2025-03-04T21:03:52.0411905Z + sudo tee -a /etc/hosts
2025-03-04T21:03:52.4397588Z 18.160.10.28 download.pytorch.org
2025-03-04T21:03:52.4422720Z + cat /etc/hosts
2025-03-04T21:03:52.4433752Z 127.0.0.1   localhost localhost.localdomain localhost4 localhost4.localdomain4
2025-03-04T21:03:52.4439133Z ::1         localhost6 localhost6.localdomain6
2025-03-04T21:03:52.4439509Z 18.160.10.28 download.pytorch.org
2025-03-04T21:03:52.4604741Z ##[group]Run pytorch/test-infra/.github/actions/calculate-docker-image@main
2025-03-04T21:03:52.4605215Z with:
2025-03-04T21:03:52.4606048Z   docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:52.4606969Z   docker-build-dir: .ci/docker
2025-03-04T21:03:52.4607264Z   working-directory: .
2025-03-04T21:03:52.4607769Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:52.4608172Z   force-push: false
2025-03-04T21:03:52.4608417Z env:
2025-03-04T21:03:52.4608647Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:52.4608917Z ##[endgroup]
2025-03-04T21:03:52.4637560Z ##[group]Run set -ex
2025-03-04T21:03:52.4637877Z set -ex
2025-03-04T21:03:52.4638133Z 
2025-03-04T21:03:52.4638558Z # If the docker build directory or the build script doesn't exist, the action will
2025-03-04T21:03:52.4639250Z # gracefully return the docker image name as it is.  Pulling docker image in Linux
2025-03-04T21:03:52.4639830Z # job could then download the pre-built image as usual
2025-03-04T21:03:52.4640355Z if [[ ! -d "${DOCKER_BUILD_DIR}" ]] || [[ ! -f "${DOCKER_BUILD_DIR}/build.sh" ]]; then
2025-03-04T21:03:52.4640839Z   echo "skip=true" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4641298Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4641716Z 
2025-03-04T21:03:52.4642092Z   echo "There is no Docker build script in ${REPO_NAME} repo, skipping..."
2025-03-04T21:03:52.4642543Z   exit 0
2025-03-04T21:03:52.4642792Z else
2025-03-04T21:03:52.4643081Z   echo "skip=false" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4643417Z fi
2025-03-04T21:03:52.4643649Z 
2025-03-04T21:03:52.4644006Z if [[ "${DOCKER_IMAGE_NAME}" == *"${DOCKER_REGISTRY}/${REPO_NAME}"* ]]; then
2025-03-04T21:03:52.4644606Z   # The docker image name already includes the ECR prefix and tag, so we can just
2025-03-04T21:03:52.4645145Z   # use it as it is, but first let's extract the tag
2025-03-04T21:03:52.4645637Z   DOCKER_TAG=$(echo "${DOCKER_IMAGE_NAME}" | awk -F '[:,]' '{print $2}')
2025-03-04T21:03:52.4646154Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4646654Z   echo "docker-image=${DOCKER_IMAGE_NAME}" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4647075Z else
2025-03-04T21:03:52.4647404Z   DOCKER_TAG=$(git rev-parse HEAD:"${DOCKER_BUILD_DIR}")
2025-03-04T21:03:52.4648009Z   echo "docker-tag=${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4648642Z   echo "docker-image=${DOCKER_REGISTRY}/${REPO_NAME}/${DOCKER_IMAGE_NAME}:${DOCKER_TAG}" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4649203Z fi
2025-03-04T21:03:52.4661920Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:52.4662308Z env:
2025-03-04T21:03:52.4662541Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:52.4662825Z   REPO_NAME: pytorch
2025-03-04T21:03:52.4663686Z   DOCKER_IMAGE_NAME: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:52.4664600Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-04T21:03:52.4664993Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:52.4665395Z ##[endgroup]
2025-03-04T21:03:52.4695217Z + [[ ! -d .ci/docker ]]
2025-03-04T21:03:52.4695536Z + [[ ! -f .ci/docker/build.sh ]]
2025-03-04T21:03:52.4695847Z + echo skip=false
2025-03-04T21:03:52.4697186Z + [[ 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* ]]
2025-03-04T21:03:52.4703763Z ++ echo 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:52.4704655Z ++ awk -F '[:,]' '{print $2}'
2025-03-04T21:03:52.4730581Z + DOCKER_TAG=e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:52.4731168Z + echo docker-tag=e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:52.4732158Z + echo docker-image=308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:52.4766825Z ##[group]Run set +e
2025-03-04T21:03:52.4767147Z set +e
2025-03-04T21:03:52.4767393Z set -x
2025-03-04T21:03:52.4767740Z 
2025-03-04T21:03:52.4767959Z login() {
2025-03-04T21:03:52.4768469Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-04T21:03:52.4768988Z }
2025-03-04T21:03:52.4769213Z 
2025-03-04T21:03:52.4769440Z retry () {
2025-03-04T21:03:52.4769731Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-04T21:03:52.4770055Z }
2025-03-04T21:03:52.4770281Z 
2025-03-04T21:03:52.4770527Z retry login "${DOCKER_REGISTRY}"
2025-03-04T21:03:52.4770843Z 
2025-03-04T21:03:52.4771075Z START_TIME=$(date +%s)
2025-03-04T21:03:52.4771379Z # Wait up to 120 minutes
2025-03-04T21:03:52.4771749Z while [[ $(( $(date +%s) - 7200 )) -lt $START_TIME ]]; do
2025-03-04T21:03:52.4772239Z   # Check if image already exists, if it does then skip building it
2025-03-04T21:03:52.4772727Z   if docker manifest inspect "${DOCKER_IMAGE}"; then
2025-03-04T21:03:52.4773090Z     exit 0
2025-03-04T21:03:52.4773340Z   fi
2025-03-04T21:03:52.4773566Z 
2025-03-04T21:03:52.4773956Z   # NB: This flag is used by Docker build workflow to push the image to ECR, so we can
2025-03-04T21:03:52.4774606Z   # use this to differentiate between the Docker build and regular build jobs. For the
2025-03-04T21:03:52.4775253Z   # latter, it will wait for the Docker images to become available before continuing
2025-03-04T21:03:52.4775768Z   if [ "${DOCKER_PUSH:-false}" == "true" ]; then
2025-03-04T21:03:52.4776167Z     # It's a Docker build job, let's build the image
2025-03-04T21:03:52.4776519Z     break
2025-03-04T21:03:52.4776765Z   else
2025-03-04T21:03:52.4777111Z     # It's a regular build job, wait for the image to become available
2025-03-04T21:03:52.4777525Z     sleep 300
2025-03-04T21:03:52.4777783Z   fi
2025-03-04T21:03:52.4778014Z done
2025-03-04T21:03:52.4778248Z 
2025-03-04T21:03:52.4778599Z # NB: This part requires a full checkout. Otherwise, the merge base will
2025-03-04T21:03:52.4779167Z # be empty.  The default action would be to continue rebuild the image
2025-03-04T21:03:52.4779680Z if [[ "$BASE_REVISION" = "$(git rev-parse HEAD)" ]]; then
2025-03-04T21:03:52.4780137Z   # if we're on the base branch then use the parent commit
2025-03-04T21:03:52.4780546Z   MERGE_BASE=$(git rev-parse HEAD~)
2025-03-04T21:03:52.4780872Z else
2025-03-04T21:03:52.4781208Z   # otherwise we're on a PR, so use the most recent base commit
2025-03-04T21:03:52.4781679Z   MERGE_BASE=$(git merge-base HEAD "$BASE_REVISION")
2025-03-04T21:03:52.4782044Z fi
2025-03-04T21:03:52.4782270Z 
2025-03-04T21:03:52.4782517Z if [[ -z "${MERGE_BASE}" ]]; then
2025-03-04T21:03:52.4782882Z   echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4783390Z 
2025-03-04T21:03:52.4784001Z   echo "Finding merge base only works with full checkout, please set fetch-depth to 0, continuing ..."
2025-03-04T21:03:52.4784550Z   exit 0
2025-03-04T21:03:52.4784779Z fi
2025-03-04T21:03:52.4785002Z 
2025-03-04T21:03:52.4785320Z if ! git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}"; then
2025-03-04T21:03:52.4785993Z   echo "Directory '${DOCKER_BUILD_DIR}' not found in commit $MERGE_BASE, you should rebase onto a more recent commit"
2025-03-04T21:03:52.4786569Z   exit 1
2025-03-04T21:03:52.4786806Z fi
2025-03-04T21:03:52.4787029Z 
2025-03-04T21:03:52.4787392Z PREVIOUS_DOCKER_TAG=$(git rev-parse "${MERGE_BASE}:${DOCKER_BUILD_DIR}")
2025-03-04T21:03:52.4788038Z # If no image exists but the hash is the same as the previous hash then we should error out here
2025-03-04T21:03:52.4788625Z if [[ "${PREVIOUS_DOCKER_TAG}" == "${DOCKER_TAG}" ]]; then
2025-03-04T21:03:52.4789306Z   echo "WARNING: Something has gone wrong and the previous image isn't available for the merge-base of your branch"
2025-03-04T21:03:52.4790058Z   echo "         Will re-build docker image to store in local cache, TTS may be longer"
2025-03-04T21:03:52.4790516Z fi
2025-03-04T21:03:52.4790740Z 
2025-03-04T21:03:52.4791011Z echo "rebuild=true" >> "${GITHUB_OUTPUT}"
2025-03-04T21:03:52.4799310Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:52.4799688Z env:
2025-03-04T21:03:52.4799921Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:52.4800206Z   DOCKER_BUILD_DIR: .ci/docker
2025-03-04T21:03:52.4800554Z   BASE_REVISION: 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:03:52.4801496Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:52.4802441Z   DOCKER_TAG: e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:52.4802900Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:52.4803300Z   DOCKER_PUSH: 
2025-03-04T21:03:52.4803541Z ##[endgroup]
2025-03-04T21:03:52.4831007Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:52.4831469Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:52.4834088Z + aws ecr get-login-password --region us-east-1
2025-03-04T21:03:52.4835716Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:52.9935713Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T21:03:52.9936313Z Configure a credential helper to remove this warning. See
2025-03-04T21:03:52.9936921Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T21:03:52.9937420Z 
2025-03-04T21:03:52.9937573Z Login Succeeded
2025-03-04T21:03:52.9961858Z ++ date +%s
2025-03-04T21:03:52.9973663Z + START_TIME=1741122232
2025-03-04T21:03:52.9978096Z ++ date +%s
2025-03-04T21:03:52.9989979Z + [[ 1741115032 -lt 1741122232 ]]
2025-03-04T21:03:52.9990921Z + docker manifest inspect 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:53.2299187Z {
2025-03-04T21:03:53.2299521Z 	"schemaVersion": 2,
2025-03-04T21:03:53.2300037Z 	"mediaType": "application/vnd.docker.distribution.manifest.v2+json",
2025-03-04T21:03:53.2300481Z 	"config": {
2025-03-04T21:03:53.2300829Z 		"mediaType": "application/vnd.docker.container.image.v1+json",
2025-03-04T21:03:53.2301265Z 		"size": 52686,
2025-03-04T21:03:53.2301683Z 		"digest": "sha256:c79be20b310e0a73ffb7cab4b71fb60706b307c1344d8b5f619c83b5644451a4"
2025-03-04T21:03:53.2302159Z 	},
2025-03-04T21:03:53.2302364Z 	"layers": [
2025-03-04T21:03:53.2302583Z 		{
2025-03-04T21:03:53.2302925Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2303639Z 			"size": 28584506,
2025-03-04T21:03:53.2304219Z 			"digest": "sha256:80888bc6716fcbb8874e75ac88898d3e38e6f1bc55678f0e97ca9d706b7f3733"
2025-03-04T21:03:53.2304718Z 		},
2025-03-04T21:03:53.2304923Z 		{
2025-03-04T21:03:53.2305257Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
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2025-03-04T21:03:53.2306571Z 		},
2025-03-04T21:03:53.2306771Z 		{
2025-03-04T21:03:53.2307105Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
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2025-03-04T21:03:53.2307954Z 			"digest": "sha256:c7232af9ae05f7de83f8d6171bd0c35a4dd0a85ebafb15b950dbc08f89ea5fb5"
2025-03-04T21:03:53.2308424Z 		},
2025-03-04T21:03:53.2308638Z 		{
2025-03-04T21:03:53.2308964Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2309390Z 			"size": 185,
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2025-03-04T21:03:53.2322640Z 		},
2025-03-04T21:03:53.2322863Z 		{
2025-03-04T21:03:53.2323209Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2323633Z 			"size": 6886,
2025-03-04T21:03:53.2324048Z 			"digest": "sha256:56dc8550293751a1604e97ac949cfae82ba20cb2a28e034737bafd7382559609"
2025-03-04T21:03:53.2324510Z 		},
2025-03-04T21:03:53.2324717Z 		{
2025-03-04T21:03:53.2325364Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2325789Z 			"size": 1374287670,
2025-03-04T21:03:53.2326218Z 			"digest": "sha256:30c0ea6140d07e2a8deb70d780f277c63cf61836ff33d66eef944728a4bef6bd"
2025-03-04T21:03:53.2326687Z 		},
2025-03-04T21:03:53.2326889Z 		{
2025-03-04T21:03:53.2327226Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2327824Z 			"size": 62641,
2025-03-04T21:03:53.2328255Z 			"digest": "sha256:71bdb1a72c2d6dc97bbdbca82383f0260c4ee87556701e8e606c08a6bb0f0da5"
2025-03-04T21:03:53.2328728Z 		},
2025-03-04T21:03:53.2328934Z 		{
2025-03-04T21:03:53.2329264Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2329704Z 			"size": 1685,
2025-03-04T21:03:53.2330406Z 			"digest": "sha256:4829486be7c30f19f4136fa56adbb3de206ed0bbf0705b59fb2147406778ce38"
2025-03-04T21:03:53.2330870Z 		},
2025-03-04T21:03:53.2331075Z 		{
2025-03-04T21:03:53.2331408Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2331821Z 			"size": 1523,
2025-03-04T21:03:53.2332230Z 			"digest": "sha256:1f4e68d7b5e4224ba1da78ef461ff7f01e8d59c09d39281277521384105a9441"
2025-03-04T21:03:53.2332691Z 		},
2025-03-04T21:03:53.2332895Z 		{
2025-03-04T21:03:53.2333225Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2333643Z 			"size": 2626741021,
2025-03-04T21:03:53.2334059Z 			"digest": "sha256:7c373e2d9b7e82a6878d4a31293dd857915a0fe47d07dce541cea03b043d57fc"
2025-03-04T21:03:53.2334530Z 		},
2025-03-04T21:03:53.2334736Z 		{
2025-03-04T21:03:53.2335065Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2335474Z 			"size": 86545,
2025-03-04T21:03:53.2335873Z 			"digest": "sha256:6223811417458a3c93b84ee3b65f8b08d9e2828b926f0aed863041610d7d95d4"
2025-03-04T21:03:53.2336322Z 		},
2025-03-04T21:03:53.2336519Z 		{
2025-03-04T21:03:53.2336848Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2337258Z 			"size": 1894,
2025-03-04T21:03:53.2337662Z 			"digest": "sha256:e3590e4c540fe3d9400cf4c90c5a138a19302c5fb9c1b96f97b6071582ed82ee"
2025-03-04T21:03:53.2338119Z 		},
2025-03-04T21:03:53.2338320Z 		{
2025-03-04T21:03:53.2338651Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2339067Z 			"size": 643747544,
2025-03-04T21:03:53.2339504Z 			"digest": "sha256:aa7bdda81edf7db339e9ddab9e6bfd4c0e312f417237e63d1fdf616e22cb95ae"
2025-03-04T21:03:53.2340168Z 		},
2025-03-04T21:03:53.2340363Z 		{
2025-03-04T21:03:53.2340822Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2341239Z 			"size": 704,
2025-03-04T21:03:53.2341653Z 			"digest": "sha256:9f9fefb5046a43ae576dbf5ee5f4ea483a6990747f4095baa22c56b003e9ac22"
2025-03-04T21:03:53.2342123Z 		},
2025-03-04T21:03:53.2342326Z 		{
2025-03-04T21:03:53.2342657Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2343069Z 			"size": 1262,
2025-03-04T21:03:53.2343470Z 			"digest": "sha256:e4186ba0d4947562804310238fc6eec98d302b5a75c0aaa8960038fbf08e1848"
2025-03-04T21:03:53.2343929Z 		},
2025-03-04T21:03:53.2344204Z + exit 0
2025-03-04T21:03:53.2344412Z 		{
2025-03-04T21:03:53.2344743Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2345159Z 			"size": 484,
2025-03-04T21:03:53.2345560Z 			"digest": "sha256:4d0730fe24079b3419eeba8068a0314e48ef06090cc97589301b00c67e7bec42"
2025-03-04T21:03:53.2346023Z 		},
2025-03-04T21:03:53.2346216Z 		{
2025-03-04T21:03:53.2346550Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2346962Z 			"size": 91727944,
2025-03-04T21:03:53.2347379Z 			"digest": "sha256:00ff3e436638480d18ea0bf109a03c6be584e61d72c25aaaea8850a6a43e999a"
2025-03-04T21:03:53.2347839Z 		},
2025-03-04T21:03:53.2348041Z 		{
2025-03-04T21:03:53.2348370Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2348783Z 			"size": 3734,
2025-03-04T21:03:53.2349201Z 			"digest": "sha256:f389b7bb3dbcab304f2766d4dd8783c64f1d2f3c5f39b20c6bc8ddd463f4a073"
2025-03-04T21:03:53.2349671Z 		},
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2025-03-04T21:03:53.2430563Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2430969Z 			"size": 164,
2025-03-04T21:03:53.2431375Z 			"digest": "sha256:c65c25a44412b9e7b406af936db844fc4205ead0e6eaf0c620e3f0ca00b41b99"
2025-03-04T21:03:53.2432015Z 		},
2025-03-04T21:03:53.2432209Z 		{
2025-03-04T21:03:53.2432674Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2433085Z 			"size": 7943,
2025-03-04T21:03:53.2433491Z 			"digest": "sha256:334af9be5f1534e96aea2dffa1695580f99e1ab9903aca2cac0866dae82f963d"
2025-03-04T21:03:53.2433956Z 		},
2025-03-04T21:03:53.2434150Z 		{
2025-03-04T21:03:53.2434472Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2434879Z 			"size": 8068,
2025-03-04T21:03:53.2435284Z 			"digest": "sha256:a68ab8ed2202e1895a9d1df9228196f5674e2334ccb32343e37c5ba5cd50d0ef"
2025-03-04T21:03:53.2435738Z 		},
2025-03-04T21:03:53.2435938Z 		{
2025-03-04T21:03:53.2436258Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2436668Z 			"size": 304,
2025-03-04T21:03:53.2437072Z 			"digest": "sha256:ecff154b79617d07547a7c71d38a59e4283b299ee5d53d8ebadfaeaa3cf89717"
2025-03-04T21:03:53.2437545Z 		},
2025-03-04T21:03:53.2437736Z 		{
2025-03-04T21:03:53.2438072Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2438487Z 			"size": 7631417,
2025-03-04T21:03:53.2438898Z 			"digest": "sha256:97244b0b2fb26637890dbe363e4b5f9d81ef33406a5d17f0099f0de19b7d1e6f"
2025-03-04T21:03:53.2439352Z 		},
2025-03-04T21:03:53.2439541Z 		{
2025-03-04T21:03:53.2439866Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2440275Z 			"size": 108,
2025-03-04T21:03:53.2440688Z 			"digest": "sha256:e41e958aa2f34e93fad64f8c4975eb27bcdcdb30c99eec6e6edbd3a728c1b50c"
2025-03-04T21:03:53.2441160Z 		},
2025-03-04T21:03:53.2441353Z 		{
2025-03-04T21:03:53.2441673Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2442079Z 			"size": 54145661,
2025-03-04T21:03:53.2442479Z 			"digest": "sha256:9155937b2d4b468c53936507ab585220008b829949cc0a580d225a717f5487be"
2025-03-04T21:03:53.2442924Z 		},
2025-03-04T21:03:53.2443124Z 		{
2025-03-04T21:03:53.2443453Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2443859Z 			"size": 496,
2025-03-04T21:03:53.2444268Z 			"digest": "sha256:b6ec22f6cdfe3bd58be4d66314320333b87c12d6fe3579dd10895141b7bbceda"
2025-03-04T21:03:53.2444790Z 		},
2025-03-04T21:03:53.2445178Z 		{
2025-03-04T21:03:53.2445611Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2446138Z 			"size": 1374859390,
2025-03-04T21:03:53.2446707Z 			"digest": "sha256:5dc326dbf4a29c72a5bfe79c37247236d63fb8b07af5457207a0761ad20d309d"
2025-03-04T21:03:53.2447276Z 		},
2025-03-04T21:03:53.2447628Z 		{
2025-03-04T21:03:53.2448107Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2448623Z 			"size": 106,
2025-03-04T21:03:53.2449143Z 			"digest": "sha256:2d9aeb8c5240100c2a720f21fe309c466d1a845e3ae10f832d1918038deacd80"
2025-03-04T21:03:53.2449719Z 		},
2025-03-04T21:03:53.2450011Z 		{
2025-03-04T21:03:53.2450471Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2459138Z 			"size": 613,
2025-03-04T21:03:53.2459577Z 			"digest": "sha256:8aec19867d7fd0045b0aea454af65799b385f327584e6c773a5144d80d004e2d"
2025-03-04T21:03:53.2460033Z 		},
2025-03-04T21:03:53.2460222Z 		{
2025-03-04T21:03:53.2460549Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2460955Z 			"size": 303706146,
2025-03-04T21:03:53.2461365Z 			"digest": "sha256:e2a360747d0d39211d9609c225ba5b9746419efe220e77fa3f967895c372b2e0"
2025-03-04T21:03:53.2461808Z 		},
2025-03-04T21:03:53.2461995Z 		{
2025-03-04T21:03:53.2462315Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2462713Z 			"size": 111,
2025-03-04T21:03:53.2463105Z 			"digest": "sha256:4fefa8e1b32c3f9081a7c72150f967e6b30d256455017b6347c69861136866ad"
2025-03-04T21:03:53.2463544Z 		},
2025-03-04T21:03:53.2463733Z 		{
2025-03-04T21:03:53.2464164Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2464561Z 			"size": 531,
2025-03-04T21:03:53.2465043Z 			"digest": "sha256:4305d8ab30091246f2d04d7b1b817f0ee5fe39e0614a46ad53bf5c0d386b1b37"
2025-03-04T21:03:53.2465492Z 		},
2025-03-04T21:03:53.2465675Z 		{
2025-03-04T21:03:53.2465995Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2466395Z 			"size": 48289034,
2025-03-04T21:03:53.2466815Z 			"digest": "sha256:b081a7af2ce1e38c63ae9a85384cfadceb21420a793725a320a2cbe7827c7add"
2025-03-04T21:03:53.2467273Z 		},
2025-03-04T21:03:53.2467458Z 		{
2025-03-04T21:03:53.2467774Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2468178Z 			"size": 106,
2025-03-04T21:03:53.2468568Z 			"digest": "sha256:49f24dd0c353387bafdb8b57f9ac7747d8667328166698835cddd7d4fbd5fa3d"
2025-03-04T21:03:53.2469019Z 		},
2025-03-04T21:03:53.2469200Z 		{
2025-03-04T21:03:53.2469522Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2469931Z 			"size": 32,
2025-03-04T21:03:53.2470337Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T21:03:53.2470789Z 		},
2025-03-04T21:03:53.2470973Z 		{
2025-03-04T21:03:53.2471290Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2471684Z 			"size": 32,
2025-03-04T21:03:53.2472080Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T21:03:53.2472531Z 		},
2025-03-04T21:03:53.2472719Z 		{
2025-03-04T21:03:53.2473036Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2473431Z 			"size": 32,
2025-03-04T21:03:53.2473825Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T21:03:53.2474277Z 		},
2025-03-04T21:03:53.2474465Z 		{
2025-03-04T21:03:53.2474775Z 			"mediaType": "application/vnd.docker.image.rootfs.diff.tar.gzip",
2025-03-04T21:03:53.2475178Z 			"size": 32,
2025-03-04T21:03:53.2475581Z 			"digest": "sha256:4f4fb700ef54461cfa02571ae0db9a0dc1e0cdb5577484a6d75e68dc38e8acc1"
2025-03-04T21:03:53.2476033Z 		}
2025-03-04T21:03:53.2476222Z 	]
2025-03-04T21:03:53.2476407Z }
2025-03-04T21:03:53.2515021Z ##[group]Run set -eux
2025-03-04T21:03:53.2515300Z set -eux
2025-03-04T21:03:53.2516130Z 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
2025-03-04T21:03:53.2526868Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:53.2527248Z env:
2025-03-04T21:03:53.2527478Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:53.2527846Z ##[endgroup]
2025-03-04T21:03:53.2559479Z + aws secretsmanager get-secret-value --secret-id docker_hub_readonly_token
2025-03-04T21:03:53.2561094Z + jq --raw-output .SecretString
2025-03-04T21:03:53.2562106Z + jq -r .docker_hub_readonly_token
2025-03-04T21:03:53.2564011Z + docker login --username pytorchbot --password-stdin
2025-03-04T21:03:53.8214776Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T21:03:53.8215392Z Configure a credential helper to remove this warning. See
2025-03-04T21:03:53.8215974Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T21:03:53.8216368Z 
2025-03-04T21:03:53.8216504Z Login Succeeded
2025-03-04T21:03:53.8325459Z ##[group]Run tag=${ECR_DOCKER_IMAGE##*/}
2025-03-04T21:03:53.8325851Z tag=${ECR_DOCKER_IMAGE##*/}
2025-03-04T21:03:53.8326253Z echo "docker pull ghcr.io/pytorch/ci-image:${tag/:/-}"
2025-03-04T21:03:53.8335334Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:53.8335717Z env:
2025-03-04T21:03:53.8335984Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:53.8337023Z   ECR_DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:53.8338097Z ##[endgroup]
2025-03-04T21:03:53.8368128Z docker pull ghcr.io/pytorch/ci-image:pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks-e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:53.8423666Z ##[group]Run pytorch/test-infra/.github/actions/pull-docker-image@main
2025-03-04T21:03:53.8424112Z with:
2025-03-04T21:03:53.8425288Z   docker-image: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:53.8426439Z   docker-registry: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:53.8426833Z env:
2025-03-04T21:03:53.8427065Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:53.8427344Z ##[endgroup]
2025-03-04T21:03:53.8464203Z ##[group]Run set -x
2025-03-04T21:03:53.8464487Z set -x
2025-03-04T21:03:53.8464726Z set +e
2025-03-04T21:03:53.8464955Z 
2025-03-04T21:03:53.8465187Z login() {
2025-03-04T21:03:53.8465663Z   aws ecr get-login-password --region us-east-1 | docker login -u AWS --password-stdin "$1"
2025-03-04T21:03:53.8466176Z }
2025-03-04T21:03:53.8466397Z 
2025-03-04T21:03:53.8466657Z retry () {
2025-03-04T21:03:53.8466935Z   $*  || (sleep 1 && $*) || (sleep 2 && $*)
2025-03-04T21:03:53.8467261Z }
2025-03-04T21:03:53.8467479Z 
2025-03-04T21:03:53.8467726Z retry login "${DOCKER_REGISTRY}"
2025-03-04T21:03:53.8468039Z 
2025-03-04T21:03:53.8468258Z set -e
2025-03-04T21:03:53.8468609Z # ignore output since only exit code is used for conditional
2025-03-04T21:03:53.8469103Z # only pull docker image if it's not available locally
2025-03-04T21:03:53.8469643Z if ! docker inspect --type=image "${DOCKER_IMAGE}" >/dev/null 2>/dev/null; then
2025-03-04T21:03:53.8470144Z   retry docker pull "${DOCKER_IMAGE}"
2025-03-04T21:03:53.8470482Z fi
2025-03-04T21:03:53.8479176Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:03:53.8479558Z env:
2025-03-04T21:03:53.8479790Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:03:53.8480657Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:53.8481632Z   DOCKER_REGISTRY: 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:53.8482027Z ##[endgroup]
2025-03-04T21:03:53.8511124Z + set +e
2025-03-04T21:03:53.8511457Z + retry login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:53.8511909Z + login 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:53.8515565Z + aws ecr get-login-password --region us-east-1
2025-03-04T21:03:53.8516567Z + docker login -u AWS --password-stdin 308535385114.dkr.ecr.us-east-1.amazonaws.com
2025-03-04T21:03:54.3611135Z WARNING! Your password will be stored unencrypted in /home/ec2-user/.docker/config.json.
2025-03-04T21:03:54.3612103Z Configure a credential helper to remove this warning. See
2025-03-04T21:03:54.3612701Z https://docs.docker.com/engine/reference/commandline/login/#credentials-store
2025-03-04T21:03:54.3613106Z 
2025-03-04T21:03:54.3613213Z Login Succeeded
2025-03-04T21:03:54.3635428Z + set -e
2025-03-04T21:03:54.3636317Z + docker inspect --type=image 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:54.3789468Z + retry docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:54.3790982Z + docker pull 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:03:54.6311195Z e4800fd93ba7d48bf4197a488fd32c12de647b0e: Pulling from pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks
2025-03-04T21:03:54.6314124Z 80888bc6716f: Pulling fs layer
2025-03-04T21:03:54.6315539Z fbcd35dc5bc3: Pulling fs layer
2025-03-04T21:03:54.6316417Z c7232af9ae05: Pulling fs layer
2025-03-04T21:03:54.6316777Z db6cdef1932a: Pulling fs layer
2025-03-04T21:03:54.6317204Z 56dc85502937: Pulling fs layer
2025-03-04T21:03:54.6317620Z 30c0ea6140d0: Pulling fs layer
2025-03-04T21:03:54.6318056Z 71bdb1a72c2d: Pulling fs layer
2025-03-04T21:03:54.6318453Z 4829486be7c3: Pulling fs layer
2025-03-04T21:03:54.6318857Z 1f4e68d7b5e4: Pulling fs layer
2025-03-04T21:03:54.6319261Z 7c373e2d9b7e: Pulling fs layer
2025-03-04T21:03:54.6319660Z 622381141745: Pulling fs layer
2025-03-04T21:03:54.6320053Z e3590e4c540f: Pulling fs layer
2025-03-04T21:03:54.6320461Z aa7bdda81edf: Pulling fs layer
2025-03-04T21:03:54.6320869Z 9f9fefb5046a: Pulling fs layer
2025-03-04T21:03:54.6321280Z e4186ba0d494: Pulling fs layer
2025-03-04T21:03:54.6321704Z 4d0730fe2407: Pulling fs layer
2025-03-04T21:03:54.6322104Z 00ff3e436638: Pulling fs layer
2025-03-04T21:03:54.6322515Z f389b7bb3dbc: Pulling fs layer
2025-03-04T21:03:54.6322924Z a252197d2b71: Pulling fs layer
2025-03-04T21:03:54.6323334Z 65a216d1be48: Pulling fs layer
2025-03-04T21:03:54.6323735Z e265fc00a638: Pulling fs layer
2025-03-04T21:03:54.6324133Z 268644de4c3a: Pulling fs layer
2025-03-04T21:03:54.6324540Z 7dd6b48df5d3: Pulling fs layer
2025-03-04T21:03:54.6325184Z 617552105e2b: Pulling fs layer
2025-03-04T21:03:54.6325615Z 2834a2b2342c: Pulling fs layer
2025-03-04T21:03:54.6326017Z 76a0ad9d05e9: Pulling fs layer
2025-03-04T21:03:54.6326410Z 7c373e2d9b7e: Waiting
2025-03-04T21:03:54.6326759Z 622381141745: Waiting
2025-03-04T21:03:54.6327107Z 71bdb1a72c2d: Waiting
2025-03-04T21:03:54.6327447Z 1f4e68d7b5e4: Waiting
2025-03-04T21:03:54.6328020Z 4829486be7c3: Waiting
2025-03-04T21:03:54.6328381Z 59ca559b9d88: Pulling fs layer
2025-03-04T21:03:54.6328785Z c63808a61495: Pulling fs layer
2025-03-04T21:03:54.6329499Z e3590e4c540f: Waiting
2025-03-04T21:03:54.6329928Z ad8b390aef2e: Pulling fs layer
2025-03-04T21:03:54.6330328Z 475565e4c86d: Pulling fs layer
2025-03-04T21:03:54.6330724Z cd07fae473e4: Pulling fs layer
2025-03-04T21:03:54.6331129Z f5604a190caf: Pulling fs layer
2025-03-04T21:03:54.6331516Z db6cdef1932a: Waiting
2025-03-04T21:03:54.6331860Z 7dd6b48df5d3: Waiting
2025-03-04T21:03:54.6332198Z 617552105e2b: Waiting
2025-03-04T21:03:54.6332503Z 475565e4c86d: Waiting
2025-03-04T21:03:54.6332747Z 2834a2b2342c: Waiting
2025-03-04T21:03:54.6333055Z 65a216d1be48: Waiting
2025-03-04T21:03:54.6333316Z bf0779a5af46: Pulling fs layer
2025-03-04T21:03:54.6333592Z e265fc00a638: Waiting
2025-03-04T21:03:54.6333829Z 76a0ad9d05e9: Waiting
2025-03-04T21:03:54.6334094Z 3a4c33aae4fa: Pulling fs layer
2025-03-04T21:03:54.6334420Z 268644de4c3a: Waiting
2025-03-04T21:03:54.6334663Z a252197d2b71: Waiting
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2025-03-04T21:03:54.6335535Z 9f9fefb5046a: Waiting
2025-03-04T21:03:54.6335781Z e4186ba0d494: Waiting
2025-03-04T21:03:54.6336354Z d91b2ac229a3: Pulling fs layer
2025-03-04T21:03:54.6336633Z f5604a190caf: Waiting
2025-03-04T21:03:54.6336885Z c63808a61495: Waiting
2025-03-04T21:03:54.6337132Z 4d0730fe2407: Waiting
2025-03-04T21:03:54.6337405Z ddbeef574b46: Pulling fs layer
2025-03-04T21:03:54.6337685Z 00ff3e436638: Waiting
2025-03-04T21:03:54.6337924Z ad8b390aef2e: Waiting
2025-03-04T21:03:54.6338184Z 94a55c9da9f6: Pulling fs layer
2025-03-04T21:03:54.6338460Z 3a4c33aae4fa: Waiting
2025-03-04T21:03:54.6338705Z 30c0ea6140d0: Waiting
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2025-03-04T21:03:54.6339992Z 59ca559b9d88: Waiting
2025-03-04T21:03:54.6340250Z 2732bb0bde0a: Pulling fs layer
2025-03-04T21:03:54.6340543Z ed3c15f0cb6d: Pulling fs layer
2025-03-04T21:03:54.6341151Z 5aa3189882c0: Pulling fs layer
2025-03-04T21:03:54.6341551Z 9d56755a4336: Pulling fs layer
2025-03-04T21:03:54.6341841Z 289b1f9140de: Pulling fs layer
2025-03-04T21:03:54.6342218Z 1a19b9ebf73a: Pulling fs layer
2025-03-04T21:03:54.6342658Z ed3c15f0cb6d: Waiting
2025-03-04T21:03:54.6342909Z 2732bb0bde0a: Waiting
2025-03-04T21:03:54.6343152Z 9d56755a4336: Waiting
2025-03-04T21:03:54.6343597Z b9c871e2218d: Pulling fs layer
2025-03-04T21:03:54.6343886Z 3e438d169ab9: Pulling fs layer
2025-03-04T21:03:54.6344164Z 1a19b9ebf73a: Waiting
2025-03-04T21:03:54.6344570Z c8151f73c550: Pulling fs layer
2025-03-04T21:03:54.6344906Z e6abce57861b: Pulling fs layer
2025-03-04T21:03:54.6345185Z b9c871e2218d: Waiting
2025-03-04T21:03:54.6345461Z 181a6d443194: Pulling fs layer
2025-03-04T21:03:54.6345747Z a4f66d4bc954: Pulling fs layer
2025-03-04T21:03:54.6346024Z 3e438d169ab9: Waiting
2025-03-04T21:03:54.6346272Z c8151f73c550: Waiting
2025-03-04T21:03:54.6346528Z 56c9d3d8c176: Pulling fs layer
2025-03-04T21:03:54.6346813Z 181a6d443194: Waiting
2025-03-04T21:03:54.6347071Z 2c4565619bd3: Pulling fs layer
2025-03-04T21:03:54.6347349Z e6abce57861b: Waiting
2025-03-04T21:03:54.6347602Z 8163be9f2fc1: Pulling fs layer
2025-03-04T21:03:54.6347879Z 56c9d3d8c176: Waiting
2025-03-04T21:03:54.6348136Z 2c4565619bd3: Waiting
2025-03-04T21:03:54.6348391Z 93e47cba5617: Pulling fs layer
2025-03-04T21:03:54.6348734Z 15595d813536: Pulling fs layer
2025-03-04T21:03:54.6349009Z 8163be9f2fc1: Waiting
2025-03-04T21:03:54.6349267Z 4ce74c7e346b: Pulling fs layer
2025-03-04T21:03:54.6349598Z 4f4fb700ef54: Pulling fs layer
2025-03-04T21:03:54.6349882Z 389263d39fc8: Pulling fs layer
2025-03-04T21:03:54.6350155Z 93e47cba5617: Waiting
2025-03-04T21:03:54.6350413Z 57f38fb7ba59: Pulling fs layer
2025-03-04T21:03:54.6350684Z 4ce74c7e346b: Waiting
2025-03-04T21:03:54.6350941Z 1cb21e96a9e2: Pulling fs layer
2025-03-04T21:03:54.6351221Z 4f4fb700ef54: Waiting
2025-03-04T21:03:54.6351560Z c65c25a44412: Pulling fs layer
2025-03-04T21:03:54.6351832Z 15595d813536: Waiting
2025-03-04T21:03:54.6352073Z 57f38fb7ba59: Waiting
2025-03-04T21:03:54.6352488Z 389263d39fc8: Waiting
2025-03-04T21:03:54.6352745Z 334af9be5f15: Pulling fs layer
2025-03-04T21:03:54.6353020Z 1cb21e96a9e2: Waiting
2025-03-04T21:03:54.6353279Z a68ab8ed2202: Pulling fs layer
2025-03-04T21:03:54.6353555Z c65c25a44412: Waiting
2025-03-04T21:03:54.6353812Z ecff154b7961: Pulling fs layer
2025-03-04T21:03:54.6355539Z 97244b0b2fb2: Pulling fs layer
2025-03-04T21:03:54.6355825Z e41e958aa2f3: Pulling fs layer
2025-03-04T21:03:54.6356111Z 9155937b2d4b: Pulling fs layer
2025-03-04T21:03:54.6356400Z b6ec22f6cdfe: Pulling fs layer
2025-03-04T21:03:54.6356686Z 5dc326dbf4a2: Pulling fs layer
2025-03-04T21:03:54.6356970Z 2d9aeb8c5240: Pulling fs layer
2025-03-04T21:03:54.6357347Z ecff154b7961: Waiting
2025-03-04T21:03:54.6357599Z 8aec19867d7f: Pulling fs layer
2025-03-04T21:03:54.6357889Z e2a360747d0d: Pulling fs layer
2025-03-04T21:03:54.6358175Z 4fefa8e1b32c: Pulling fs layer
2025-03-04T21:03:54.6358467Z 4305d8ab3009: Pulling fs layer
2025-03-04T21:03:54.6358753Z b081a7af2ce1: Pulling fs layer
2025-03-04T21:03:54.6359029Z 97244b0b2fb2: Waiting
2025-03-04T21:03:54.6359287Z 49f24dd0c353: Pulling fs layer
2025-03-04T21:03:54.6359657Z e41e958aa2f3: Waiting
2025-03-04T21:03:54.6359913Z 2d9aeb8c5240: Waiting
2025-03-04T21:03:54.6360243Z e2a360747d0d: Waiting
2025-03-04T21:03:54.6360491Z 5dc326dbf4a2: Waiting
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2025-03-04T21:03:54.6360988Z b6ec22f6cdfe: Waiting
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2025-03-04T21:03:54.6361556Z 4305d8ab3009: Waiting
2025-03-04T21:03:54.6361794Z b081a7af2ce1: Waiting
2025-03-04T21:03:54.7663436Z fbcd35dc5bc3: Verifying Checksum
2025-03-04T21:03:54.7664013Z fbcd35dc5bc3: Download complete
2025-03-04T21:03:54.8582374Z db6cdef1932a: Verifying Checksum
2025-03-04T21:03:54.8582857Z db6cdef1932a: Download complete
2025-03-04T21:03:54.9379149Z 56dc85502937: Verifying Checksum
2025-03-04T21:03:54.9379588Z 56dc85502937: Download complete
2025-03-04T21:03:55.1907345Z 80888bc6716f: Verifying Checksum
2025-03-04T21:03:55.1907706Z 80888bc6716f: Download complete
2025-03-04T21:03:55.2538084Z c7232af9ae05: Verifying Checksum
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2025-03-04T21:03:55.2666513Z 71bdb1a72c2d: Verifying Checksum
2025-03-04T21:03:55.2667015Z 71bdb1a72c2d: Download complete
2025-03-04T21:03:55.3436023Z 4829486be7c3: Verifying Checksum
2025-03-04T21:03:55.3436512Z 4829486be7c3: Download complete
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2025-03-04T21:03:56.2717506Z 80888bc6716f: Pull complete
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2025-03-04T21:04:02.0278987Z aa7bdda81edf: Verifying Checksum
2025-03-04T21:04:02.0279563Z aa7bdda81edf: Download complete
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2025-03-04T21:04:03.4251788Z f389b7bb3dbc: Verifying Checksum
2025-03-04T21:04:03.4252170Z f389b7bb3dbc: Download complete
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2025-03-04T21:04:03.5976246Z 65a216d1be48: Download complete
2025-03-04T21:04:03.6881684Z e265fc00a638: Verifying Checksum
2025-03-04T21:04:03.6882133Z e265fc00a638: Download complete
2025-03-04T21:04:08.7302806Z 30c0ea6140d0: Download complete
2025-03-04T21:04:08.8155859Z 7dd6b48df5d3: Verifying Checksum
2025-03-04T21:04:08.8156344Z 7dd6b48df5d3: Download complete
2025-03-04T21:04:08.9039104Z 617552105e2b: Download complete
2025-03-04T21:04:08.9978941Z 2834a2b2342c: Verifying Checksum
2025-03-04T21:04:08.9979425Z 2834a2b2342c: Download complete
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2025-03-04T21:04:10.4738508Z c63808a61495: Verifying Checksum
2025-03-04T21:04:10.4738994Z c63808a61495: Download complete
2025-03-04T21:04:10.5752070Z ad8b390aef2e: Verifying Checksum
2025-03-04T21:04:10.5752456Z ad8b390aef2e: Download complete
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2025-03-04T21:04:10.9407840Z bf0779a5af46: Verifying Checksum
2025-03-04T21:04:10.9408191Z bf0779a5af46: Download complete
2025-03-04T21:04:15.2911750Z 3a4c33aae4fa: Verifying Checksum
2025-03-04T21:04:15.2912167Z 3a4c33aae4fa: Download complete
2025-03-04T21:04:15.3732809Z d91b2ac229a3: Download complete
2025-03-04T21:04:15.4552581Z ddbeef574b46: Verifying Checksum
2025-03-04T21:04:15.4552982Z ddbeef574b46: Download complete
2025-03-04T21:04:15.8864689Z 94a55c9da9f6: Verifying Checksum
2025-03-04T21:04:15.8865106Z 94a55c9da9f6: Download complete
2025-03-04T21:04:16.1924651Z 2732bb0bde0a: Verifying Checksum
2025-03-04T21:04:16.1925353Z 2732bb0bde0a: Download complete
2025-03-04T21:04:16.4890095Z ed3c15f0cb6d: Verifying Checksum
2025-03-04T21:04:16.4890594Z ed3c15f0cb6d: Download complete
2025-03-04T21:04:16.7625749Z 5aa3189882c0: Verifying Checksum
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2025-03-04T21:04:16.8465151Z 9d56755a4336: Download complete
2025-03-04T21:04:16.9193176Z 289b1f9140de: Verifying Checksum
2025-03-04T21:04:16.9193692Z 289b1f9140de: Download complete
2025-03-04T21:04:17.0048518Z 1a19b9ebf73a: Verifying Checksum
2025-03-04T21:04:17.0049235Z 1a19b9ebf73a: Download complete
2025-03-04T21:04:21.0149358Z 30c0ea6140d0: Pull complete
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2025-03-04T21:04:21.4992262Z 1f4e68d7b5e4: Pull complete
2025-03-04T21:04:21.6721716Z 7c373e2d9b7e: Verifying Checksum
2025-03-04T21:04:21.6722089Z 7c373e2d9b7e: Download complete
2025-03-04T21:04:21.7524550Z 3e438d169ab9: Verifying Checksum
2025-03-04T21:04:21.7525249Z 3e438d169ab9: Download complete
2025-03-04T21:04:21.8624314Z c8151f73c550: Download complete
2025-03-04T21:04:22.3649713Z e6abce57861b: Verifying Checksum
2025-03-04T21:04:22.3650237Z e6abce57861b: Download complete
2025-03-04T21:04:22.4393660Z 181a6d443194: Verifying Checksum
2025-03-04T21:04:22.4394284Z 181a6d443194: Download complete
2025-03-04T21:04:22.5236000Z a4f66d4bc954: Verifying Checksum
2025-03-04T21:04:22.5236483Z a4f66d4bc954: Download complete
2025-03-04T21:04:22.5868336Z 56c9d3d8c176: Download complete
2025-03-04T21:04:22.6643409Z 2c4565619bd3: Verifying Checksum
2025-03-04T21:04:22.6643859Z 2c4565619bd3: Download complete
2025-03-04T21:04:32.8305954Z 268644de4c3a: Verifying Checksum
2025-03-04T21:04:32.8306318Z 268644de4c3a: Download complete
2025-03-04T21:04:32.9360373Z 93e47cba5617: Verifying Checksum
2025-03-04T21:04:32.9360738Z 93e47cba5617: Download complete
2025-03-04T21:04:33.0317741Z 15595d813536: Verifying Checksum
2025-03-04T21:04:33.0318125Z 15595d813536: Download complete
2025-03-04T21:04:33.1457795Z 4ce74c7e346b: Verifying Checksum
2025-03-04T21:04:33.1458186Z 4ce74c7e346b: Download complete
2025-03-04T21:04:33.1590461Z 4f4fb700ef54: Download complete
2025-03-04T21:04:33.2439777Z 389263d39fc8: Verifying Checksum
2025-03-04T21:04:33.2440141Z 389263d39fc8: Download complete
2025-03-04T21:04:33.3432996Z 57f38fb7ba59: Verifying Checksum
2025-03-04T21:04:33.3433445Z 57f38fb7ba59: Download complete
2025-03-04T21:04:35.3703743Z 1cb21e96a9e2: Verifying Checksum
2025-03-04T21:04:35.3704166Z 1cb21e96a9e2: Download complete
2025-03-04T21:04:35.4700432Z c65c25a44412: Verifying Checksum
2025-03-04T21:04:35.4700772Z c65c25a44412: Download complete
2025-03-04T21:04:35.7774416Z 334af9be5f15: Verifying Checksum
2025-03-04T21:04:35.7774817Z 334af9be5f15: Download complete
2025-03-04T21:04:35.8551378Z a68ab8ed2202: Verifying Checksum
2025-03-04T21:04:35.8551744Z a68ab8ed2202: Download complete
2025-03-04T21:04:35.9741541Z ecff154b7961: Verifying Checksum
2025-03-04T21:04:35.9741882Z ecff154b7961: Download complete
2025-03-04T21:04:36.1145379Z 97244b0b2fb2: Verifying Checksum
2025-03-04T21:04:36.1145751Z 97244b0b2fb2: Download complete
2025-03-04T21:04:36.1929536Z e41e958aa2f3: Verifying Checksum
2025-03-04T21:04:36.1929908Z e41e958aa2f3: Download complete
2025-03-04T21:04:36.9652159Z 9155937b2d4b: Verifying Checksum
2025-03-04T21:04:36.9652673Z 9155937b2d4b: Download complete
2025-03-04T21:04:37.0483915Z b6ec22f6cdfe: Verifying Checksum
2025-03-04T21:04:37.0484286Z b6ec22f6cdfe: Download complete
2025-03-04T21:05:15.2532487Z 5dc326dbf4a2: Verifying Checksum
2025-03-04T21:05:15.2533028Z 5dc326dbf4a2: Download complete
2025-03-04T21:05:15.3442566Z 2d9aeb8c5240: Verifying Checksum
2025-03-04T21:05:15.3443011Z 2d9aeb8c5240: Download complete
2025-03-04T21:05:15.4203453Z 8aec19867d7f: Verifying Checksum
2025-03-04T21:05:15.4203876Z 8aec19867d7f: Download complete
2025-03-04T21:05:23.8352410Z e2a360747d0d: Verifying Checksum
2025-03-04T21:05:23.8352848Z e2a360747d0d: Download complete
2025-03-04T21:05:23.9247111Z 4fefa8e1b32c: Download complete
2025-03-04T21:05:24.0833536Z 4305d8ab3009: Verifying Checksum
2025-03-04T21:05:24.0833909Z 4305d8ab3009: Download complete
2025-03-04T21:05:25.3329781Z b081a7af2ce1: Verifying Checksum
2025-03-04T21:05:25.3330177Z b081a7af2ce1: Download complete
2025-03-04T21:05:25.4209411Z 49f24dd0c353: Verifying Checksum
2025-03-04T21:05:25.4209765Z 49f24dd0c353: Download complete
2025-03-04T21:05:52.2530401Z b9c871e2218d: Verifying Checksum
2025-03-04T21:05:52.2532356Z b9c871e2218d: Download complete
2025-03-04T21:06:02.8879975Z 8163be9f2fc1: Verifying Checksum
2025-03-04T21:06:02.8880340Z 8163be9f2fc1: Download complete
2025-03-04T21:06:14.1999249Z 7c373e2d9b7e: Pull complete
2025-03-04T21:06:14.4308822Z 622381141745: Pull complete
2025-03-04T21:06:14.6540082Z e3590e4c540f: Pull complete
2025-03-04T21:06:25.6242213Z aa7bdda81edf: Pull complete
2025-03-04T21:06:25.8244205Z 9f9fefb5046a: Pull complete
2025-03-04T21:06:26.0244610Z e4186ba0d494: Pull complete
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2025-03-04T21:06:28.5311981Z 00ff3e436638: Pull complete
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2025-03-04T21:06:29.0007387Z a252197d2b71: Pull complete
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2025-03-04T21:07:34.1122287Z d91b2ac229a3: Pull complete
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2025-03-04T21:07:35.9209312Z 5aa3189882c0: Pull complete
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2025-03-04T21:08:10.4766621Z b9c871e2218d: Pull complete
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2025-03-04T21:10:16.3823435Z 97244b0b2fb2: Pull complete
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2025-03-04T21:10:35.1922805Z 4305d8ab3009: Pull complete
2025-03-04T21:10:35.8244180Z b081a7af2ce1: Pull complete
2025-03-04T21:10:36.0361584Z 49f24dd0c353: Pull complete
2025-03-04T21:10:36.9466402Z Digest: sha256:10bcf94a2effce3256f6633d4b69f9e7f22aa569173ddbb4eab262e4cf671bbc
2025-03-04T21:10:36.9885325Z 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
2025-03-04T21:10:37.0129200Z 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:10:37.0197896Z ##[group]Run echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T21:10:37.0198834Z echo "IN_CONTAINER_RUNNER=$(if [ -f /.inarc ] || [ -f /.incontainer ]; then echo true ; else echo false; fi)" >> "$GITHUB_OUTPUT"
2025-03-04T21:10:37.0210467Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:10:37.0210851Z env:
2025-03-04T21:10:37.0211082Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:10:37.0211357Z ##[endgroup]
2025-03-04T21:10:37.0488213Z ##[group]Run pytorch/test-infra/.github/actions/setup-nvidia@main
2025-03-04T21:10:37.0488644Z with:
2025-03-04T21:10:37.0488883Z   driver-version: 550.54.15
2025-03-04T21:10:37.0489157Z env:
2025-03-04T21:10:37.0489382Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:10:37.0489658Z ##[endgroup]
2025-03-04T21:10:37.0690938Z ##[group]Run nick-fields/retry@3e91a01664abd3c5cd539100d10d33b9c5b68482
2025-03-04T21:10:37.0691354Z with:
2025-03-04T21:10:37.0691572Z   timeout_minutes: 10
2025-03-04T21:10:37.0691828Z   max_attempts: 3
2025-03-04T21:10:37.0716233Z   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

2025-03-04T21:10:37.0740816Z   retry_wait_seconds: 10
2025-03-04T21:10:37.0741106Z   polling_interval_seconds: 1
2025-03-04T21:10:37.0741397Z   warning_on_retry: true
2025-03-04T21:10:37.0741672Z   continue_on_error: false
2025-03-04T21:10:37.0741939Z env:
2025-03-04T21:10:37.0742159Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:10:37.0742431Z   DRIVER_VERSION: 550.54.15
2025-03-04T21:10:37.0742693Z ##[endgroup]
2025-03-04T21:10:37.1706060Z == Installing nvidia driver NVIDIA-Linux-x86_64-550.54.15.run ==
2025-03-04T21:10:37.1707835Z + pre_install_nvidia_driver_amzn2
2025-03-04T21:10:37.1708481Z + sudo yum remove -y nvidia-driver-latest-dkms
2025-03-04T21:10:37.7289405Z No match for argument: nvidia-driver-latest-dkms
2025-03-04T21:10:37.7289862Z No packages marked for removal.
2025-03-04T21:10:37.7353633Z Dependencies resolved.
2025-03-04T21:10:37.7363131Z Nothing to do.
2025-03-04T21:10:37.7363665Z Complete!
2025-03-04T21:10:37.7977042Z + install_nvidia_driver_common
2025-03-04T21:10:37.7980968Z + echo 'Before installing NVIDIA driver'
2025-03-04T21:10:37.7981320Z + lspci
2025-03-04T21:10:37.7982508Z Before installing NVIDIA driver
2025-03-04T21:10:37.8928761Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma]
2025-03-04T21:10:37.8929302Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II]
2025-03-04T21:10:37.8929886Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08)
2025-03-04T21:10:37.8930442Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111
2025-03-04T21:10:37.8930956Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller
2025-03-04T21:10:37.8931521Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA)
2025-03-04T21:10:37.8932047Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1)
2025-03-04T21:10:37.8932567Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller
2025-03-04T21:10:37.8933334Z + lsmod
2025-03-04T21:10:37.8973362Z Module                  Size  Used by
2025-03-04T21:10:37.8973876Z xt_conntrack           16384  1
2025-03-04T21:10:37.8974285Z nft_chain_nat          16384  3
2025-03-04T21:10:37.8974579Z xt_MASQUERADE          20480  1
2025-03-04T21:10:37.8974912Z nf_nat                 57344  2 nft_chain_nat,xt_MASQUERADE
2025-03-04T21:10:37.8975281Z nf_conntrack_netlink    57344  0
2025-03-04T21:10:37.8975796Z nf_conntrack          184320  4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE
2025-03-04T21:10:37.8976486Z nf_defrag_ipv6         24576  1 nf_conntrack
2025-03-04T21:10:37.8976834Z nf_defrag_ipv4         16384  1 nf_conntrack
2025-03-04T21:10:37.8977333Z xfrm_user              57344  1
2025-03-04T21:10:37.8977783Z xfrm_algo              16384  1 xfrm_user
2025-03-04T21:10:37.8978240Z xt_addrtype            16384  2
2025-03-04T21:10:37.8978633Z nft_compat             20480  4
2025-03-04T21:10:37.8978976Z nf_tables             311296  57 nft_compat,nft_chain_nat
2025-03-04T21:10:37.8979458Z nfnetlink              20480  4 nft_compat,nf_conntrack_netlink,nf_tables
2025-03-04T21:10:37.8991443Z br_netfilter           36864  0
2025-03-04T21:10:37.8991866Z bridge                323584  1 br_netfilter
2025-03-04T21:10:37.8992304Z stp                    16384  1 bridge
2025-03-04T21:10:37.8992626Z llc                    16384  2 bridge,stp
2025-03-04T21:10:37.8992945Z overlay               167936  0
2025-03-04T21:10:37.8993228Z tls                   135168  0
2025-03-04T21:10:37.8993506Z nls_ascii              16384  1
2025-03-04T21:10:37.8993787Z nls_cp437              20480  1
2025-03-04T21:10:37.8994066Z vfat                   24576  1
2025-03-04T21:10:37.8994348Z fat                    86016  1 vfat
2025-03-04T21:10:37.8994642Z sunrpc                696320  1
2025-03-04T21:10:37.8994916Z ena                   180224  0
2025-03-04T21:10:37.8995190Z i8042                  45056  0
2025-03-04T21:10:37.8995476Z serio                  28672  3 i8042
2025-03-04T21:10:37.8995779Z button                 24576  0
2025-03-04T21:10:37.8996250Z ghash_clmulni_intel    16384  0
2025-03-04T21:10:37.8996546Z sch_fq_codel           20480  17
2025-03-04T21:10:37.8996842Z dm_mod                188416  0
2025-03-04T21:10:37.8997123Z loop                   36864  0
2025-03-04T21:10:37.8997438Z fuse                  163840  1
2025-03-04T21:10:37.8997859Z configfs               57344  1
2025-03-04T21:10:37.8998147Z dax                    45056  1 dm_mod
2025-03-04T21:10:37.8998492Z dmi_sysfs              20480  0
2025-03-04T21:10:37.8998774Z crc32_pclmul           16384  0
2025-03-04T21:10:37.8999056Z crc32c_intel           24576  0
2025-03-04T21:10:37.8999330Z efivarfs               24576  1
2025-03-04T21:10:37.8999595Z + modinfo nvidia
2025-03-04T21:10:37.9000003Z filename:       /lib/modules/6.1.128-136.201.amzn2023.x86_64/kernel/drivers/video/nvidia.ko
2025-03-04T21:10:37.9000497Z alias:          char-major-195-*
2025-03-04T21:10:37.9000794Z version:        550.54.15
2025-03-04T21:10:37.9001074Z supported:      external
2025-03-04T21:10:37.9001342Z license:        NVIDIA
2025-03-04T21:10:37.9001630Z firmware:       nvidia/550.54.15/gsp_tu10x.bin
2025-03-04T21:10:37.9002261Z firmware:       nvidia/550.54.15/gsp_ga10x.bin
2025-03-04T21:10:37.9002610Z srcversion:     833721318DA517F0C2FEC97
2025-03-04T21:10:37.9002945Z alias:          pci:v000010DEd*sv*sd*bc06sc80i00*
2025-03-04T21:10:37.9003314Z alias:          pci:v000010DEd*sv*sd*bc03sc02i00*
2025-03-04T21:10:37.9003674Z alias:          pci:v000010DEd*sv*sd*bc03sc00i00*
2025-03-04T21:10:37.9004017Z depends:        i2c-core,drm
2025-03-04T21:10:37.9004299Z retpoline:      Y
2025-03-04T21:10:37.9004543Z name:           nvidia
2025-03-04T21:10:37.9004928Z vermagic:       6.1.128-136.201.amzn2023.x86_64 SMP preempt mod_unload modversions 
2025-03-04T21:10:37.9005436Z parm:           NvSwitchRegDwords:NvSwitch regkey (charp)
2025-03-04T21:10:37.9005919Z parm:           NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp)
2025-03-04T21:10:37.9006473Z parm:           NVreg_ResmanDebugLevel:int
2025-03-04T21:10:37.9006819Z parm:           NVreg_RmLogonRC:int
2025-03-04T21:10:37.9007159Z parm:           NVreg_ModifyDeviceFiles:int
2025-03-04T21:10:37.9007509Z parm:           NVreg_DeviceFileUID:int
2025-03-04T21:10:37.9008087Z parm:           NVreg_DeviceFileGID:int
2025-03-04T21:10:37.9008421Z parm:           NVreg_DeviceFileMode:int
2025-03-04T21:10:37.9008815Z parm:           NVreg_InitializeSystemMemoryAllocations:int
2025-03-04T21:10:37.9009233Z parm:           NVreg_UsePageAttributeTable:int
2025-03-04T21:10:37.9009602Z parm:           NVreg_EnablePCIeGen3:int
2025-03-04T21:10:37.9009968Z parm:           NVreg_EnableMSI:int
2025-03-04T21:10:37.9010288Z parm:           NVreg_TCEBypassMode:int
2025-03-04T21:10:37.9010638Z parm:           NVreg_EnableStreamMemOPs:int
2025-03-04T21:10:37.9011037Z parm:           NVreg_RestrictProfilingToAdminUsers:int
2025-03-04T21:10:37.9011473Z parm:           NVreg_PreserveVideoMemoryAllocations:int
2025-03-04T21:10:37.9011898Z parm:           NVreg_EnableS0ixPowerManagement:int
2025-03-04T21:10:37.9012346Z parm:           NVreg_S0ixPowerManagementVideoMemoryThreshold:int
2025-03-04T21:10:37.9012797Z parm:           NVreg_DynamicPowerManagement:int
2025-03-04T21:10:37.9013248Z parm:           NVreg_DynamicPowerManagementVideoMemoryThreshold:int
2025-03-04T21:10:37.9013697Z parm:           NVreg_EnableGpuFirmware:int
2025-03-04T21:10:37.9014065Z parm:           NVreg_EnableGpuFirmwareLogs:int
2025-03-04T21:10:37.9014473Z parm:           NVreg_OpenRmEnableUnsupportedGpus:int
2025-03-04T21:10:37.9014885Z parm:           NVreg_EnableUserNUMAManagement:int
2025-03-04T21:10:37.9015256Z parm:           NVreg_MemoryPoolSize:int
2025-03-04T21:10:37.9015603Z parm:           NVreg_KMallocHeapMaxSize:int
2025-03-04T21:10:37.9015963Z parm:           NVreg_VMallocHeapMaxSize:int
2025-03-04T21:10:37.9016322Z parm:           NVreg_IgnoreMMIOCheck:int
2025-03-04T21:10:37.9016660Z parm:           NVreg_NvLinkDisable:int
2025-03-04T21:10:37.9017035Z parm:           NVreg_EnablePCIERelaxedOrderingMode:int
2025-03-04T21:10:37.9017430Z parm:           NVreg_RegisterPCIDriver:int
2025-03-04T21:10:37.9017790Z parm:           NVreg_EnableResizableBar:int
2025-03-04T21:10:37.9018155Z parm:           NVreg_EnableDbgBreakpoint:int
2025-03-04T21:10:37.9025412Z parm:           NVreg_EnableNonblockingOpen:int
2025-03-04T21:10:37.9025789Z parm:           NVreg_RegistryDwords:charp
2025-03-04T21:10:37.9026159Z parm:           NVreg_RegistryDwordsPerDevice:charp
2025-03-04T21:10:37.9026662Z parm:           NVreg_RmMsg:charp
2025-03-04T21:10:37.9026996Z parm:           NVreg_GpuBlacklist:charp
2025-03-04T21:10:37.9027349Z parm:           NVreg_TemporaryFilePath:charp
2025-03-04T21:10:37.9027701Z parm:           NVreg_ExcludedGpus:charp
2025-03-04T21:10:37.9028043Z parm:           NVreg_DmaRemapPeerMmio:int
2025-03-04T21:10:37.9028400Z parm:           NVreg_RmNvlinkBandwidth:charp
2025-03-04T21:10:37.9028756Z parm:           NVreg_ImexChannelCount:int
2025-03-04T21:10:37.9029106Z parm:           rm_firmware_active:charp
2025-03-04T21:10:37.9029420Z + HAS_NVIDIA_DRIVER=0
2025-03-04T21:10:37.9029867Z ++ command -v nvidia-smi
2025-03-04T21:10:37.9030161Z + '[' -x /usr/bin/nvidia-smi ']'
2025-03-04T21:10:37.9030434Z + set +e
2025-03-04T21:10:37.9030771Z ++ nvidia-smi --query-gpu=driver_version --format=csv,noheader --id=0
2025-03-04T21:10:40.1518016Z + INSTALLED_DRIVER_VERSION=550.54.15
2025-03-04T21:10:40.1518385Z + NVIDIA_SMI_STATUS=0
2025-03-04T21:10:40.1518681Z + '[' 0 -ne 0 ']'
2025-03-04T21:10:40.1518945Z + '[' 550.54.15 '!=' 550.54.15 ']'
2025-03-04T21:10:40.1519241Z + HAS_NVIDIA_DRIVER=1
2025-03-04T21:10:40.1519709Z + echo 'NVIDIA driver (550.54.15) has already been installed. Skipping NVIDIA driver installation'
2025-03-04T21:10:40.1520220Z + set -e
2025-03-04T21:10:40.1520441Z + '[' 1 -eq 0 ']'
2025-03-04T21:10:40.1520864Z NVIDIA driver (550.54.15) has already been installed. Skipping NVIDIA driver installation
2025-03-04T21:10:40.1523115Z + post_install_nvidia_driver_common
2025-03-04T21:10:40.1528733Z + sudo modprobe nvidia
2025-03-04T21:10:40.2422027Z + echo 'After installing NVIDIA driver'
2025-03-04T21:10:40.2423133Z + lspci
2025-03-04T21:10:40.2423534Z After installing NVIDIA driver
2025-03-04T21:10:40.2540235Z 00:00.0 Host bridge: Intel Corporation 440FX - 82441FX PMC [Natoma]
2025-03-04T21:10:40.2540782Z 00:01.0 ISA bridge: Intel Corporation 82371SB PIIX3 ISA [Natoma/Triton II]
2025-03-04T21:10:40.2541370Z 00:01.3 Non-VGA unclassified device: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08)
2025-03-04T21:10:40.2541931Z 00:03.0 VGA compatible controller: Amazon.com, Inc. Device 1111
2025-03-04T21:10:40.2542449Z 00:04.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe EBS Controller
2025-03-04T21:10:40.2543018Z 00:05.0 Ethernet controller: Amazon.com, Inc. Elastic Network Adapter (ENA)
2025-03-04T21:10:40.2543550Z 00:1e.0 3D controller: NVIDIA Corporation GA102GL [A10G] (rev a1)
2025-03-04T21:10:40.2544113Z 00:1f.0 Non-Volatile memory controller: Amazon.com, Inc. NVMe SSD Controller
2025-03-04T21:10:40.2544554Z + lsmod
2025-03-04T21:10:40.2572019Z Module                  Size  Used by
2025-03-04T21:10:40.2572379Z nvidia_uvm           4706304  0
2025-03-04T21:10:40.2572757Z nvidia              54071296  1 nvidia_uvm
2025-03-04T21:10:40.2573127Z drm                   602112  1 nvidia
2025-03-04T21:10:40.2573465Z drm_panel_orientation_quirks    32768  1 drm
2025-03-04T21:10:40.2573810Z backlight              24576  1 drm
2025-03-04T21:10:40.2574130Z i2c_core              110592  2 nvidia,drm
2025-03-04T21:10:40.2574451Z xt_conntrack           16384  1
2025-03-04T21:10:40.2574736Z nft_chain_nat          16384  3
2025-03-04T21:10:40.2575034Z xt_MASQUERADE          20480  1
2025-03-04T21:10:40.2575363Z nf_nat                 57344  2 nft_chain_nat,xt_MASQUERADE
2025-03-04T21:10:40.2575730Z nf_conntrack_netlink    57344  0
2025-03-04T21:10:40.2576161Z nf_conntrack          184320  4 xt_conntrack,nf_nat,nf_conntrack_netlink,xt_MASQUERADE
2025-03-04T21:10:40.2576638Z nf_defrag_ipv6         24576  1 nf_conntrack
2025-03-04T21:10:40.2576984Z nf_defrag_ipv4         16384  1 nf_conntrack
2025-03-04T21:10:40.2577314Z xfrm_user              57344  1
2025-03-04T21:10:40.2577609Z xfrm_algo              16384  1 xfrm_user
2025-03-04T21:10:40.2577955Z xt_addrtype            16384  2
2025-03-04T21:10:40.2578264Z nft_compat             20480  4
2025-03-04T21:10:40.2578598Z nf_tables             311296  57 nft_compat,nft_chain_nat
2025-03-04T21:10:40.2579042Z nfnetlink              20480  4 nft_compat,nf_conntrack_netlink,nf_tables
2025-03-04T21:10:40.2579449Z br_netfilter           36864  0
2025-03-04T21:10:40.2579755Z bridge                323584  1 br_netfilter
2025-03-04T21:10:40.2580082Z stp                    16384  1 bridge
2025-03-04T21:10:40.2580396Z llc                    16384  2 bridge,stp
2025-03-04T21:10:40.2580713Z overlay               167936  0
2025-03-04T21:10:40.2580987Z tls                   135168  0
2025-03-04T21:10:40.2581267Z nls_ascii              16384  1
2025-03-04T21:10:40.2581555Z nls_cp437              20480  1
2025-03-04T21:10:40.2581831Z vfat                   24576  1
2025-03-04T21:10:40.2582348Z fat                    86016  1 vfat
2025-03-04T21:10:40.2582648Z sunrpc                696320  1
2025-03-04T21:10:40.2582927Z ena                   180224  0
2025-03-04T21:10:40.2583200Z i8042                  45056  0
2025-03-04T21:10:40.2583493Z serio                  28672  3 i8042
2025-03-04T21:10:40.2583794Z button                 24576  0
2025-03-04T21:10:40.2584079Z ghash_clmulni_intel    16384  0
2025-03-04T21:10:40.2584374Z sch_fq_codel           20480  17
2025-03-04T21:10:40.2584661Z dm_mod                188416  0
2025-03-04T21:10:40.2584937Z loop                   36864  0
2025-03-04T21:10:40.2585212Z fuse                  163840  1
2025-03-04T21:10:40.2585495Z configfs               57344  1
2025-03-04T21:10:40.2585785Z dax                    45056  1 dm_mod
2025-03-04T21:10:40.2586082Z dmi_sysfs              20480  0
2025-03-04T21:10:40.2586530Z crc32_pclmul           16384  0
2025-03-04T21:10:40.2586819Z crc32c_intel           24576  0
2025-03-04T21:10:40.2587101Z efivarfs               24576  1
2025-03-04T21:10:40.2587384Z + modinfo nvidia
2025-03-04T21:10:40.2589493Z filename:       /lib/modules/6.1.128-136.201.amzn2023.x86_64/kernel/drivers/video/nvidia.ko
2025-03-04T21:10:40.2590011Z alias:          char-major-195-*
2025-03-04T21:10:40.2590310Z version:        550.54.15
2025-03-04T21:10:40.2590586Z supported:      external
2025-03-04T21:10:40.2590860Z license:        NVIDIA
2025-03-04T21:10:40.2591153Z firmware:       nvidia/550.54.15/gsp_tu10x.bin
2025-03-04T21:10:40.2591522Z firmware:       nvidia/550.54.15/gsp_ga10x.bin
2025-03-04T21:10:40.2591872Z srcversion:     833721318DA517F0C2FEC97
2025-03-04T21:10:40.2592219Z alias:          pci:v000010DEd*sv*sd*bc06sc80i00*
2025-03-04T21:10:40.2592589Z alias:          pci:v000010DEd*sv*sd*bc03sc02i00*
2025-03-04T21:10:40.2592953Z alias:          pci:v000010DEd*sv*sd*bc03sc00i00*
2025-03-04T21:10:40.2593308Z depends:        i2c-core,drm
2025-03-04T21:10:40.2593593Z retpoline:      Y
2025-03-04T21:10:40.2593837Z name:           nvidia
2025-03-04T21:10:40.2594229Z vermagic:       6.1.128-136.201.amzn2023.x86_64 SMP preempt mod_unload modversions 
2025-03-04T21:10:40.2594830Z parm:           NvSwitchRegDwords:NvSwitch regkey (charp)
2025-03-04T21:10:40.2595304Z parm:           NvSwitchBlacklist:NvSwitchBlacklist=uuid[,uuid...] (charp)
2025-03-04T21:10:40.2595757Z parm:           NVreg_ResmanDebugLevel:int
2025-03-04T21:10:40.2596096Z parm:           NVreg_RmLogonRC:int
2025-03-04T21:10:40.2596425Z parm:           NVreg_ModifyDeviceFiles:int
2025-03-04T21:10:40.2596771Z parm:           NVreg_DeviceFileUID:int
2025-03-04T21:10:40.2597104Z parm:           NVreg_DeviceFileGID:int
2025-03-04T21:10:40.2597444Z parm:           NVreg_DeviceFileMode:int
2025-03-04T21:10:40.2597839Z parm:           NVreg_InitializeSystemMemoryAllocations:int
2025-03-04T21:10:40.2598260Z parm:           NVreg_UsePageAttributeTable:int
2025-03-04T21:10:40.2598631Z parm:           NVreg_EnablePCIeGen3:int
2025-03-04T21:10:40.2598960Z parm:           NVreg_EnableMSI:int
2025-03-04T21:10:40.2599285Z parm:           NVreg_TCEBypassMode:int
2025-03-04T21:10:40.2599633Z parm:           NVreg_EnableStreamMemOPs:int
2025-03-04T21:10:40.2600029Z parm:           NVreg_RestrictProfilingToAdminUsers:int
2025-03-04T21:10:40.2600461Z parm:           NVreg_PreserveVideoMemoryAllocations:int
2025-03-04T21:10:40.2600878Z parm:           NVreg_EnableS0ixPowerManagement:int
2025-03-04T21:10:40.2601322Z parm:           NVreg_S0ixPowerManagementVideoMemoryThreshold:int
2025-03-04T21:10:40.2601765Z parm:           NVreg_DynamicPowerManagement:int
2025-03-04T21:10:40.2602219Z parm:           NVreg_DynamicPowerManagementVideoMemoryThreshold:int
2025-03-04T21:10:40.2602661Z parm:           NVreg_EnableGpuFirmware:int
2025-03-04T21:10:40.2603030Z parm:           NVreg_EnableGpuFirmwareLogs:int
2025-03-04T21:10:40.2603444Z parm:           NVreg_OpenRmEnableUnsupportedGpus:int
2025-03-04T21:10:40.2603851Z parm:           NVreg_EnableUserNUMAManagement:int
2025-03-04T21:10:40.2604227Z parm:           NVreg_MemoryPoolSize:int
2025-03-04T21:10:40.2604694Z parm:           NVreg_KMallocHeapMaxSize:int
2025-03-04T21:10:40.2605058Z parm:           NVreg_VMallocHeapMaxSize:int
2025-03-04T21:10:40.2605412Z parm:           NVreg_IgnoreMMIOCheck:int
2025-03-04T21:10:40.2605754Z parm:           NVreg_NvLinkDisable:int
2025-03-04T21:10:40.2606134Z parm:           NVreg_EnablePCIERelaxedOrderingMode:int
2025-03-04T21:10:40.2606532Z parm:           NVreg_RegisterPCIDriver:int
2025-03-04T21:10:40.2606890Z parm:           NVreg_EnableResizableBar:int
2025-03-04T21:10:40.2607259Z parm:           NVreg_EnableDbgBreakpoint:int
2025-03-04T21:10:40.2607776Z parm:           NVreg_EnableNonblockingOpen:int
2025-03-04T21:10:40.2608151Z parm:           NVreg_RegistryDwords:charp
2025-03-04T21:10:40.2608522Z parm:           NVreg_RegistryDwordsPerDevice:charp
2025-03-04T21:10:40.2608977Z parm:           NVreg_RmMsg:charp
2025-03-04T21:10:40.2609301Z parm:           NVreg_GpuBlacklist:charp
2025-03-04T21:10:40.2609658Z parm:           NVreg_TemporaryFilePath:charp
2025-03-04T21:10:40.2610019Z parm:           NVreg_ExcludedGpus:charp
2025-03-04T21:10:40.2610369Z parm:           NVreg_DmaRemapPeerMmio:int
2025-03-04T21:10:40.2610736Z parm:           NVreg_RmNvlinkBandwidth:charp
2025-03-04T21:10:40.2611098Z parm:           NVreg_ImexChannelCount:int
2025-03-04T21:10:40.2611443Z parm:           rm_firmware_active:charp
2025-03-04T21:10:40.2611759Z + set +e
2025-03-04T21:10:40.2611980Z + nvidia-smi
2025-03-04T21:10:41.8152650Z Tue Mar  4 21:10:41 2025       
2025-03-04T21:10:41.8153460Z +-----------------------------------------------------------------------------------------+
2025-03-04T21:10:41.8154521Z | NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
2025-03-04T21:10:41.8155581Z |-----------------------------------------+------------------------+----------------------+
2025-03-04T21:10:41.8156630Z | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
2025-03-04T21:10:41.8157773Z | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
2025-03-04T21:10:41.8158455Z |                                         |                        |               MIG M. |
2025-03-04T21:10:41.8158834Z |=========================================+========================+======================|
2025-03-04T21:10:41.8253868Z |   0  NVIDIA A10G                    Off |   00000000:00:1E.0 Off |                    0 |
2025-03-04T21:10:41.8254373Z |  0%   24C    P0             54W /  300W |       0MiB /  23028MiB |      5%      Default |
2025-03-04T21:10:41.8254802Z |                                         |                        |                  N/A |
2025-03-04T21:10:41.8255236Z +-----------------------------------------+------------------------+----------------------+
2025-03-04T21:10:41.8255704Z                                                                                          
2025-03-04T21:10:41.8256148Z +-----------------------------------------------------------------------------------------+
2025-03-04T21:10:41.8256621Z | Processes:                                                                              |
2025-03-04T21:10:41.8257117Z |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
2025-03-04T21:10:41.8257584Z |        ID   ID                                                               Usage      |
2025-03-04T21:10:41.8257965Z |=========================================================================================|
2025-03-04T21:10:41.8259018Z |  No running processes found                                                             |
2025-03-04T21:10:41.8259546Z +-----------------------------------------------------------------------------------------+
2025-03-04T21:10:42.4257861Z + nvidia-smi --query-gpu=gpu_name --format=csv,noheader --id=0
2025-03-04T21:10:43.9791281Z NVIDIA A10G
2025-03-04T21:10:44.4211597Z + NVIDIA_SMI_STATUS=0
2025-03-04T21:10:44.4211915Z + '[' 0 -eq 0 ']'
2025-03-04T21:10:44.4212494Z + echo 'INFO: Ignoring allowed status 0'
2025-03-04T21:10:44.4212835Z + set -e
2025-03-04T21:10:44.4213083Z INFO: Ignoring allowed status 0
2025-03-04T21:10:44.4222031Z == Installing nvidia container toolkit for amzn2023 ==
2025-03-04T21:10:44.4225159Z + sudo yum install -y yum-utils
2025-03-04T21:10:44.8525733Z Last metadata expiration check: 0:11:02 ago on Tue Mar  4 20:59:42 2025.
2025-03-04T21:10:44.8769933Z Package dnf-utils-4.3.0-13.amzn2023.0.5.noarch is already installed.
2025-03-04T21:10:44.9156420Z Dependencies resolved.
2025-03-04T21:10:44.9334307Z Nothing to do.
2025-03-04T21:10:44.9334842Z Complete!
2025-03-04T21:10:45.0324189Z + [[ amzn2023 == \a\m\z\n\2\0\2\3 ]]
2025-03-04T21:10:45.0325074Z + YUM_REPO_URL=https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
2025-03-04T21:10:45.0326480Z + sudo yum-config-manager --add-repo https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
2025-03-04T21:10:45.3048357Z Adding repo from: https://nvidia.github.io/libnvidia-container/stable/rpm/nvidia-container-toolkit.repo
2025-03-04T21:10:45.3638919Z + sudo yum install -y nvidia-docker2 nvidia-container-toolkit-1.16.2
2025-03-04T21:10:45.9399372Z nvidia-container-toolkit                         14 kB/s | 833  B     00:00    
2025-03-04T21:10:45.9648583Z Package nvidia-docker2-2.14.0-1.noarch is already installed.
2025-03-04T21:10:46.0033396Z Dependencies resolved.
2025-03-04T21:10:46.0205359Z ================================================================================
2025-03-04T21:10:46.0205809Z  Package                       Arch   Version    Repository                Size
2025-03-04T21:10:46.0206224Z ================================================================================
2025-03-04T21:10:46.0206557Z Downgrading:
2025-03-04T21:10:46.0206987Z  nvidia-container-toolkit      x86_64 1.16.2-1   nvidia-container-toolkit 1.2 M
2025-03-04T21:10:46.0207734Z  nvidia-container-toolkit-base x86_64 1.16.2-1   nvidia-container-toolkit 5.6 M
2025-03-04T21:10:46.0208118Z 
2025-03-04T21:10:46.0208230Z Transaction Summary
2025-03-04T21:10:46.0208505Z ================================================================================
2025-03-04T21:10:46.0208842Z Downgrade  2 Packages
2025-03-04T21:10:46.0209013Z 
2025-03-04T21:10:46.0209131Z Total download size: 6.8 M
2025-03-04T21:10:46.0210588Z Downloading Packages:
2025-03-04T21:10:46.1018516Z (1/2): nvidia-container-toolkit-1.16.2-1.x86_64  15 MB/s | 1.2 MB     00:00    
2025-03-04T21:10:46.2703467Z (2/2): nvidia-container-toolkit-base-1.16.2-1.x  23 MB/s | 5.6 MB     00:00    
2025-03-04T21:10:46.2712459Z --------------------------------------------------------------------------------
2025-03-04T21:10:46.2715865Z Total                                            27 MB/s | 6.8 MB     00:00     
2025-03-04T21:10:46.2718750Z Running transaction check
2025-03-04T21:10:46.2815015Z Transaction check succeeded.
2025-03-04T21:10:46.2815861Z Running transaction test
2025-03-04T21:10:46.3108767Z Transaction test succeeded.
2025-03-04T21:10:46.3111346Z Running transaction
2025-03-04T21:10:47.0159162Z   Preparing        :                                                        1/1 
2025-03-04T21:10:47.1719053Z   Downgrading      : nvidia-container-toolkit-base-1.16.2-1.x86_64          1/4 
2025-03-04T21:10:47.2039897Z   Downgrading      : nvidia-container-toolkit-1.16.2-1.x86_64               2/4 
2025-03-04T21:10:47.2353141Z   Running scriptlet: nvidia-container-toolkit-1.16.2-1.x86_64               2/4 
2025-03-04T21:10:47.2353754Z   Cleanup          : nvidia-container-toolkit-1.17.4-1.x86_64               3/4 
2025-03-04T21:10:47.2455450Z   Running scriptlet: nvidia-container-toolkit-1.17.4-1.x86_64               3/4 
2025-03-04T21:10:47.2477139Z   Cleanup          : nvidia-container-toolkit-base-1.17.4-1.x86_64          4/4 
2025-03-04T21:11:34.3436890Z   Running scriptlet: nvidia-container-toolkit-1.16.2-1.x86_64               4/4 
2025-03-04T21:11:34.3448684Z   Verifying        : nvidia-container-toolkit-1.16.2-1.x86_64               1/4 
2025-03-04T21:11:34.3449681Z   Verifying        : nvidia-container-toolkit-1.17.4-1.x86_64               2/4 
2025-03-04T21:11:34.3450267Z   Verifying        : nvidia-container-toolkit-base-1.16.2-1.x86_64          3/4 
2025-03-04T21:11:34.4981901Z   Verifying        : nvidia-container-toolkit-base-1.17.4-1.x86_64          4/4 
2025-03-04T21:11:34.4982281Z 
2025-03-04T21:11:34.4982398Z Downgraded:
2025-03-04T21:11:34.4982792Z   nvidia-container-toolkit-1.16.2-1.x86_64                                      
2025-03-04T21:11:34.4983409Z   nvidia-container-toolkit-base-1.16.2-1.x86_64                                 
2025-03-04T21:11:34.4983797Z 
2025-03-04T21:11:34.4983908Z Complete!
2025-03-04T21:11:34.5426647Z + sudo systemctl restart docker
2025-03-04T21:11:40.4508520Z Tue Mar  4 21:11:40 2025       
2025-03-04T21:11:40.4509351Z +-----------------------------------------------------------------------------------------+
2025-03-04T21:11:40.4509899Z | NVIDIA-SMI 550.54.15              Driver Version: 550.54.15      CUDA Version: 12.4     |
2025-03-04T21:11:40.4510436Z |-----------------------------------------+------------------------+----------------------+
2025-03-04T21:11:40.4510972Z | GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |
2025-03-04T21:11:40.4511552Z | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
2025-03-04T21:11:40.4512030Z |                                         |                        |               MIG M. |
2025-03-04T21:11:40.4512402Z |=========================================+========================+======================|
2025-03-04T21:11:40.4634409Z |   0  NVIDIA A10G                    On  |   00000000:00:1E.0 Off |                    0 |
2025-03-04T21:11:40.4635153Z |  0%   24C    P0             55W /  300W |       0MiB /  23028MiB |      5%      Default |
2025-03-04T21:11:40.4635579Z |                                         |                        |                  N/A |
2025-03-04T21:11:40.4636012Z +-----------------------------------------+------------------------+----------------------+
2025-03-04T21:11:40.4636447Z                                                                                          
2025-03-04T21:11:40.4636872Z +-----------------------------------------------------------------------------------------+
2025-03-04T21:11:40.4637335Z | Processes:                                                                              |
2025-03-04T21:11:40.4637826Z |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
2025-03-04T21:11:40.4638289Z |        ID   ID                                                               Usage      |
2025-03-04T21:11:40.4638669Z |=========================================================================================|
2025-03-04T21:11:40.4639155Z |  No running processes found                                                             |
2025-03-04T21:11:40.4639663Z +-----------------------------------------------------------------------------------------+
2025-03-04T21:11:41.2113126Z Command completed after 1 attempt(s).
2025-03-04T21:11:41.2211730Z Prepare all required actions
2025-03-04T21:11:41.2241856Z ##[group]Run ./.github/actions/get-workflow-job-id
2025-03-04T21:11:41.2242217Z with:
2025-03-04T21:11:41.2242630Z   github-token: ***
2025-03-04T21:11:41.2242885Z env:
2025-03-04T21:11:41.2243123Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:41.2243478Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:11:41.2243847Z ##[endgroup]
2025-03-04T21:11:41.2264968Z ##[group]Run set -eux
2025-03-04T21:11:41.2265262Z set -eux
2025-03-04T21:11:41.2265705Z python3 .github/scripts/get_workflow_job_id.py "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-04T21:11:41.2280225Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:41.2280615Z env:
2025-03-04T21:11:41.2280854Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:41.2281386Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:11:41.2281908Z   GITHUB_TOKEN: ***
2025-03-04T21:11:41.2282168Z ##[endgroup]
2025-03-04T21:11:41.2314775Z + python3 .github/scripts/get_workflow_job_id.py 13661696663 i-05d5204902c63dc95
2025-03-04T21:11:42.3458860Z setting job-id=38197725386
2025-03-04T21:11:42.3459436Z setting job-name=cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:11:42.3574219Z ##[group]Run python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-04T21:11:42.3574946Z python3 -m pip install psutil==5.9.1 nvidia-ml-py==11.525.84 dataclasses_json==0.6.7
2025-03-04T21:11:42.3575515Z python3 -m tools.stats.monitor > usage_log.txt 2>&1 &
2025-03-04T21:11:42.3575991Z echo "monitor-script-pid=${!}" >> "${GITHUB_OUTPUT}"
2025-03-04T21:11:42.3584853Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:11:42.3585235Z env:
2025-03-04T21:11:42.3585476Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:42.3585839Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:11:42.3586211Z   JOB_ID: 38197725386
2025-03-04T21:11:42.3586693Z   JOB_NAME: cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:11:42.3587230Z   WORKFLOW_NAME: inductor
2025-03-04T21:11:42.3587517Z   WORKFLOW_RUN_ID: 13661696663
2025-03-04T21:11:42.3587832Z ##[endgroup]
2025-03-04T21:11:42.6424658Z Defaulting to user installation because normal site-packages is not writeable
2025-03-04T21:11:43.0351991Z Collecting psutil==5.9.1
2025-03-04T21:11:43.0629994Z   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)
2025-03-04T21:11:43.1041403Z Collecting nvidia-ml-py==11.525.84
2025-03-04T21:11:43.1080872Z   Downloading nvidia_ml_py-11.525.84-py3-none-any.whl (34 kB)
2025-03-04T21:11:43.1718610Z Collecting dataclasses_json==0.6.7
2025-03-04T21:11:43.1756471Z   Downloading dataclasses_json-0.6.7-py3-none-any.whl (28 kB)
2025-03-04T21:11:43.2082059Z Collecting typing-inspect<1,>=0.4.0
2025-03-04T21:11:43.2115815Z   Downloading typing_inspect-0.9.0-py3-none-any.whl (8.8 kB)
2025-03-04T21:11:43.3268860Z Collecting marshmallow<4.0.0,>=3.18.0
2025-03-04T21:11:43.3315780Z   Downloading marshmallow-3.26.1-py3-none-any.whl (50 kB)
2025-03-04T21:11:43.3870468Z Collecting packaging>=17.0
2025-03-04T21:11:43.3904653Z   Downloading packaging-24.2-py3-none-any.whl (65 kB)
2025-03-04T21:11:43.4120696Z Collecting mypy-extensions>=0.3.0
2025-03-04T21:11:43.4153511Z   Downloading mypy_extensions-1.0.0-py3-none-any.whl (4.7 kB)
2025-03-04T21:11:43.4596363Z Collecting typing-extensions>=3.7.4
2025-03-04T21:11:43.4630713Z   Downloading typing_extensions-4.12.2-py3-none-any.whl (37 kB)
2025-03-04T21:11:43.5534147Z Installing collected packages: typing-extensions, packaging, mypy-extensions, typing-inspect, marshmallow, psutil, nvidia-ml-py, dataclasses-json
2025-03-04T21:11:43.8027224Z 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
2025-03-04T21:11:44.0032876Z Prepare all required actions
2025-03-04T21:11:44.0033269Z Getting action download info
2025-03-04T21:11:44.1664111Z Download action repository 'seemethere/download-artifact-s3@v4' (SHA:1da556a7aa0a088e3153970611f6c432d58e80e6)
2025-03-04T21:11:44.3867226Z Download action repository 'actions/download-artifact@v4' (SHA:cc203385981b70ca67e1cc392babf9cc229d5806)
2025-03-04T21:11:44.6723593Z ##[group]Run ./.github/actions/download-build-artifacts
2025-03-04T21:11:44.6723980Z with:
2025-03-04T21:11:44.6724256Z   name: linux-focal-cuda12.4-py3.10-gcc9-sm86
2025-03-04T21:11:44.6724605Z   s3-bucket: gha-artifacts
2025-03-04T21:11:44.6725235Z env:
2025-03-04T21:11:44.6725531Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:44.6725881Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:11:44.6727024Z ##[endgroup]
2025-03-04T21:11:44.6781340Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-04T21:11:44.6781692Z with:
2025-03-04T21:11:44.6782052Z   name: linux-focal-cuda12.4-py3.10-gcc9-sm86
2025-03-04T21:11:44.6782437Z   s3-bucket: gha-artifacts
2025-03-04T21:11:44.6782758Z   region: us-east-1
2025-03-04T21:11:44.6783105Z env:
2025-03-04T21:11:44.6783395Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:11:44.6788341Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:11:44.6788743Z ##[endgroup]
2025-03-04T21:11:45.1518930Z (node:54168) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-04T21:11:45.1519893Z 
2025-03-04T21:11:45.1520285Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-04T21:11:45.1521325Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-04T21:11:45.1522430Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-04T21:11:45.3740619Z Found 1 objects with prefix pytorch/pytorch/13661696663/linux-focal-cuda12.4-py3.10-gcc9-sm86/
2025-03-04T21:11:45.3741382Z Starting download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-04T21:12:04.4271473Z Finished download (1/1): /home/ec2-user/actions-runner/_work/pytorch/pytorch/artifacts.zip
2025-03-04T21:12:04.4276860Z Artifact download has finished successfully
2025-03-04T21:12:04.4636769Z ##[group]Run unzip -o artifacts.zip
2025-03-04T21:12:04.4637141Z unzip -o artifacts.zip
2025-03-04T21:12:04.4647079Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:12:04.4647470Z env:
2025-03-04T21:12:04.4647817Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:04.4648168Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:04.4648542Z ##[endgroup]
2025-03-04T21:12:04.4728586Z Archive:  artifacts.zip
2025-03-04T21:12:04.4729999Z    creating: dist/
2025-03-04T21:12:06.8107845Z   inflating: dist/torch-2.7.0a0+git1b74980-cp310-cp310-linux_x86_64.whl  
2025-03-04T21:12:06.8110572Z    creating: build/custom_test_artifacts/
2025-03-04T21:12:06.8111109Z    creating: build/custom_test_artifacts/custom-op-build/
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2025-03-04T21:12:06.8117678Z    creating: build/custom_test_artifacts/custom-op-build/CMakeFiles/3.31.2/
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2025-03-04T21:12:16.2902386Z   inflating: .additional_ci_files/test-times.json  
2025-03-04T21:12:16.3288929Z   inflating: .additional_ci_files/test-class-times.json  
2025-03-04T21:12:16.3329690Z ##[group]Run rm artifacts.zip
2025-03-04T21:12:16.3330026Z rm artifacts.zip
2025-03-04T21:12:16.3338882Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:12:16.3339267Z env:
2025-03-04T21:12:16.3339499Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:16.3339845Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:16.3340216Z ##[endgroup]
2025-03-04T21:12:16.4556343Z ##[group]Run df -H
2025-03-04T21:12:16.4556616Z df -H
2025-03-04T21:12:16.4565291Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:12:16.4565704Z env:
2025-03-04T21:12:16.4565950Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:16.4566293Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:16.4566663Z ##[endgroup]
2025-03-04T21:12:16.4614529Z Filesystem        Size  Used Avail Use% Mounted on
2025-03-04T21:12:16.4614941Z devtmpfs          4.2M     0  4.2M   0% /dev
2025-03-04T21:12:16.4615289Z tmpfs              34G     0   34G   0% /dev/shm
2025-03-04T21:12:16.4615634Z tmpfs              14G  553k   14G   1% /run
2025-03-04T21:12:16.4615968Z /dev/nvme0n1p1    161G   62G  100G  39% /
2025-03-04T21:12:16.4616304Z tmpfs              34G   13k   34G   1% /tmp
2025-03-04T21:12:16.4616659Z /dev/nvme0n1p128   11M  1.4M  9.2M  13% /boot/efi
2025-03-04T21:12:16.4617029Z tmpfs             6.7G     0  6.7G   0% /run/user/0
2025-03-04T21:12:16.4650800Z Prepare all required actions
2025-03-04T21:12:16.4651186Z Getting action download info
2025-03-04T21:12:16.6009800Z ##[group]Run ./.github/actions/download-td-artifacts
2025-03-04T21:12:16.6010162Z with:
2025-03-04T21:12:16.6010567Z env:
2025-03-04T21:12:16.6010800Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:16.6011151Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:16.6011522Z ##[endgroup]
2025-03-04T21:12:16.6044200Z ##[group]Run seemethere/download-artifact-s3@v4
2025-03-04T21:12:16.6044537Z with:
2025-03-04T21:12:16.6044755Z   name: td_results
2025-03-04T21:12:16.6045006Z   s3-bucket: gha-artifacts
2025-03-04T21:12:16.6045281Z   region: us-east-1
2025-03-04T21:12:16.6045511Z env:
2025-03-04T21:12:16.6045733Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:16.6046070Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:16.6046431Z ##[endgroup]
2025-03-04T21:12:17.0576161Z (node:54193) NOTE: We are formalizing our plans to enter AWS SDK for JavaScript (v2) into maintenance mode in 2023.
2025-03-04T21:12:17.0576652Z 
2025-03-04T21:12:17.0576859Z Please migrate your code to use AWS SDK for JavaScript (v3).
2025-03-04T21:12:17.0577395Z For more information, check the migration guide at https://a.co/7PzMCcy
2025-03-04T21:12:17.0577966Z (Use `node --trace-warnings ...` to show where the warning was created)
2025-03-04T21:12:17.1528454Z Found 0 objects with prefix pytorch/pytorch/13661696663/td_results/
2025-03-04T21:12:17.1534460Z Artifact download has finished successfully
2025-03-04T21:12:17.1878196Z ##[group]Run mkdir -p .additional_ci_files
2025-03-04T21:12:17.1878572Z mkdir -p .additional_ci_files
2025-03-04T21:12:17.1879015Z mv td_results.json .additional_ci_files/td_results.json || true
2025-03-04T21:12:17.1888006Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:12:17.1888399Z env:
2025-03-04T21:12:17.1888639Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:17.1889007Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:17.1889369Z ##[endgroup]
2025-03-04T21:12:17.1959023Z mv: cannot stat 'td_results.json': No such file or directory
2025-03-04T21:12:17.1993958Z ##[group]Run .github/scripts/parse_ref.py
2025-03-04T21:12:17.1994357Z .github/scripts/parse_ref.py
2025-03-04T21:12:17.2002875Z shell: /usr/bin/bash -e {0}
2025-03-04T21:12:17.2003161Z env:
2025-03-04T21:12:17.2003390Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:17.2003736Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:17.2004106Z ##[endgroup]
2025-03-04T21:12:17.2343195Z Prepare all required actions
2025-03-04T21:12:17.2344002Z Getting action download info
2025-03-04T21:12:17.3584429Z ##[group]Run ./.github/actions/filter-test-configs
2025-03-04T21:12:17.3584786Z with:
2025-03-04T21:12:17.3585191Z   github-token: ***
2025-03-04T21:12:17.3586905Z   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"}]}
2025-03-04T21:12:17.3588916Z   job-name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:12:17.3589449Z env:
2025-03-04T21:12:17.3589886Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:17.3590241Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:17.3590609Z ##[endgroup]
2025-03-04T21:12:17.3656618Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-04T21:12:17.3656935Z with:
2025-03-04T21:12:17.3657160Z   shell: bash
2025-03-04T21:12:17.3657402Z   timeout_minutes: 10
2025-03-04T21:12:17.3657660Z   max_attempts: 5
2025-03-04T21:12:17.3657908Z   retry_wait_seconds: 30
2025-03-04T21:12:17.3658698Z   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

2025-03-04T21:12:17.3659715Z   polling_interval_seconds: 1
2025-03-04T21:12:17.3660001Z   warning_on_retry: true
2025-03-04T21:12:17.3660280Z   continue_on_error: false
2025-03-04T21:12:17.3660550Z env:
2025-03-04T21:12:17.3660783Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:17.3661139Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:17.3675802Z   GITHUB_TOKEN: ***
2025-03-04T21:12:17.3676144Z ##[endgroup]
2025-03-04T21:12:17.4670751Z + python3 -m pip install requests==2.27.1 pyyaml==6.0.1
2025-03-04T21:12:17.6953546Z Defaulting to user installation because normal site-packages is not writeable
2025-03-04T21:12:17.8202804Z Collecting requests==2.27.1
2025-03-04T21:12:17.8441514Z   Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)
2025-03-04T21:12:18.0161913Z Collecting pyyaml==6.0.1
2025-03-04T21:12:18.0203473Z   Downloading PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (738 kB)
2025-03-04T21:12:18.0437705Z Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (1.25.10)
2025-03-04T21:12:18.0884258Z Collecting certifi>=2017.4.17
2025-03-04T21:12:18.0987334Z   Downloading certifi-2025.1.31-py3-none-any.whl (166 kB)
2025-03-04T21:12:18.4390352Z Collecting charset-normalizer~=2.0.0
2025-03-04T21:12:18.4426756Z   Downloading charset_normalizer-2.0.12-py3-none-any.whl (39 kB)
2025-03-04T21:12:18.4467872Z Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.9/site-packages (from requests==2.27.1) (2.10)
2025-03-04T21:12:18.5309704Z Installing collected packages: charset-normalizer, certifi, requests, pyyaml
2025-03-04T21:12:18.6425771Z Successfully installed certifi-2025.1.31 charset-normalizer-2.0.12 pyyaml-6.0.1 requests-2.27.1
2025-03-04T21:12:19.4451313Z Command completed after 1 attempt(s).
2025-03-04T21:12:19.4525675Z ##[group]Run set -x
2025-03-04T21:12:19.4526100Z set -x
2025-03-04T21:12:19.4526335Z 
2025-03-04T21:12:19.4526720Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-04T21:12:19.4527215Z # in runner workspace
2025-03-04T21:12:19.4527720Z python3 "${GITHUB_ACTION_PATH}/../../scripts/parse_ref.py"
2025-03-04T21:12:19.4536681Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:12:19.4537288Z env:
2025-03-04T21:12:19.4537526Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:19.4537871Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:19.4538237Z ##[endgroup]
2025-03-04T21:12:19.4567130Z + python3 /home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/filter-test-configs/../../scripts/parse_ref.py
2025-03-04T21:12:19.4801820Z ##[group]Run echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-04T21:12:19.4802241Z echo "Workflow: ${GITHUB_WORKFLOW}"
2025-03-04T21:12:19.4802601Z echo "Job name: ${JOB_NAME}"
2025-03-04T21:12:19.4802908Z 
2025-03-04T21:12:19.4803301Z # Use relative path here as this could be checked out anywhere, not necessarily
2025-03-04T21:12:19.4803802Z # in runner workspace
2025-03-04T21:12:19.4804238Z python3 "${GITHUB_ACTION_PATH}/../../scripts/filter_test_configs.py" \
2025-03-04T21:12:19.4804729Z   --workflow "${GITHUB_WORKFLOW}" \
2025-03-04T21:12:19.4805094Z   --job-name "${JOB_NAME}" \
2025-03-04T21:12:19.4806950Z   --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"}]}" \
2025-03-04T21:12:19.4808891Z   --selected-test-configs "" \
2025-03-04T21:12:19.4809422Z   --pr-number "${PR_NUMBER}" \
2025-03-04T21:12:19.4809747Z   --tag "${TAG}" \
2025-03-04T21:12:19.4810053Z   --event-name "${EVENT_NAME}" \
2025-03-04T21:12:19.4810391Z   --schedule "${SCHEDULE}" \
2025-03-04T21:12:19.4810719Z   --branch "${HEAD_BRANCH}"
2025-03-04T21:12:19.4819263Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:12:19.4819644Z env:
2025-03-04T21:12:19.4819875Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:19.4820218Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:19.4820796Z   GITHUB_TOKEN: ***
2025-03-04T21:12:19.4821320Z   JOB_NAME: cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:12:19.4821902Z   PR_NUMBER: 
2025-03-04T21:12:19.4822163Z   TAG: ciflow/inductor/148205
2025-03-04T21:12:19.4822465Z   EVENT_NAME: push
2025-03-04T21:12:19.4822719Z   SCHEDULE: 
2025-03-04T21:12:19.4822953Z   HEAD_BRANCH: 
2025-03-04T21:12:19.4823199Z ##[endgroup]
2025-03-04T21:12:19.4849811Z Workflow: inductor
2025-03-04T21:12:19.4850321Z Job name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:12:19.7036750Z INFO:root:Found no test-config label on the PR, so all test configs are included
2025-03-04T21:12:19.8307064Z ##[group]Run echo "Filtered matrix:"
2025-03-04T21:12:19.8307419Z echo "Filtered matrix:"
2025-03-04T21:12:19.8309187Z 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"}]}"
2025-03-04T21:12:19.8310947Z 
2025-03-04T21:12:19.8311173Z echo
2025-03-04T21:12:19.8311466Z echo "Is the current job unstable? False"
2025-03-04T21:12:19.8311817Z 
2025-03-04T21:12:19.8312038Z echo
2025-03-04T21:12:19.8312497Z echo "Is keep-going label set? False"
2025-03-04T21:12:19.8312908Z 
2025-03-04T21:12:19.8313129Z echo
2025-03-04T21:12:19.8313379Z echo "Renabled issues? "
2025-03-04T21:12:19.8321987Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:12:19.8322369Z env:
2025-03-04T21:12:19.8322601Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:19.8322950Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:19.8323317Z ##[endgroup]
2025-03-04T21:12:19.8351284Z Filtered matrix:
2025-03-04T21:12:19.8353611Z {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}]}
2025-03-04T21:12:19.8355804Z 
2025-03-04T21:12:19.8355935Z Is the current job unstable? False
2025-03-04T21:12:19.8356157Z 
2025-03-04T21:12:19.8356276Z Is keep-going label set? False
2025-03-04T21:12:19.8356475Z 
2025-03-04T21:12:19.8356576Z Renabled issues? 
2025-03-04T21:12:19.8409559Z ##[group]Run echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-04T21:12:19.8410089Z echo "timeout=$((JOB_TIMEOUT-30))" >> "${GITHUB_OUTPUT}"
2025-03-04T21:12:19.8419101Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T21:12:19.8419483Z env:
2025-03-04T21:12:19.8419708Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:19.8420053Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:19.8420584Z   JOB_TIMEOUT: 240
2025-03-04T21:12:19.8420827Z ##[endgroup]
2025-03-04T21:12:19.8495530Z ##[group]Run set -x
2025-03-04T21:12:19.8495868Z set -x
2025-03-04T21:12:19.8496109Z 
2025-03-04T21:12:19.8496372Z if [[ $TEST_CONFIG == 'multigpu' ]]; then
2025-03-04T21:12:19.8496776Z   TEST_COMMAND=.ci/pytorch/multigpu-test.sh
2025-03-04T21:12:19.8497180Z elif [[ $BUILD_ENVIRONMENT == *onnx* ]]; then
2025-03-04T21:12:19.8497551Z   TEST_COMMAND=.ci/onnx/test.sh
2025-03-04T21:12:19.8497859Z else
2025-03-04T21:12:19.8498122Z   TEST_COMMAND=.ci/pytorch/test.sh
2025-03-04T21:12:19.8498428Z fi
2025-03-04T21:12:19.8498646Z 
2025-03-04T21:12:19.8498917Z # Leaving 1GB for the runner and other things
2025-03-04T21:12:19.8499482Z TOTAL_AVAILABLE_MEMORY_IN_GB=$(awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo)
2025-03-04T21:12:19.8500336Z # https://docs.docker.com/engine/containers/resource_constraints/#--memory-swap-details, the 3GB swap
2025-03-04T21:12:19.8501029Z # comes from https://github.com/pytorch/test-infra/pull/6058
2025-03-04T21:12:19.8501558Z TOTAL_MEMORY_WITH_SWAP=$(("${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}" + 3))
2025-03-04T21:12:19.8501974Z 
2025-03-04T21:12:19.8502249Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-04T21:12:19.8502602Z   SHM_OPTS=
2025-03-04T21:12:19.8502860Z   JENKINS_USER=
2025-03-04T21:12:19.8503217Z   # ensure that docker container cleanly exits in 12 hours
2025-03-04T21:12:19.8503685Z   # if for some reason cleanup action doesn't stop container
2025-03-04T21:12:19.8504085Z   # when job is cancelled
2025-03-04T21:12:19.8504401Z   DOCKER_SHELL_CMD="sleep 12h"
2025-03-04T21:12:19.8504703Z 
2025-03-04T21:12:19.8505076Z   # since some steps are skipped on s390x, if they are necessary, run them here
2025-03-04T21:12:19.8505625Z   env | grep '^GITHUB' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T21:12:19.8506075Z   env | grep '^CI' >> "/tmp/github_env_${GITHUB_RUN_ID}"
2025-03-04T21:12:19.8506481Z else
2025-03-04T21:12:19.8506744Z   SHM_OPTS="--shm-size=${SHM_SIZE}"
2025-03-04T21:12:19.8507088Z   JENKINS_USER="--user jenkins"
2025-03-04T21:12:19.8507412Z   DOCKER_SHELL_CMD=
2025-03-04T21:12:19.8507684Z fi
2025-03-04T21:12:19.8507903Z 
2025-03-04T21:12:19.8508242Z # detached container should get cleaned up by teardown_ec2_linux
2025-03-04T21:12:19.8508771Z # TODO: Stop building test binaries as part of the build phase
2025-03-04T21:12:19.8509376Z # Used for GPU_FLAG, SHM_OPTS, JENKINS_USER and DOCKER_SHELL_CMD since that doesn't play nice
2025-03-04T21:12:19.8509911Z # shellcheck disable=SC2086,SC2090
2025-03-04T21:12:19.8510258Z container_name=$(docker run \
2025-03-04T21:12:19.8510574Z   ${GPU_FLAG:-} \
2025-03-04T21:12:19.8510888Z   ${SCCACHE_SERVER_PORT_DOCKER_FLAG:-} \
2025-03-04T21:12:19.8511238Z   -e BUILD_ENVIRONMENT \
2025-03-04T21:12:19.8511543Z   -e PR_NUMBER \
2025-03-04T21:12:19.8511822Z   -e GITHUB_ACTIONS \
2025-03-04T21:12:19.8512111Z   -e GITHUB_REPOSITORY \
2025-03-04T21:12:19.8512417Z   -e GITHUB_WORKFLOW \
2025-03-04T21:12:19.8512710Z   -e GITHUB_JOB \
2025-03-04T21:12:19.8513001Z   -e GITHUB_RUN_ID \
2025-03-04T21:12:19.8513286Z   -e GITHUB_RUN_NUMBER \
2025-03-04T21:12:19.8513592Z   -e GITHUB_RUN_ATTEMPT \
2025-03-04T21:12:19.8513884Z   -e JOB_ID \
2025-03-04T21:12:19.8514148Z   -e JOB_NAME \
2025-03-04T21:12:19.8514415Z   -e BASE_SHA \
2025-03-04T21:12:19.8514680Z   -e BRANCH \
2025-03-04T21:12:19.8514935Z   -e SHA1 \
2025-03-04T21:12:19.8515346Z   -e AWS_DEFAULT_REGION \
2025-03-04T21:12:19.8515763Z   -e IN_WHEEL_TEST \
2025-03-04T21:12:19.8516047Z   -e SHARD_NUMBER \
2025-03-04T21:12:19.8516327Z   -e TEST_CONFIG \
2025-03-04T21:12:19.8516613Z   -e NUM_TEST_SHARDS \
2025-03-04T21:12:19.8516917Z   -e REENABLED_ISSUES \
2025-03-04T21:12:19.8517227Z   -e CONTINUE_THROUGH_ERROR \
2025-03-04T21:12:19.8517547Z   -e VERBOSE_TEST_LOGS \
2025-03-04T21:12:19.8517853Z   -e TEST_SHOWLOCALS \
2025-03-04T21:12:19.8518149Z   -e NO_TEST_TIMEOUT \
2025-03-04T21:12:19.8518431Z   -e NO_TD \
2025-03-04T21:12:19.8518692Z   -e TD_DISTRIBUTED \
2025-03-04T21:12:19.8518973Z   -e PR_LABELS \
2025-03-04T21:12:19.8519272Z   -e MAX_JOBS="$(nproc --ignore=2)" \
2025-03-04T21:12:19.8519608Z   -e SCCACHE_BUCKET \
2025-03-04T21:12:19.8519896Z   -e SCCACHE_REGION \
2025-03-04T21:12:19.8520183Z   -e XLA_CUDA \
2025-03-04T21:12:19.8520477Z   -e XLA_CLANG_CACHE_S3_BUCKET_NAME \
2025-03-04T21:12:19.8520846Z   -e PYTORCH_TEST_CUDA_MEM_LEAK_CHECK \
2025-03-04T21:12:19.8521214Z   -e PYTORCH_TEST_RERUN_DISABLED_TESTS \
2025-03-04T21:12:19.8521582Z   -e SKIP_SCCACHE_INITIALIZATION=1 \
2025-03-04T21:12:19.8521933Z   -e HUGGING_FACE_HUB_TOKEN \
2025-03-04T21:12:19.8522266Z   -e SCRIBE_GRAPHQL_ACCESS_TOKEN \
2025-03-04T21:12:19.8522595Z   -e DASHBOARD_TAG \
2025-03-04T21:12:19.8522879Z   -e IS_A100_RUNNER \
2025-03-04T21:12:19.8523175Z   -e ARTIFACTS_FILE_SUFFIX \
2025-03-04T21:12:19.8523538Z   --memory="${TOTAL_AVAILABLE_MEMORY_IN_GB%.*}g" \
2025-03-04T21:12:19.8523953Z   --memory-swap="${TOTAL_MEMORY_WITH_SWAP}g" \
2025-03-04T21:12:19.8524366Z   --env-file="/tmp/github_env_${GITHUB_RUN_ID}" \
2025-03-04T21:12:19.8524763Z   --security-opt seccomp=unconfined \
2025-03-04T21:12:19.8525436Z   --cap-add=SYS_PTRACE \
2025-03-04T21:12:19.8525746Z   --ipc=host \
2025-03-04T21:12:19.8526014Z   ${SHM_OPTS} \
2025-03-04T21:12:19.8526270Z   --tty \
2025-03-04T21:12:19.8526511Z   --detach \
2025-03-04T21:12:19.8526784Z   --name="${container_name}" \
2025-03-04T21:12:19.8527102Z   ${JENKINS_USER} \
2025-03-04T21:12:19.8527548Z   -v "${GITHUB_WORKSPACE}:/var/lib/jenkins/workspace" \
2025-03-04T21:12:19.8527952Z   -w /var/lib/jenkins/workspace \
2025-03-04T21:12:19.8528276Z   "${DOCKER_IMAGE}" \
2025-03-04T21:12:19.8528563Z   ${DOCKER_SHELL_CMD}
2025-03-04T21:12:19.8528836Z )
2025-03-04T21:12:19.8529134Z # Propagate download.pytorch.org IP to container
2025-03-04T21:12:19.8529789Z grep download.pytorch.org /etc/hosts | docker exec -i "${container_name}" sudo bash -c "/bin/cat >> /etc/hosts"
2025-03-04T21:12:19.8530485Z echo "DOCKER_CONTAINER_ID=${container_name}" >> "${GITHUB_ENV}"
2025-03-04T21:12:19.8530900Z 
2025-03-04T21:12:19.8531172Z if [[ ${BUILD_ENVIRONMENT} == *"s390x"* ]]; then
2025-03-04T21:12:19.8531745Z   docker exec -t "${container_name}" sh -c "python3 -m pip install -r .ci/docker/requirements-ci.txt"
2025-03-04T21:12:19.8532259Z fi
2025-03-04T21:12:19.8532481Z 
2025-03-04T21:12:19.8532968Z docker exec -t "${container_name}" sh -c "python3 -m pip install $(echo dist/*.whl)[opt-einsum] && ${TEST_COMMAND}"
2025-03-04T21:12:19.8542777Z shell: /usr/bin/bash -e {0}
2025-03-04T21:12:19.8543053Z env:
2025-03-04T21:12:19.8543277Z   GIT_DEFAULT_BRANCH: main
2025-03-04T21:12:19.8543615Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:19.8544064Z   BUILD_ENVIRONMENT: linux-focal-cuda12.4-py3.10-gcc9-sm86
2025-03-04T21:12:19.8544441Z   PR_NUMBER: 
2025-03-04T21:12:19.8544696Z   GITHUB_REPOSITORY: pytorch/pytorch
2025-03-04T21:12:19.8545149Z   GITHUB_WORKFLOW: inductor
2025-03-04T21:12:19.8545426Z   GITHUB_JOB: test
2025-03-04T21:12:19.8545815Z   GITHUB_RUN_ID: 13661696663
2025-03-04T21:12:19.8546100Z   GITHUB_RUN_NUMBER: 120837
2025-03-04T21:12:19.8546377Z   GITHUB_RUN_ATTEMPT: 1
2025-03-04T21:12:19.8546633Z   JOB_ID: 38197725386
2025-03-04T21:12:19.8547111Z   JOB_NAME: cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:12:19.8547628Z   BRANCH: 
2025-03-04T21:12:19.8547893Z   SHA1: 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:19.8548271Z   BASE_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:19.8548628Z   TEST_CONFIG: inductor_torchbench
2025-03-04T21:12:19.8548928Z   SHARD_NUMBER: 1
2025-03-04T21:12:19.8549172Z   NUM_TEST_SHARDS: 2
2025-03-04T21:12:19.8549427Z   REENABLED_ISSUES: 
2025-03-04T21:12:19.8549696Z   CONTINUE_THROUGH_ERROR: False
2025-03-04T21:12:19.8549990Z   VERBOSE_TEST_LOGS: False
2025-03-04T21:12:19.8550268Z   TEST_SHOWLOCALS: False
2025-03-04T21:12:19.8550550Z   NO_TEST_TIMEOUT: False
2025-03-04T21:12:19.8550808Z   NO_TD: False
2025-03-04T21:12:19.8551056Z   TD_DISTRIBUTED: False
2025-03-04T21:12:19.8551372Z   SCCACHE_BUCKET: ossci-compiler-cache-circleci-v2
2025-03-04T21:12:19.8551743Z   SCCACHE_REGION: us-east-1
2025-03-04T21:12:19.8552016Z   SHM_SIZE: 2g
2025-03-04T21:12:19.8552830Z   DOCKER_IMAGE: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T21:12:19.8553704Z   XLA_CUDA: 
2025-03-04T21:12:19.8554076Z   XLA_CLANG_CACHE_S3_BUCKET_NAME: ossci-compiler-clang-cache-circleci-xla
2025-03-04T21:12:19.8554540Z   PYTORCH_TEST_CUDA_MEM_LEAK_CHECK: 0
2025-03-04T21:12:19.8554873Z   PYTORCH_TEST_RERUN_DISABLED_TESTS: 0
2025-03-04T21:12:19.8555190Z   DASHBOARD_TAG: 
2025-03-04T21:12:19.8555609Z   HUGGING_FACE_HUB_TOKEN: ***
2025-03-04T21:12:19.8556026Z   SCRIBE_GRAPHQL_ACCESS_TOKEN: ***
2025-03-04T21:12:19.8556327Z   IS_A100_RUNNER: 0
2025-03-04T21:12:19.8556805Z   ARTIFACTS_FILE_SUFFIX: test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386
2025-03-04T21:12:19.8557338Z ##[endgroup]
2025-03-04T21:12:19.8584238Z + [[ inductor_torchbench == \m\u\l\t\i\g\p\u ]]
2025-03-04T21:12:19.8584671Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *onnx* ]]
2025-03-04T21:12:19.8585062Z + TEST_COMMAND=.ci/pytorch/test.sh
2025-03-04T21:12:19.8587780Z ++ awk '/MemTotal/ { printf "%.3f \n", $2/1024/1024 - 1 }' /proc/meminfo
2025-03-04T21:12:19.8610685Z + TOTAL_AVAILABLE_MEMORY_IN_GB='61.094 '
2025-03-04T21:12:19.8611025Z + TOTAL_MEMORY_WITH_SWAP=64
2025-03-04T21:12:19.8611515Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *\s\3\9\0\x* ]]
2025-03-04T21:12:19.8611928Z + SHM_OPTS=--shm-size=2g
2025-03-04T21:12:19.8612215Z + JENKINS_USER='--user jenkins'
2025-03-04T21:12:19.8612499Z + DOCKER_SHELL_CMD=
2025-03-04T21:12:19.8621624Z +++ nproc --ignore=2
2025-03-04T21:12:19.8651296Z ++ 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
2025-03-04T21:12:32.2729242Z + container_name=135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T21:12:32.2731198Z + grep download.pytorch.org /etc/hosts
2025-03-04T21:12:32.2733484Z + docker exec -i 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de sudo bash -c '/bin/cat >> /etc/hosts'
2025-03-04T21:12:32.3694490Z + echo DOCKER_CONTAINER_ID=135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T21:12:32.3695156Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *\s\3\9\0\x* ]]
2025-03-04T21:12:32.3698110Z ++ echo dist/torch-2.7.0a0+git1b74980-cp310-cp310-linux_x86_64.whl
2025-03-04T21:12:32.3701108Z + docker exec -t 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de sh -c 'python3 -m pip install dist/torch-2.7.0a0+git1b74980-cp310-cp310-linux_x86_64.whl[opt-einsum] && .ci/pytorch/test.sh'
2025-03-04T21:12:32.8285362Z Processing ./dist/torch-2.7.0a0+git1b74980-cp310-cp310-linux_x86_64.whl (from torch==2.7.0a0+git1b74980)
2025-03-04T21:12:33.1799376Z 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)
2025-03-04T21:12:33.1801333Z 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)
2025-03-04T21:12:33.2282454Z Collecting sympy==1.13.3 (from torch==2.7.0a0+git1b74980->torch==2.7.0a0+git1b74980)
2025-03-04T21:12:33.2295891Z   Using cached sympy-1.13.3-py3-none-any.whl.metadata (12 kB)
2025-03-04T21:12:33.2313709Z 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)
2025-03-04T21:12:33.2317170Z 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)
2025-03-04T21:12:33.2320526Z 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)
2025-03-04T21:12:33.2331365Z 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)
2025-03-04T21:12:33.2345139Z 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)
2025-03-04T21:12:33.2363407Z 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)
2025-03-04T21:12:33.2753140Z 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)
2025-03-04T21:12:33.2834740Z Using cached sympy-1.13.3-py3-none-any.whl (6.2 MB)
2025-03-04T21:12:33.9177083Z Installing collected packages: sympy, torch
2025-03-04T21:12:33.9177497Z   Attempting uninstall: sympy
2025-03-04T21:12:33.9188233Z     Found existing installation: sympy 1.13.1
2025-03-04T21:12:34.1166528Z     Uninstalling sympy-1.13.1:
2025-03-04T21:12:35.3292147Z       Successfully uninstalled sympy-1.13.1
2025-03-04T21:12:50.0237651Z 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.
2025-03-04T21:12:50.0238656Z timm 1.0.14 requires torchvision, which is not installed.
2025-03-04T21:12:50.0239230Z Successfully installed sympy-1.13.3 torch-2.7.0a0+git1b74980
2025-03-04T21:12:50.1300520Z + export TERM=vt100
2025-03-04T21:12:50.1301064Z + TERM=vt100
2025-03-04T21:12:50.1303091Z ++ dirname .ci/pytorch/test.sh
2025-03-04T21:12:50.1312448Z + source .ci/pytorch/common.sh
2025-03-04T21:12:50.1315483Z +++ dirname .ci/pytorch/common.sh
2025-03-04T21:12:50.1325127Z ++ source .ci/pytorch/common_utils.sh
2025-03-04T21:12:50.1326776Z +++ declare -f -t trap_add
2025-03-04T21:12:50.1333044Z ++ set -ex -o pipefail
2025-03-04T21:12:50.1333708Z ++ [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *rocm* ]]
2025-03-04T21:12:50.1334135Z ++ BUILD_TEST_LIBTORCH=0
2025-03-04T21:12:50.1334490Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *rocm* ]]
2025-03-04T21:12:50.1334978Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *s390x* ]]
2025-03-04T21:12:50.1335435Z + [[ -d /var/lib/jenkins/workspace ]]
2025-03-04T21:12:50.1337208Z ++ stat -c %u /var/lib/jenkins/workspace
2025-03-04T21:12:50.1355698Z + WORKSPACE_ORIGINAL_OWNER_ID=1000
2025-03-04T21:12:50.1356664Z + trap_add cleanup_workspace EXIT
2025-03-04T21:12:50.1357061Z + trap_add_cmd=cleanup_workspace
2025-03-04T21:12:50.1357402Z + shift
2025-03-04T21:12:50.1357674Z + for trap_add_name in "$@"
2025-03-04T21:12:50.1364076Z +++ trap -p EXIT
2025-03-04T21:12:50.1367609Z ++ eval 'extract_trap_cmd '
2025-03-04T21:12:50.1368005Z +++ extract_trap_cmd
2025-03-04T21:12:50.1368271Z +++ printf '%s\n' ''
2025-03-04T21:12:50.1368548Z ++ printf '%s\n' cleanup_workspace
2025-03-04T21:12:50.1371389Z + trap -- '
2025-03-04T21:12:50.1371649Z cleanup_workspace' EXIT
2025-03-04T21:12:50.1372021Z + sudo chown -R jenkins /var/lib/jenkins/workspace
2025-03-04T21:12:50.9316502Z + git config --global --add safe.directory /var/lib/jenkins/workspace
2025-03-04T21:12:50.9339064Z + echo 'Environment variables:'
2025-03-04T21:12:50.9339448Z Environment variables:
2025-03-04T21:12:50.9339719Z + env
2025-03-04T21:12:50.9349991Z INSTALLED_DB=yes
2025-03-04T21:12:50.9350340Z NV_LIBCUBLAS_VERSION=12.4.5.8-1
2025-03-04T21:12:50.9350835Z NVIDIA_VISIBLE_DEVICES=all
2025-03-04T21:12:50.9351273Z NV_NVML_DEV_VERSION=12.4.127-1
2025-03-04T21:12:50.9351865Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T21:12:50.9352331Z CONTINUE_THROUGH_ERROR=False
2025-03-04T21:12:50.9352678Z NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.21.5-1+cuda12.4
2025-03-04T21:12:50.9353064Z NV_LIBNCCL_DEV_PACKAGE_VERSION=2.21.5-1
2025-03-04T21:12:50.9353466Z BUILD_ENVIRONMENT=linux-focal-cuda12.4-py3.10-gcc9-sm86
2025-03-04T21:12:50.9353894Z HOSTNAME=135038d589b4
2025-03-04T21:12:50.9354472Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9355110Z GITHUB_ACTION=__self
2025-03-04T21:12:50.9355434Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-04T21:12:50.9359477Z 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
2025-03-04T21:12:50.9363782Z NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-4=12.4.5.8-1
2025-03-04T21:12:50.9364174Z NV_NVTX_VERSION=12.4.127-1
2025-03-04T21:12:50.9364745Z GITHUB_RUN_NUMBER=120837
2025-03-04T21:12:50.9365045Z TEST_CONFIG=inductor_torchbench
2025-03-04T21:12:50.9365569Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-04T21:12:50.9365921Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-04T21:12:50.9366245Z IS_A100_RUNNER=0
2025-03-04T21:12:50.9366519Z NV_CUDA_CUDART_DEV_VERSION=12.4.127-1
2025-03-04T21:12:50.9366855Z NV_LIBCUSPARSE_VERSION=12.3.1.170-1
2025-03-04T21:12:50.9367378Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-04T21:12:50.9367884Z NV_LIBNPP_VERSION=12.2.5.30-1
2025-03-04T21:12:50.9368212Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot]
2025-03-04T21:12:50.9368601Z CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
2025-03-04T21:12:50.9368958Z GITHUB_REF_TYPE=tag
2025-03-04T21:12:50.9369230Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-04T21:12:50.9369524Z NCCL_VERSION=2.21.5-1
2025-03-04T21:12:50.9369832Z BASE_SHA=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:50.9370173Z XLA_CUDA=
2025-03-04T21:12:50.9370542Z HUGGING_FACE_HUB_TOKEN=***
2025-03-04T21:12:50.9372906Z ***
2025-03-04T21:12:50.9373164Z CARGO_NET_GIT_FETCH_WITH_CLI=true
2025-03-04T21:12:50.9373649Z GITHUB_REPOSITORY_ID=65600975
2025-03-04T21:12:50.9373942Z GITHUB_ACTIONS=true
2025-03-04T21:12:50.9374211Z NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:50.9374552Z NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-4=12.4.127-1
2025-03-04T21:12:50.9374933Z NV_LIBNPP_PACKAGE=libnpp-12-4=12.2.5.30-1
2025-03-04T21:12:50.9375295Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:50.9375653Z NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
2025-03-04T21:12:50.9376019Z GITHUB_SHA=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:50.9376616Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/tags/ciflow/inductor/148205
2025-03-04T21:12:50.9377167Z UCC_HOME=/usr
2025-03-04T21:12:50.9377424Z NV_LIBCUBLAS_DEV_VERSION=12.4.5.8-1
2025-03-04T21:12:50.9377734Z VERBOSE_TEST_LOGS=False
2025-03-04T21:12:50.9378007Z NVIDIA_PRODUCT_NAME=CUDA
2025-03-04T21:12:50.9378332Z NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-4
2025-03-04T21:12:50.9378713Z GITHUB_REF=refs/tags/ciflow/inductor/148205
2025-03-04T21:12:50.9379063Z NV_CUDA_CUDART_VERSION=12.4.127-1
2025-03-04T21:12:50.9379359Z SHARD_NUMBER=1
2025-03-04T21:12:50.9379614Z GITHUB_REF_PROTECTED=false
2025-03-04T21:12:50.9379896Z HOME=/var/lib/jenkins
2025-03-04T21:12:50.9380192Z GITHUB_API_URL=https://api.github.com
2025-03-04T21:12:50.9380538Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-04T21:12:50.9380900Z UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb
2025-03-04T21:12:50.9381258Z CUDA_VERSION=12.4.1
2025-03-04T21:12:50.9381546Z NV_LIBCUBLAS_PACKAGE=libcublas-12-4=12.4.5.8-1
2025-03-04T21:12:50.9381882Z NUM_TEST_SHARDS=2
2025-03-04T21:12:50.9382130Z UCX_HOME=/usr
2025-03-04T21:12:50.9382486Z NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-4=12.4.1-1
2025-03-04T21:12:50.9383223Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9384107Z JOB_NAME=cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:12:50.9384931Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9385763Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-04T21:12:50.9386261Z GITHUB_EVENT_NAME=push
2025-03-04T21:12:50.9386534Z DASHBOARD_TAG=
2025-03-04T21:12:50.9386778Z GITHUB_RUN_ID=13661696663
2025-03-04T21:12:50.9387100Z NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-4=12.2.5.30-1
2025-03-04T21:12:50.9387481Z NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-4
2025-03-04T21:12:50.9388156Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9388824Z GITHUB_ACTOR=pytorch-bot[bot]
2025-03-04T21:12:50.9389133Z NV_LIBNPP_DEV_VERSION=12.2.5.30-1
2025-03-04T21:12:50.9389428Z PR_NUMBER=
2025-03-04T21:12:50.9389660Z GITHUB_RUN_ATTEMPT=1
2025-03-04T21:12:50.9390044Z ANACONDA_PYTHON_VERSION=3.10
2025-03-04T21:12:50.9390476Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-04T21:12:50.9390831Z TERM=vt100
2025-03-04T21:12:50.9391083Z NV_LIBCUSPARSE_DEV_VERSION=12.3.1.170-1
2025-03-04T21:12:50.9391400Z INSTALLED_VISION=yes
2025-03-04T21:12:50.9391650Z BRANCH=
2025-03-04T21:12:50.9391884Z SCCACHE_REGION=us-east-1
2025-03-04T21:12:50.9392170Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-04T21:12:50.9392475Z LIBRARY_PATH=/usr/local/cuda/lib64/stubs
2025-03-04T21:12:50.9392803Z CUDA_PATH=/usr/local/cuda
2025-03-04T21:12:50.9393320Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-04T21:12:50.9393899Z GITHUB_SERVER_URL=https://github.com
2025-03-04T21:12:50.9394271Z UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
2025-03-04T21:12:50.9394629Z REENABLED_ISSUES=
2025-03-04T21:12:50.9394867Z SHLVL=1
2025-03-04T21:12:50.9395086Z MAX_JOBS=14
2025-03-04T21:12:50.9395324Z NV_CUDA_LIB_VERSION=12.4.1-1
2025-03-04T21:12:50.9395626Z NVARCH=x86_64
2025-03-04T21:12:50.9395876Z GITHUB_ACTOR_ID=54816060
2025-03-04T21:12:50.9396225Z GITHUB_WORKFLOW_SHA=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:50.9396635Z GITHUB_REF_NAME=ciflow/inductor/148205
2025-03-04T21:12:50.9396980Z NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-4
2025-03-04T21:12:50.9397429Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-04T21:12:50.9397855Z GITHUB_JOB=test
2025-03-04T21:12:50.9398132Z NV_LIBNCCL_PACKAGE=libnccl2=2.21.5-1+cuda12.4
2025-03-04T21:12:50.9398552Z LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
2025-03-04T21:12:50.9398977Z NO_TEST_TIMEOUT=False
2025-03-04T21:12:50.9399255Z TD_DISTRIBUTED=False
2025-03-04T21:12:50.9399551Z NV_CUDA_NSIGHT_COMPUTE_VERSION=12.4.1-1
2025-03-04T21:12:50.9399916Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-04T21:12:50.9400252Z NV_NVPROF_VERSION=12.4.127-1
2025-03-04T21:12:50.9400560Z GITHUB_RETENTION_DAYS=90
2025-03-04T21:12:50.9400860Z OPENSSL_DIR=/opt/openssl
2025-03-04T21:12:50.9401156Z GITHUB_ACTION_REPOSITORY=
2025-03-04T21:12:50.9402023Z 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
2025-03-04T21:12:50.9402909Z GITHUB_BASE_REF=
2025-03-04T21:12:50.9403422Z ARTIFACTS_FILE_SUFFIX=test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386
2025-03-04T21:12:50.9404035Z NV_LIBNCCL_PACKAGE_NAME=libnccl2
2025-03-04T21:12:50.9404340Z CI=true
2025-03-04T21:12:50.9404588Z NV_LIBNCCL_PACKAGE_VERSION=2.21.5-1
2025-03-04T21:12:50.9404926Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-04T21:12:50.9405273Z JOB_ID=38197725386
2025-03-04T21:12:50.9405545Z INSTALLED_PROTOBUF=yes
2025-03-04T21:12:50.9405820Z GITHUB_HEAD_REF=
2025-03-04T21:12:50.9406072Z GITHUB_ACTION_REF=
2025-03-04T21:12:50.9406402Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-04T21:12:50.9406795Z TEST_SHOWLOCALS=False
2025-03-04T21:12:50.9407084Z GITHUB_WORKFLOW=inductor
2025-03-04T21:12:50.9407387Z DEBIAN_FRONTEND=noninteractive
2025-03-04T21:12:50.9408118Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9408741Z NO_TD=False
2025-03-04T21:12:50.9408990Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-04T21:12:50.9409281Z _=/usr/bin/env
2025-03-04T21:12:50.9409620Z ++ python -c 'import site; print(site.getsitepackages()[0])'
2025-03-04T21:12:50.9530260Z + TORCH_INSTALL_DIR=/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch
2025-03-04T21:12:50.9530871Z + TORCH_BIN_DIR=/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/bin
2025-03-04T21:12:50.9531481Z + TORCH_LIB_DIR=/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/lib
2025-03-04T21:12:50.9532250Z + TORCH_TEST_DIR=/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/test
2025-03-04T21:12:50.9532705Z + BUILD_DIR=build
2025-03-04T21:12:50.9532969Z + BUILD_RENAMED_DIR=build_renamed
2025-03-04T21:12:50.9533485Z + BUILD_BIN_DIR=build/bin
2025-03-04T21:12:50.9533752Z + SHARD_NUMBER=1
2025-03-04T21:12:50.9534126Z + NUM_TEST_SHARDS=2
2025-03-04T21:12:50.9534409Z + export TORCH_SERIALIZATION_DEBUG=1
2025-03-04T21:12:50.9534741Z + TORCH_SERIALIZATION_DEBUG=1
2025-03-04T21:12:50.9535038Z + export VALGRIND=ON
2025-03-04T21:12:50.9535406Z + VALGRIND=ON
2025-03-04T21:12:50.9535797Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *clang9* ]]
2025-03-04T21:12:50.9536267Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *xpu* ]]
2025-03-04T21:12:50.9536804Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *s390x* ]]
2025-03-04T21:12:50.9537167Z + [[ 0 == \1 ]]
2025-03-04T21:12:50.9537407Z + [[ False == \1 ]]
2025-03-04T21:12:50.9537722Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *bazel* ]]
2025-03-04T21:12:50.9538124Z ++ realpath build/custom_test_artifacts
2025-03-04T21:12:50.9547634Z + CUSTOM_TEST_ARTIFACT_BUILD_DIR=/var/lib/jenkins/workspace/build/custom_test_artifacts
2025-03-04T21:12:50.9548325Z + [[ -n '' ]]
2025-03-04T21:12:50.9548678Z + echo 'Environment variables'
2025-03-04T21:12:50.9549093Z Environment variables
2025-03-04T21:12:50.9549449Z + env
2025-03-04T21:12:50.9560700Z INSTALLED_DB=yes
2025-03-04T21:12:50.9561128Z NV_LIBCUBLAS_VERSION=12.4.5.8-1
2025-03-04T21:12:50.9561550Z NVIDIA_VISIBLE_DEVICES=all
2025-03-04T21:12:50.9561983Z NV_NVML_DEV_VERSION=12.4.127-1
2025-03-04T21:12:50.9562404Z GITHUB_WORKSPACE=/home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T21:12:50.9562864Z CONTINUE_THROUGH_ERROR=False
2025-03-04T21:12:50.9563366Z NV_LIBNCCL_DEV_PACKAGE=libnccl-dev=2.21.5-1+cuda12.4
2025-03-04T21:12:50.9563757Z NV_LIBNCCL_DEV_PACKAGE_VERSION=2.21.5-1
2025-03-04T21:12:50.9564303Z BUILD_ENVIRONMENT=linux-focal-cuda12.4-py3.10-gcc9-sm86
2025-03-04T21:12:50.9564696Z HOSTNAME=135038d589b4
2025-03-04T21:12:50.9565369Z GITHUB_PATH=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/add_path_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9566100Z GITHUB_ACTION=__self
2025-03-04T21:12:50.9566403Z PYTORCH_TEST_CUDA_MEM_LEAK_CHECK=0
2025-03-04T21:12:50.9570887Z 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
2025-03-04T21:12:50.9575841Z NV_LIBCUBLAS_DEV_PACKAGE=libcublas-dev-12-4=12.4.5.8-1
2025-03-04T21:12:50.9576220Z NV_NVTX_VERSION=12.4.127-1
2025-03-04T21:12:50.9576495Z GITHUB_RUN_NUMBER=120837
2025-03-04T21:12:50.9576778Z TEST_CONFIG=inductor_torchbench
2025-03-04T21:12:50.9577086Z GITHUB_REPOSITORY_OWNER_ID=21003710
2025-03-04T21:12:50.9577630Z TORCH_NVCC_FLAGS=-Xfatbin -compress-all
2025-03-04T21:12:50.9577946Z IS_A100_RUNNER=0
2025-03-04T21:12:50.9578206Z NV_CUDA_CUDART_DEV_VERSION=12.4.127-1
2025-03-04T21:12:50.9578535Z NV_LIBCUSPARSE_VERSION=12.3.1.170-1
2025-03-04T21:12:50.9579006Z SCRIBE_GRAPHQL_ACCESS_TOKEN=***
2025-03-04T21:12:50.9579313Z NV_LIBNPP_VERSION=12.2.5.30-1
2025-03-04T21:12:50.9579628Z GITHUB_TRIGGERING_ACTOR=pytorch-bot[bot]
2025-03-04T21:12:50.9580008Z CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
2025-03-04T21:12:50.9581639Z GITHUB_REF_TYPE=tag
2025-03-04T21:12:50.9582000Z TORCH_CUDA_ARCH_LIST=Maxwell
2025-03-04T21:12:50.9582292Z NCCL_VERSION=2.21.5-1
2025-03-04T21:12:50.9582590Z BASE_SHA=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:50.9582923Z XLA_CUDA=
2025-03-04T21:12:50.9583264Z HUGGING_FACE_HUB_TOKEN=***
2025-03-04T21:12:50.9583708Z ***
2025-03-04T21:12:50.9583941Z CARGO_NET_GIT_FETCH_WITH_CLI=true
2025-03-04T21:12:50.9584258Z GITHUB_REPOSITORY_ID=65600975
2025-03-04T21:12:50.9584547Z GITHUB_ACTIONS=true
2025-03-04T21:12:50.9584806Z NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T21:12:50.9585171Z NV_NVPROF_DEV_PACKAGE=cuda-nvprof-12-4=12.4.127-1
2025-03-04T21:12:50.9585576Z NV_LIBNPP_PACKAGE=libnpp-12-4=12.2.5.30-1
2025-03-04T21:12:50.9585932Z SHA1=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:50.9586289Z NV_LIBNCCL_DEV_PACKAGE_NAME=libnccl-dev
2025-03-04T21:12:50.9586652Z GITHUB_SHA=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:50.9587255Z GITHUB_WORKFLOW_REF=pytorch/pytorch/.github/workflows/inductor.yml@refs/tags/ciflow/inductor/148205
2025-03-04T21:12:50.9587809Z UCC_HOME=/usr
2025-03-04T21:12:50.9588060Z TORCH_SERIALIZATION_DEBUG=1
2025-03-04T21:12:50.9588361Z NV_LIBCUBLAS_DEV_VERSION=12.4.5.8-1
2025-03-04T21:12:50.9588675Z VERBOSE_TEST_LOGS=False
2025-03-04T21:12:50.9588949Z NVIDIA_PRODUCT_NAME=CUDA
2025-03-04T21:12:50.9589267Z NV_LIBCUBLAS_DEV_PACKAGE_NAME=libcublas-dev-12-4
2025-03-04T21:12:50.9589649Z GITHUB_REF=refs/tags/ciflow/inductor/148205
2025-03-04T21:12:50.9589991Z NV_CUDA_CUDART_VERSION=12.4.127-1
2025-03-04T21:12:50.9590284Z SHARD_NUMBER=1
2025-03-04T21:12:50.9590529Z GITHUB_REF_PROTECTED=false
2025-03-04T21:12:50.9590805Z HOME=/var/lib/jenkins
2025-03-04T21:12:50.9591093Z GITHUB_API_URL=https://api.github.com
2025-03-04T21:12:50.9591430Z PYTORCH_TEST_RERUN_DISABLED_TESTS=0
2025-03-04T21:12:50.9591785Z UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb
2025-03-04T21:12:50.9592136Z CUDA_VERSION=12.4.1
2025-03-04T21:12:50.9592425Z NV_LIBCUBLAS_PACKAGE=libcublas-12-4=12.4.5.8-1
2025-03-04T21:12:50.9592763Z NUM_TEST_SHARDS=2
2025-03-04T21:12:50.9593011Z UCX_HOME=/usr
2025-03-04T21:12:50.9593361Z NV_CUDA_NSIGHT_COMPUTE_DEV_PACKAGE=cuda-nsight-compute-12-4=12.4.1-1
2025-03-04T21:12:50.9594097Z GITHUB_STATE=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/save_state_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9594937Z JOB_NAME=cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T21:12:50.9595750Z GITHUB_ENV=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_env_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9596531Z GITHUB_EVENT_PATH=/home/ec2-user/actions-runner/_work/_temp/_github_workflow/event.json
2025-03-04T21:12:50.9597029Z GITHUB_EVENT_NAME=push
2025-03-04T21:12:50.9597293Z DASHBOARD_TAG=
2025-03-04T21:12:50.9597539Z GITHUB_RUN_ID=13661696663
2025-03-04T21:12:50.9597856Z NV_LIBNPP_DEV_PACKAGE=libnpp-dev-12-4=12.2.5.30-1
2025-03-04T21:12:50.9598237Z NV_LIBCUBLAS_PACKAGE_NAME=libcublas-12-4
2025-03-04T21:12:50.9598917Z GITHUB_STEP_SUMMARY=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/step_summary_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9599584Z GITHUB_ACTOR=pytorch-bot[bot]
2025-03-04T21:12:50.9599885Z NV_LIBNPP_DEV_VERSION=12.2.5.30-1
2025-03-04T21:12:50.9600175Z PR_NUMBER=
2025-03-04T21:12:50.9600406Z GITHUB_RUN_ATTEMPT=1
2025-03-04T21:12:50.9600662Z VALGRIND=ON
2025-03-04T21:12:50.9600905Z ANACONDA_PYTHON_VERSION=3.10
2025-03-04T21:12:50.9601250Z GITHUB_GRAPHQL_URL=https://api.github.com/graphql
2025-03-04T21:12:50.9601603Z TERM=vt100
2025-03-04T21:12:50.9601859Z NV_LIBCUSPARSE_DEV_VERSION=12.3.1.170-1
2025-03-04T21:12:50.9602180Z INSTALLED_VISION=yes
2025-03-04T21:12:50.9602431Z BRANCH=
2025-03-04T21:12:50.9602663Z SCCACHE_REGION=us-east-1
2025-03-04T21:12:50.9602949Z OPENSSL_ROOT_DIR=/opt/openssl
2025-03-04T21:12:50.9603264Z LIBRARY_PATH=/usr/local/cuda/lib64/stubs
2025-03-04T21:12:50.9603685Z CUDA_PATH=/usr/local/cuda
2025-03-04T21:12:50.9604284Z GITHUB_ACTION_PATH=/home/ec2-user/actions-runner/_work/pytorch/pytorch/./.github/actions/setup-linux
2025-03-04T21:12:50.9604853Z GITHUB_SERVER_URL=https://github.com
2025-03-04T21:12:50.9605222Z UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
2025-03-04T21:12:50.9605583Z REENABLED_ISSUES=
2025-03-04T21:12:50.9605820Z SHLVL=1
2025-03-04T21:12:50.9606035Z MAX_JOBS=14
2025-03-04T21:12:50.9606275Z NV_CUDA_LIB_VERSION=12.4.1-1
2025-03-04T21:12:50.9606556Z NVARCH=x86_64
2025-03-04T21:12:50.9606796Z GITHUB_ACTOR_ID=54816060
2025-03-04T21:12:50.9607167Z GITHUB_WORKFLOW_SHA=1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T21:12:50.9607690Z GITHUB_REF_NAME=ciflow/inductor/148205
2025-03-04T21:12:50.9608033Z NV_CUDA_COMPAT_PACKAGE=cuda-compat-12-4
2025-03-04T21:12:50.9608486Z XLA_CLANG_CACHE_S3_BUCKET_NAME=ossci-compiler-clang-cache-circleci-xla
2025-03-04T21:12:50.9608929Z GITHUB_JOB=test
2025-03-04T21:12:50.9609205Z NV_LIBNCCL_PACKAGE=libnccl2=2.21.5-1+cuda12.4
2025-03-04T21:12:50.9609641Z LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64
2025-03-04T21:12:50.9610039Z NO_TEST_TIMEOUT=False
2025-03-04T21:12:50.9610306Z TD_DISTRIBUTED=False
2025-03-04T21:12:50.9610593Z NV_CUDA_NSIGHT_COMPUTE_VERSION=12.4.1-1
2025-03-04T21:12:50.9622260Z GITHUB_REPOSITORY=pytorch/pytorch
2025-03-04T21:12:50.9622644Z NV_NVPROF_VERSION=12.4.127-1
2025-03-04T21:12:50.9622954Z GITHUB_RETENTION_DAYS=90
2025-03-04T21:12:50.9623233Z OPENSSL_DIR=/opt/openssl
2025-03-04T21:12:50.9623521Z GITHUB_ACTION_REPOSITORY=
2025-03-04T21:12:50.9624293Z 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
2025-03-04T21:12:50.9625546Z GITHUB_BASE_REF=
2025-03-04T21:12:50.9626020Z ARTIFACTS_FILE_SUFFIX=test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386
2025-03-04T21:12:50.9626566Z NV_LIBNCCL_PACKAGE_NAME=libnccl2
2025-03-04T21:12:50.9626854Z CI=true
2025-03-04T21:12:50.9627104Z NV_LIBNCCL_PACKAGE_VERSION=2.21.5-1
2025-03-04T21:12:50.9627433Z GITHUB_REPOSITORY_OWNER=pytorch
2025-03-04T21:12:50.9627722Z JOB_ID=38197725386
2025-03-04T21:12:50.9627975Z INSTALLED_PROTOBUF=yes
2025-03-04T21:12:50.9628232Z GITHUB_HEAD_REF=
2025-03-04T21:12:50.9628479Z GITHUB_ACTION_REF=
2025-03-04T21:12:50.9628780Z SCCACHE_BUCKET=ossci-compiler-cache-circleci-v2
2025-03-04T21:12:50.9629140Z TEST_SHOWLOCALS=False
2025-03-04T21:12:50.9629405Z GITHUB_WORKFLOW=inductor
2025-03-04T21:12:50.9629694Z DEBIAN_FRONTEND=noninteractive
2025-03-04T21:12:50.9630305Z GITHUB_OUTPUT=/home/ec2-user/actions-runner/_work/_temp/_runner_file_commands/set_output_3b4c0200-773d-4566-81b3-555e6e13c8bd
2025-03-04T21:12:50.9630923Z NO_TD=False
2025-03-04T21:12:50.9631168Z SKIP_SCCACHE_INITIALIZATION=1
2025-03-04T21:12:50.9631446Z _=/usr/bin/env
2025-03-04T21:12:50.9631686Z + echo 'Testing pytorch'
2025-03-04T21:12:50.9631952Z Testing pytorch
2025-03-04T21:12:50.9632201Z + export LANG=C.UTF-8
2025-03-04T21:12:50.9632459Z + LANG=C.UTF-8
2025-03-04T21:12:50.9632687Z + PR_NUMBER=
2025-03-04T21:12:50.9632949Z + [[ inductor_torchbench == \d\e\f\a\u\l\t ]]
2025-03-04T21:12:50.9633331Z + [[ inductor_torchbench == \d\i\s\t\r\i\b\u\t\e\d ]]
2025-03-04T21:12:50.9633692Z + [[ inductor_torchbench == \s\l\o\w ]]
2025-03-04T21:12:50.9634107Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *slow-gradcheck* ]]
2025-03-04T21:12:50.9634573Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *cuda* ]]
2025-03-04T21:12:50.9634969Z + export PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
2025-03-04T21:12:50.9635324Z + PYTORCH_TESTING_DEVICE_ONLY_FOR=cuda
2025-03-04T21:12:50.9635660Z + [[ inductor_torchbench == *crossref* ]]
2025-03-04T21:12:50.9636041Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *rocm* ]]
2025-03-04T21:12:50.9636459Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *xpu* ]]
2025-03-04T21:12:50.9636886Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *-bazel-* ]]
2025-03-04T21:12:50.9637280Z + pip_install --user ninja==1.10.2
2025-03-04T21:12:50.9637865Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T21:12:50.9638468Z + python3 -m pip install --progress-bar off --user ninja==1.10.2
2025-03-04T21:12:51.4904213Z Collecting ninja==1.10.2
2025-03-04T21:12:51.5236576Z   Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl.metadata (5.0 kB)
2025-03-04T21:12:51.5361794Z Downloading ninja-1.10.2-py2.py3-none-manylinux_2_5_x86_64.manylinux1_x86_64.whl (108 kB)
2025-03-04T21:12:52.1285897Z Installing collected packages: ninja
2025-03-04T21:12:52.1365174Z   WARNING: The script ninja is installed in '/var/lib/jenkins/.local/bin' which is not on PATH.
2025-03-04T21:12:52.1366698Z   Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
2025-03-04T21:12:52.1422757Z Successfully installed ninja-1.10.2
2025-03-04T21:12:52.2383322Z + 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
2025-03-04T21:12:52.2384936Z + 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
2025-03-04T21:12:52.2385964Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *aarch64* ]]
2025-03-04T21:12:52.2386352Z + install_tlparse
2025-03-04T21:12:52.2386636Z + pip_install --user tlparse==0.3.30
2025-03-04T21:12:52.2387050Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T21:12:52.2387555Z + python3 -m pip install --progress-bar off --user tlparse==0.3.30
2025-03-04T21:12:52.7084842Z Collecting tlparse==0.3.30
2025-03-04T21:12:52.7423438Z   Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.9 kB)
2025-03-04T21:12:52.7578088Z Downloading tlparse-0.3.30-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.3 MB)
2025-03-04T21:12:53.3701078Z Installing collected packages: tlparse
2025-03-04T21:12:53.4052476Z Successfully installed tlparse-0.3.30
2025-03-04T21:12:53.5030839Z ++ python -m site --user-base
2025-03-04T21:12:53.5214043Z + 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
2025-03-04T21:12:53.5215555Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *asan* ]]
2025-03-04T21:12:53.5216131Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *-debug* ]]
2025-03-04T21:12:53.5216577Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 != *-bazel-* ]]
2025-03-04T21:12:53.5217194Z + echo 'We are not in debug mode: linux-focal-cuda12.4-py3.10-gcc9-sm86. Expect the assertion to pass'
2025-03-04T21:12:53.5217934Z We are not in debug mode: linux-focal-cuda12.4-py3.10-gcc9-sm86. Expect the assertion to pass
2025-03-04T21:12:53.5221376Z + cd test
2025-03-04T21:12:53.5221795Z + python -c 'import torch; torch._C._crash_if_debug_asserts_fail(424242)'
2025-03-04T21:12:55.2443814Z + [[ inductor_torchbench == \n\o\g\p\u\_\N\O\_\A\V\X\2 ]]
2025-03-04T21:12:55.2444298Z + [[ inductor_torchbench == \n\o\g\p\u\_\A\V\X\5\1\2 ]]
2025-03-04T21:12:55.2449084Z + DYNAMO_BENCHMARK_FLAGS=()
2025-03-04T21:12:55.2449465Z + [[ inductor_torchbench == *pr_time_benchmarks* ]]
2025-03-04T21:12:55.2449861Z + [[ inductor_torchbench == *dynamo_eager* ]]
2025-03-04T21:12:55.2450219Z + [[ inductor_torchbench == *aot_eager* ]]
2025-03-04T21:12:55.2450575Z + [[ inductor_torchbench == *aot_inductor* ]]
2025-03-04T21:12:55.2450931Z + [[ inductor_torchbench == *inductor* ]]
2025-03-04T21:12:55.2451280Z + [[ inductor_torchbench != *perf* ]]
2025-03-04T21:12:55.2451636Z + DYNAMO_BENCHMARK_FLAGS+=(--inductor)
2025-03-04T21:12:55.2451982Z + [[ inductor_torchbench == *dynamic* ]]
2025-03-04T21:12:55.2452317Z + [[ inductor_torchbench == *cpu* ]]
2025-03-04T21:12:55.2452649Z + DYNAMO_BENCHMARK_FLAGS+=(--device cuda)
2025-03-04T21:12:55.2484848Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *libtorch* ]]
2025-03-04T21:12:55.2485544Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *-bazel-* ]]
2025-03-04T21:12:55.2488811Z + cd test
2025-03-04T21:12:55.2489132Z + python -c 'import torch; print(torch.__config__.show())'
2025-03-04T21:12:56.7771628Z PyTorch built with:
2025-03-04T21:12:56.7771957Z   - GCC 9.4
2025-03-04T21:12:56.7772200Z   - C++ Version: 201703
2025-03-04T21:12:56.7772756Z   - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-04T21:12:56.7773474Z   - Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
2025-03-04T21:12:56.7773936Z   - OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-04T21:12:56.7774290Z   - LAPACK is enabled (usually provided by MKL)
2025-03-04T21:12:56.7774635Z   - NNPACK is enabled
2025-03-04T21:12:56.7774915Z   - CPU capability usage: AVX2
2025-03-04T21:12:56.7775227Z   - CUDA Runtime 12.4
2025-03-04T21:12:56.7775622Z   - NVCC architecture flags: -gencode;arch=compute_86,code=sm_86
2025-03-04T21:12:56.7776034Z   - CuDNN 90.1
2025-03-04T21:12:56.7776274Z   - Magma 2.6.1
2025-03-04T21:12:56.7780697Z   - 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, 
2025-03-04T21:12:56.7785372Z 
2025-03-04T21:12:57.1185535Z + cd test
2025-03-04T21:12:57.1185949Z + python -c 'import torch; print(torch.__config__.parallel_info())'
2025-03-04T21:12:58.5116209Z ATen/Parallel:
2025-03-04T21:12:58.5116820Z 	at::get_num_threads() : 8
2025-03-04T21:12:58.5117417Z 	at::get_num_interop_threads() : 16
2025-03-04T21:12:58.5117971Z OpenMP 201511 (a.k.a. OpenMP 4.5)
2025-03-04T21:12:58.5118325Z 	omp_get_max_threads() : 8
2025-03-04T21:12:58.5118881Z Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
2025-03-04T21:12:58.5119466Z 	mkl_get_max_threads() : 8
2025-03-04T21:12:58.5119911Z Intel(R) MKL-DNN v3.5.3 (Git Hash 66f0cb9eb66affd2da3bf5f8d897376f04aae6af)
2025-03-04T21:12:58.5120378Z std::thread::hardware_concurrency() : 16
2025-03-04T21:12:58.5120711Z Environment variables:
2025-03-04T21:12:58.5120990Z 	OMP_NUM_THREADS : [not set]
2025-03-04T21:12:58.5121283Z 	MKL_NUM_THREADS : [not set]
2025-03-04T21:12:58.5121580Z ATen parallel backend: OpenMP
2025-03-04T21:12:58.5121769Z 
2025-03-04T21:12:58.8137306Z + [[ inductor_torchbench == *numpy_2* ]]
2025-03-04T21:12:58.8137809Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *aarch64* ]]
2025-03-04T21:12:58.8138228Z + [[ inductor_torchbench == *backward* ]]
2025-03-04T21:12:58.8138572Z + [[ inductor_torchbench == *xla* ]]
2025-03-04T21:12:58.8138905Z + [[ inductor_torchbench == *executorch* ]]
2025-03-04T21:12:58.8139271Z + [[ inductor_torchbench == \j\i\t\_\l\e\g\a\c\y ]]
2025-03-04T21:12:58.8139697Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *libtorch* ]]
2025-03-04T21:12:58.8140110Z + [[ inductor_torchbench == distributed ]]
2025-03-04T21:12:58.8140878Z + [[ inductor_torchbench == *inductor_distributed* ]]
2025-03-04T21:12:58.8141452Z + [[ inductor_torchbench == *inductor-halide* ]]
2025-03-04T21:12:58.8141855Z + [[ inductor_torchbench == *inductor-triton-cpu* ]]
2025-03-04T21:12:58.8142281Z + [[ inductor_torchbench == *inductor-micro-benchmark* ]]
2025-03-04T21:12:58.8142683Z + [[ inductor_torchbench == *huggingface* ]]
2025-03-04T21:12:58.8143029Z + [[ inductor_torchbench == *timm* ]]
2025-03-04T21:12:58.8143364Z + [[ inductor_torchbench == cachebench ]]
2025-03-04T21:12:58.8143721Z + [[ inductor_torchbench == verify_cachebench ]]
2025-03-04T21:12:58.8144084Z + [[ inductor_torchbench == *torchbench* ]]
2025-03-04T21:12:58.8144421Z + [[ inductor_torchbench == *cpu* ]]
2025-03-04T21:12:58.8144743Z + install_torchaudio cuda
2025-03-04T21:12:58.8145017Z + local commit
2025-03-04T21:12:58.8145277Z ++ get_pinned_commit audio
2025-03-04T21:12:58.8145579Z ++ cat .github/ci_commit_pins/audio.txt
2025-03-04T21:12:58.8164713Z + commit=c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-04T21:12:58.8165098Z + [[ cuda == \c\u\d\a ]]
2025-03-04T21:12:58.8165389Z + TORCH_CUDA_ARCH_LIST='8.0;8.6'
2025-03-04T21:12:58.8166012Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-04T21:12:58.8166717Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T21:12:58.8167604Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-04T21:12:59.2122399Z Collecting git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-04T21:12:59.2127039Z   Cloning https://github.com/pytorch/audio.git (to revision c670ad81fda266b6598aeeef434583eb98197ae8) to /tmp/pip-req-build-twlb2_xs
2025-03-04T21:12:59.2158491Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/audio.git /tmp/pip-req-build-twlb2_xs
2025-03-04T21:13:00.0268296Z   Running command git rev-parse -q --verify 'sha^c670ad81fda266b6598aeeef434583eb98197ae8'
2025-03-04T21:13:00.0296029Z   Running command git fetch -q https://github.com/pytorch/audio.git c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-04T21:13:00.4424163Z   Resolved https://github.com/pytorch/audio.git to commit c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-04T21:13:00.4425053Z   Running command git submodule update --init --recursive -q
2025-03-04T21:13:03.0632050Z   Preparing metadata (setup.py) ... [?25l- done
2025-03-04T21:13:03.0666848Z [?25hRequirement already satisfied: torch in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchaudio==2.6.0a0+c670ad8) (2.7.0a0+git1b74980)
2025-03-04T21:13:03.0692759Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.16.1)
2025-03-04T21:13:03.0696681Z Requirement already satisfied: typing-extensions>=4.10.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (4.12.2)
2025-03-04T21:13:03.0700869Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (1.13.3)
2025-03-04T21:13:03.0703881Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2.8.8)
2025-03-04T21:13:03.0707318Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (3.1.5)
2025-03-04T21:13:03.0710302Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch->torchaudio==2.6.0a0+c670ad8) (2024.10.0)
2025-03-04T21:13:03.0725495Z 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->torchaudio==2.6.0a0+c670ad8) (1.3.0)
2025-03-04T21:13:03.1210795Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from jinja2->torch->torchaudio==2.6.0a0+c670ad8) (3.0.2)
2025-03-04T21:13:03.1275744Z Building wheels for collected packages: torchaudio
2025-03-04T21:15:32.7712805Z   Building wheel for torchaudio (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | done
2025-03-04T21:15:32.7752633Z [?25h  Created wheel for torchaudio: filename=torchaudio-2.6.0a0+c670ad8-cp310-cp310-linux_x86_64.whl size=2363927 sha256=f5436bf633fcb5adc9d75dbadf215f03e01341c816c05e05e609840285b78b6f
2025-03-04T21:15:32.7757386Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/d3/a5/a0/32c6aea783135ce13a14d2b2ccb6f8d5ce3b45e87b2c1918f9
2025-03-04T21:15:32.7783939Z Successfully built torchaudio
2025-03-04T21:15:33.2972483Z Installing collected packages: torchaudio
2025-03-04T21:15:33.5124586Z Successfully installed torchaudio-2.6.0a0+c670ad8
2025-03-04T21:15:33.7143549Z + install_torchvision
2025-03-04T21:15:33.7143857Z + local orig_preload
2025-03-04T21:15:33.7144119Z + local commit
2025-03-04T21:15:33.7148810Z ++ get_pinned_commit vision
2025-03-04T21:15:33.7149193Z ++ cat .github/ci_commit_pins/vision.txt
2025-03-04T21:15:33.7178392Z + commit=d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T21:15:33.7178745Z + orig_preload=
2025-03-04T21:15:33.7178996Z + '[' -n '' ']'
2025-03-04T21:15:33.7179553Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T21:15:33.7180249Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T21:15:33.7181073Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T21:15:34.1205531Z Collecting git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T21:15:34.1209061Z   Cloning https://github.com/pytorch/vision.git (to revision d23a6e1664d20707c11781299611436e1f0c104f) to /tmp/pip-req-build-4saill7f
2025-03-04T21:15:34.1250615Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/vision.git /tmp/pip-req-build-4saill7f
2025-03-04T21:15:35.6631363Z   Running command git rev-parse -q --verify 'sha^d23a6e1664d20707c11781299611436e1f0c104f'
2025-03-04T21:15:35.6661811Z   Running command git fetch -q https://github.com/pytorch/vision.git d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T21:15:37.0830962Z   Running command git checkout -q d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T21:15:37.4682578Z   Resolved https://github.com/pytorch/vision.git to commit d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T21:15:40.3355952Z   Preparing metadata (setup.py) ... [?25l- \ done
2025-03-04T21:15:40.3389851Z [?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)
2025-03-04T21:15:40.3393308Z 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)
2025-03-04T21:15:40.3398142Z 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)
2025-03-04T21:15:40.3466165Z 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)
2025-03-04T21:15:40.3470500Z 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)
2025-03-04T21:15:40.3475087Z 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)
2025-03-04T21:15:40.3478379Z 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)
2025-03-04T21:15:40.3481915Z 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)
2025-03-04T21:15:40.3485487Z 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)
2025-03-04T21:15:40.3501079Z 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)
2025-03-04T21:15:40.3985761Z 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)
2025-03-04T21:15:40.4051422Z Building wheels for collected packages: torchvision
2025-03-04T21:16:57.5044819Z   Building wheel for torchvision (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done
2025-03-04T21:16:57.5077701Z [?25h  Created wheel for torchvision: filename=torchvision-0.19.0a0+d23a6e1-cp310-cp310-linux_x86_64.whl size=2074053 sha256=cfb92cf46e34e886a4e6d9f6c586229b8ff58e89bd07d47430f10edb26ffbf1f
2025-03-04T21:16:57.5080650Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/0e/56/35/02931e71eb23fd2b85591c7ec05b733ca7c8b328a2fd151f96
2025-03-04T21:16:57.5115077Z Successfully built torchvision
2025-03-04T21:16:58.0312839Z Installing collected packages: torchvision
2025-03-04T21:16:58.4476773Z Successfully installed torchvision-0.19.0a0+d23a6e1
2025-03-04T21:16:58.6104368Z + '[' -n '' ']'
2025-03-04T21:16:58.6104700Z + TORCH_CUDA_ARCH_LIST='8.0;8.6'
2025-03-04T21:16:58.6105099Z + pip_install git+https://github.com/pytorch/ao.git
2025-03-04T21:16:58.6105567Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T21:16:58.6106129Z + python3 -m pip install --progress-bar off git+https://github.com/pytorch/ao.git
2025-03-04T21:16:59.0114330Z Collecting git+https://github.com/pytorch/ao.git
2025-03-04T21:16:59.0117397Z   Cloning https://github.com/pytorch/ao.git to /tmp/pip-req-build-l2y_5zet
2025-03-04T21:16:59.0148662Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/ao.git /tmp/pip-req-build-l2y_5zet
2025-03-04T21:16:59.8452375Z   Resolved https://github.com/pytorch/ao.git to commit d4be9e422e1f4e213903cb48319050a689a4d1d7
2025-03-04T21:16:59.8453132Z   Running command git submodule update --init --recursive -q
2025-03-04T21:17:05.4305933Z   Preparing metadata (setup.py) ... [?25l- done
2025-03-04T21:17:05.4347893Z [?25hBuilding wheels for collected packages: torchao
2025-03-04T21:19:48.0858350Z   Building wheel for torchao (setup.py) ... [?25l- \ | / - \ | / - \ | / - done
2025-03-04T21:19:48.0906441Z [?25h  Created wheel for torchao: filename=torchao-0.10.0+gitd4be9e42-cp39-abi3-linux_x86_64.whl size=4085429 sha256=cfe3cd43c8fee94eabca7a9604a40d77cb45b1733bb24689ed4bb94ae0c2ca50
2025-03-04T21:19:48.0909130Z   Stored in directory: /tmp/pip-ephem-wheel-cache-84y3p51r/wheels/c0/df/d8/b1e2f5f1ea3bf141268a83a667137647b234f04c636e1e7a5e
2025-03-04T21:19:48.0947006Z Successfully built torchao
2025-03-04T21:19:48.6758015Z Installing collected packages: torchao
2025-03-04T21:19:49.3429423Z Successfully installed torchao-0.10.0+gitd4be9e42
2025-03-04T21:19:49.8016317Z + id=0
2025-03-04T21:19:49.8016650Z + pip_install opencv-python==4.8.0.74
2025-03-04T21:19:49.8017116Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T21:19:49.8017640Z + python3 -m pip install --progress-bar off opencv-python==4.8.0.74
2025-03-04T21:19:50.3978841Z Collecting opencv-python==4.8.0.74
2025-03-04T21:19:50.4312292Z   Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)
2025-03-04T21:19:50.4414679Z Requirement already satisfied: numpy>=1.21.2 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from opencv-python==4.8.0.74) (1.22.4)
2025-03-04T21:19:50.4493503Z Downloading opencv_python-4.8.0.74-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (61.7 MB)
2025-03-04T21:19:51.6018745Z Installing collected packages: opencv-python
2025-03-04T21:19:52.4700255Z Successfully installed opencv-python-4.8.0.74
2025-03-04T21:19:52.5819170Z + [[ inductor_torchbench == *inductor_torchbench_smoketest_perf* ]]
2025-03-04T21:19:52.5819740Z + [[ inductor_torchbench == *inductor_torchbench_cpu_smoketest_perf* ]]
2025-03-04T21:19:52.5820237Z + [[ inductor_torchbench == *torchbench_gcp_smoketest* ]]
2025-03-04T21:19:52.5820629Z + checkout_install_torchbench
2025-03-04T21:19:52.5826430Z + local commit
2025-03-04T21:19:52.5842853Z ++ get_pinned_commit torchbench
2025-03-04T21:19:52.5843209Z ++ cat .github/ci_commit_pins/torchbench.txt
2025-03-04T21:19:52.5912521Z + commit=373ffb19dc470f4423a3176a4133f8f4b3cdb5bd
2025-03-04T21:19:52.5913426Z + git clone https://github.com/pytorch/benchmark torchbench
2025-03-04T21:19:52.5948079Z Cloning into 'torchbench'...
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2025-03-04T21:19:52.7693000Z remote: Counting objects:  65% (3644/5606)
2025-03-04T21:19:52.7693409Z remote: Counting objects:  66% (3700/5606)
2025-03-04T21:19:52.7693839Z remote: Counting objects:  67% (3757/5606)
2025-03-04T21:19:52.7694256Z remote: Counting objects:  68% (3813/5606)
2025-03-04T21:19:52.7694660Z remote: Counting objects:  69% (3869/5606)
2025-03-04T21:19:52.7695067Z remote: Counting objects:  70% (3925/5606)
2025-03-04T21:19:52.7695477Z remote: Counting objects:  71% (3981/5606)
2025-03-04T21:19:52.7695883Z remote: Counting objects:  72% (4037/5606)
2025-03-04T21:19:52.7696287Z remote: Counting objects:  73% (4093/5606)
2025-03-04T21:19:52.7696696Z remote: Counting objects:  74% (4149/5606)
2025-03-04T21:19:52.7697100Z remote: Counting objects:  75% (4205/5606)
2025-03-04T21:19:52.7697510Z remote: Counting objects:  76% (4261/5606)
2025-03-04T21:19:52.7697918Z remote: Counting objects:  77% (4317/5606)
2025-03-04T21:19:52.7698334Z remote: Counting objects:  78% (4373/5606)
2025-03-04T21:19:52.7698741Z remote: Counting objects:  79% (4429/5606)
2025-03-04T21:19:52.7699150Z remote: Counting objects:  80% (4485/5606)
2025-03-04T21:19:52.7699556Z remote: Counting objects:  81% (4541/5606)
2025-03-04T21:19:52.7699962Z remote: Counting objects:  82% (4597/5606)
2025-03-04T21:19:52.7700368Z remote: Counting objects:  83% (4653/5606)
2025-03-04T21:19:52.7700773Z remote: Counting objects:  84% (4710/5606)
2025-03-04T21:19:52.7701179Z remote: Counting objects:  85% (4766/5606)
2025-03-04T21:19:52.7701585Z remote: Counting objects:  86% (4822/5606)
2025-03-04T21:19:52.7701992Z remote: Counting objects:  87% (4878/5606)
2025-03-04T21:19:52.7702398Z remote: Counting objects:  88% (4934/5606)
2025-03-04T21:19:52.7702806Z remote: Counting objects:  89% (4990/5606)
2025-03-04T21:19:52.7703340Z remote: Counting objects:  90% (5046/5606)
2025-03-04T21:19:52.7703865Z remote: Counting objects:  91% (5102/5606)
2025-03-04T21:19:52.7704274Z remote: Counting objects:  92% (5158/5606)
2025-03-04T21:19:52.7704685Z remote: Counting objects:  93% (5214/5606)
2025-03-04T21:19:52.7705101Z remote: Counting objects:  94% (5270/5606)
2025-03-04T21:19:52.7705508Z remote: Counting objects:  95% (5326/5606)
2025-03-04T21:19:52.7705912Z remote: Counting objects:  96% (5382/5606)
2025-03-04T21:19:52.7706370Z remote: Counting objects:  97% (5438/5606)
2025-03-04T21:19:52.7706777Z remote: Counting objects:  98% (5494/5606)
2025-03-04T21:19:52.7707180Z remote: Counting objects:  99% (5550/5606)
2025-03-04T21:19:52.7707582Z remote: Counting objects: 100% (5606/5606)
2025-03-04T21:19:52.7708016Z remote: Counting objects: 100% (5606/5606), done.
2025-03-04T21:19:52.7708451Z remote: Compressing objects:   0% (1/677)
2025-03-04T21:19:52.7732245Z remote: Compressing objects:   1% (7/677)
2025-03-04T21:19:52.7749303Z remote: Compressing objects:   2% (14/677)
2025-03-04T21:19:52.7774559Z remote: Compressing objects:   3% (21/677)
2025-03-04T21:19:52.7808235Z remote: Compressing objects:   4% (28/677)
2025-03-04T21:19:52.7847997Z remote: Compressing objects:   5% (34/677)
2025-03-04T21:19:52.7866583Z remote: Compressing objects:   6% (41/677)
2025-03-04T21:19:52.7888957Z remote: Compressing objects:   7% (48/677)
2025-03-04T21:19:52.7900747Z remote: Compressing objects:   8% (55/677)
2025-03-04T21:19:52.7908769Z remote: Compressing objects:   9% (61/677)
2025-03-04T21:19:52.7929813Z remote: Compressing objects:  10% (68/677)
2025-03-04T21:19:52.8037537Z remote: Compressing objects:  11% (75/677)
2025-03-04T21:19:52.8117747Z remote: Compressing objects:  12% (82/677)
2025-03-04T21:19:52.8194018Z remote: Compressing objects:  13% (89/677)
2025-03-04T21:19:52.8276245Z remote: Compressing objects:  14% (95/677)
2025-03-04T21:19:52.8308248Z remote: Compressing objects:  15% (102/677)
2025-03-04T21:19:52.8361959Z remote: Compressing objects:  16% (109/677)
2025-03-04T21:19:52.8406304Z remote: Compressing objects:  17% (116/677)
2025-03-04T21:19:52.8441388Z remote: Compressing objects:  18% (122/677)
2025-03-04T21:19:52.8455804Z remote: Compressing objects:  19% (129/677)
2025-03-04T21:19:52.8497038Z remote: Compressing objects:  20% (136/677)
2025-03-04T21:19:52.8539727Z remote: Compressing objects:  21% (143/677)
2025-03-04T21:19:52.8560872Z remote: Compressing objects:  22% (149/677)
2025-03-04T21:19:52.8591086Z remote: Compressing objects:  23% (156/677)
2025-03-04T21:19:52.8610285Z remote: Compressing objects:  24% (163/677)
2025-03-04T21:19:52.8632829Z remote: Compressing objects:  25% (170/677)
2025-03-04T21:19:52.8646992Z remote: Compressing objects:  26% (177/677)
2025-03-04T21:19:52.8671489Z remote: Compressing objects:  27% (183/677)
2025-03-04T21:19:52.8686994Z remote: Compressing objects:  28% (190/677)
2025-03-04T21:19:52.8705853Z remote: Compressing objects:  29% (197/677)
2025-03-04T21:19:52.8718769Z remote: Compressing objects:  30% (204/677)
2025-03-04T21:19:52.8736434Z remote: Compressing objects:  31% (210/677)
2025-03-04T21:19:52.8750086Z remote: Compressing objects:  32% (217/677)
2025-03-04T21:19:52.8759310Z remote: Compressing objects:  33% (224/677)
2025-03-04T21:19:52.8764525Z remote: Compressing objects:  34% (231/677)
2025-03-04T21:19:52.8774711Z remote: Compressing objects:  35% (237/677)
2025-03-04T21:19:52.8779808Z remote: Compressing objects:  36% (244/677)
2025-03-04T21:19:52.8782463Z remote: Compressing objects:  37% (251/677)
2025-03-04T21:19:52.8783671Z remote: Compressing objects:  38% (258/677)
2025-03-04T21:19:52.8786212Z remote: Compressing objects:  39% (265/677)
2025-03-04T21:19:52.8786691Z remote: Compressing objects:  40% (271/677)
2025-03-04T21:19:52.8787198Z remote: Compressing objects:  41% (278/677)
2025-03-04T21:19:52.8787929Z remote: Compressing objects:  42% (285/677)
2025-03-04T21:19:52.8788505Z remote: Compressing objects:  43% (292/677)
2025-03-04T21:19:52.8789035Z remote: Compressing objects:  44% (298/677)
2025-03-04T21:19:52.8789500Z remote: Compressing objects:  45% (305/677)
2025-03-04T21:19:52.8790591Z remote: Compressing objects:  46% (312/677)
2025-03-04T21:19:52.8795209Z remote: Compressing objects:  47% (319/677)
2025-03-04T21:19:52.8807071Z remote: Compressing objects:  48% (325/677)
2025-03-04T21:19:52.8813122Z remote: Compressing objects:  49% (332/677)
2025-03-04T21:19:52.8814989Z remote: Compressing objects:  50% (339/677)
2025-03-04T21:19:52.8817788Z remote: Compressing objects:  51% (346/677)
2025-03-04T21:19:52.8818451Z remote: Compressing objects:  52% (353/677)
2025-03-04T21:19:52.8820242Z remote: Compressing objects:  53% (359/677)
2025-03-04T21:19:52.8824972Z remote: Compressing objects:  54% (366/677)
2025-03-04T21:19:52.8825663Z remote: Compressing objects:  55% (373/677)
2025-03-04T21:19:52.8828950Z remote: Compressing objects:  56% (380/677)
2025-03-04T21:19:52.8830188Z remote: Compressing objects:  57% (386/677)
2025-03-04T21:19:52.8832760Z remote: Compressing objects:  58% (393/677)
2025-03-04T21:19:52.8836169Z remote: Compressing objects:  59% (400/677)
2025-03-04T21:19:52.8836609Z remote: Compressing objects:  60% (407/677)
2025-03-04T21:19:52.8841347Z remote: Compressing objects:  61% (413/677)
2025-03-04T21:19:52.8844828Z remote: Compressing objects:  62% (420/677)
2025-03-04T21:19:52.8847162Z remote: Compressing objects:  63% (427/677)
2025-03-04T21:19:52.8850656Z remote: Compressing objects:  64% (434/677)
2025-03-04T21:19:52.8855268Z remote: Compressing objects:  65% (441/677)
2025-03-04T21:19:52.8856339Z remote: Compressing objects:  66% (447/677)
2025-03-04T21:19:52.8856878Z remote: Compressing objects:  67% (454/677)
2025-03-04T21:19:52.8859936Z remote: Compressing objects:  68% (461/677)
2025-03-04T21:19:52.8860624Z remote: Compressing objects:  69% (468/677)
2025-03-04T21:19:52.8862952Z remote: Compressing objects:  70% (474/677)
2025-03-04T21:19:52.8865345Z remote: Compressing objects:  71% (481/677)
2025-03-04T21:19:52.8867691Z remote: Compressing objects:  72% (488/677)
2025-03-04T21:19:52.8869284Z remote: Compressing objects:  73% (495/677)
2025-03-04T21:19:52.8876560Z remote: Compressing objects:  74% (501/677)
2025-03-04T21:19:52.8877014Z remote: Compressing objects:  75% (508/677)
2025-03-04T21:19:52.8877437Z remote: Compressing objects:  76% (515/677)
2025-03-04T21:19:52.8877856Z remote: Compressing objects:  77% (522/677)
2025-03-04T21:19:52.8878275Z remote: Compressing objects:  78% (529/677)
2025-03-04T21:19:52.8878700Z remote: Compressing objects:  79% (535/677)
2025-03-04T21:19:52.8879119Z remote: Compressing objects:  80% (542/677)
2025-03-04T21:19:52.8884655Z remote: Compressing objects:  81% (549/677)
2025-03-04T21:19:52.8885122Z remote: Compressing objects:  82% (556/677)
2025-03-04T21:19:52.8885542Z remote: Compressing objects:  83% (562/677)
2025-03-04T21:19:52.8885967Z remote: Compressing objects:  84% (569/677)
2025-03-04T21:19:52.8886388Z remote: Compressing objects:  85% (576/677)
2025-03-04T21:19:52.8886809Z remote: Compressing objects:  86% (583/677)
2025-03-04T21:19:52.8887259Z remote: Compressing objects:  87% (589/677)
2025-03-04T21:19:52.8887829Z remote: Compressing objects:  88% (596/677)
2025-03-04T21:19:52.8888264Z remote: Compressing objects:  89% (603/677)
2025-03-04T21:19:52.8895761Z remote: Compressing objects:  90% (610/677)
2025-03-04T21:19:52.8896201Z remote: Compressing objects:  91% (617/677)
2025-03-04T21:19:52.8897292Z remote: Compressing objects:  92% (623/677)
2025-03-04T21:19:52.8901073Z remote: Compressing objects:  93% (630/677)
2025-03-04T21:19:52.8903662Z remote: Compressing objects:  94% (637/677)
2025-03-04T21:19:52.8904374Z remote: Compressing objects:  95% (644/677)
2025-03-04T21:19:52.8905523Z remote: Compressing objects:  96% (650/677)
2025-03-04T21:19:52.8906222Z remote: Compressing objects:  97% (657/677)
2025-03-04T21:19:52.8907204Z remote: Compressing objects:  98% (664/677)
2025-03-04T21:19:52.8909562Z remote: Compressing objects:  99% (671/677)
2025-03-04T21:19:52.8910168Z remote: Compressing objects: 100% (677/677)
2025-03-04T21:19:52.8910621Z remote: Compressing objects: 100% (677/677), done.
2025-03-04T21:19:52.9011623Z Receiving objects:   0% (1/35224)
2025-03-04T21:19:52.9061673Z Receiving objects:   1% (353/35224)
2025-03-04T21:19:52.9104779Z Receiving objects:   2% (705/35224)
2025-03-04T21:19:52.9143680Z Receiving objects:   3% (1057/35224)
2025-03-04T21:19:52.9180101Z Receiving objects:   4% (1409/35224)
2025-03-04T21:19:52.9218814Z Receiving objects:   5% (1762/35224)
2025-03-04T21:19:52.9267205Z Receiving objects:   6% (2114/35224)
2025-03-04T21:19:52.9301477Z Receiving objects:   7% (2466/35224)
2025-03-04T21:19:52.9338593Z Receiving objects:   8% (2818/35224)
2025-03-04T21:19:52.9412281Z Receiving objects:   9% (3171/35224)
2025-03-04T21:19:52.9473270Z Receiving objects:  10% (3523/35224)
2025-03-04T21:19:52.9543240Z Receiving objects:  11% (3875/35224)
2025-03-04T21:19:52.9719570Z Receiving objects:  12% (4227/35224)
2025-03-04T21:19:52.9830859Z Receiving objects:  13% (4580/35224)
2025-03-04T21:19:52.9910520Z Receiving objects:  14% (4932/35224)
2025-03-04T21:19:52.9958279Z Receiving objects:  15% (5284/35224)
2025-03-04T21:19:52.9978733Z Receiving objects:  16% (5636/35224)
2025-03-04T21:19:53.0041654Z Receiving objects:  17% (5989/35224)
2025-03-04T21:19:53.0059416Z Receiving objects:  18% (6341/35224)
2025-03-04T21:19:53.0104113Z Receiving objects:  19% (6693/35224)
2025-03-04T21:19:53.8937491Z Receiving objects:  20% (7045/35224)
2025-03-04T21:19:53.9941307Z Receiving objects:  20% (7346/35224), 76.15 MiB | 76.14 MiB/s
2025-03-04T21:19:54.5647045Z Receiving objects:  21% (7398/35224), 76.15 MiB | 76.14 MiB/s
2025-03-04T21:19:54.6491598Z Receiving objects:  22% (7750/35224), 113.50 MiB | 75.66 MiB/s
2025-03-04T21:19:54.7354780Z Receiving objects:  23% (8102/35224), 113.50 MiB | 75.66 MiB/s
2025-03-04T21:19:54.8168713Z Receiving objects:  24% (8454/35224), 113.50 MiB | 75.66 MiB/s
2025-03-04T21:19:54.8936940Z Receiving objects:  25% (8806/35224), 113.50 MiB | 75.66 MiB/s
2025-03-04T21:19:54.8978861Z Receiving objects:  25% (9138/35224), 151.24 MiB | 75.62 MiB/s
2025-03-04T21:19:54.9795729Z Receiving objects:  26% (9159/35224), 151.24 MiB | 75.62 MiB/s
2025-03-04T21:19:55.0596675Z Receiving objects:  27% (9511/35224), 151.24 MiB | 75.62 MiB/s
2025-03-04T21:19:55.2994426Z Receiving objects:  28% (9863/35224), 151.24 MiB | 75.62 MiB/s
2025-03-04T21:19:55.3835446Z Receiving objects:  29% (10215/35224), 151.24 MiB | 75.62 MiB/s
2025-03-04T21:19:55.5819543Z Receiving objects:  30% (10568/35224), 151.24 MiB | 75.62 MiB/s
2025-03-04T21:19:55.8940011Z Receiving objects:  31% (10920/35224), 184.61 MiB | 73.84 MiB/s
2025-03-04T21:19:55.9269180Z Receiving objects:  31% (11252/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9483742Z Receiving objects:  32% (11272/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9503870Z Receiving objects:  33% (11624/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9518531Z Receiving objects:  34% (11977/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9533418Z Receiving objects:  35% (12329/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9542718Z Receiving objects:  36% (12681/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9560673Z Receiving objects:  37% (13033/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9571549Z Receiving objects:  38% (13386/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9578255Z Receiving objects:  39% (13738/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9603037Z Receiving objects:  40% (14090/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9617595Z Receiving objects:  41% (14442/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9817460Z Receiving objects:  42% (14795/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9825509Z Receiving objects:  43% (15147/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9833161Z Receiving objects:  44% (15499/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9849669Z Receiving objects:  45% (15851/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9859009Z Receiving objects:  46% (16204/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9877438Z Receiving objects:  47% (16556/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:55.9890202Z Receiving objects:  48% (16908/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0027681Z Receiving objects:  49% (17260/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0036356Z Receiving objects:  50% (17612/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0045903Z Receiving objects:  51% (17965/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0295721Z Receiving objects:  52% (18317/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0700298Z Receiving objects:  53% (18669/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0713079Z Receiving objects:  54% (19021/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0728508Z Receiving objects:  55% (19374/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0744112Z Receiving objects:  56% (19726/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0756324Z Receiving objects:  57% (20078/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0777530Z Receiving objects:  58% (20430/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0785364Z Receiving objects:  59% (20783/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.0795395Z Receiving objects:  60% (21135/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1011981Z Receiving objects:  61% (21487/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1047861Z Receiving objects:  62% (21839/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1062756Z Receiving objects:  63% (22192/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1075318Z Receiving objects:  64% (22544/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1134609Z Receiving objects:  65% (22896/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1308945Z Receiving objects:  66% (23248/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1317907Z Receiving objects:  67% (23601/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1328252Z Receiving objects:  68% (23953/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1336053Z Receiving objects:  69% (24305/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1342630Z Receiving objects:  70% (24657/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1461454Z Receiving objects:  71% (25010/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1479887Z Receiving objects:  72% (25362/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1495258Z Receiving objects:  73% (25714/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1517452Z Receiving objects:  74% (26066/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1547636Z Receiving objects:  75% (26418/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1583369Z Receiving objects:  76% (26771/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1601390Z Receiving objects:  77% (27123/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1622151Z Receiving objects:  78% (27475/35224), 217.31 MiB | 72.43 MiB/s
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2025-03-04T21:19:56.1896954Z Receiving objects:  89% (31350/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1902674Z Receiving objects:  90% (31702/35224), 217.31 MiB | 72.43 MiB/s
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2025-03-04T21:19:56.1912479Z Receiving objects:  92% (32407/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1918143Z Receiving objects:  93% (32759/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1923832Z Receiving objects:  94% (33111/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.1985013Z Receiving objects:  95% (33463/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.2022243Z Receiving objects:  96% (33816/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.2043128Z Receiving objects:  97% (34168/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.2094824Z Receiving objects:  98% (34520/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.2139266Z Receiving objects:  99% (34872/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.2140233Z remote: Total 35224 (delta 5252), reused 4971 (delta 4929), pack-reused 29618 (from 2)
2025-03-04T21:19:56.2157418Z Receiving objects: 100% (35224/35224), 217.31 MiB | 72.43 MiB/s
2025-03-04T21:19:56.2157996Z Receiving objects: 100% (35224/35224), 233.51 MiB | 70.29 MiB/s, done.
2025-03-04T21:19:56.2189702Z Resolving deltas:   0% (0/19868)
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2025-03-04T21:19:56.3092653Z Resolving deltas:  44% (8744/19868)
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2025-03-04T21:19:56.3107617Z Resolving deltas:  46% (9201/19868)
2025-03-04T21:19:56.3115392Z Resolving deltas:  47% (9429/19868)
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2025-03-04T21:19:56.3491238Z Resolving deltas:  75% (15055/19868)
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2025-03-04T21:19:56.3517213Z Resolving deltas:  77% (15311/19868)
2025-03-04T21:19:56.3526868Z Resolving deltas:  78% (15576/19868)
2025-03-04T21:19:56.3538922Z Resolving deltas:  79% (15717/19868)
2025-03-04T21:19:56.3580101Z Resolving deltas:  80% (15906/19868)
2025-03-04T21:19:56.3596202Z Resolving deltas:  83% (16566/19868)
2025-03-04T21:19:56.3622009Z Resolving deltas:  84% (16821/19868)
2025-03-04T21:19:56.3628826Z Resolving deltas:  86% (17205/19868)
2025-03-04T21:19:56.3647747Z Resolving deltas:  87% (17315/19868)
2025-03-04T21:19:56.3654296Z Resolving deltas:  88% (17588/19868)
2025-03-04T21:19:56.3667903Z Resolving deltas:  89% (17693/19868)
2025-03-04T21:19:56.3673613Z Resolving deltas:  90% (17983/19868)
2025-03-04T21:19:56.3682262Z Resolving deltas:  91% (18083/19868)
2025-03-04T21:19:56.3694256Z Resolving deltas:  92% (18282/19868)
2025-03-04T21:19:56.3711227Z Resolving deltas:  93% (18485/19868)
2025-03-04T21:19:56.3726838Z Resolving deltas:  94% (18708/19868)
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2025-03-04T21:19:56.3899804Z Resolving deltas:  98% (19473/19868)
2025-03-04T21:19:56.4092901Z Resolving deltas:  99% (19682/19868)
2025-03-04T21:19:56.4093336Z Resolving deltas: 100% (19868/19868)
2025-03-04T21:19:56.4093764Z Resolving deltas: 100% (19868/19868), done.
2025-03-04T21:19:57.4160620Z + pushd torchbench
2025-03-04T21:19:57.4161055Z ~/workspace/torchbench ~/workspace
2025-03-04T21:19:57.4161459Z + git checkout 373ffb19dc470f4423a3176a4133f8f4b3cdb5bd
2025-03-04T21:19:57.4585189Z Note: switching to '373ffb19dc470f4423a3176a4133f8f4b3cdb5bd'.
2025-03-04T21:19:57.4585838Z 
2025-03-04T21:19:57.4586232Z You are in 'detached HEAD' state. You can look around, make experimental
2025-03-04T21:19:57.4586844Z changes and commit them, and you can discard any commits you make in this
2025-03-04T21:19:57.4587417Z state without impacting any branches by switching back to a branch.
2025-03-04T21:19:57.4587758Z 
2025-03-04T21:19:57.4587974Z If you want to create a new branch to retain commits you create, you may
2025-03-04T21:19:57.4588483Z do so (now or later) by using -c with the switch command. Example:
2025-03-04T21:19:57.4588773Z 
2025-03-04T21:19:57.4588901Z   git switch -c <new-branch-name>
2025-03-04T21:19:57.4589106Z 
2025-03-04T21:19:57.4589228Z Or undo this operation with:
2025-03-04T21:19:57.4589414Z 
2025-03-04T21:19:57.4589523Z   git switch -
2025-03-04T21:19:57.4589681Z 
2025-03-04T21:19:57.4590003Z Turn off this advice by setting config variable advice.detachedHead to false
2025-03-04T21:19:57.4590362Z 
2025-03-04T21:19:57.4590612Z HEAD is now at 373ffb19 Copy model before benchmark warmup runs (#145858)
2025-03-04T21:19:57.4591180Z + '[' '' ']'
2025-03-04T21:19:57.4591521Z + python install.py --continue_on_fail
2025-03-04T21:20:05.0989363Z checking packages numpy, torch, torchvision, torchaudio are installed, generating constaints...OK
2025-03-04T21:20:37.5835486Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/BERT_pytorch...OK
2025-03-04T21:20:52.8963601Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Background_Matting...OK
2025-03-04T21:21:08.3116913Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/LearningToPaint...OK
2025-03-04T21:21:23.4571161Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/Super_SloMo...OK
2025-03-04T21:21:36.8929718Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/alexnet...OK
2025-03-04T21:21:58.5235625Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_edgecnn...OK
2025-03-04T21:22:14.8855901Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gcn...OK
2025-03-04T21:22:31.1727850Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_gin...OK
2025-03-04T21:22:47.4743761Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/basic_gnn_sage...OK
2025-03-04T21:22:47.4748463Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/cm3leon_generate...SKIP - No install.py is found
2025-03-04T21:23:02.0392156Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dcgan...OK
2025-03-04T21:23:18.8321744Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/demucs...OK
2025-03-04T21:23:32.2515844Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/densenet121...OK
2025-03-04T21:24:43.8203625Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_c4...OK
2025-03-04T21:25:08.1584017Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_dc5...OK
2025-03-04T21:25:30.7539171Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_101_fpn...OK
2025-03-04T21:25:52.8556141Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_c4...OK
2025-03-04T21:26:16.5225613Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_dc5...OK
2025-03-04T21:26:38.8995207Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fasterrcnn_r_50_fpn...OK
2025-03-04T21:27:00.4457961Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_fcos_r_50_fpn...OK
2025-03-04T21:27:22.6309753Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn...OK
2025-03-04T21:27:45.0338394Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_101_c4...OK
2025-03-04T21:28:07.3522067Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_101_fpn...OK
2025-03-04T21:28:29.5913512Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_50_c4...OK
2025-03-04T21:28:51.7366636Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/detectron2_maskrcnn_r_50_fpn...OK
2025-03-04T21:29:06.6218854Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dlrm...OK
2025-03-04T21:29:43.0064151Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/doctr_det_predictor...OK
2025-03-04T21:30:03.3767847Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/doctr_reco_predictor...OK
2025-03-04T21:30:22.4689908Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/drq...OK
2025-03-04T21:30:46.8885819Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/fastNLP_Bert...OK
2025-03-04T21:31:01.8783818Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_dp_cifar10...OK
2025-03-04T21:31:17.1667131Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/functorch_maml_omniglot...OK
2025-03-04T21:31:39.0585659Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Albert...OK
2025-03-04T21:32:01.3980851Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bart...OK
2025-03-04T21:32:22.1469434Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert...OK
2025-03-04T21:32:45.0726835Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Bert_large...OK
2025-03-04T21:33:06.2734987Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_BigBird...OK
2025-03-04T21:33:26.5540954Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_DistilBert...OK
2025-03-04T21:33:48.1757035Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2...OK
2025-03-04T21:34:19.7321258Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_GPT2_large...OK
2025-03-04T21:34:41.3219015Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Longformer...OK
2025-03-04T21:35:00.6335140Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Reformer...OK
2025-03-04T21:35:25.6591936Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Roberta_base...OK
2025-03-04T21:35:45.9936400Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5...OK
2025-03-04T21:36:08.1926050Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_base...OK
2025-03-04T21:36:08.1929603Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_generate...SKIP - No install.py is found
2025-03-04T21:36:37.4819334Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_T5_large...OK
2025-03-04T21:36:55.4709077Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_Whisper...OK
2025-03-04T21:36:55.4712060Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_clip...SKIP - No install.py is found
2025-03-04T21:37:18.3739905Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_distil_whisper...OK
2025-03-04T21:37:33.4471011Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/lennard_jones...OK
2025-03-04T21:37:48.5581948Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama...OK
2025-03-04T21:38:47.4299830Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llama_v2_7b_16h...OK
2025-03-04T21:40:33.2585861Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/llava...OK
2025-03-04T21:40:47.0527397Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml...OK
2025-03-04T21:41:02.5048204Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/maml_omniglot...OK
2025-03-04T21:41:02.5051801Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/microbench_unbacked_tolist_sum...SKIP - No install.py is found
2025-03-04T21:41:16.2549958Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mnasnet1_0...OK
2025-03-04T21:41:30.0263245Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2...OK
2025-03-04T21:41:43.8344443Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v2_quantized_qat...OK
2025-03-04T21:41:57.6297243Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/mobilenet_v3_large...OK
2025-03-04T21:42:11.4385450Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moco...OK
2025-03-04T21:42:47.3815957Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/moondream...OK
2025-03-04T21:42:47.3818810Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nanogpt...SKIP - No install.py is found
2025-03-04T21:43:02.4848992Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/nvidia_deeprecommender...OK
2025-03-04T21:43:18.8868720Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/opacus_cifar10...OK
2025-03-04T21:43:32.6818748Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_densenet...OK
2025-03-04T21:43:46.4767284Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/phlippe_resnet...OK
2025-03-04T21:44:00.2790616Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_equation_of_state...OK
2025-03-04T21:44:14.0309111Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_isoneutral_mixing...OK
2025-03-04T21:44:27.8131013Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pyhpc_turbulent_kinetic_energy...OK
2025-03-04T21:44:48.6152005Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_CycleGAN_and_pix2pix...OK
2025-03-04T21:45:03.9582566Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_stargan...OK
2025-03-04T21:45:21.7040763Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/pytorch_unet...OK
2025-03-04T21:45:35.5031225Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet152...OK
2025-03-04T21:45:49.3248985Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet18...OK
2025-03-04T21:46:03.1172619Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50...OK
2025-03-04T21:46:16.8822867Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnet50_quantized_qat...OK
2025-03-04T21:46:30.6800623Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/resnext50_32x4d...OK
2025-03-04T21:46:53.9709059Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam...OK
2025-03-04T21:47:18.5888675Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/sam_fast...OK
2025-03-04T21:47:32.2642963Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/shufflenet_v2_x1_0...OK
2025-03-04T21:47:32.2647057Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt...SKIP - No install.py is found
2025-03-04T21:47:32.2648651Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/simple_gpt_tp_manual...SKIP - No install.py is found
2025-03-04T21:47:48.8657429Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/soft_actor_critic...OK
2025-03-04T21:48:04.3955804Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/speech_transformer...OK
2025-03-04T21:48:18.0490278Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/squeezenet1_1...OK
2025-03-04T21:48:57.4463161Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_text_encoder...OK
2025-03-04T21:49:19.1848889Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/stable_diffusion_unet...OK
2025-03-04T21:49:37.8196637Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tacotron2...OK
2025-03-04T21:49:58.6574530Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientdet...OK
2025-03-04T21:50:12.2984496Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_efficientnet...OK
2025-03-04T21:50:25.9805210Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_nfnet...OK
2025-03-04T21:50:39.6669929Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_regnet...OK
2025-03-04T21:50:53.3084698Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_resnest...OK
2025-03-04T21:51:06.9720793Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer...OK
2025-03-04T21:51:20.6501448Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vision_transformer_large...OK
2025-03-04T21:51:34.3243957Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/timm_vovnet...OK
2025-03-04T21:51:54.4109370Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/torch_multimodal_clip...OK
2025-03-04T21:52:13.7175839Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/tts_angular...OK
2025-03-04T21:52:27.3876190Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vgg16...OK
2025-03-04T21:52:42.7880246Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/vision_maskrcnn...OK
2025-03-04T21:52:58.5285867Z running setup for /var/lib/jenkins/workspace/torchbench/torchbenchmark/models/yolov3...OK
2025-03-04T21:53:05.3618069Z installed torchbench with package constraints: {'numpy': '1.22.4', 'torch': '2.7.0a0+git1b74980', 'torchvision': '0.19.0a0+d23a6e1', 'torchaudio': '2.6.0a0+c670ad8'}
2025-03-04T21:53:05.6767277Z + pip install transformers==4.38.1
2025-03-04T21:53:06.1996928Z Collecting transformers==4.38.1
2025-03-04T21:53:06.2350833Z   Downloading transformers-4.38.1-py3-none-any.whl.metadata (131 kB)
2025-03-04T21:53:06.4440753Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (3.16.1)
2025-03-04T21:53:06.4443755Z Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (0.29.1)
2025-03-04T21:53:06.4448470Z Requirement already satisfied: numpy>=1.17 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (1.22.4)
2025-03-04T21:53:06.4452859Z Requirement already satisfied: packaging>=20.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (24.2)
2025-03-04T21:53:06.4457736Z Requirement already satisfied: pyyaml>=5.1 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (6.0.2)
2025-03-04T21:53:06.4462661Z Requirement already satisfied: regex!=2019.12.17 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (2024.11.6)
2025-03-04T21:53:06.4466188Z Requirement already satisfied: requests in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (2.32.3)
2025-03-04T21:53:06.5919654Z Collecting tokenizers<0.19,>=0.14 (from transformers==4.38.1)
2025-03-04T21:53:06.5939445Z   Using cached tokenizers-0.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)
2025-03-04T21:53:06.5970652Z Requirement already satisfied: safetensors>=0.4.1 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (0.5.3)
2025-03-04T21:53:06.5974645Z Requirement already satisfied: tqdm>=4.27 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from transformers==4.38.1) (4.67.1)
2025-03-04T21:53:06.6187989Z Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (2024.10.0)
2025-03-04T21:53:06.6194851Z Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.38.1) (4.12.2)
2025-03-04T21:53:06.6370250Z Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from requests->transformers==4.38.1) (3.4.1)
2025-03-04T21:53:06.6373581Z Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from requests->transformers==4.38.1) (3.10)
2025-03-04T21:53:06.6376872Z Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from requests->transformers==4.38.1) (2.3.0)
2025-03-04T21:53:06.6380562Z Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from requests->transformers==4.38.1) (2025.1.31)
2025-03-04T21:53:06.6824570Z Downloading transformers-4.38.1-py3-none-any.whl (8.5 MB)
2025-03-04T21:53:06.7542267Z [?25l   ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 0.0/8.5 MB ? eta -:--:--
2025-03-04T21:53:06.7543030Z    ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 8.5/8.5 MB 124.8 MB/s eta 0:00:00
2025-03-04T21:53:06.7558559Z [?25hUsing cached tokenizers-0.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)
2025-03-04T21:53:07.7452041Z Installing collected packages: tokenizers, transformers
2025-03-04T21:53:07.7452491Z   Attempting uninstall: tokenizers
2025-03-04T21:53:07.7467549Z     Found existing installation: tokenizers 0.19.1
2025-03-04T21:53:07.7511475Z     Uninstalling tokenizers-0.19.1:
2025-03-04T21:53:07.7525998Z       Successfully uninstalled tokenizers-0.19.1
2025-03-04T21:53:07.8297222Z   Attempting uninstall: transformers
2025-03-04T21:53:07.8324177Z     Found existing installation: transformers 4.44.2
2025-03-04T21:53:08.0444086Z     Uninstalling transformers-4.44.2:
2025-03-04T21:53:08.0766735Z       Successfully uninstalled transformers-4.44.2
2025-03-04T21:53:12.2094786Z Successfully installed tokenizers-0.15.2 transformers-4.38.1
2025-03-04T21:53:12.3621690Z + echo 'Print all dependencies after TorchBench is installed'
2025-03-04T21:53:12.3622257Z Print all dependencies after TorchBench is installed
2025-03-04T21:53:12.3622684Z + python -mpip freeze
2025-03-04T21:53:12.8029231Z absl-py==2.1.0
2025-03-04T21:53:12.8029653Z accelerate==1.4.0
2025-03-04T21:53:12.8030283Z aiohappyeyeballs==2.4.6
2025-03-04T21:53:12.8030728Z aiohttp==3.11.13
2025-03-04T21:53:12.8031091Z aiosignal==1.3.2
2025-03-04T21:53:12.8031441Z annotated-types==0.7.0
2025-03-04T21:53:12.8031824Z antlr4-python3-runtime==4.9.3
2025-03-04T21:53:12.8032184Z anyascii==0.3.2
2025-03-04T21:53:12.8032477Z astroid==3.3.8
2025-03-04T21:53:12.8032822Z astunparse==1.6.3
2025-03-04T21:53:12.8033127Z async-timeout==5.0.1
2025-03-04T21:53:12.8033486Z attrs==23.1.0
2025-03-04T21:53:12.8033802Z audioread==3.0.1
2025-03-04T21:53:12.8034140Z babel==2.17.0
2025-03-04T21:53:12.8034456Z beautifulsoup4==4.13.3
2025-03-04T21:53:12.8035460Z -e git+https://github.com/pytorch/benchmark@373ffb19dc470f4423a3176a4133f8f4b3cdb5bd#egg=bert_pytorch&subdirectory=torchbenchmark/models/BERT_pytorch
2025-03-04T21:53:12.8036456Z black==25.1.0
2025-03-04T21:53:12.8037156Z blinker==1.9.0
2025-03-04T21:53:12.8037512Z blis==1.2.0
2025-03-04T21:53:12.8038045Z blobfile==3.0.0
2025-03-04T21:53:12.8038434Z bokeh==3.6.3
2025-03-04T21:53:12.8038737Z boto3==1.35.42
2025-03-04T21:53:12.8038996Z botocore==1.35.99
2025-03-04T21:53:12.8039335Z bs4==0.0.2
2025-03-04T21:53:12.8039618Z cachetools==5.5.2
2025-03-04T21:53:12.8039957Z cardboardlint==1.3.1
2025-03-04T21:53:12.8040329Z catalogue==2.0.10
2025-03-04T21:53:12.8040679Z certifi==2025.1.31
2025-03-04T21:53:12.8040946Z cffi==1.17.1
2025-03-04T21:53:12.8041187Z charset-normalizer==3.4.1
2025-03-04T21:53:12.8041548Z click==8.1.8
2025-03-04T21:53:12.8041778Z cloudpathlib==0.21.0
2025-03-04T21:53:12.8042064Z cloudpickle==3.1.1
2025-03-04T21:53:12.8042417Z colorama==0.4.6
2025-03-04T21:53:12.8042679Z confection==0.1.5
2025-03-04T21:53:12.8043000Z contourpy==1.2.1
2025-03-04T21:53:12.8043334Z coremltools==5.0b5
2025-03-04T21:53:12.8043605Z cryptography==44.0.1
2025-03-04T21:53:12.8043853Z csvw==3.5.1
2025-03-04T21:53:12.8044154Z cycler==0.12.1
2025-03-04T21:53:12.8044392Z cymem==2.0.11
2025-03-04T21:53:12.8044718Z Cython==3.0.12
2025-03-04T21:53:12.8045044Z DALL-E==0.1
2025-03-04T21:53:12.8045375Z dataclasses-json==0.6.7
2025-03-04T21:53:12.8045669Z datasets==3.3.2
2025-03-04T21:53:12.8046002Z decorator==5.2.1
2025-03-04T21:53:12.8046332Z defusedxml==0.7.1
2025-03-04T21:53:12.8046671Z Deprecated==1.2.18
2025-03-04T21:53:12.8047369Z detectron2 @ git+https://github.com/facebookresearch/detectron2.git@0df2d73d0013db7de629602c23cc120219b4f2b8
2025-03-04T21:53:12.8048214Z diffusers==0.30.3
2025-03-04T21:53:12.8048519Z dill==0.3.8
2025-03-04T21:53:12.8048840Z diskcache==5.6.3
2025-03-04T21:53:12.8049182Z distro==1.9.0
2025-03-04T21:53:12.8049466Z dlinfo==2.0.0
2025-03-04T21:53:12.8049768Z docker-pycreds==0.4.0
2025-03-04T21:53:12.8050101Z dominate==2.9.1
2025-03-04T21:53:12.8050825Z effdet @ git+https://github.com/rwightman/efficientdet-pytorch.git@d43c9e34cd62d22b4205831bb735f6dd83b8e881
2025-03-04T21:53:12.8051673Z einops==0.8.1
2025-03-04T21:53:12.8051952Z exceptiongroup==1.2.2
2025-03-04T21:53:12.8052287Z execnet==2.1.1
2025-03-04T21:53:12.8052642Z expecttest==0.3.0
2025-03-04T21:53:12.8053004Z FastNLP==0.6.0
2025-03-04T21:53:12.8053287Z fbscribelogger==0.1.7
2025-03-04T21:53:12.8053550Z ffmpeg-python==0.2.0
2025-03-04T21:53:12.8053827Z filelock==3.16.1
2025-03-04T21:53:12.8054158Z Flask==3.1.0
2025-03-04T21:53:12.8054471Z flatbuffers==2.0
2025-03-04T21:53:12.8054715Z fonttools==4.56.0
2025-03-04T21:53:12.8054952Z frozenlist==1.5.0
2025-03-04T21:53:12.8055236Z fsspec==2024.10.0
2025-03-04T21:53:12.8055508Z ftfy==6.3.1
2025-03-04T21:53:12.8056111Z functorch @ git+https://github.com/pytorch/functorch.git@b71aa0b4387b86c278132209b99538be48ef4c74
2025-03-04T21:53:12.8056645Z future==1.0.0
2025-03-04T21:53:12.8056879Z fvcore==0.1.5.post20221221
2025-03-04T21:53:12.8057145Z gdown==5.2.0
2025-03-04T21:53:12.8057370Z ghstack==0.8.0
2025-03-04T21:53:12.8057603Z gitdb==4.0.12
2025-03-04T21:53:12.8057833Z GitPython==3.1.44
2025-03-04T21:53:12.8058109Z google-auth==2.38.0
2025-03-04T21:53:12.8058393Z google-auth-oauthlib==1.0.0
2025-03-04T21:53:12.8058671Z grpcio==1.70.0
2025-03-04T21:53:12.8058894Z gym==0.26.2
2025-03-04T21:53:12.8059116Z gym-notices==0.0.8
2025-03-04T21:53:12.8059353Z h5py==3.13.0
2025-03-04T21:53:12.8059575Z higher==0.2.1
2025-03-04T21:53:12.8059891Z huggingface-hub==0.29.1
2025-03-04T21:53:12.8060177Z hydra-core==1.3.2
2025-03-04T21:53:12.8060411Z hypothesis==5.35.1
2025-03-04T21:53:12.8060649Z idna==3.10
2025-03-04T21:53:12.8060869Z imageio==2.37.0
2025-03-04T21:53:12.8061119Z importlib_metadata==8.6.1
2025-03-04T21:53:12.8061386Z inflect==7.5.0
2025-03-04T21:53:12.8061618Z iniconfig==2.0.0
2025-03-04T21:53:12.8061852Z iopath==0.1.9
2025-03-04T21:53:12.8062076Z isodate==0.7.2
2025-03-04T21:53:12.8062302Z isort==6.0.1
2025-03-04T21:53:12.8062531Z itsdangerous==2.2.0
2025-03-04T21:53:12.8062771Z Jinja2==3.1.5
2025-03-04T21:53:12.8062998Z jmespath==1.0.1
2025-03-04T21:53:12.8063231Z joblib==1.4.2
2025-03-04T21:53:12.8063589Z jsonpatch==1.33
2025-03-04T21:53:12.8063828Z jsonpointer==3.0.0
2025-03-04T21:53:12.8064151Z jsonschema==4.23.0
2025-03-04T21:53:12.8064425Z jsonschema-specifications==2024.10.1
2025-03-04T21:53:12.8064741Z junitparser==2.1.1
2025-03-04T21:53:12.8064982Z kaldi-io==0.9.8
2025-03-04T21:53:12.8065219Z kiwisolver==1.4.8
2025-03-04T21:53:12.8065455Z kornia==0.8.0
2025-03-04T21:53:12.8065680Z kornia_rs==0.1.8
2025-03-04T21:53:12.8065915Z lameenc==1.8.1
2025-03-04T21:53:12.8066145Z langcodes==3.5.0
2025-03-04T21:53:12.8066383Z langdetect==1.0.9
2025-03-04T21:53:12.8066624Z language-tags==1.2.0
2025-03-04T21:53:12.8066874Z language_data==1.3.0
2025-03-04T21:53:12.8067117Z lark==0.12.0
2025-03-04T21:53:12.8067341Z lazy_loader==0.4
2025-03-04T21:53:12.8067573Z libcst==1.6.0
2025-03-04T21:53:12.8067799Z librosa==0.9.2
2025-03-04T21:53:12.8068031Z lintrunner==0.12.7
2025-03-04T21:53:12.8068270Z llvmlite==0.38.1
2025-03-04T21:53:12.8068513Z lxml==5.3.0
2025-03-04T21:53:12.8068741Z marisa-trie==1.2.1
2025-03-04T21:53:12.8080811Z Markdown==3.7
2025-03-04T21:53:12.8081071Z markdown-it-py==3.0.0
2025-03-04T21:53:12.8081351Z MarkupSafe==3.0.2
2025-03-04T21:53:12.8081600Z marshmallow==3.26.1
2025-03-04T21:53:12.8081857Z matplotlib==3.8.4
2025-03-04T21:53:12.8082098Z mccabe==0.7.0
2025-03-04T21:53:12.8082327Z mdurl==0.1.2
2025-03-04T21:53:12.8082556Z ml_dtypes==0.5.1
2025-03-04T21:53:12.8082802Z MonkeyType==23.3.0
2025-03-04T21:53:12.8083050Z more-itertools==10.6.0
2025-03-04T21:53:12.8083310Z mpmath==1.3.0
2025-03-04T21:53:12.8083544Z msgpack==1.1.0
2025-03-04T21:53:12.8083831Z multidict==6.1.0
2025-03-04T21:53:12.8084078Z multiprocess==0.70.16
2025-03-04T21:53:12.8084350Z murmurhash==1.0.12
2025-03-04T21:53:12.8084598Z musdb==0.4.2
2025-03-04T21:53:12.8084822Z museval==0.4.1
2025-03-04T21:53:12.8085057Z mypy==1.13.0
2025-03-04T21:53:12.8085293Z mypy-extensions==1.0.0
2025-03-04T21:53:12.8085556Z networkx==2.8.8
2025-03-04T21:53:12.8085798Z ninja==1.10.2
2025-03-04T21:53:12.8086026Z nose==1.3.7
2025-03-04T21:53:12.8086251Z numba==0.55.2
2025-03-04T21:53:12.8086478Z numpy==1.22.4
2025-03-04T21:53:12.8086733Z nvidia-cublas-cu12==12.4.5.8
2025-03-04T21:53:12.8087042Z nvidia-cuda-cupti-cu12==12.4.127
2025-03-04T21:53:12.8087358Z nvidia-cuda-nvrtc-cu12==12.4.127
2025-03-04T21:53:12.8087871Z nvidia-cuda-runtime-cu12==12.4.127
2025-03-04T21:53:12.8088191Z nvidia-cudnn-cu12==9.1.0.70
2025-03-04T21:53:12.8088483Z nvidia-cufft-cu12==11.2.1.3
2025-03-04T21:53:12.8088765Z nvidia-curand-cu12==10.3.5.147
2025-03-04T21:53:12.8089069Z nvidia-cusolver-cu12==11.6.1.9
2025-03-04T21:53:12.8089371Z nvidia-cusparse-cu12==12.3.1.170
2025-03-04T21:53:12.8089686Z nvidia-cusparselt-cu12==0.6.2
2025-03-04T21:53:12.8089983Z nvidia-ml-py==12.570.86
2025-03-04T21:53:12.8090256Z nvidia-nccl-cu12==2.21.5
2025-03-04T21:53:12.8090543Z nvidia-nvjitlink-cu12==12.4.127
2025-03-04T21:53:12.8090851Z nvidia-nvtx-cu12==12.4.127
2025-03-04T21:53:12.8091126Z oauthlib==3.2.2
2025-03-04T21:53:12.8091369Z omegaconf==2.3.0
2025-03-04T21:53:12.8091608Z onnx==1.17.0
2025-03-04T21:53:12.8091847Z onnxscript==0.1.0
2025-03-04T21:53:12.8092097Z opacus==1.5.3
2025-03-04T21:53:12.8092345Z opencv-python==4.8.0.74
2025-03-04T21:53:12.8092608Z opt-einsum==3.3.0
2025-03-04T21:53:12.8092858Z optree==0.13.0
2025-03-04T21:53:12.8093098Z packaging==24.2
2025-03-04T21:53:12.8093339Z pandas==2.0.3
2025-03-04T21:53:12.8093585Z parameterized==0.8.1
2025-03-04T21:53:12.8093844Z patch==1.16
2025-03-04T21:53:12.8094076Z pathspec==0.12.1
2025-03-04T21:53:12.8094323Z phonemizer==3.3.0
2025-03-04T21:53:12.8094578Z pillow==11.0.0
2025-03-04T21:53:12.8094827Z platformdirs==4.3.6
2025-03-04T21:53:12.8095075Z pluggy==1.5.0
2025-03-04T21:53:12.8095315Z ply==3.11
2025-03-04T21:53:12.8095535Z pooch==1.8.2
2025-03-04T21:53:12.8095767Z portalocker==3.1.1
2025-03-04T21:53:12.8096008Z preshed==3.0.9
2025-03-04T21:53:12.8096245Z prettytable==3.15.1
2025-03-04T21:53:12.8096497Z propcache==0.3.0
2025-03-04T21:53:12.8096738Z protobuf==3.20.2
2025-03-04T21:53:12.8096980Z psutil==7.0.0
2025-03-04T21:53:12.8097390Z PuLP==2.9.0
2025-03-04T21:53:12.8097617Z pwlf==2.2.1
2025-03-04T21:53:12.8097849Z py-cpuinfo==9.0.0
2025-03-04T21:53:12.8098203Z pyaml==25.1.0
2025-03-04T21:53:12.8098452Z pyarrow==19.0.1
2025-03-04T21:53:12.8098685Z pyasn1==0.6.1
2025-03-04T21:53:12.8098922Z pyasn1_modules==0.4.1
2025-03-04T21:53:12.8099187Z pyclipper==1.3.0.post6
2025-03-04T21:53:12.8099442Z pycocotools==2.0.8
2025-03-04T21:53:12.8099690Z pycparser==2.22
2025-03-04T21:53:12.8099935Z pycryptodomex==3.21.0
2025-03-04T21:53:12.8100193Z pydantic==2.10.6
2025-03-04T21:53:12.8100446Z pydantic_core==2.27.2
2025-03-04T21:53:12.8100698Z pyDOE==0.3.8
2025-03-04T21:53:12.8100924Z pydot==3.0.4
2025-03-04T21:53:12.8101156Z pygame==2.6.1
2025-03-04T21:53:12.8101388Z PyGithub==2.3.0
2025-03-04T21:53:12.8101623Z Pygments==2.15.0
2025-03-04T21:53:12.8101856Z PyJWT==2.10.1
2025-03-04T21:53:12.8102084Z pylint==3.3.4
2025-03-04T21:53:12.8102311Z PyNaCl==1.5.0
2025-03-04T21:53:12.8102535Z pynvml==12.0.0
2025-03-04T21:53:12.8102775Z pyparsing==3.2.1
2025-03-04T21:53:12.8103029Z pypdfium2==4.30.1
2025-03-04T21:53:12.8103269Z pysbd==0.3.4
2025-03-04T21:53:12.8103505Z PySocks==1.7.1
2025-03-04T21:53:12.8103742Z pytest==8.3.5
2025-03-04T21:53:12.8103987Z pytest-benchmark==5.1.0
2025-03-04T21:53:12.8104261Z pytest-cpp==2.3.0
2025-03-04T21:53:12.8104522Z pytest-flakefinder==1.1.0
2025-03-04T21:53:12.8104818Z pytest-rerunfailures==14.0
2025-03-04T21:53:12.8105114Z pytest-subtests==0.13.1
2025-03-04T21:53:12.8105386Z pytest-xdist==3.3.1
2025-03-04T21:53:12.8105653Z python-dateutil==2.9.0.post0
2025-03-04T21:53:12.8105945Z python-doctr==0.11.0
2025-03-04T21:53:12.8106654Z pytorch-labs-segment-anything-fast @ git+https://github.com/pytorch-labs/segment-anything-fast.git@e6aadeb86f3ae1f58c3f98e2a91e251716e0f2aa
2025-03-04T21:53:12.8107400Z pytz==2025.1
2025-03-04T21:53:12.8107640Z PyWavelets==1.4.1
2025-03-04T21:53:12.8107888Z PyYAML==6.0.2
2025-03-04T21:53:12.8108145Z pyzstd==0.16.2
2025-03-04T21:53:12.8108419Z RapidFuzz==3.12.2
2025-03-04T21:53:12.8108657Z rdflib==7.1.3
2025-03-04T21:53:12.8108895Z redis==5.2.1
2025-03-04T21:53:12.8109128Z referencing==0.36.2
2025-03-04T21:53:12.8109380Z regex==2024.11.6
2025-03-04T21:53:12.8109615Z requests==2.32.3
2025-03-04T21:53:12.8109863Z requests-oauthlib==2.0.0
2025-03-04T21:53:12.8110135Z resampy==0.4.3
2025-03-04T21:53:12.8110359Z rfc3986==1.5.0
2025-03-04T21:53:12.8110588Z rich==13.9.4
2025-03-04T21:53:12.8110816Z rpds-py==0.23.1
2025-03-04T21:53:12.8111043Z rsa==4.9
2025-03-04T21:53:12.8111256Z s3transfer==0.10.4
2025-03-04T21:53:12.8111495Z safetensors==0.5.3
2025-03-04T21:53:12.8111740Z scikit-image==0.22.0
2025-03-04T21:53:12.8111991Z scikit-learn==1.6.1
2025-03-04T21:53:12.8112236Z scipy==1.10.1
2025-03-04T21:53:12.8112459Z segments==2.3.0
2025-03-04T21:53:12.8112698Z sentencepiece==0.2.0
2025-03-04T21:53:12.8112952Z sentry-sdk==2.22.0
2025-03-04T21:53:12.8113194Z setproctitle==1.3.5
2025-03-04T21:53:12.8113436Z shapely==2.0.7
2025-03-04T21:53:12.8113668Z shellingham==1.5.4
2025-03-04T21:53:12.8113913Z simplejson==3.20.1
2025-03-04T21:53:12.8114152Z six==1.17.0
2025-03-04T21:53:12.8114374Z smart-open==7.1.0
2025-03-04T21:53:12.8114616Z smmap==5.0.2
2025-03-04T21:53:12.8114853Z sortedcontainers==2.4.0
2025-03-04T21:53:12.8115116Z soundfile==0.13.1
2025-03-04T21:53:12.8115354Z soupsieve==2.6
2025-03-04T21:53:12.8115586Z soxr==0.5.0.post1
2025-03-04T21:53:12.8115810Z spacy==3.8.4
2025-03-04T21:53:12.8116044Z spacy-legacy==3.0.12
2025-03-04T21:53:12.8116302Z spacy-loggers==1.0.5
2025-03-04T21:53:12.8116556Z srsly==2.5.1
2025-03-04T21:53:12.8116788Z stempeg==0.2.3
2025-03-04T21:53:12.8117022Z submitit==1.5.2
2025-03-04T21:53:12.8117252Z sympy==1.13.3
2025-03-04T21:53:12.8117480Z tabulate==0.9.0
2025-03-04T21:53:12.8117721Z tb-nightly==2.13.0a20230426
2025-03-04T21:53:12.8118000Z tensorboard==2.13.0
2025-03-04T21:53:12.8118269Z tensorboard-data-server==0.7.2
2025-03-04T21:53:12.8118564Z tensorboardX==2.6.2.2
2025-03-04T21:53:12.8118819Z termcolor==2.5.0
2025-03-04T21:53:12.8119047Z thinc==8.3.4
2025-03-04T21:53:12.8119281Z threadpoolctl==3.5.0
2025-03-04T21:53:12.8119632Z thriftpy2==0.5.2
2025-03-04T21:53:12.8119871Z tifffile==2025.2.18
2025-03-04T21:53:12.8120474Z timm @ git+https://github.com/huggingface/pytorch-image-models.git@730b907b4d45a4713cbc425cbf224c46089fd514
2025-03-04T21:53:12.8121064Z tlparse==0.3.30
2025-03-04T21:53:12.8121301Z tokenizers==0.15.2
2025-03-04T21:53:12.8121534Z tomli==2.2.1
2025-03-04T21:53:12.8121756Z tomlkit==0.13.2
2025-03-04T21:53:12.8122542Z torch @ file:///var/lib/jenkins/workspace/dist/torch-2.7.0a0%2Bgit1b74980-cp310-cp310-linux_x86_64.whl#sha256=7fbd769826ccb5a3fd728133246ae8c4faf5e660c8486ba8f8d23f3bd020e473
2025-03-04T21:53:12.8123695Z torch_geometric @ git+https://github.com/pyg-team/pytorch_geometric.git@cabcd4097442ba60aa1efa11e1619dd9bb8fb527
2025-03-04T21:53:12.8124479Z torchao @ git+https://github.com/pytorch/ao.git@d4be9e422e1f4e213903cb48319050a689a4d1d7
2025-03-04T21:53:12.8125698Z torchaudio @ git+https://github.com/pytorch/audio.git@c670ad81fda266b6598aeeef434583eb98197ae8
2025-03-04T21:53:12.8126554Z torchmultimodal @ git+https://github.com/facebookresearch/multimodal.git@6569fcc03450c2360b50d772bf9b18ec3487fcf4
2025-03-04T21:53:12.8127390Z torchvision @ git+https://github.com/pytorch/vision.git@d23a6e1664d20707c11781299611436e1f0c104f
2025-03-04T21:53:12.8128048Z tornado==6.4.2
2025-03-04T21:53:12.8128281Z tqdm==4.67.1
2025-03-04T21:53:12.8128510Z transformers==4.38.1
2025-03-04T21:53:12.8128762Z treetable==0.2.5
2025-03-04T21:53:12.8129047Z triton @ file:///var/lib/jenkins/triton/python
2025-03-04T21:53:12.8129377Z typeguard==4.4.2
2025-03-04T21:53:12.8129612Z typer==0.15.2
2025-03-04T21:53:12.8129848Z typing-inspect==0.9.0
2025-03-04T21:53:12.8130118Z typing_extensions==4.12.2
2025-03-04T21:53:12.8130391Z tzdata==2025.1
2025-03-04T21:53:12.8130627Z Unidecode==1.3.8
2025-03-04T21:53:12.8130885Z unittest-xml-reporting==3.2.0
2025-03-04T21:53:12.8131180Z uritemplate==4.1.1
2025-03-04T21:53:12.8131423Z urllib3==2.3.0
2025-03-04T21:53:12.8131659Z visdom==0.2.4
2025-03-04T21:53:12.8131883Z wandb==0.19.7
2025-03-04T21:53:12.8132114Z wasabi==1.1.3
2025-03-04T21:53:12.8132341Z wcwidth==0.2.13
2025-03-04T21:53:12.8132570Z weasel==0.4.1
2025-03-04T21:53:12.8132806Z websocket-client==1.8.0
2025-03-04T21:53:12.8133069Z Werkzeug==3.1.3
2025-03-04T21:53:12.8133302Z wrapt==1.17.2
2025-03-04T21:53:12.8133529Z xdoctest==1.1.0
2025-03-04T21:53:12.8133760Z xxhash==3.5.0
2025-03-04T21:53:12.8133996Z xyzservices==2025.1.0
2025-03-04T21:53:12.8134249Z yacs==0.1.8
2025-03-04T21:53:12.8134465Z yarl==1.18.3
2025-03-04T21:53:12.8134690Z z3-solver==4.12.6.0
2025-03-04T21:53:12.8134932Z zipp==3.21.0
2025-03-04T21:53:12.8357007Z + popd
2025-03-04T21:53:12.8357245Z ~/workspace
2025-03-04T21:53:12.8357492Z + [[ inductor_torchbench != *cpu* ]]
2025-03-04T21:53:12.8357810Z + install_torchrec_and_fbgemm
2025-03-04T21:53:12.8363304Z + local torchrec_commit
2025-03-04T21:53:12.8367160Z ++ get_pinned_commit torchrec
2025-03-04T21:53:12.8372491Z ++ cat .github/ci_commit_pins/torchrec.txt
2025-03-04T21:53:12.8399466Z + torchrec_commit=6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2
2025-03-04T21:53:12.8399872Z + local fbgemm_commit
2025-03-04T21:53:12.8403407Z ++ get_pinned_commit fbgemm
2025-03-04T21:53:12.8403734Z ++ cat .github/ci_commit_pins/fbgemm.txt
2025-03-04T21:53:12.8424495Z + fbgemm_commit=de731af65b4f04696e85c729e3282450b51b95fd
2025-03-04T21:53:12.8425175Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *rocm* ]]
2025-03-04T21:53:12.8425703Z + pip_uninstall torchrec-nightly
2025-03-04T21:53:12.8426046Z + pip3 uninstall -y torchrec-nightly
2025-03-04T21:53:13.2796829Z WARNING: Skipping torchrec-nightly as it is not installed.
2025-03-04T21:53:13.3164934Z + pip_uninstall fbgemm-gpu-nightly
2025-03-04T21:53:13.3165471Z + pip3 uninstall -y fbgemm-gpu-nightly
2025-03-04T21:53:13.7484424Z WARNING: Skipping fbgemm-gpu-nightly as it is not installed.
2025-03-04T21:53:13.7857220Z + pip_install setuptools-git-versioning scikit-build pyre-extensions
2025-03-04T21:53:13.7857803Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T21:53:13.7859027Z + python3 -m pip install --progress-bar off setuptools-git-versioning scikit-build pyre-extensions
2025-03-04T21:53:14.2890190Z Collecting setuptools-git-versioning
2025-03-04T21:53:14.3227125Z   Downloading setuptools_git_versioning-2.1.0-py3-none-any.whl.metadata (6.1 kB)
2025-03-04T21:53:14.3537747Z Collecting scikit-build
2025-03-04T21:53:14.3581026Z   Downloading scikit_build-0.18.1-py3-none-any.whl.metadata (18 kB)
2025-03-04T21:53:14.3944542Z Collecting pyre-extensions
2025-03-04T21:53:14.3980560Z   Downloading pyre_extensions-0.0.32-py3-none-any.whl.metadata (4.0 kB)
2025-03-04T21:53:14.4048575Z Requirement already satisfied: packaging in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from setuptools-git-versioning) (24.2)
2025-03-04T21:53:14.4052048Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from setuptools-git-versioning) (75.8.0)
2025-03-04T21:53:14.4056299Z Requirement already satisfied: tomli>=2.0.1 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from setuptools-git-versioning) (2.2.1)
2025-03-04T21:53:14.4066833Z Requirement already satisfied: distro in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from scikit-build) (1.9.0)
2025-03-04T21:53:14.4072142Z Requirement already satisfied: wheel>=0.32.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from scikit-build) (0.45.1)
2025-03-04T21:53:14.4078389Z Requirement already satisfied: typing-inspect in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from pyre-extensions) (0.9.0)
2025-03-04T21:53:14.4081260Z Requirement already satisfied: typing-extensions in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from pyre-extensions) (4.12.2)
2025-03-04T21:53:14.4166449Z Requirement already satisfied: mypy-extensions>=0.3.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from typing-inspect->pyre-extensions) (1.0.0)
2025-03-04T21:53:14.4204950Z Downloading setuptools_git_versioning-2.1.0-py3-none-any.whl (10 kB)
2025-03-04T21:53:14.4296139Z Downloading scikit_build-0.18.1-py3-none-any.whl (85 kB)
2025-03-04T21:53:14.4451599Z Downloading pyre_extensions-0.0.32-py3-none-any.whl (12 kB)
2025-03-04T21:53:15.3141503Z Installing collected packages: setuptools-git-versioning, scikit-build, pyre-extensions
2025-03-04T21:53:15.4262325Z Successfully installed pyre-extensions-0.0.32 scikit-build-0.18.1 setuptools-git-versioning-2.1.0
2025-03-04T21:53:15.5562569Z + [[ linux-focal-cuda12.4-py3.10-gcc9-sm86 == *rocm* ]]
2025-03-04T21:53:15.5562976Z + CUDA_PATH=/usr/local/cuda-12.1
2025-03-04T21:53:15.5563785Z + pip_install --no-use-pep517 --user 'git+https://github.com/pytorch/FBGEMM.git@de731af65b4f04696e85c729e3282450b51b95fd#egg=fbgemm-gpu&subdirectory=fbgemm_gpu'
2025-03-04T21:53:15.5564671Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T21:53:15.5565666Z + python3 -m pip install --progress-bar off --no-use-pep517 --user 'git+https://github.com/pytorch/FBGEMM.git@de731af65b4f04696e85c729e3282450b51b95fd#egg=fbgemm-gpu&subdirectory=fbgemm_gpu'
2025-03-04T21:53:15.9843741Z Collecting fbgemm-gpu
2025-03-04T21:53:15.9846332Z   Cloning https://github.com/pytorch/FBGEMM.git (to revision de731af65b4f04696e85c729e3282450b51b95fd) to /tmp/pip-install-7m8fdbve/fbgemm-gpu_7a983d50ce47421298b2110974675876
2025-03-04T21:53:15.9876453Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/FBGEMM.git /tmp/pip-install-7m8fdbve/fbgemm-gpu_7a983d50ce47421298b2110974675876
2025-03-04T21:53:17.0304333Z   Running command git rev-parse -q --verify 'sha^de731af65b4f04696e85c729e3282450b51b95fd'
2025-03-04T21:53:17.0334170Z   Running command git fetch -q https://github.com/pytorch/FBGEMM.git de731af65b4f04696e85c729e3282450b51b95fd
2025-03-04T21:53:17.7431189Z   Running command git checkout -q de731af65b4f04696e85c729e3282450b51b95fd
2025-03-04T21:53:18.1906261Z   Resolved https://github.com/pytorch/FBGEMM.git to commit de731af65b4f04696e85c729e3282450b51b95fd
2025-03-04T21:53:18.1907269Z   Running command git submodule update --init --recursive -q
2025-03-04T21:53:26.6204032Z   Preparing metadata (setup.py) ... [?25l- done
2025-03-04T21:53:26.6224119Z [?25hRequirement already satisfied: numpy in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from fbgemm-gpu) (1.22.4)
2025-03-04T21:53:26.6232691Z Building wheels for collected packages: fbgemm-gpu
2025-03-04T22:39:42.3875375Z   Building wheel for fbgemm-gpu (setup.py) ... [?25l- \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - \ | / - done
2025-03-04T22:39:42.5720935Z [?25h  Created wheel for fbgemm-gpu: filename=fbgemm_gpu-0.4.1rc0.post421-cp310-cp310-linux_x86_64.whl size=253383917 sha256=520b7395708c216cc30d82e6bb6703622619432be7d5f65eeeea66e26fae8820
2025-03-04T22:39:42.5722162Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/d8/d9/f3/f5c260aba9c9f0a533444c12621a0c2ceb8fbcffce2b5beb39
2025-03-04T22:39:42.5744396Z Successfully built fbgemm-gpu
2025-03-04T22:39:43.4472773Z Installing collected packages: fbgemm-gpu
2025-03-04T22:39:46.6978750Z Successfully installed fbgemm-gpu-0.4.1rc0.post421
2025-03-04T22:39:47.6359933Z + pip_install --no-use-pep517 --user git+https://github.com/pytorch/torchrec.git@6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2
2025-03-04T22:39:47.6360697Z + pip_install_pkg='python3 -m pip install --progress-bar off'
2025-03-04T22:39:47.6361524Z + python3 -m pip install --progress-bar off --no-use-pep517 --user git+https://github.com/pytorch/torchrec.git@6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2
2025-03-04T22:39:48.0773455Z Collecting git+https://github.com/pytorch/torchrec.git@6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2
2025-03-04T22:39:48.0776910Z   Cloning https://github.com/pytorch/torchrec.git (to revision 6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2) to /tmp/pip-req-build-7kzu_5t0
2025-03-04T22:39:48.0807563Z   Running command git clone --filter=blob:none --quiet https://github.com/pytorch/torchrec.git /tmp/pip-req-build-7kzu_5t0
2025-03-04T22:39:48.8117595Z   Running command git rev-parse -q --verify 'sha^6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2'
2025-03-04T22:39:48.8147947Z   Running command git fetch -q https://github.com/pytorch/torchrec.git 6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2
2025-03-04T22:39:49.2265532Z   Running command git checkout -q 6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2
2025-03-04T22:39:49.5992360Z   Resolved https://github.com/pytorch/torchrec.git to commit 6cd9fd362514d14ebb9ed51314c62ac1e1e2bbf2
2025-03-04T22:39:49.8727499Z   Preparing metadata (setup.py) ... [?25l- done
2025-03-04T22:39:49.8762886Z [?25hRequirement already satisfied: iopath in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchrec==0.3.2) (0.1.9)
2025-03-04T22:39:49.8766207Z Requirement already satisfied: pandas in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchrec==0.3.2) (2.0.3)
2025-03-04T22:39:49.8776816Z Requirement already satisfied: tabulate in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchrec==0.3.2) (0.9.0)
2025-03-04T22:39:49.9374846Z Collecting torchmetrics (from torchrec==0.3.2)
2025-03-04T22:39:49.9756418Z   Downloading torchmetrics-1.6.2-py3-none-any.whl.metadata (20 kB)
2025-03-04T22:39:50.0223000Z Requirement already satisfied: tqdm in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchrec==0.3.2) (4.67.1)
2025-03-04T22:39:50.0224475Z Requirement already satisfied: fbgemm-gpu in /var/lib/jenkins/.local/lib/python3.10/site-packages (from torchrec==0.3.2) (0.4.1rc0.post421)
2025-03-04T22:39:50.0236568Z Requirement already satisfied: numpy in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from fbgemm-gpu->torchrec==0.3.2) (1.22.4)
2025-03-04T22:39:50.0246116Z Requirement already satisfied: portalocker in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from iopath->torchrec==0.3.2) (3.1.1)
2025-03-04T22:39:50.0522780Z Requirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from pandas->torchrec==0.3.2) (2.9.0.post0)
2025-03-04T22:39:50.0526422Z Requirement already satisfied: pytz>=2020.1 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from pandas->torchrec==0.3.2) (2025.1)
2025-03-04T22:39:50.0531294Z Requirement already satisfied: tzdata>=2022.1 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from pandas->torchrec==0.3.2) (2025.1)
2025-03-04T22:39:50.0554604Z Requirement already satisfied: packaging>17.1 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchmetrics->torchrec==0.3.2) (24.2)
2025-03-04T22:39:50.0559097Z Requirement already satisfied: torch>=2.0.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torchmetrics->torchrec==0.3.2) (2.7.0a0+git1b74980)
2025-03-04T22:39:50.0728924Z Collecting lightning-utilities>=0.8.0 (from torchmetrics->torchrec==0.3.2)
2025-03-04T22:39:50.0763571Z   Downloading lightning_utilities-0.13.1-py3-none-any.whl.metadata (5.6 kB)
2025-03-04T22:39:50.0855701Z Requirement already satisfied: setuptools in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from lightning-utilities>=0.8.0->torchmetrics->torchrec==0.3.2) (75.8.0)
2025-03-04T22:39:50.0859401Z Requirement already satisfied: typing_extensions in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from lightning-utilities>=0.8.0->torchmetrics->torchrec==0.3.2) (4.12.2)
2025-03-04T22:39:50.0882229Z Requirement already satisfied: six>=1.5 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->torchrec==0.3.2) (1.17.0)
2025-03-04T22:39:50.0913823Z Requirement already satisfied: filelock in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch>=2.0.0->torchmetrics->torchrec==0.3.2) (3.16.1)
2025-03-04T22:39:50.0918708Z Requirement already satisfied: sympy==1.13.3 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch>=2.0.0->torchmetrics->torchrec==0.3.2) (1.13.3)
2025-03-04T22:39:50.0921832Z Requirement already satisfied: networkx in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch>=2.0.0->torchmetrics->torchrec==0.3.2) (2.8.8)
2025-03-04T22:39:50.0923708Z Requirement already satisfied: jinja2 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch>=2.0.0->torchmetrics->torchrec==0.3.2) (3.1.5)
2025-03-04T22:39:50.0927528Z Requirement already satisfied: fsspec in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from torch>=2.0.0->torchmetrics->torchrec==0.3.2) (2024.10.0)
2025-03-04T22:39:50.0944969Z 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>=2.0.0->torchmetrics->torchrec==0.3.2) (1.3.0)
2025-03-04T22:39:50.1358967Z Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/py_3.10/lib/python3.10/site-packages (from jinja2->torch>=2.0.0->torchmetrics->torchrec==0.3.2) (3.0.2)
2025-03-04T22:39:50.1513474Z Downloading torchmetrics-1.6.2-py3-none-any.whl (931 kB)
2025-03-04T22:39:50.1795400Z Downloading lightning_utilities-0.13.1-py3-none-any.whl (28 kB)
2025-03-04T22:39:50.1885123Z Building wheels for collected packages: torchrec
2025-03-04T22:39:50.7575247Z   Building wheel for torchrec (setup.py) ... [?25l- \ | done
2025-03-04T22:39:50.7583730Z [?25h  Created wheel for torchrec: filename=torchrec-0.3.2-py3-none-any.whl size=374547 sha256=afb56f53733d808346cbf6721256c221f985d344775a1b3e84267a926e428d6d
2025-03-04T22:39:50.7585235Z   Stored in directory: /var/lib/jenkins/.cache/pip/wheels/8e/a1/47/39ede01672ba82c08fb521bfc057cc4347e4b0e951738c8ca8
2025-03-04T22:39:50.7608514Z Successfully built torchrec
2025-03-04T22:39:51.5683610Z Installing collected packages: lightning-utilities, torchmetrics, torchrec
2025-03-04T22:39:52.2059040Z Successfully installed lightning-utilities-0.13.1 torchmetrics-1.6.2 torchrec-0.3.2
2025-03-04T22:39:52.3742065Z ++ pwd
2025-03-04T22:39:52.3744297Z + PYTHONPATH=/var/lib/jenkins/workspace/torchbench
2025-03-04T22:39:52.3744749Z + test_dynamo_benchmark torchbench 0
2025-03-04T22:39:52.3755617Z ++ pwd
2025-03-04T22:39:52.3759443Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports
2025-03-04T22:39:52.3759966Z + local suite=torchbench
2025-03-04T22:39:52.3760241Z + shift
2025-03-04T22:39:52.3760549Z + local shard_id=0
2025-03-04T22:39:52.3760807Z + shift
2025-03-04T22:39:52.3761058Z + [[ inductor_torchbench == *perf_compare* ]]
2025-03-04T22:39:52.3761427Z + [[ inductor_torchbench == *perf* ]]
2025-03-04T22:39:52.3761763Z + [[ inductor_torchbench == *cpu* ]]
2025-03-04T22:39:52.3762231Z + [[ inductor_torchbench == *aot_inductor* ]]
2025-03-04T22:39:52.3762901Z + test_single_dynamo_benchmark inference torchbench 0 --inference --bfloat16
2025-03-04T22:39:52.3790980Z ++ pwd
2025-03-04T22:39:52.3794089Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports
2025-03-04T22:39:52.3794782Z + mkdir -p /var/lib/jenkins/workspace/test/test-reports
2025-03-04T22:39:52.3842189Z + local name=inference
2025-03-04T22:39:52.3842593Z + shift
2025-03-04T22:39:52.3842943Z + local suite=torchbench
2025-03-04T22:39:52.3843315Z + shift
2025-03-04T22:39:52.3843607Z + local shard_id=0
2025-03-04T22:39:52.3843859Z + shift
2025-03-04T22:39:52.3844092Z + partition_flags=()
2025-03-04T22:39:52.3844361Z + local partition_flags
2025-03-04T22:39:52.3844626Z + [[ -n 2 ]]
2025-03-04T22:39:52.3844855Z + [[ -n 0 ]]
2025-03-04T22:39:52.3845271Z + partition_flags=(--total-partitions "$NUM_TEST_SHARDS" --partition-id "$shard_id")
2025-03-04T22:39:52.3845786Z + [[ inductor_torchbench == *perf_compare* ]]
2025-03-04T22:39:52.3846148Z + [[ inductor_torchbench == *perf* ]]
2025-03-04T22:39:52.3846481Z + [[ inductor_torchbench == *_avx2* ]]
2025-03-04T22:39:52.3846813Z + [[ inductor_torchbench == *_avx512* ]]
2025-03-04T22:39:52.3848209Z + python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --print-compilation-time --inductor --device cuda --inference --bfloat16 --total-partitions 2 --partition-id 0 --output /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv
2025-03-04T22:39:59.7509558Z 
2025-03-04T22:40:01.0430867Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:40:01.0431329Z loading model: 0it [00:01, ?it/s]
2025-03-04T22:40:01.0431784Z cuda eval  torchrec_dlrm                      
2025-03-04T22:40:01.0472445Z Traceback (most recent call last):
2025-03-04T22:40:01.0473248Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:40:01.0473959Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:40:01.0474544Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:40:01.0475094Z     return mod(*inputs)
2025-03-04T22:40:01.0475712Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:40:01.0476831Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:40:01.0477462Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:40:01.0478088Z     return forward_call(*args, **kwargs)
2025-03-04T22:40:01.0478686Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchrec/models/dlrm.py", line 893, in forward
2025-03-04T22:40:01.0479375Z     logits = self.model(batch.dense_features, batch.sparse_features)
2025-03-04T22:40:01.0480118Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:40:01.0480785Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:40:01.0481398Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:40:01.0482019Z     return forward_call(*args, **kwargs)
2025-03-04T22:40:01.0482620Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchrec/models/dlrm.py", line 572, in forward
2025-03-04T22:40:01.0483229Z     concatenated_dense = self.inter_arch(
2025-03-04T22:40:01.0483883Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:40:01.0484545Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:40:01.0485158Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:40:01.0485778Z     return forward_call(*args, **kwargs)
2025-03-04T22:40:01.0486366Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchrec/models/dlrm.py", line 284, in forward
2025-03-04T22:40:01.0487051Z     return self.crossnet(combined_values.reshape([B, -1]))
2025-03-04T22:40:01.0487921Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:40:01.0488590Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:40:01.0489209Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:40:01.0489824Z     return forward_call(*args, **kwargs)
2025-03-04T22:40:01.0490425Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchrec/modules/crossnet.py", line 175, in forward
2025-03-04T22:40:01.0491118Z     x_l_v = torch.nn.functional.linear(x_l, self.V_kernels[layer])
2025-03-04T22:40:01.0491734Z RuntimeError: expected mat1 and mat2 to have the same dtype, but got: float != c10::BFloat16
2025-03-04T22:40:01.0492149Z 
2025-03-04T22:40:01.0492367Z The above exception was the direct cause of the following exception:
2025-03-04T22:40:01.0492693Z 
2025-03-04T22:40:01.0492816Z Traceback (most recent call last):
2025-03-04T22:40:01.0493302Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:40:01.0493800Z     ) = runner.load_model(
2025-03-04T22:40:01.0494313Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:40:01.0494884Z     self.validate_model(model, example_inputs)
2025-03-04T22:40:01.0495447Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:40:01.0496025Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:40:01.0496390Z RuntimeError: Eager run failed
2025-03-04T22:40:01.0496587Z 
2025-03-04T22:40:01.0496702Z eager_fail_to_run
2025-03-04T22:40:01.6439902Z [rank0]:[W304 22:40:01.630890544 ProcessGroupNCCL.cpp:1477] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
2025-03-04T22:40:05.5449715Z 
2025-03-04T22:40:07.9244806Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:40:07.9245170Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:40:07.9309157Z cuda eval  BERT_pytorch                       
2025-03-04T22:40:27.3604886Z Compilation time (from dynamo_timed): 16.657362783
2025-03-04T22:40:27.3605304Z pass
2025-03-04T22:40:27.3646564Z TIMING: _recursive_pre_grad_passes:0.08503 pad_mm_benchmark:0.76283 _recursive_joint_graph_passes:1.32688 _recursive_post_grad_passes:0.53333 async_compile.wait:1.78426 code_gen:4.12479 inductor_compile:7.02797 backend_compile:13.59791 entire_frame_compile:16.65736 gc:0.00016 total_wall_time:16.65736
2025-03-04T22:40:27.3648333Z STATS: call_* op count: 543 | FakeTensor.__torch_dispatch__:2100 | FakeTensorMode.__torch_dispatch__:17497 | ProxyTorchDispatchMode.__torch_dispatch__:7364
2025-03-04T22:40:27.3649162Z Dynamo produced 1 graphs covering 543 ops with 0 graph breaks (0 unique)
2025-03-04T22:40:33.0295538Z 
2025-03-04T22:40:35.9390665Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:40:35.9391151Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:40:35.9440814Z cuda eval  Background_Matting                 
2025-03-04T22:40:35.9698510Z Compilation time (from dynamo_timed): 0
2025-03-04T22:40:35.9698958Z pass_due_to_skip
2025-03-04T22:40:36.0195350Z TIMING: total_wall_time:0
2025-03-04T22:40:36.0195790Z STATS: call_* op count: 0
2025-03-04T22:40:36.0196218Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-04T22:40:40.3766688Z 
2025-03-04T22:40:43.6493981Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:40:43.6494359Z loading model: 0it [00:03, ?it/s]
2025-03-04T22:40:43.6518912Z cuda eval  LearningToPaint                    
2025-03-04T22:40:51.3384223Z Compilation time (from dynamo_timed): 5.733532396
2025-03-04T22:40:51.3384627Z pass
2025-03-04T22:40:51.3392629Z TIMING: _recursive_pre_grad_passes:0.00538 pad_mm_benchmark:0.07054 _recursive_joint_graph_passes:0.42089 _recursive_post_grad_passes:0.07272 async_compile.wait:0.50885 code_gen:1.40647 inductor_compile:2.35389 backend_compile:4.3786 entire_frame_compile:5.73353 gc:0.00052 total_wall_time:5.73353
2025-03-04T22:40:51.3394265Z STATS: call_* op count: 71 | FakeTensorMode.__torch_dispatch__:4570 | ProxyTorchDispatchMode.__torch_dispatch__:1724 | FakeTensor.__torch_dispatch__:933
2025-03-04T22:40:51.3395283Z Dynamo produced 1 graphs covering 71 ops with 0 graph breaks (0 unique)
2025-03-04T22:40:56.9918370Z 
2025-03-04T22:40:58.4990884Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/vgg16-397923af.pth
2025-03-04T22:40:58.5306073Z 
2025-03-04T22:40:58.5306259Z 
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2025-03-04T22:40:58.7312452Z 
2025-03-04T22:40:58.8314029Z  16% 86.6M/528M [00:00<00:01, 454MB/s]
2025-03-04T22:40:58.8314300Z 
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2025-03-04T22:40:58.9316301Z 
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2025-03-04T22:40:59.3321608Z 
2025-03-04T22:40:59.4330521Z  67% 351M/528M [00:00<00:00, 462MB/s]
2025-03-04T22:40:59.4330809Z 
2025-03-04T22:40:59.5336913Z  75% 395M/528M [00:00<00:00, 461MB/s]
2025-03-04T22:40:59.5337148Z 
2025-03-04T22:40:59.6337362Z  83% 439M/528M [00:01<00:00, 460MB/s]
2025-03-04T22:40:59.6337597Z 
2025-03-04T22:40:59.7339743Z  92% 483M/528M [00:01<00:00, 460MB/s]
2025-03-04T22:40:59.7339969Z 
2025-03-04T22:40:59.7353875Z 100% 528M/528M [00:01<00:00, 461MB/s]
2025-03-04T22:40:59.7354205Z 100% 528M/528M [00:01<00:00, 459MB/s]
2025-03-04T22:41:00.3376211Z WARNING:common:Model Super_SloMo does not support bfloat16, running with amp instead
2025-03-04T22:41:00.7026537Z 
2025-03-04T22:41:00.7027025Z loading model: 0it [00:03, ?it/s]
2025-03-04T22:41:00.7028269Z WARNING:common:Model Super_SloMo does not support bfloat16, running with amp instead
2025-03-04T22:41:00.7028962Z cuda eval  Super_SloMo                        
2025-03-04T22:41:11.9233025Z WARNING:common:Model Super_SloMo does not support bfloat16, running with amp instead
2025-03-04T22:41:41.6099928Z Compilation time (from dynamo_timed): 25.400709641
2025-03-04T22:41:41.6100328Z pass
2025-03-04T22:41:41.6161008Z TIMING: _recursive_pre_grad_passes:0.00952 _recursive_joint_graph_passes:0.30747 _recursive_post_grad_passes:0.26431 async_compile.wait:2.24833 code_gen:13.5082 inductor_compile:18.45257 backend_compile:22.86896 entire_frame_compile:25.40071 gc:0.00124 total_wall_time:25.40071
2025-03-04T22:41:41.6162579Z STATS: call_* op count: 376 | FakeTensorMode.__torch_dispatch__:18486 | ProxyTorchDispatchMode.__torch_dispatch__:7576 | FakeTensor.__torch_dispatch__:3165
2025-03-04T22:41:41.6163454Z Dynamo produced 1 graphs covering 376 ops with 0 graph breaks (0 unique)
2025-03-04T22:41:47.6149962Z 
2025-03-04T22:41:48.1901331Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/alexnet-owt-7be5be79.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/alexnet-owt-7be5be79.pth
2025-03-04T22:41:48.2236249Z 
2025-03-04T22:41:48.2236446Z 
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2025-03-04T22:41:48.4241912Z  15% 36.0M/233M [00:00<00:00, 377MB/s]
2025-03-04T22:41:48.4242315Z 
2025-03-04T22:41:48.5243167Z  32% 74.4M/233M [00:00<00:00, 392MB/s]
2025-03-04T22:41:48.5244406Z 
2025-03-04T22:41:48.6245513Z  49% 114M/233M [00:00<00:00, 402MB/s] 
2025-03-04T22:41:48.6245894Z 
2025-03-04T22:41:48.7247620Z  66% 154M/233M [00:00<00:00, 408MB/s]
2025-03-04T22:41:48.7248052Z 
2025-03-04T22:41:48.8247096Z  83% 193M/233M [00:00<00:00, 410MB/s]
2025-03-04T22:41:48.8247516Z 100% 233M/233M [00:00<00:00, 407MB/s]
2025-03-04T22:41:49.6436662Z 
2025-03-04T22:41:49.6437164Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:41:49.6442505Z cuda eval  alexnet                            
2025-03-04T22:41:54.7338140Z Compilation time (from dynamo_timed): 2.928060109
2025-03-04T22:41:54.7338686Z pass
2025-03-04T22:41:54.7342252Z TIMING: _recursive_pre_grad_passes:0.00387 pad_mm_benchmark:0.2305 _recursive_joint_graph_passes:0.36668 _recursive_post_grad_passes:0.02492 async_compile.wait:0.43893 code_gen:1.05298 inductor_compile:1.30782 backend_compile:2.58649 entire_frame_compile:2.92806 gc:0.00054 total_wall_time:2.92806
2025-03-04T22:41:54.7344098Z STATS: call_* op count: 22 | FakeTensorMode.__torch_dispatch__:1202 | ProxyTorchDispatchMode.__torch_dispatch__:264 | FakeTensor.__torch_dispatch__:72
2025-03-04T22:41:54.7345028Z Dynamo produced 1 graphs covering 22 ops with 0 graph breaks (0 unique)
2025-03-04T22:42:00.0148545Z 
2025-03-04T22:42:01.9731505Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:42:01.9732020Z loading model: 0it [00:01, ?it/s]
2025-03-04T22:42:01.9735593Z cuda eval  basic_gnn_edgecnn                  
2025-03-04T22:42:04.2298142Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_reduce.two Found from   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:42:04.2299574Z     return mod(*inputs)
2025-03-04T22:42:04.2300358Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/models/basic_gnn.py", line 226, in forward
2025-03-04T22:42:04.2301011Z     x = self.convs[i](x, edge_index)
2025-03-04T22:42:04.2301412Z   File "/tmp/jenkins_pyg/tmp1s39521j.py", line 233, in forward
2025-03-04T22:42:04.2301859Z     return self.propagate(edge_index, x=x, size=None)
2025-03-04T22:42:04.2302312Z   File "/tmp/jenkins_pyg/tmp1s39521j.py", line 192, in propagate
2025-03-04T22:42:04.2302884Z     out = self.aggregate(out, index=kwargs.index, ptr=kwargs.ptr, dim_size=kwargs.dim_size)
2025-03-04T22:42:04.2303720Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T22:42:04.2305066Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T22:42:04.2305773Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T22:42:04.2306464Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T22:42:04.2307147Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 48, in forward
2025-03-04T22:42:04.2307836Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='max')
2025-03-04T22:42:04.2308515Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T22:42:04.2309148Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T22:42:04.2309780Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 98, in scatter
2025-03-04T22:42:04.2310420Z     return src.new_zeros(size).scatter_reduce_(
2025-03-04T22:42:04.2310663Z 
2025-03-04T22:42:04.2310673Z 
2025-03-04T22:42:04.4133682Z Compilation time (from dynamo_timed): 2.081313206
2025-03-04T22:42:04.4134209Z pass
2025-03-04T22:42:04.4170556Z TIMING: _recursive_pre_grad_passes:0.00423 pad_mm_benchmark:0.00444 _recursive_joint_graph_passes:0.16359 _recursive_post_grad_passes:0.03301 async_compile.wait:0.27771 code_gen:0.45053 inductor_compile:0.65747 backend_compile:1.69405 entire_frame_compile:2.08131 gc:0.00036 total_wall_time:2.08131
2025-03-04T22:42:04.4172168Z STATS: call_* op count: 55 | FakeTensorMode.__torch_dispatch__:1456 | ProxyTorchDispatchMode.__torch_dispatch__:467 | FakeTensor.__torch_dispatch__:79
2025-03-04T22:42:04.4172998Z Dynamo produced 1 graphs covering 55 ops with 0 graph breaks (0 unique)
2025-03-04T22:42:09.7220905Z 
2025-03-04T22:42:11.9271584Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:42:11.9271981Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:42:11.9272319Z cuda eval  basic_gnn_gcn                      
2025-03-04T22:42:14.6702773Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/gcn_conv.py", line 100, in torch_dynamo_resume_in_gcn_norm_at_91
2025-03-04T22:42:14.6704105Z     deg = scatter(edge_weight, idx, dim=0, dim_size=num_nodes, reduce='sum')
2025-03-04T22:42:14.6704830Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T22:42:14.6705500Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T22:42:14.6705789Z 
2025-03-04T22:42:14.6705793Z 
2025-03-04T22:42:15.2422081Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/tmp/jenkins_pyg/tmpwfqezivw.py", line 243, in torch_dynamo_resume_in_forward_at_221
2025-03-04T22:42:15.2423327Z     out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
2025-03-04T22:42:15.2423885Z   File "/tmp/jenkins_pyg/tmpwfqezivw.py", line 188, in propagate
2025-03-04T22:42:15.2424551Z     out = self.aggregate(out, dim_size=kwargs.dim_size, ptr=kwargs.ptr, index=kwargs.index)
2025-03-04T22:42:15.2425713Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T22:42:15.2426576Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T22:42:15.2427375Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T22:42:15.2428159Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T22:42:15.2428925Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 21, in forward
2025-03-04T22:42:15.2429703Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='sum')
2025-03-04T22:42:15.2430470Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T22:42:15.2431621Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T22:42:15.2432357Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T22:42:15.2433116Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T22:42:15.2433399Z 
2025-03-04T22:42:15.2433404Z 
2025-03-04T22:42:15.8173088Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/tmp/jenkins_pyg/tmpwfqezivw.py", line 243, in torch_dynamo_resume_in_forward_at_221
2025-03-04T22:42:15.8174233Z     out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
2025-03-04T22:42:15.8174824Z   File "/tmp/jenkins_pyg/tmpwfqezivw.py", line 188, in propagate
2025-03-04T22:42:15.8175483Z     out = self.aggregate(out, dim_size=kwargs.dim_size, ptr=kwargs.ptr, index=kwargs.index)
2025-03-04T22:42:15.8176464Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T22:42:15.8177330Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T22:42:15.8178120Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T22:42:15.8178889Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T22:42:15.8179619Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 21, in forward
2025-03-04T22:42:15.8180366Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='sum')
2025-03-04T22:42:15.8181133Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T22:42:15.8181855Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T22:42:15.8182576Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T22:42:15.8183339Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T22:42:15.8183628Z 
2025-03-04T22:42:15.8183632Z 
2025-03-04T22:42:15.9527579Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/tmp/jenkins_pyg/tmpwfqezivw.py", line 243, in torch_dynamo_resume_in_forward_at_221
2025-03-04T22:42:15.9529804Z     out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
2025-03-04T22:42:15.9530794Z   File "/tmp/jenkins_pyg/tmpwfqezivw.py", line 188, in propagate
2025-03-04T22:42:15.9531938Z     out = self.aggregate(out, dim_size=kwargs.dim_size, ptr=kwargs.ptr, index=kwargs.index)
2025-03-04T22:42:15.9533390Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T22:42:15.9534174Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T22:42:15.9534893Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T22:42:15.9535582Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T22:42:15.9536263Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 21, in forward
2025-03-04T22:42:15.9536951Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='sum')
2025-03-04T22:42:15.9537624Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T22:42:15.9538258Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T22:42:15.9538889Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T22:42:15.9539556Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T22:42:15.9539841Z 
2025-03-04T22:42:15.9539845Z 
2025-03-04T22:42:16.1022606Z Compilation time (from dynamo_timed): 3.029574173
2025-03-04T22:42:16.1023001Z pass
2025-03-04T22:42:16.1138418Z TIMING: entire_frame_compile:3.02957 gc:0.0058 _recursive_pre_grad_passes:0.0066 _recursive_joint_graph_passes:0.14115 _recursive_post_grad_passes:0.01407 async_compile.wait:1.07504 code_gen:1.18923 inductor_compile:1.34392 backend_compile:2.42107 pad_mm_benchmark:0.00063 total_wall_time:3.02957
2025-03-04T22:42:16.1140074Z STATS: call_* op count: 57 | FakeTensorMode.__torch_dispatch__:1059 | FakeTensor.__torch_dispatch__:65 | ProxyTorchDispatchMode.__torch_dispatch__:218
2025-03-04T22:42:16.1140899Z Dynamo produced 6 graphs covering 57 ops with 6 graph breaks (1 unique)
2025-03-04T22:42:21.4589423Z 
2025-03-04T22:42:23.5117213Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:42:23.5117692Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:42:23.5120874Z cuda eval  basic_gnn_gin                      
2025-03-04T22:42:25.6839022Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:42:25.6840039Z     return mod(*inputs)
2025-03-04T22:42:25.6840691Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/models/basic_gnn.py", line 226, in forward
2025-03-04T22:42:25.6841535Z     x = self.convs[i](x, edge_index)
2025-03-04T22:42:25.6841934Z   File "/tmp/jenkins_pyg/tmpxmcvtf8q.py", line 225, in forward
2025-03-04T22:42:25.6842373Z     out = self.propagate(edge_index, x=x, size=size)
2025-03-04T22:42:25.6842820Z   File "/tmp/jenkins_pyg/tmpxmcvtf8q.py", line 182, in propagate
2025-03-04T22:42:25.6843415Z     out = self.aggregate(out, ptr=kwargs.ptr, index=kwargs.index, dim_size=kwargs.dim_size)
2025-03-04T22:42:25.6852360Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T22:42:25.6853136Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T22:42:25.6853857Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T22:42:25.6854547Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T22:42:25.6855257Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 21, in forward
2025-03-04T22:42:25.6855967Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='sum')
2025-03-04T22:42:25.6856646Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T22:42:25.6857278Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T22:42:25.6857901Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T22:42:25.6858567Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T22:42:25.6858850Z 
2025-03-04T22:42:25.6858858Z 
2025-03-04T22:42:25.9396040Z Compilation time (from dynamo_timed): 1.856634224
2025-03-04T22:42:25.9396469Z pass
2025-03-04T22:42:25.9493817Z TIMING: _recursive_pre_grad_passes:0.00422 pad_mm_benchmark:0.00366 _recursive_joint_graph_passes:0.14635 _recursive_post_grad_passes:0.04247 async_compile.wait:0.13142 code_gen:0.26853 inductor_compile:0.46205 backend_compile:1.47385 entire_frame_compile:1.85663 gc:0.00091 total_wall_time:1.85663
2025-03-04T22:42:25.9496084Z STATS: call_* op count: 52 | FakeTensorMode.__torch_dispatch__:1368 | ProxyTorchDispatchMode.__torch_dispatch__:474 | FakeTensor.__torch_dispatch__:81
2025-03-04T22:42:25.9496905Z Dynamo produced 1 graphs covering 52 ops with 0 graph breaks (0 unique)
2025-03-04T22:42:31.2186991Z 
2025-03-04T22:42:33.2754002Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:42:33.2754388Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:42:33.2756403Z cuda eval  basic_gnn_sage                     
2025-03-04T22:42:35.4760942Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:42:35.4762112Z     return mod(*inputs)
2025-03-04T22:42:35.4762718Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/models/basic_gnn.py", line 226, in forward
2025-03-04T22:42:35.4763360Z     x = self.convs[i](x, edge_index)
2025-03-04T22:42:35.4763752Z   File "/tmp/jenkins_pyg/tmpl_o48gns.py", line 228, in forward
2025-03-04T22:42:35.4764189Z     out = self.propagate(edge_index, x=x, size=size)
2025-03-04T22:42:35.4764632Z   File "/tmp/jenkins_pyg/tmpl_o48gns.py", line 182, in propagate
2025-03-04T22:42:35.4765205Z     out = self.aggregate(out, ptr=kwargs.ptr, index=kwargs.index, dim_size=kwargs.dim_size)
2025-03-04T22:42:35.4766035Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T22:42:35.4766796Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T22:42:35.4767506Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T22:42:35.4768289Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T22:42:35.4768971Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 34, in forward
2025-03-04T22:42:35.4769662Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='mean')
2025-03-04T22:42:35.4770342Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T22:42:35.4770971Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T22:42:35.4771602Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 82, in scatter
2025-03-04T22:42:35.4772263Z     out = src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T22:42:35.4772551Z 
2025-03-04T22:42:35.4772555Z 
2025-03-04T22:42:35.7090394Z Compilation time (from dynamo_timed): 1.862858111
2025-03-04T22:42:35.7090790Z pass
2025-03-04T22:42:35.7196869Z TIMING: _recursive_pre_grad_passes:0.00413 pad_mm_benchmark:0.00384 _recursive_joint_graph_passes:0.15244 _recursive_post_grad_passes:0.03649 async_compile.wait:0.14184 code_gen:0.32315 inductor_compile:0.52372 backend_compile:1.54273 entire_frame_compile:1.86286 gc:0.0006 total_wall_time:1.86286
2025-03-04T22:42:35.7198525Z STATS: call_* op count: 58 | FakeTensorMode.__torch_dispatch__:1392 | ProxyTorchDispatchMode.__torch_dispatch__:466 | FakeTensor.__torch_dispatch__:89
2025-03-04T22:42:35.7199360Z Dynamo produced 1 graphs covering 58 ops with 0 graph breaks (0 unique)
2025-03-04T22:42:41.0274615Z 
2025-03-04T22:42:41.5578506Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:42:41.5578863Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:42:41.5583397Z cuda eval  dcgan                              
2025-03-04T22:42:45.6811889Z Compilation time (from dynamo_timed): 2.557970907
2025-03-04T22:42:45.6812302Z pass
2025-03-04T22:42:45.6815205Z TIMING: _recursive_pre_grad_passes:0.00358 _recursive_joint_graph_passes:0.13402 _recursive_post_grad_passes:0.01947 async_compile.precompile:0.41293 async_compile.wait:0.23643 code_gen:0.9519 inductor_compile:1.20262 backend_compile:2.19254 entire_frame_compile:2.55797 gc:0.00153 total_wall_time:2.55797
2025-03-04T22:42:45.6816846Z STATS: call_* op count: 13 | FakeTensorMode.__torch_dispatch__:1049 | FakeTensor.__torch_dispatch__:165 | ProxyTorchDispatchMode.__torch_dispatch__:308
2025-03-04T22:42:45.6817675Z Dynamo produced 1 graphs covering 13 ops with 0 graph breaks (0 unique)
2025-03-04T22:42:50.9457406Z 
2025-03-04T22:42:53.9099625Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:42:53.9100005Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:42:53.9119170Z cuda eval  demucs                             
2025-03-04T22:43:38.3981510Z Compilation time (from dynamo_timed): 4.645212486
2025-03-04T22:43:38.3982217Z pass
2025-03-04T22:43:38.4049832Z TIMING: entire_frame_compile:4.64521 gc:0.00205 _recursive_pre_grad_passes:0.00495 _recursive_joint_graph_passes:0.13642 _recursive_post_grad_passes:0.01634 async_compile.precompile:0.12006 async_compile.wait:0.2799 code_gen:1.32412 inductor_compile:1.83463 backend_compile:3.08319 total_wall_time:4.64521
2025-03-04T22:43:38.4051455Z STATS: call_* op count: 62 | FakeTensorMode.__torch_dispatch__:2262 | ProxyTorchDispatchMode.__torch_dispatch__:595 | FakeTensor.__torch_dispatch__:201
2025-03-04T22:43:38.4052278Z Dynamo produced 3 graphs covering 62 ops with 3 graph breaks (1 unique)
2025-03-04T22:43:43.7596782Z 
2025-03-04T22:43:44.0696136Z loading model: 0it [00:00, ?it/s]Downloading: "https://download.pytorch.org/models/densenet121-a639ec97.pth" to /var/lib/jenkins/.cache/torch/hub/checkpoints/densenet121-a639ec97.pth
2025-03-04T22:43:44.1026655Z 
2025-03-04T22:43:44.1026978Z 
2025-03-04T22:43:44.1781507Z   0% 0.00/30.8M [00:00<?, ?B/s]
2025-03-04T22:43:44.1782231Z 100% 30.8M/30.8M [00:00<00:00, 430MB/s]
2025-03-04T22:43:45.2816938Z 
2025-03-04T22:43:45.2817299Z loading model: 0it [00:01, ?it/s]
2025-03-04T22:43:45.2953262Z cuda eval  densenet121                        
2025-03-04T22:44:24.5103704Z Compilation time (from dynamo_timed): 34.620813318
2025-03-04T22:44:24.5104098Z pass
2025-03-04T22:44:24.5107870Z TIMING: _recursive_pre_grad_passes:0.01627 pad_mm_benchmark:0.06866 _recursive_joint_graph_passes:0.34455 _recursive_post_grad_passes:0.34759 async_compile.wait:1.81862 code_gen:12.39323 inductor_compile:17.95099 backend_compile:25.85448 entire_frame_compile:34.62081 gc:0.00015 total_wall_time:34.62081
2025-03-04T22:44:24.5109546Z STATS: call_* op count: 431 | FakeTensorMode.__torch_dispatch__:25401 | ProxyTorchDispatchMode.__torch_dispatch__:9582 | FakeTensor.__torch_dispatch__:5390
2025-03-04T22:44:24.5110411Z Dynamo produced 1 graphs covering 431 ops with 0 graph breaks (0 unique)
2025-03-04T22:44:31.0263474Z 
2025-03-04T22:44:34.3012554Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:44:34.3012969Z loading model: 0it [00:03, ?it/s]
2025-03-04T22:44:34.3013355Z cuda eval  detectron2_fasterrcnn_r_101_c4     
2025-03-04T22:44:34.3031118Z Traceback (most recent call last):
2025-03-04T22:44:34.3031749Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:44:34.3032328Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:44:34.3032896Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:44:34.3033444Z     return mod(*inputs)
2025-03-04T22:44:34.3034051Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:34.3034714Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:34.3035324Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:34.3035936Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:34.3036591Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:44:34.3037241Z     return self.inference(batched_inputs)
2025-03-04T22:44:34.3037889Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:44:34.3038620Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:44:34.3039339Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:34.3039992Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:34.3040598Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:34.3041210Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:34.3041865Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 477, in forward
2025-03-04T22:44:34.3042995Z     box_features = self._shared_roi_transform(
2025-03-04T22:44:34.3043729Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 456, in _shared_roi_transform
2025-03-04T22:44:34.3044447Z     x = self.pooler(features, boxes)
2025-03-04T22:44:34.3045081Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:34.3045734Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:34.3046344Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:34.3046954Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:34.3047722Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 246, in forward
2025-03-04T22:44:34.3048385Z     return self.level_poolers[0](x[0], pooler_fmt_boxes)
2025-03-04T22:44:34.3049081Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:34.3049734Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:34.3050342Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:34.3050956Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:34.3051552Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:44:34.3052147Z     return roi_align(
2025-03-04T22:44:34.3052704Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:44:34.3053525Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:44:34.3054346Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:44:34.3055156Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:44:34.3055994Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:44:34.3056656Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:44:34.3057256Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:44:34.3057861Z     return input[
2025-03-04T22:44:34.3059841Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 26886.45 GiB. GPU 0 has a total capacity of 21.98 GiB of which 19.01 GiB is free. Process 104197 has 2.96 GiB memory in use. Of the allocated memory 2.51 GiB is allocated by PyTorch, and 162.68 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)
2025-03-04T22:44:34.3061776Z 
2025-03-04T22:44:34.3062000Z The above exception was the direct cause of the following exception:
2025-03-04T22:44:34.3062321Z 
2025-03-04T22:44:34.3062451Z Traceback (most recent call last):
2025-03-04T22:44:34.3062940Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:44:34.3063438Z     ) = runner.load_model(
2025-03-04T22:44:34.3063932Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:44:34.3064504Z     self.validate_model(model, example_inputs)
2025-03-04T22:44:34.3065061Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:44:34.3065629Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:44:34.3065994Z RuntimeError: Eager run failed
2025-03-04T22:44:34.3066277Z 
2025-03-04T22:44:34.3066380Z eager_fail_to_run
2025-03-04T22:44:38.7770189Z 
2025-03-04T22:44:43.3740996Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:44:43.3741412Z loading model: 0it [00:04, ?it/s]
2025-03-04T22:44:43.3741749Z cuda eval  detectron2_fasterrcnn_r_101_dc5    
2025-03-04T22:44:43.3754822Z Traceback (most recent call last):
2025-03-04T22:44:43.3755445Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:44:43.3756119Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:44:43.3756749Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:44:43.3757356Z     return mod(*inputs)
2025-03-04T22:44:43.3758017Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:43.3758756Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:43.3759436Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:43.3760138Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:43.3760851Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:44:43.3761589Z     return self.inference(batched_inputs)
2025-03-04T22:44:43.3762324Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:44:43.3763146Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:44:43.3763955Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:43.3764686Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:43.3765364Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:43.3766046Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:43.3766790Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 747, in forward
2025-03-04T22:44:43.3767723Z     pred_instances = self._forward_box(features, proposals)
2025-03-04T22:44:43.3768557Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 798, in _forward_box
2025-03-04T22:44:43.3769484Z     box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])
2025-03-04T22:44:43.3770369Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:43.3771102Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:43.3771778Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:43.3772460Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:43.3773141Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 246, in forward
2025-03-04T22:44:43.3773895Z     return self.level_poolers[0](x[0], pooler_fmt_boxes)
2025-03-04T22:44:43.3774665Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:43.3775399Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:43.3776073Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:43.3776756Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:43.3777423Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:44:43.3778081Z     return roi_align(
2025-03-04T22:44:43.3778693Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:44:43.3779624Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:44:43.3780704Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:44:43.3781498Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:44:43.3782334Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:44:43.3782994Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:44:43.3783592Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:44:43.3784195Z     return input[
2025-03-04T22:44:43.3786176Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 13324.51 GiB. GPU 0 has a total capacity of 21.98 GiB of which 18.15 GiB is free. Process 104259 has 3.82 GiB memory in use. Of the allocated memory 3.34 GiB is allocated by PyTorch, and 149.04 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)
2025-03-04T22:44:43.3788103Z 
2025-03-04T22:44:43.3788327Z The above exception was the direct cause of the following exception:
2025-03-04T22:44:43.3788647Z 
2025-03-04T22:44:43.3788771Z Traceback (most recent call last):
2025-03-04T22:44:43.3789261Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:44:43.3789751Z     ) = runner.load_model(
2025-03-04T22:44:43.3790244Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:44:43.3790812Z     self.validate_model(model, example_inputs)
2025-03-04T22:44:43.3791365Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:44:43.3791946Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:44:43.3792310Z RuntimeError: Eager run failed
2025-03-04T22:44:43.3792500Z 
2025-03-04T22:44:43.3792607Z eager_fail_to_run
2025-03-04T22:44:47.7930186Z 
2025-03-04T22:44:50.7515348Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:44:50.7516009Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:44:50.7516654Z cuda eval  detectron2_fasterrcnn_r_101_fpn    
2025-03-04T22:44:50.7532487Z Traceback (most recent call last):
2025-03-04T22:44:50.7533067Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:44:50.7533652Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:44:50.7534226Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:44:50.7534770Z     return mod(*inputs)
2025-03-04T22:44:50.7535372Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:50.7536052Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:50.7536670Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:50.7537282Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:50.7537921Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:44:50.7538571Z     return self.inference(batched_inputs)
2025-03-04T22:44:50.7539233Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:44:50.7539958Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:44:50.7540676Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:50.7541328Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:50.7542337Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:50.7542947Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:50.7543602Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 747, in forward
2025-03-04T22:44:50.7544324Z     pred_instances = self._forward_box(features, proposals)
2025-03-04T22:44:50.7545058Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 798, in _forward_box
2025-03-04T22:44:50.7545875Z     box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])
2025-03-04T22:44:50.7546655Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:50.7547306Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:50.7547900Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:50.7548520Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:50.7549120Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 261, in forward
2025-03-04T22:44:50.7549818Z     output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
2025-03-04T22:44:50.7550550Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:50.7551202Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:50.7551803Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:50.7552410Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:50.7553003Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:44:50.7553588Z     return roi_align(
2025-03-04T22:44:50.7554150Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:44:50.7554965Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:44:50.7555779Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:44:50.7556567Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:44:50.7557398Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:44:50.7558057Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:44:50.7558657Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:44:50.7559257Z     return input[
2025-03-04T22:44:50.7561234Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 19073.68 GiB. GPU 0 has a total capacity of 21.98 GiB of which 18.27 GiB is free. Process 104321 has 3.69 GiB memory in use. Of the allocated memory 3.30 GiB is allocated by PyTorch, and 96.89 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)
2025-03-04T22:44:50.7563147Z 
2025-03-04T22:44:50.7563364Z The above exception was the direct cause of the following exception:
2025-03-04T22:44:50.7563689Z 
2025-03-04T22:44:50.7563809Z Traceback (most recent call last):
2025-03-04T22:44:50.7564291Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:44:50.7564787Z     ) = runner.load_model(
2025-03-04T22:44:50.7565280Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:44:50.7565937Z     self.validate_model(model, example_inputs)
2025-03-04T22:44:50.7566641Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:44:50.7567254Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:44:50.7567712Z RuntimeError: Eager run failed
2025-03-04T22:44:50.7567912Z 
2025-03-04T22:44:50.7568011Z eager_fail_to_run
2025-03-04T22:44:55.1877198Z 
2025-03-04T22:44:57.8190229Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:44:57.8190619Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:44:57.8190974Z cuda eval  detectron2_fasterrcnn_r_50_c4      
2025-03-04T22:44:57.8204022Z Traceback (most recent call last):
2025-03-04T22:44:57.8204629Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:44:57.8205211Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:44:57.8205789Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:44:57.8206374Z     return mod(*inputs)
2025-03-04T22:44:57.8207007Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:57.8207828Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:57.8208518Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:57.8209214Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:57.8209937Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:44:57.8210678Z     return self.inference(batched_inputs)
2025-03-04T22:44:57.8211422Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:44:57.8212251Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:44:57.8213069Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:57.8213819Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:57.8214507Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:57.8215200Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:57.8215951Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 477, in forward
2025-03-04T22:44:57.8216726Z     box_features = self._shared_roi_transform(
2025-03-04T22:44:57.8217562Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 456, in _shared_roi_transform
2025-03-04T22:44:57.8218388Z     x = self.pooler(features, boxes)
2025-03-04T22:44:57.8219106Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:57.8219839Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:57.8220527Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:57.8221216Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:57.8221898Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 246, in forward
2025-03-04T22:44:57.8222643Z     return self.level_poolers[0](x[0], pooler_fmt_boxes)
2025-03-04T22:44:57.8223416Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:44:57.8224151Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:44:57.8225086Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:44:57.8225710Z     return forward_call(*args, **kwargs)
2025-03-04T22:44:57.8226321Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:44:57.8227207Z     return roi_align(
2025-03-04T22:44:57.8227993Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:44:57.8228875Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:44:57.8229705Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:44:57.8230507Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:44:57.8231352Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:44:57.8232024Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:44:57.8232637Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:44:57.8233255Z     return input[
2025-03-04T22:44:57.8235240Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 27475.92 GiB. GPU 0 has a total capacity of 21.98 GiB of which 20.15 GiB is free. Process 104383 has 1.81 GiB memory in use. Of the allocated memory 1.37 GiB is allocated by PyTorch, and 150.43 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)
2025-03-04T22:44:57.8237169Z 
2025-03-04T22:44:57.8237404Z The above exception was the direct cause of the following exception:
2025-03-04T22:44:57.8237737Z 
2025-03-04T22:44:57.8237860Z Traceback (most recent call last):
2025-03-04T22:44:57.8238376Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:44:57.8246634Z     ) = runner.load_model(
2025-03-04T22:44:57.8247175Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:44:57.8247888Z     self.validate_model(model, example_inputs)
2025-03-04T22:44:57.8248453Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:44:57.8249080Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:44:57.8249444Z RuntimeError: Eager run failed
2025-03-04T22:44:57.8249635Z 
2025-03-04T22:44:57.8249743Z eager_fail_to_run
2025-03-04T22:45:02.4679295Z 
2025-03-04T22:45:06.7010568Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:45:06.7010960Z loading model: 0it [00:04, ?it/s]
2025-03-04T22:45:06.7011306Z cuda eval  detectron2_fasterrcnn_r_50_dc5     
2025-03-04T22:45:06.7025536Z Traceback (most recent call last):
2025-03-04T22:45:06.7026138Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:45:06.7026718Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:45:06.7027325Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:45:06.7027885Z     return mod(*inputs)
2025-03-04T22:45:06.7028501Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:45:06.7029176Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:45:06.7029791Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:45:06.7030415Z     return forward_call(*args, **kwargs)
2025-03-04T22:45:06.7031058Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:45:06.7031758Z     return self.inference(batched_inputs)
2025-03-04T22:45:06.7032411Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:45:06.7033144Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:45:06.7034454Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:45:06.7035116Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:45:06.7035731Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:45:06.7036348Z     return forward_call(*args, **kwargs)
2025-03-04T22:45:06.7037009Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 747, in forward
2025-03-04T22:45:06.7037735Z     pred_instances = self._forward_box(features, proposals)
2025-03-04T22:45:06.7038475Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 798, in _forward_box
2025-03-04T22:45:06.7039297Z     box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])
2025-03-04T22:45:06.7040096Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:45:06.7040755Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:45:06.7041366Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:45:06.7041979Z     return forward_call(*args, **kwargs)
2025-03-04T22:45:06.7042592Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 246, in forward
2025-03-04T22:45:06.7043260Z     return self.level_poolers[0](x[0], pooler_fmt_boxes)
2025-03-04T22:45:06.7043967Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:45:06.7044628Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:45:06.7045239Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:45:06.7045857Z     return forward_call(*args, **kwargs)
2025-03-04T22:45:06.7046465Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:45:06.7047061Z     return roi_align(
2025-03-04T22:45:06.7047705Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:45:06.7048534Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:45:06.7049362Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:45:06.7050161Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:45:06.7051003Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:45:06.7051672Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:45:06.7052295Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:45:06.7052907Z     return input[
2025-03-04T22:45:06.7054890Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 13541.47 GiB. GPU 0 has a total capacity of 21.98 GiB of which 19.31 GiB is free. Process 104445 has 2.65 GiB memory in use. Of the allocated memory 2.21 GiB is allocated by PyTorch, and 119.29 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)
2025-03-04T22:45:06.7056821Z 
2025-03-04T22:45:06.7057043Z The above exception was the direct cause of the following exception:
2025-03-04T22:45:06.7057374Z 
2025-03-04T22:45:06.7057498Z Traceback (most recent call last):
2025-03-04T22:45:06.7058080Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:45:06.7058655Z     ) = runner.load_model(
2025-03-04T22:45:06.7059160Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:45:06.7059736Z     self.validate_model(model, example_inputs)
2025-03-04T22:45:06.7060303Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:45:06.7060884Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:45:06.7061255Z RuntimeError: Eager run failed
2025-03-04T22:45:06.7061448Z 
2025-03-04T22:45:06.7061556Z eager_fail_to_run
2025-03-04T22:45:11.1072095Z 
2025-03-04T22:45:13.6831564Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:45:13.6831980Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:45:13.6832341Z cuda eval  detectron2_fasterrcnn_r_50_fpn     
2025-03-04T22:45:13.6845841Z Traceback (most recent call last):
2025-03-04T22:45:13.6846431Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:45:13.6847051Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:45:13.6847732Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:45:13.6848283Z     return mod(*inputs)
2025-03-04T22:45:13.6848895Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:45:13.6849559Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:45:13.6850170Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:45:13.6850787Z     return forward_call(*args, **kwargs)
2025-03-04T22:45:13.6851425Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:45:13.6852085Z     return self.inference(batched_inputs)
2025-03-04T22:45:13.6852741Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:45:13.6853477Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:45:13.6854199Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:45:13.6854859Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:45:13.6855468Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:45:13.6856080Z     return forward_call(*args, **kwargs)
2025-03-04T22:45:13.6856755Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 747, in forward
2025-03-04T22:45:13.6857475Z     pred_instances = self._forward_box(features, proposals)
2025-03-04T22:45:13.6858213Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 798, in _forward_box
2025-03-04T22:45:13.6859038Z     box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])
2025-03-04T22:45:13.6859822Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:45:13.6860479Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:45:13.6861094Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:45:13.6861710Z     return forward_call(*args, **kwargs)
2025-03-04T22:45:13.6862319Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 261, in forward
2025-03-04T22:45:13.6863030Z     output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
2025-03-04T22:45:13.6863772Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:45:13.6864427Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:45:13.6865482Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:45:13.6866101Z     return forward_call(*args, **kwargs)
2025-03-04T22:45:13.6866694Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:45:13.6867288Z     return roi_align(
2025-03-04T22:45:13.6867850Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:45:13.6868682Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:45:13.6869514Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:45:13.6870306Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:45:13.6871150Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:45:13.6871820Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:45:13.6872423Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:45:13.6873026Z     return input[
2025-03-04T22:45:13.6875051Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 21403.23 GiB. GPU 0 has a total capacity of 21.98 GiB of which 19.47 GiB is free. Process 104509 has 2.50 GiB memory in use. Of the allocated memory 2.16 GiB is allocated by PyTorch, and 47.33 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)
2025-03-04T22:45:13.6876971Z 
2025-03-04T22:45:13.6877195Z The above exception was the direct cause of the following exception:
2025-03-04T22:45:13.6877522Z 
2025-03-04T22:45:13.6877651Z Traceback (most recent call last):
2025-03-04T22:45:13.6878138Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:45:13.6878634Z     ) = runner.load_model(
2025-03-04T22:45:13.6879130Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:45:13.6879702Z     self.validate_model(model, example_inputs)
2025-03-04T22:45:13.6880261Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:45:13.6880834Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:45:13.6881196Z RuntimeError: Eager run failed
2025-03-04T22:45:13.6881393Z 
2025-03-04T22:45:13.6881491Z eager_fail_to_run
2025-03-04T22:45:18.1361815Z 
2025-03-04T22:45:20.0084437Z loading model: 0it [00:00, ?it/s]WARNING:common:Model detectron2_fcos_r_50_fpn does not support bfloat16, running with amp instead
2025-03-04T22:45:20.8395057Z 
2025-03-04T22:45:20.8395865Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:45:20.8396439Z WARNING:common:Model detectron2_fcos_r_50_fpn does not support bfloat16, running with amp instead
2025-03-04T22:45:20.8396991Z cuda eval  detectron2_fcos_r_50_fpn           
2025-03-04T22:45:33.2803194Z WARNING:common:Model detectron2_fcos_r_50_fpn does not support bfloat16, running with amp instead
2025-03-04T22:45:43.5809372Z skipping cudagraphs due to mutated inputs (1 instances). Found from : 
2025-03-04T22:45:43.5812517Z    File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/structures/image_list.py", line 127, in torch_dynamo_resume_in_from_tensors_at_122
2025-03-04T22:45:43.5813390Z     batched_imgs[i, ..., : img.shape[-2], : img.shape[-1]].copy_(img)
2025-03-04T22:45:43.5813692Z 
2025-03-04T22:46:19.4031440Z skipping cudagraphs due to skipping cudagraphs due to cpu device (output)
2025-03-04T22:46:21.8239694Z skipping cudagraphs due to skipping cudagraphs due to cpu device (output)
2025-03-04T22:46:24.2811375Z Compilation time (from dynamo_timed): 46.328406401
2025-03-04T22:46:24.2812799Z pass
2025-03-04T22:46:24.2842787Z TIMING: _recursive_pre_grad_passes:0.02922 _recursive_joint_graph_passes:0.42144 inductor_compile:24.48361 backend_compile:33.06506 entire_frame_compile:46.32841 gc:0.02493 _recursive_post_grad_passes:0.41875 async_compile.precompile:0.38087 async_compile.wait:7.92394 code_gen:19.09201 total_wall_time:46.32841
2025-03-04T22:46:24.2844949Z STATS: call_* op count: 951 | FakeTensorMode.__torch_dispatch__:31745 | FakeTensor.__torch_dispatch__:4984 | ProxyTorchDispatchMode.__torch_dispatch__:10552
2025-03-04T22:46:24.2845908Z Dynamo produced 28 graphs covering 951 ops with 20 graph breaks (3 unique)
2025-03-04T22:46:31.0103115Z 
2025-03-04T22:46:34.1070941Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:46:34.1071368Z loading model: 0it [00:03, ?it/s]
2025-03-04T22:46:34.1071720Z cuda eval  detectron2_maskrcnn_r_101_c4       
2025-03-04T22:46:34.1085207Z Traceback (most recent call last):
2025-03-04T22:46:34.1085840Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:46:34.1086424Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:46:34.1087000Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:46:34.1087687Z     return mod(*inputs)
2025-03-04T22:46:34.1088310Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:34.1088977Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:34.1089593Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:34.1090210Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:34.1090849Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:46:34.1091511Z     return self.inference(batched_inputs)
2025-03-04T22:46:34.1092167Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:46:34.1092898Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:46:34.1093619Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:34.1094273Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:34.1094883Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:34.1095491Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:34.1096147Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 477, in forward
2025-03-04T22:46:34.1096826Z     box_features = self._shared_roi_transform(
2025-03-04T22:46:34.1097560Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 456, in _shared_roi_transform
2025-03-04T22:46:34.1098286Z     x = self.pooler(features, boxes)
2025-03-04T22:46:34.1098919Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:34.1099574Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:34.1100181Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:34.1100790Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:34.1101395Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 246, in forward
2025-03-04T22:46:34.1102051Z     return self.level_poolers[0](x[0], pooler_fmt_boxes)
2025-03-04T22:46:34.1102741Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:34.1104232Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:34.1104958Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:34.1105576Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:34.1106172Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:46:34.1106764Z     return roi_align(
2025-03-04T22:46:34.1107320Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:46:34.1108142Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:46:34.1108965Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:46:34.1109757Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:46:34.1110609Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:46:34.1111277Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:46:34.1111882Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:46:34.1112488Z     return input[
2025-03-04T22:46:34.1114467Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 26943.76 GiB. GPU 0 has a total capacity of 21.98 GiB of which 19.01 GiB is free. Process 105409 has 2.96 GiB memory in use. Of the allocated memory 2.51 GiB is allocated by PyTorch, and 154.91 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)
2025-03-04T22:46:34.1116414Z 
2025-03-04T22:46:34.1116639Z The above exception was the direct cause of the following exception:
2025-03-04T22:46:34.1116965Z 
2025-03-04T22:46:34.1117093Z Traceback (most recent call last):
2025-03-04T22:46:34.1117596Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:46:34.1118095Z     ) = runner.load_model(
2025-03-04T22:46:34.1118590Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:46:34.1119163Z     self.validate_model(model, example_inputs)
2025-03-04T22:46:34.1119727Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:46:34.1120300Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:46:34.1120664Z RuntimeError: Eager run failed
2025-03-04T22:46:34.1120861Z 
2025-03-04T22:46:34.1120960Z eager_fail_to_run
2025-03-04T22:46:38.5465916Z 
2025-03-04T22:46:41.5348272Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:46:41.5348687Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:46:41.5349068Z cuda eval  detectron2_maskrcnn_r_101_fpn      
2025-03-04T22:46:41.5362349Z Traceback (most recent call last):
2025-03-04T22:46:41.5362936Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:46:41.5363504Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:46:41.5364071Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:46:41.5364613Z     return mod(*inputs)
2025-03-04T22:46:41.5365212Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:41.5365864Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:41.5366469Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:41.5367077Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:41.5367775Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:46:41.5368812Z     return self.inference(batched_inputs)
2025-03-04T22:46:41.5369464Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:46:41.5370193Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:46:41.5370912Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:41.5371565Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:41.5372172Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:41.5372780Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:41.5373429Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 747, in forward
2025-03-04T22:46:41.5374158Z     pred_instances = self._forward_box(features, proposals)
2025-03-04T22:46:41.5374900Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 798, in _forward_box
2025-03-04T22:46:41.5375712Z     box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])
2025-03-04T22:46:41.5376490Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:41.5377144Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:41.5377751Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:41.5378360Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:41.5378967Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 261, in forward
2025-03-04T22:46:41.5379671Z     output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
2025-03-04T22:46:41.5380423Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:41.5381072Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:41.5381676Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:41.5382286Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:41.5382880Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:46:41.5383469Z     return roi_align(
2025-03-04T22:46:41.5384026Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:46:41.5384848Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:46:41.5385669Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:46:41.5386472Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:46:41.5387313Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:46:41.5387976Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:46:41.5388582Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:46:41.5389187Z     return input[
2025-03-04T22:46:41.5391247Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 19183.01 GiB. GPU 0 has a total capacity of 21.98 GiB of which 18.26 GiB is free. Process 105471 has 3.71 GiB memory in use. Of the allocated memory 3.32 GiB is allocated by PyTorch, and 95.81 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)
2025-03-04T22:46:41.5393301Z 
2025-03-04T22:46:41.5393525Z The above exception was the direct cause of the following exception:
2025-03-04T22:46:41.5393847Z 
2025-03-04T22:46:41.5393984Z Traceback (most recent call last):
2025-03-04T22:46:41.5394472Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:46:41.5394959Z     ) = runner.load_model(
2025-03-04T22:46:41.5395456Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:46:41.5396022Z     self.validate_model(model, example_inputs)
2025-03-04T22:46:41.5396581Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:46:41.5397149Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:46:41.5397508Z RuntimeError: Eager run failed
2025-03-04T22:46:41.5397717Z 
2025-03-04T22:46:41.5397814Z eager_fail_to_run
2025-03-04T22:46:46.0032211Z 
2025-03-04T22:46:48.6528295Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:46:48.6528694Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:46:48.6529038Z cuda eval  detectron2_maskrcnn_r_50_c4        
2025-03-04T22:46:48.6542581Z Traceback (most recent call last):
2025-03-04T22:46:48.6543338Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:46:48.6543946Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:46:48.6544521Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:46:48.6545067Z     return mod(*inputs)
2025-03-04T22:46:48.6545674Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:48.6546337Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:48.6546950Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:48.6547591Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:48.6548232Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:46:48.6548890Z     return self.inference(batched_inputs)
2025-03-04T22:46:48.6549540Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:46:48.6550272Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:46:48.6551045Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:48.6551700Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:48.6552310Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:48.6552925Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:48.6553583Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 477, in forward
2025-03-04T22:46:48.6554268Z     box_features = self._shared_roi_transform(
2025-03-04T22:46:48.6554999Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 456, in _shared_roi_transform
2025-03-04T22:46:48.6555723Z     x = self.pooler(features, boxes)
2025-03-04T22:46:48.6556361Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:48.6557019Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:48.6557630Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:48.6558246Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:48.6558852Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 246, in forward
2025-03-04T22:46:48.6559909Z     return self.level_poolers[0](x[0], pooler_fmt_boxes)
2025-03-04T22:46:48.6560603Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:48.6561267Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:48.6561874Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:48.6562487Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:48.6563087Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:46:48.6563679Z     return roi_align(
2025-03-04T22:46:48.6564239Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:46:48.6565065Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:46:48.6565899Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:46:48.6566696Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:46:48.6567539Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:46:48.6568384Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:46:48.6568998Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:46:48.6569609Z     return input[
2025-03-04T22:46:48.6571602Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 27336.74 GiB. GPU 0 has a total capacity of 21.98 GiB of which 20.16 GiB is free. Process 105532 has 1.81 GiB memory in use. Of the allocated memory 1.38 GiB is allocated by PyTorch, and 135.76 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)
2025-03-04T22:46:48.6573540Z 
2025-03-04T22:46:48.6573760Z The above exception was the direct cause of the following exception:
2025-03-04T22:46:48.6574090Z 
2025-03-04T22:46:48.6574212Z Traceback (most recent call last):
2025-03-04T22:46:48.6574727Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:46:48.6575230Z     ) = runner.load_model(
2025-03-04T22:46:48.6583728Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:46:48.6584360Z     self.validate_model(model, example_inputs)
2025-03-04T22:46:48.6584926Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:46:48.6585507Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:46:48.6585884Z RuntimeError: Eager run failed
2025-03-04T22:46:48.6586091Z 
2025-03-04T22:46:48.6586194Z eager_fail_to_run
2025-03-04T22:46:53.0599384Z 
2025-03-04T22:46:55.6414229Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:46:55.6414590Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:46:55.6414931Z cuda eval  detectron2_maskrcnn_r_50_fpn       
2025-03-04T22:46:55.6428785Z Traceback (most recent call last):
2025-03-04T22:46:55.6429348Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:46:55.6429948Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:46:55.6430524Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:46:55.6431067Z     return mod(*inputs)
2025-03-04T22:46:55.6431676Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:55.6432340Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:55.6433423Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:55.6434047Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:55.6434685Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T22:46:55.6435338Z     return self.inference(batched_inputs)
2025-03-04T22:46:55.6435988Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T22:46:55.6436716Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T22:46:55.6437442Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:55.6438413Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:55.6439031Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:55.6439658Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:55.6440314Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 747, in forward
2025-03-04T22:46:55.6441035Z     pred_instances = self._forward_box(features, proposals)
2025-03-04T22:46:55.6441771Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 798, in _forward_box
2025-03-04T22:46:55.6442585Z     box_features = self.box_pooler(features, [x.proposal_boxes for x in proposals])
2025-03-04T22:46:55.6443366Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:55.6444021Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:55.6444632Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:55.6445247Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:55.6445865Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 261, in forward
2025-03-04T22:46:55.6446579Z     output.index_put_((inds,), pooler(x[level], pooler_fmt_boxes_level))
2025-03-04T22:46:55.6447324Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:46:55.6448087Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:46:55.6448699Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:46:55.6449311Z     return forward_call(*args, **kwargs)
2025-03-04T22:46:55.6449911Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T22:46:55.6450502Z     return roi_align(
2025-03-04T22:46:55.6451062Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T22:46:55.6451899Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T22:46:55.6452719Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T22:46:55.6453517Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T22:46:55.6454360Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T22:46:55.6455030Z     v1 = masked_index(y_low, x_low)
2025-03-04T22:46:55.6455637Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T22:46:55.6456249Z     return input[
2025-03-04T22:46:55.6458320Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20915.45 GiB. GPU 0 has a total capacity of 21.98 GiB of which 19.45 GiB is free. Process 105593 has 2.52 GiB memory in use. Of the allocated memory 2.17 GiB is allocated by PyTorch, and 54.95 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)
2025-03-04T22:46:55.6460396Z 
2025-03-04T22:46:55.6460620Z The above exception was the direct cause of the following exception:
2025-03-04T22:46:55.6460951Z 
2025-03-04T22:46:55.6461075Z Traceback (most recent call last):
2025-03-04T22:46:55.6461567Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:46:55.6462071Z     ) = runner.load_model(
2025-03-04T22:46:55.6462574Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:46:55.6463151Z     self.validate_model(model, example_inputs)
2025-03-04T22:46:55.6463724Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:46:55.6464304Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:46:55.6464678Z RuntimeError: Eager run failed
2025-03-04T22:46:55.6464871Z 
2025-03-04T22:46:55.6464983Z eager_fail_to_run
2025-03-04T22:47:00.0860977Z 
2025-03-04T22:47:09.2242338Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:47:09.2242707Z loading model: 0it [00:09, ?it/s]
2025-03-04T22:47:09.2248429Z cuda eval  dlrm                               
2025-03-04T22:47:13.4824355Z Compilation time (from dynamo_timed): 2.617532843
2025-03-04T22:47:13.4825101Z pass
2025-03-04T22:47:13.4903650Z TIMING: _recursive_pre_grad_passes:0.00441 pad_mm_benchmark:0.44168 _recursive_joint_graph_passes:0.58636 _recursive_post_grad_passes:0.04973 async_compile.wait:0.14591 code_gen:0.33812 inductor_compile:0.61435 backend_compile:2.16195 entire_frame_compile:2.61753 gc:0.00046 total_wall_time:2.61753
2025-03-04T22:47:13.4905624Z STATS: call_* op count: 36 | FakeTensorMode.__torch_dispatch__:1912 | ProxyTorchDispatchMode.__torch_dispatch__:568 | FakeTensor.__torch_dispatch__:147
2025-03-04T22:47:13.4906467Z Dynamo produced 1 graphs covering 36 ops with 0 graph breaks (0 unique)
2025-03-04T22:47:18.7612031Z 
2025-03-04T22:47:20.2384618Z loading model: 0it [00:00, ?it/s]Downloading https://doctr-static.mindee.com/models?id=v0.7.0/db_resnet50-79bd7d70.pt&src=0 to /var/lib/jenkins/.cache/doctr/models/db_resnet50-79bd7d70.pt
2025-03-04T22:47:20.6137017Z 
2025-03-04T22:47:20.6137081Z 
2025-03-04T22:47:20.7139640Z   0% 0/102021912 [00:00<?, ?it/s]
2025-03-04T22:47:20.7140004Z 
2025-03-04T22:47:20.8139771Z  25% 25750528/102021912 [00:00<00:00, 257504911.64it/s]
2025-03-04T22:47:20.8140156Z 
2025-03-04T22:47:20.9139824Z  53% 54301696/102021912 [00:00<00:00, 273963872.07it/s]
2025-03-04T22:47:20.9140208Z 
2025-03-04T22:47:20.9783935Z  82% 83466240/102021912 [00:00<00:00, 282031377.23it/s]
2025-03-04T22:47:20.9784497Z 102022144it [00:00, 280159623.48it/s]                  
2025-03-04T22:47:22.7073421Z Downloading https://doctr-static.mindee.com/models?id=v0.3.1/crnn_vgg16_bn-9762b0b0.pt&src=0 to /var/lib/jenkins/.cache/doctr/models/crnn_vgg16_bn-9762b0b0.pt
2025-03-04T22:47:23.0954817Z 
2025-03-04T22:47:23.0954825Z 
2025-03-04T22:47:23.1954594Z   0% 0/63286381 [00:00<?, ?it/s]
2025-03-04T22:47:23.1954811Z 
2025-03-04T22:47:23.2954261Z  46% 29227008/63286381 [00:00<00:00, 292269661.90it/s]
2025-03-04T22:47:23.2954539Z 
2025-03-04T22:47:23.3064214Z  95% 60240896/63286381 [00:00<00:00, 302770514.73it/s]
2025-03-04T22:47:23.3064651Z 63287296it [00:00, 300206464.92it/s]                  
2025-03-04T22:47:23.9277030Z WARNING:common:Model doctr_det_predictor does not support bfloat16, running with amp instead
2025-03-04T22:47:24.4553977Z 
2025-03-04T22:47:24.4554359Z loading model: 0it [00:05, ?it/s]
2025-03-04T22:47:24.4554898Z WARNING:common:Model doctr_det_predictor does not support bfloat16, running with amp instead
2025-03-04T22:47:24.4555713Z cuda eval  doctr_det_predictor                
2025-03-04T22:47:32.6339904Z WARNING:common:Model doctr_det_predictor does not support bfloat16, running with amp instead
2025-03-04T22:47:56.9500487Z Compilation time (from dynamo_timed): 21.452997848
2025-03-04T22:47:56.9500883Z pass
2025-03-04T22:47:56.9530609Z TIMING: _recursive_pre_grad_passes:0.01032 _recursive_joint_graph_passes:0.43304 inductor_compile:6.8659 backend_compile:12.40002 entire_frame_compile:21.453 gc:0.00355 _recursive_post_grad_passes:0.18929 async_compile.wait:0.11396 code_gen:3.59671 total_wall_time:21.453
2025-03-04T22:47:56.9532153Z STATS: call_* op count: 219 | FakeTensorMode.__torch_dispatch__:19827 | ProxyTorchDispatchMode.__torch_dispatch__:7726 | FakeTensor.__torch_dispatch__:3114
2025-03-04T22:47:56.9533006Z Dynamo produced 3 graphs covering 219 ops with 4 graph breaks (3 unique)
2025-03-04T22:48:02.9490406Z 
2025-03-04T22:48:06.2564325Z loading model: 0it [00:00, ?it/s]WARNING:common:Model doctr_reco_predictor does not support bfloat16, running with amp instead
2025-03-04T22:48:06.6504965Z 
2025-03-04T22:48:06.6505920Z loading model: 0it [00:03, ?it/s]
2025-03-04T22:48:06.6506548Z WARNING:common:Model doctr_reco_predictor does not support bfloat16, running with amp instead
2025-03-04T22:48:06.6507130Z cuda eval  doctr_reco_predictor               
2025-03-04T22:48:06.8147932Z WARNING:common:Model doctr_reco_predictor does not support bfloat16, running with amp instead
2025-03-04T22:48:13.7401309Z Compilation time (from dynamo_timed): 5.315782829
2025-03-04T22:48:13.7401709Z pass
2025-03-04T22:48:13.7410126Z TIMING: _recursive_pre_grad_passes:0.00508 _recursive_joint_graph_passes:0.14788 inductor_compile:1.78911 backend_compile:3.45982 entire_frame_compile:5.31578 gc:0.00132 _recursive_post_grad_passes:0.04305 async_compile.wait:0.10356 code_gen:0.91171 total_wall_time:5.31578
2025-03-04T22:48:13.7411653Z STATS: call_* op count: 48 | FakeTensorMode.__torch_dispatch__:4336 | ProxyTorchDispatchMode.__torch_dispatch__:1491 | FakeTensor.__torch_dispatch__:601
2025-03-04T22:48:13.7412518Z Dynamo produced 2 graphs covering 48 ops with 1 graph breaks (1 unique)
2025-03-04T22:48:19.1878838Z 
2025-03-04T22:48:20.9471182Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:48:20.9471533Z loading model: 0it [00:01, ?it/s]
2025-03-04T22:48:20.9476426Z cuda eval  drq                                
2025-03-04T22:48:25.6898487Z Compilation time (from dynamo_timed): 3.473373458
2025-03-04T22:48:25.6898872Z pass
2025-03-04T22:48:25.6901928Z TIMING: _recursive_pre_grad_passes:0.0049 pad_mm_benchmark:0.23727 _recursive_joint_graph_passes:0.37852 _recursive_post_grad_passes:0.03397 async_compile.precompile:0.23567 async_compile.wait:0.82174 code_gen:1.12422 inductor_compile:1.34452 backend_compile:2.84439 entire_frame_compile:3.47337 gc:0.0007 total_wall_time:3.47337
2025-03-04T22:48:25.6903643Z STATS: call_* op count: 58 | FakeTensorMode.__torch_dispatch__:1275 | FakeTensor.__torch_dispatch__:115 | ProxyTorchDispatchMode.__torch_dispatch__:328
2025-03-04T22:48:25.6904497Z Dynamo produced 2 graphs covering 58 ops with 0 graph breaks (0 unique)
2025-03-04T22:48:31.1360301Z 
2025-03-04T22:48:33.0106737Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:48:33.0107496Z loading model: 0it [00:01, ?it/s]
2025-03-04T22:48:33.0161454Z cuda eval  fastNLP_Bert                       
2025-03-04T22:48:36.1527935Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break from `Tensor.item()`, consider setting:
2025-03-04T22:48:36.1528965Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-04T22:48:36.1529783Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] or:
2025-03-04T22:48:36.1530573Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-04T22:48:36.1532007Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] to include these operations in the captured graph.
2025-03-04T22:48:36.1533027Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-04T22:48:36.1533764Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] Graph break: from user code at:
2025-03-04T22:48:36.1534861Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 455, in forward_pass
2025-03-04T22:48:36.1535919Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     return mod(*inputs)
2025-03-04T22:48:36.1537034Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/fastNLP/models/bert.py", line 265, in forward
2025-03-04T22:48:36.1538171Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     sequence_output = self.bert(words)
2025-03-04T22:48:36.1539393Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/fastNLP/embeddings/bert_embedding.py", line 137, in forward
2025-03-04T22:48:36.1540572Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     outputs = self.model(words)
2025-03-04T22:48:36.1541752Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/fastNLP/embeddings/bert_embedding.py", line 445, in forward
2025-03-04T22:48:36.1543638Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0]     max_word_piece_length = batch_word_pieces_length.sum(dim=-1).max().item()  # 表示word piece的长度(包括padding)
2025-03-04T22:48:36.1544617Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-04T22:48:36.1545258Z W0304 22:48:36.151000 48344 site-packages/torch/_dynamo/variables/tensor.py:913] [0/0] 
2025-03-04T22:48:49.2230367Z Compilation time (from dynamo_timed): 11.797823602
2025-03-04T22:48:49.2230780Z pass
2025-03-04T22:48:49.2242606Z TIMING: entire_frame_compile:11.79782 gc:0.00339 _recursive_pre_grad_passes:0.01064 _recursive_joint_graph_passes:1.00994 _recursive_post_grad_passes:0.29527 async_compile.precompile:0.22618 async_compile.wait:0.23384 code_gen:1.80432 inductor_compile:4.0003 backend_compile:8.68041 pad_mm_benchmark:0.41629 total_wall_time:11.79782
2025-03-04T22:48:49.2244360Z STATS: call_* op count: 440 | FakeTensorMode.__torch_dispatch__:16443 | ProxyTorchDispatchMode.__torch_dispatch__:6925 | FakeTensor.__torch_dispatch__:1763
2025-03-04T22:48:49.2245259Z Dynamo produced 4 graphs covering 440 ops with 4 graph breaks (1 unique)
2025-03-04T22:48:54.8346652Z 
2025-03-04T22:48:55.8008184Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:48:55.8008708Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:48:55.8024508Z cuda eval  functorch_dp_cifar10               
2025-03-04T22:49:03.2122792Z Compilation time (from dynamo_timed): 5.629436983
2025-03-04T22:49:03.2123190Z pass
2025-03-04T22:49:03.2127143Z TIMING: _recursive_pre_grad_passes:0.00457 pad_mm_benchmark:0.09817 _recursive_joint_graph_passes:0.45014 _recursive_post_grad_passes:0.05132 async_compile.wait:0.07452 code_gen:1.75483 inductor_compile:2.72956 backend_compile:4.67074 entire_frame_compile:5.62944 gc:0.00049 total_wall_time:5.62944
2025-03-04T22:49:03.2128835Z STATS: call_* op count: 69 | FakeTensorMode.__torch_dispatch__:3555 | ProxyTorchDispatchMode.__torch_dispatch__:1397 | FakeTensor.__torch_dispatch__:751
2025-03-04T22:49:03.2129659Z Dynamo produced 1 graphs covering 69 ops with 0 graph breaks (0 unique)
2025-03-04T22:49:08.6314444Z 
2025-03-04T22:49:09.2917747Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:49:09.2918123Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:49:09.2922207Z cuda eval  functorch_maml_omniglot            
2025-03-04T22:49:13.4820469Z Compilation time (from dynamo_timed): 2.607489532
2025-03-04T22:49:13.4820882Z pass
2025-03-04T22:49:13.4822016Z TIMING: _recursive_pre_grad_passes:0.00365 pad_mm_benchmark:0.0736 _recursive_joint_graph_passes:0.20978 _recursive_post_grad_passes:0.01553 async_compile.wait:0.33214 code_gen:0.91321 inductor_compile:1.19733 backend_compile:2.29957 entire_frame_compile:2.60749 gc:0.00086 total_wall_time:2.60749
2025-03-04T22:49:13.4823630Z STATS: call_* op count: 14 | FakeTensorMode.__torch_dispatch__:1155 | ProxyTorchDispatchMode.__torch_dispatch__:282 | FakeTensor.__torch_dispatch__:118
2025-03-04T22:49:13.4824471Z Dynamo produced 1 graphs covering 14 ops with 0 graph breaks (0 unique)
2025-03-04T22:49:18.7071188Z 
2025-03-04T22:49:20.3549969Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:49:20.3550876Z loading model: 0it [00:01, ?it/s]
2025-03-04T22:49:20.3557439Z cuda eval  hf_Albert                          
2025-03-04T22:49:32.5732756Z Compilation time (from dynamo_timed): 8.478502833
2025-03-04T22:49:32.5733152Z pass
2025-03-04T22:49:32.5878989Z TIMING: _recursive_pre_grad_passes:0.00879 pad_mm_benchmark:0.0456 _recursive_joint_graph_passes:0.79761 _recursive_post_grad_passes:0.29391 async_compile.wait:0.15343 code_gen:1.51837 inductor_compile:3.25419 backend_compile:6.72516 entire_frame_compile:8.4785 gc:0.00035 total_wall_time:8.4785
2025-03-04T22:49:32.5880684Z STATS: call_* op count: 436 | FakeTensorMode.__torch_dispatch__:14198 | FakeTensor.__torch_dispatch__:1489 | ProxyTorchDispatchMode.__torch_dispatch__:6073
2025-03-04T22:49:32.5881528Z Dynamo produced 1 graphs covering 436 ops with 0 graph breaks (0 unique)
2025-03-04T22:49:38.1382125Z 
2025-03-04T22:49:42.5124401Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:49:42.5124956Z loading model: 0it [00:04, ?it/s]
2025-03-04T22:49:42.5187454Z cuda eval  hf_Bart                            
2025-03-04T22:50:00.6516633Z Compilation time (from dynamo_timed): 13.140838765
2025-03-04T22:50:00.6517184Z pass
2025-03-04T22:50:00.6771930Z TIMING: _recursive_pre_grad_passes:0.00992 pad_mm_benchmark:0.19412 _recursive_joint_graph_passes:0.70982 _recursive_post_grad_passes:0.64474 async_compile.wait:0.08582 code_gen:2.33724 inductor_compile:5.04181 backend_compile:9.87804 entire_frame_compile:13.14084 gc:0.0008 total_wall_time:13.14084
2025-03-04T22:50:00.6773563Z STATS: call_* op count: 451 | FakeTensorMode.__torch_dispatch__:17919 | FakeTensor.__torch_dispatch__:2365 | ProxyTorchDispatchMode.__torch_dispatch__:7713
2025-03-04T22:50:00.6774410Z Dynamo produced 1 graphs covering 451 ops with 0 graph breaks (0 unique)
2025-03-04T22:50:06.3776634Z 
2025-03-04T22:50:09.3644266Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:50:09.3644636Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:50:09.3697258Z cuda eval  hf_Bert                            
2025-03-04T22:50:24.6658311Z Compilation time (from dynamo_timed): 11.11063402
2025-03-04T22:50:24.6658716Z pass
2025-03-04T22:50:24.6792602Z TIMING: _recursive_pre_grad_passes:0.00849 pad_mm_benchmark:0.16177 _recursive_joint_graph_passes:0.71848 _recursive_post_grad_passes:0.30197 async_compile.wait:0.15277 code_gen:1.51343 inductor_compile:3.38273 backend_compile:7.83902 entire_frame_compile:11.11063 gc:0.00131 total_wall_time:11.11063
2025-03-04T22:50:24.6794225Z STATS: call_* op count: 367 | FakeTensorMode.__torch_dispatch__:15513 | FakeTensor.__torch_dispatch__:1780 | ProxyTorchDispatchMode.__torch_dispatch__:6647
2025-03-04T22:50:24.6795063Z Dynamo produced 1 graphs covering 367 ops with 0 graph breaks (0 unique)
2025-03-04T22:50:30.3454232Z 
2025-03-04T22:50:35.4675460Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:50:35.4675844Z loading model: 0it [00:05, ?it/s]
2025-03-04T22:50:35.4770166Z cuda eval  hf_Bert_large                      
2025-03-04T22:51:04.0383521Z Compilation time (from dynamo_timed): 20.386936301
2025-03-04T22:51:04.0385410Z pass
2025-03-04T22:51:04.0589672Z TIMING: _recursive_pre_grad_passes:0.01344 pad_mm_benchmark:0.21393 _recursive_joint_graph_passes:1.17798 _recursive_post_grad_passes:0.58484 async_compile.wait:0.10951 code_gen:2.17269 inductor_compile:6.00344 backend_compile:14.02978 entire_frame_compile:20.38694 gc:7e-05 total_wall_time:20.38694
2025-03-04T22:51:04.0593226Z STATS: call_* op count: 715 | FakeTensorMode.__torch_dispatch__:29853 | FakeTensor.__torch_dispatch__:3448 | ProxyTorchDispatchMode.__torch_dispatch__:13007
2025-03-04T22:51:04.0594396Z Dynamo produced 1 graphs covering 715 ops with 0 graph breaks (0 unique)
2025-03-04T22:51:09.9803482Z 
2025-03-04T22:51:13.6423784Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:51:13.6424479Z loading model: 0it [00:03, ?it/s]
2025-03-04T22:51:13.6474660Z cuda eval  hf_BigBird                         
2025-03-04T22:52:04.3487109Z W0304 22:52:04.347000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:04.8864075Z W0304 22:52:04.885000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:05.5448499Z W0304 22:52:05.543000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:05.8070993Z W0304 22:52:05.806000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:06.0726962Z W0304 22:52:06.072000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:06.3363088Z W0304 22:52:06.335000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:06.5967320Z W0304 22:52:06.596000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:06.8579303Z W0304 22:52:06.857000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:07.1202278Z W0304 22:52:07.119000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:07.3806685Z W0304 22:52:07.379000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:07.6406726Z W0304 22:52:07.640000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:07.9011022Z W0304 22:52:07.900000 49413 site-packages/torch/_inductor/utils.py:1714] [0/0] DeviceCopy in input program
2025-03-04T22:52:28.4343255Z skipping cudagraphs due to skipping cudagraphs due to cpu device (cat_1). Found from : 
2025-03-04T22:52:28.4344238Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 453, in forward_pass
2025-03-04T22:52:28.4344797Z     return mod(**inputs)
2025-03-04T22:52:28.4345474Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 2450, in forward
2025-03-04T22:52:28.4346176Z     outputs = self.bert(
2025-03-04T22:52:28.4346820Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 2133, in forward
2025-03-04T22:52:28.4347555Z     encoder_outputs = self.encoder(
2025-03-04T22:52:28.4348247Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 1631, in forward
2025-03-04T22:52:28.4348940Z     layer_outputs = layer_module(
2025-03-04T22:52:28.4349610Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 1488, in forward
2025-03-04T22:52:28.4350321Z     self_attention_outputs = self.attention(
2025-03-04T22:52:28.4351028Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 1401, in forward
2025-03-04T22:52:28.4351719Z     self_outputs = self.self(
2025-03-04T22:52:28.4352375Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 472, in forward
2025-03-04T22:52:28.4353191Z     context_layer, attention_probs = self.bigbird_block_sparse_attention(
2025-03-04T22:52:28.4354586Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 593, in bigbird_block_sparse_attention
2025-03-04T22:52:28.4363111Z     rand_attn = np.stack(rand_attn, axis=0)
2025-03-04T22:52:28.4363352Z 
2025-03-04T22:52:30.1372647Z Compilation time (from dynamo_timed): 51.99615915
2025-03-04T22:52:30.1373212Z fail_accuracy
2025-03-04T22:52:30.1553492Z TIMING: _recursive_pre_grad_passes:0.04641 pad_mm_benchmark:0.97906 _recursive_joint_graph_passes:2.9186 _recursive_post_grad_passes:1.52206 async_compile.wait:1.86466 code_gen:19.59576 inductor_compile:28.86665 backend_compile:44.49549 entire_frame_compile:51.99616 gc:0.00032 total_wall_time:51.99616
2025-03-04T22:52:30.1555978Z STATS: call_* op count: 3347 | FakeTensorMode.__torch_dispatch__:83870 | FakeTensor.__torch_dispatch__:6691 | ProxyTorchDispatchMode.__torch_dispatch__:30625
2025-03-04T22:52:30.1557231Z Dynamo produced 1 graphs covering 3347 ops with 0 graph breaks (0 unique)
2025-03-04T22:52:37.1277378Z 
2025-03-04T22:52:39.6128810Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:52:39.6129212Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:52:39.6154169Z cuda eval  hf_DistilBert                      
2025-03-04T22:52:49.8217557Z Compilation time (from dynamo_timed): 7.121307615
2025-03-04T22:52:49.8217941Z pass
2025-03-04T22:52:49.8363904Z TIMING: _recursive_pre_grad_passes:0.00666 pad_mm_benchmark:0.05672 _recursive_joint_graph_passes:0.40452 _recursive_post_grad_passes:0.29204 async_compile.wait:0.1673 code_gen:1.27003 inductor_compile:2.89674 backend_compile:5.41227 entire_frame_compile:7.12131 gc:0.00069 total_wall_time:7.12131
2025-03-04T22:52:49.8365577Z STATS: call_* op count: 203 | FakeTensorMode.__torch_dispatch__:8688 | FakeTensor.__torch_dispatch__:1122 | ProxyTorchDispatchMode.__torch_dispatch__:3590
2025-03-04T22:52:49.8366414Z Dynamo produced 1 graphs covering 203 ops with 0 graph breaks (0 unique)
2025-03-04T22:52:55.3644874Z 
2025-03-04T22:52:58.8552088Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:52:58.8552496Z loading model: 0it [00:03, ?it/s]
2025-03-04T22:52:58.8594640Z cuda eval  hf_GPT2                            
2025-03-04T22:53:13.5717379Z Compilation time (from dynamo_timed): 10.024809075
2025-03-04T22:53:13.5718653Z pass
2025-03-04T22:53:13.6108913Z TIMING: _recursive_pre_grad_passes:0.0122 pad_mm_benchmark:0.16773 _recursive_joint_graph_passes:0.70724 _recursive_post_grad_passes:0.28314 async_compile.wait:0.13666 code_gen:1.34482 inductor_compile:3.08839 backend_compile:7.22956 entire_frame_compile:10.02481 gc:0.00012 total_wall_time:10.02481
2025-03-04T22:53:13.6110535Z STATS: call_* op count: 622 | FakeTensorMode.__torch_dispatch__:14238 | FakeTensor.__torch_dispatch__:1611 | ProxyTorchDispatchMode.__torch_dispatch__:6105
2025-03-04T22:53:13.6111432Z Dynamo produced 1 graphs covering 622 ops with 0 graph breaks (0 unique)
2025-03-04T22:53:19.2577577Z 
2025-03-04T22:53:32.6676101Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:53:32.6676504Z loading model: 0it [00:13, ?it/s]
2025-03-04T22:53:32.6787493Z cuda eval  hf_GPT2_large                      
2025-03-04T22:53:32.7049588Z Compilation time (from dynamo_timed): 0
2025-03-04T22:53:32.7049954Z pass_due_to_skip
2025-03-04T22:53:33.0351362Z TIMING: total_wall_time:0
2025-03-04T22:53:33.0351694Z STATS: call_* op count: 0
2025-03-04T22:53:33.0352110Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-04T22:53:37.4097129Z 
2025-03-04T22:53:39.0354416Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:53:39.0355071Z loading model: 0it [00:01, ?it/s]
2025-03-04T22:53:39.0375755Z cuda eval  hf_Reformer                        
2025-03-04T22:53:41.5830746Z 
2025-03-04T22:53:41.5831676Z class GraphModule(torch.nn.Module):
2025-03-04T22:53:41.5832448Z     def forward(self, L_inputs_input_ids_: "i64[4, 4096][4096, 1]cuda:0"):
2025-03-04T22:53:41.5833186Z         l_inputs_input_ids_ = L_inputs_input_ids_
2025-03-04T22:53:41.5833746Z         
2025-03-04T22:53:41.5835973Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-04T22:53:41.5838238Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-04T22:53:41.5838929Z         
2025-03-04T22:53:41.5840166Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-04T22:53:41.5842133Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(4096, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-04T22:53:41.5843453Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-04T22:53:41.5844263Z         
2025-03-04T22:53:41.5844487Z 
2025-03-04T22:53:41.5844681Z class GraphModule(torch.nn.Module):
2025-03-04T22:53:41.5845371Z     def forward(self, L_inputs_input_ids_: "i64[4, 4096][4096, 1]cuda:0"):
2025-03-04T22:53:41.5846008Z         l_inputs_input_ids_ = L_inputs_input_ids_
2025-03-04T22:53:41.5846497Z         
2025-03-04T22:53:41.5848175Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-04T22:53:41.5850107Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-04T22:53:41.5850839Z         
2025-03-04T22:53:41.5852127Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-04T22:53:41.5854088Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(4096, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-04T22:53:41.5855500Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-04T22:53:41.5856340Z         
2025-03-04T22:53:41.6332503Z 
2025-03-04T22:53:41.6333284Z class GraphModule(torch.nn.Module):
2025-03-04T22:53:41.6333949Z     def forward(self, L_input_ids_: "i64[4, 4096][4096, 1]cuda:0"):
2025-03-04T22:53:41.6334573Z         l_input_ids_ = L_input_ids_
2025-03-04T22:53:41.6335008Z         
2025-03-04T22:53:41.6336042Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-04T22:53:41.6337323Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-04T22:53:41.6337783Z         
2025-03-04T22:53:41.6338634Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-04T22:53:41.6339853Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(4096, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-04T22:53:41.6340685Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-04T22:53:41.6341197Z         
2025-03-04T22:53:46.3571670Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] Graph break from `Tensor.item()`, consider setting:
2025-03-04T22:53:46.3572694Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-04T22:53:46.3573522Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] or:
2025-03-04T22:53:46.3574708Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-04T22:53:46.3575837Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] to include these operations in the captured graph.
2025-03-04T22:53:46.3576636Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-04T22:53:46.3577376Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] Graph break: from user code at:
2025-03-04T22:53:46.3578769Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 2094, in torch_dynamo_resume_in_forward_at_2066
2025-03-04T22:53:46.3580191Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     embedding_output = self.embeddings(
2025-03-04T22:53:46.3581487Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 266, in forward
2025-03-04T22:53:46.3582848Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     position_embeddings = self.position_embeddings(position_ids)
2025-03-04T22:53:46.3584218Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 190, in forward
2025-03-04T22:53:46.3585501Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0]     max_position_id = position_ids.max().item()
2025-03-04T22:53:46.3586279Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-04T22:53:46.3586898Z W0304 22:53:46.356000 50107 site-packages/torch/_dynamo/variables/tensor.py:913] [4/0] 
2025-03-04T22:53:55.7693076Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter.src Found from   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 2101, in torch_dynamo_resume_in_forward_at_2094
2025-03-04T22:53:55.7694368Z     encoder_outputs = self.encoder(
2025-03-04T22:53:55.7695068Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1727, in forward
2025-03-04T22:53:55.7695805Z     hidden_states = _ReversibleFunction.apply(
2025-03-04T22:53:55.7696530Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1615, in forward
2025-03-04T22:53:55.7697228Z     layer_outputs = layer(
2025-03-04T22:53:55.7697882Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1480, in forward
2025-03-04T22:53:55.7698596Z     attn_outputs = self.attention(
2025-03-04T22:53:55.7699285Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1313, in forward
2025-03-04T22:53:55.7700014Z     self_attention_outputs = self.self_attention(
2025-03-04T22:53:55.7700737Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 482, in forward
2025-03-04T22:53:55.7701630Z     sorted_bucket_idx, undo_sorted_bucket_idx = self._get_sorted_bucket_idx_and_undo_sorted_bucket_idx(
2025-03-04T22:53:55.7702679Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 702, in _get_sorted_bucket_idx_and_undo_sorted_bucket_idx
2025-03-04T22:53:55.7703610Z     undo_sorted_bucket_idx.scatter_(-1, sorted_bucket_idx, indices)
2025-03-04T22:53:55.7703917Z 
2025-03-04T22:53:55.7703922Z 
2025-03-04T22:53:57.8575009Z Compilation time (from dynamo_timed): 14.78403816
2025-03-04T22:53:57.8575928Z pass
2025-03-04T22:53:57.8635903Z TIMING: entire_frame_compile:14.78404 gc:0.0064 _recursive_pre_grad_passes:0.01533 _recursive_joint_graph_passes:0.42699 _recursive_post_grad_passes:0.17742 async_compile.wait:2.62458 code_gen:7.01153 inductor_compile:9.62745 backend_compile:12.15434 pad_mm_benchmark:0.0296 total_wall_time:14.78404
2025-03-04T22:53:57.8637524Z STATS: call_* op count: 482 | FakeTensorMode.__torch_dispatch__:12914 | ProxyTorchDispatchMode.__torch_dispatch__:4923 | FakeTensor.__torch_dispatch__:1227
2025-03-04T22:53:57.8638366Z Dynamo produced 7 graphs covering 482 ops with 5 graph breaks (2 unique)
2025-03-04T22:54:03.5578475Z 
2025-03-04T22:54:10.2797572Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:54:10.2798032Z loading model: 0it [00:06, ?it/s]
2025-03-04T22:54:10.2848263Z cuda eval  hf_Roberta_base                    
2025-03-04T22:54:29.0630453Z Compilation time (from dynamo_timed): 11.4485048
2025-03-04T22:54:29.0630989Z pass
2025-03-04T22:54:29.0920038Z TIMING: _recursive_pre_grad_passes:0.00879 pad_mm_benchmark:0.48362 _recursive_joint_graph_passes:1.04058 _recursive_post_grad_passes:0.30347 async_compile.wait:0.15101 code_gen:1.51978 inductor_compile:3.39899 backend_compile:8.18296 entire_frame_compile:11.4485 gc:0.00022 total_wall_time:11.4485
2025-03-04T22:54:29.0921667Z STATS: call_* op count: 374 | FakeTensorMode.__torch_dispatch__:15609 | FakeTensor.__torch_dispatch__:1796 | ProxyTorchDispatchMode.__torch_dispatch__:6686
2025-03-04T22:54:29.0922509Z Dynamo produced 1 graphs covering 374 ops with 0 graph breaks (0 unique)
2025-03-04T22:54:34.7420052Z 
2025-03-04T22:54:37.2122790Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:54:37.2123527Z loading model: 0it [00:02, ?it/s]
2025-03-04T22:54:37.2160650Z cuda eval  hf_T5                              
2025-03-04T22:55:02.0084808Z Compilation time (from dynamo_timed): 13.370944723
2025-03-04T22:55:02.0085211Z pass
2025-03-04T22:55:02.0555196Z TIMING: _recursive_pre_grad_passes:0.01433 pad_mm_benchmark:0.07631 _recursive_joint_graph_passes:0.71836 _recursive_post_grad_passes:0.12346 async_compile.wait:0.44207 code_gen:2.69518 inductor_compile:5.14093 backend_compile:10.11928 entire_frame_compile:13.37094 gc:0.00099 total_wall_time:13.37094
2025-03-04T22:55:02.0556908Z STATS: call_* op count: 827 | FakeTensorMode.__torch_dispatch__:21630 | ProxyTorchDispatchMode.__torch_dispatch__:8376 | FakeTensor.__torch_dispatch__:2040
2025-03-04T22:55:02.0557762Z Dynamo produced 1 graphs covering 827 ops with 0 graph breaks (0 unique)
2025-03-04T22:55:07.7423893Z 
2025-03-04T22:55:12.5307591Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:55:12.5308146Z loading model: 0it [00:04, ?it/s]
2025-03-04T22:55:12.5308573Z cuda eval  hf_T5_base                         
2025-03-04T22:55:12.5320200Z Traceback (most recent call last):
2025-03-04T22:55:12.5321004Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T22:55:12.5321594Z     self.model_iter_fn(model, example_inputs)
2025-03-04T22:55:12.5322223Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 453, in forward_pass
2025-03-04T22:55:12.5322962Z     return mod(**inputs)
2025-03-04T22:55:12.5323760Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:55:12.5324676Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:55:12.5325634Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:55:12.5326254Z     return forward_call(*args, **kwargs)
2025-03-04T22:55:12.5326898Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1748, in forward
2025-03-04T22:55:12.5327707Z     decoder_outputs = self.decoder(
2025-03-04T22:55:12.5328345Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:55:12.5329003Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:55:12.5330291Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:55:12.5330907Z     return forward_call(*args, **kwargs)
2025-03-04T22:55:12.5331550Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1115, in forward
2025-03-04T22:55:12.5332196Z     layer_outputs = layer_module(
2025-03-04T22:55:12.5332825Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:55:12.5333478Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:55:12.5334086Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:55:12.5334697Z     return forward_call(*args, **kwargs)
2025-03-04T22:55:12.5335331Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 695, in forward
2025-03-04T22:55:12.5335994Z     self_attention_outputs = self.layer[0](
2025-03-04T22:55:12.5336648Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:55:12.5337304Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:55:12.5337908Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:55:12.5338518Z     return forward_call(*args, **kwargs)
2025-03-04T22:55:12.5339156Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 602, in forward
2025-03-04T22:55:12.5339818Z     attention_output = self.SelfAttention(
2025-03-04T22:55:12.5340474Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T22:55:12.5341129Z     return self._call_impl(*args, **kwargs)
2025-03-04T22:55:12.5341739Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T22:55:12.5342363Z     return forward_call(*args, **kwargs)
2025-03-04T22:55:12.5343002Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 562, in forward
2025-03-04T22:55:12.5343757Z     attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:12.5345963Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU 0 has a total capacity of 21.98 GiB of which 58.44 MiB is free. Process 109319 has 21.91 GiB memory in use. Of the allocated memory 20.94 GiB is allocated by PyTorch, and 666.49 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)
2025-03-04T22:55:12.5347891Z 
2025-03-04T22:55:12.5348113Z The above exception was the direct cause of the following exception:
2025-03-04T22:55:12.5348452Z 
2025-03-04T22:55:12.5348575Z Traceback (most recent call last):
2025-03-04T22:55:12.5349066Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T22:55:12.5349567Z     ) = runner.load_model(
2025-03-04T22:55:12.5350072Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T22:55:12.5350647Z     self.validate_model(model, example_inputs)
2025-03-04T22:55:12.5351213Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T22:55:12.5351787Z     raise RuntimeError("Eager run failed") from e
2025-03-04T22:55:12.5352157Z RuntimeError: Eager run failed
2025-03-04T22:55:12.5352350Z 
2025-03-04T22:55:12.5352458Z eager_fail_to_run
2025-03-04T22:55:16.9364298Z 
2025-03-04T22:55:24.7006973Z loading model: 0it [00:00, ?it/s]
2025-03-04T22:55:24.7007812Z loading model: 0it [00:07, ?it/s]
2025-03-04T22:55:24.7043368Z cuda eval  hf_T5_generate                     
2025-03-04T22:55:54.2560082Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0] Graph break from `Tensor.item()`, consider setting:
2025-03-04T22:55:54.2561123Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-04T22:55:54.2561956Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0] or:
2025-03-04T22:55:54.2562844Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-04T22:55:54.2564138Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0] to include these operations in the captured graph.
2025-03-04T22:55:54.2564953Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0] 
2025-03-04T22:55:54.2565742Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0] Graph break: from user code at:
2025-03-04T22:55:54.2567060Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py", line 1392, in torch_dynamo_resume_in_generate_at_1348
2025-03-04T22:55:54.2568666Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0]     model_kwargs["attention_mask"] = self._prepare_attention_mask_for_generation(
2025-03-04T22:55:54.2570126Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py", line 476, in _prepare_attention_mask_for_generation
2025-03-04T22:55:54.2571566Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0]     is_pad_token_in_inputs = (pad_token_id is not None) and (pad_token_id in inputs)
2025-03-04T22:55:54.2572473Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0] 
2025-03-04T22:55:54.2573090Z W0304 22:55:54.254000 50884 site-packages/torch/_dynamo/variables/tensor.py:913] [3/0] 
2025-03-04T22:55:57.6324317Z 
2025-03-04T22:55:57.6326146Z class GraphModule(torch.nn.Module):
2025-03-04T22:55:57.6402422Z     def forward(self, L_stack0_: "i64[4, 2048][2048, 1]cuda:0", L_inputs_tensor_: "i64[4, 2048][2048, 1]cuda:0", L_self_modules_encoder_modules_embed_tokens_parameters_weight_: "bf16[32128, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_relative_attention_bias_parameters_weight_: "bf16[32, 8][8, 1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_encoder_modules_final_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_relative_attention_bias_parameters_weight_: "bf16[32, 8][8, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_: "bf16[512, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_layer_norm_parameters_weight_: "bf16[512][1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_: "bf16[2048, 512][512, 1]cuda:0", L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_: "bf16[512, 2048][2048, 1]cuda:0", L_self_modules_decoder_modules_final_layer_norm_parameters_weight_: "bf16[512][1]cuda:0"):
2025-03-04T22:55:57.6466660Z         l_stack0_ = L_stack0_
2025-03-04T22:55:57.6466974Z         l_inputs_tensor_ = L_inputs_tensor_
2025-03-04T22:55:57.6467617Z         l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = L_self_modules_encoder_modules_embed_tokens_parameters_weight_
2025-03-04T22:55:57.6468861Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6470499Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6472227Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6473958Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6475858Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_relative_attention_bias_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_relative_attention_bias_parameters_weight_
2025-03-04T22:55:57.6477745Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6479420Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6481206Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6483066Z         l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6484706Z         l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6486337Z         l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6488192Z         l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6489921Z         l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6491644Z         l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6493268Z         l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6494905Z         l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6496647Z         l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6498285Z         l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6499911Z         l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6501638Z         l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6503356Z         l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6505077Z         l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6506787Z         l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6508489Z         l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6510234Z         l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6511864Z         l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6513518Z         l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6527146Z         l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6528994Z         l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6530739Z         l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6532368Z         l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6534008Z         l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6535750Z         l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6537392Z         l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6539069Z         l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6540789Z         l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6542505Z         l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6544518Z         l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6546140Z         l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6547772Z         l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6549521Z         l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6551164Z         l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6552786Z         l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6554506Z         l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6556232Z         l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6557951Z         l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6559569Z         l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6561205Z         l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6562951Z         l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6564301Z         l_self_modules_encoder_modules_final_layer_norm_parameters_weight_ = L_self_modules_encoder_modules_final_layer_norm_parameters_weight_
2025-03-04T22:55:57.6565548Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6567165Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6569246Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6570976Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6572862Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_relative_attention_bias_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_relative_attention_bias_parameters_weight_
2025-03-04T22:55:57.6574760Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6576389Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6578030Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6579784Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6581537Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6583287Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6584922Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6586552Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6588352Z         l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6589981Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6591604Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6593403Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6595196Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6596913Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6598541Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6600189Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6601945Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6603691Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6605446Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6607093Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6608878Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6610615Z         l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6612253Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6613884Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6615609Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6617325Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6619279Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6620901Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6622538Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6624286Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6626309Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6628058Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6629693Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6631335Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6633081Z         l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6634713Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6636334Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6638097Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6639827Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6641545Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6643161Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6645065Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6647038Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6648931Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6650700Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6652335Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6653959Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6655694Z         l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6657323Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6658989Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6660708Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6662425Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6664152Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6665771Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6667407Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6669295Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6671114Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6672854Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6674484Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6676118Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6677858Z         l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6679484Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6681111Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6682834Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6684551Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6686275Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_SelfAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6687977Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6689621Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_EncDecAttention_modules_q_parameters_weight_
2025-03-04T22:55:57.6691368Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_EncDecAttention_modules_k_parameters_weight_
2025-03-04T22:55:57.6693114Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_EncDecAttention_modules_v_parameters_weight_
2025-03-04T22:55:57.6695018Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_EncDecAttention_modules_o_parameters_weight_
2025-03-04T22:55:57.6696656Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_layer_norm_parameters_weight_
2025-03-04T22:55:57.6698333Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_DenseReluDense_modules_wi_parameters_weight_
2025-03-04T22:55:57.6701532Z         l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = L_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_DenseReluDense_modules_wo_parameters_weight_
2025-03-04T22:55:57.6702893Z         l_self_modules_decoder_modules_final_layer_norm_parameters_weight_ = L_self_modules_decoder_modules_final_layer_norm_parameters_weight_
2025-03-04T22:55:57.6703525Z         
2025-03-04T22:55:57.6704282Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1011 in forward, code: input_ids = input_ids.view(-1, input_shape[-1])
2025-03-04T22:55:57.6705275Z         input_ids: "i64[4, 2048][2048, 1]cuda:0" = l_inputs_tensor_.view(-1, 2048);  l_inputs_tensor_ = None
2025-03-04T22:55:57.6705746Z         
2025-03-04T22:55:57.6706468Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1021 in forward, code: inputs_embeds = self.embed_tokens(input_ids)
2025-03-04T22:55:57.6707842Z         inputs_embeds: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids, l_self_modules_encoder_modules_embed_tokens_parameters_weight_, None, None, 2.0, False, False);  input_ids = None
2025-03-04T22:55:57.6708710Z         
2025-03-04T22:55:57.6709505Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:978 in get_extended_attention_mask, code: extended_attention_mask = attention_mask[:, None, None, :]
2025-03-04T22:55:57.6710686Z         extended_attention_mask: "i64[4, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = l_stack0_[(slice(None, None, None), None, None, slice(None, None, None))]
2025-03-04T22:55:57.6711286Z         
2025-03-04T22:55:57.6712188Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:989 in get_extended_attention_mask, code: extended_attention_mask = extended_attention_mask.to(dtype=dtype)  # fp16 compatibility
2025-03-04T22:55:57.6713545Z         extended_attention_mask_1: "bf16[4, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = extended_attention_mask.to(dtype = torch.bfloat16);  extended_attention_mask = None
2025-03-04T22:55:57.6714211Z         
2025-03-04T22:55:57.6715074Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:990 in get_extended_attention_mask, code: extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(dtype).min
2025-03-04T22:55:57.6716242Z         sub: "bf16[4, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = 1.0 - extended_attention_mask_1;  extended_attention_mask_1 = None
2025-03-04T22:55:57.6716979Z         extended_attention_mask_2: "bf16[4, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = sub * -3.3895313892515355e+38;  sub = None
2025-03-04T22:55:57.6717486Z         
2025-03-04T22:55:57.6718236Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1073 in forward, code: hidden_states = self.dropout(inputs_embeds)
2025-03-04T22:55:57.6719370Z         hidden_states: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(inputs_embeds, 0.1, False, False);  inputs_embeds = None
2025-03-04T22:55:57.6720181Z         
2025-03-04T22:55:57.6720980Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.6721988Z         to_1: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states.to(torch.float32)
2025-03-04T22:55:57.6722497Z         pow_1: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_1.pow(2);  to_1 = None
2025-03-04T22:55:57.6723040Z         variance: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_1.mean(-1, keepdim = True);  pow_1 = None
2025-03-04T22:55:57.6723479Z         
2025-03-04T22:55:57.6724313Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.6725569Z         add: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance + 1e-06;  variance = None
2025-03-04T22:55:57.6726072Z         rsqrt: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add);  add = None
2025-03-04T22:55:57.6726647Z         hidden_states_1: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states * rsqrt;  rsqrt = None
2025-03-04T22:55:57.6727109Z         
2025-03-04T22:55:57.6727933Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.6728998Z         hidden_states_2: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_1.to(torch.bfloat16);  hidden_states_1 = None
2025-03-04T22:55:57.6729526Z         
2025-03-04T22:55:57.6730194Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.6731903Z         normed_hidden_states: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_2;  l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_2 = None
2025-03-04T22:55:57.6733140Z         
2025-03-04T22:55:57.6734008Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.6735997Z         linear: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states, l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.6737324Z         
2025-03-04T22:55:57.6738180Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6739261Z         view_1: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear.view(4, -1, 8, 64);  linear = None
2025-03-04T22:55:57.6739883Z         query_states: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_1.transpose(1, 2);  view_1 = None
2025-03-04T22:55:57.6740347Z         
2025-03-04T22:55:57.6741076Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.6743063Z         linear_1: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states, l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.6744502Z         
2025-03-04T22:55:57.6745350Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6746432Z         view_2: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_1.view(4, -1, 8, 64);  linear_1 = None
2025-03-04T22:55:57.6747072Z         hidden_states_3: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_2.transpose(1, 2);  view_2 = None
2025-03-04T22:55:57.6747540Z         
2025-03-04T22:55:57.6748262Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.6750187Z         linear_2: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states, l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states = l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.6751585Z         
2025-03-04T22:55:57.6752433Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6753513Z         view_3: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_2.view(4, -1, 8, 64);  linear_2 = None
2025-03-04T22:55:57.6754151Z         hidden_states_4: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_3.transpose(1, 2);  view_3 = None
2025-03-04T22:55:57.6754617Z         
2025-03-04T22:55:57.6755320Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.6756317Z         transpose_3: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_3.transpose(3, 2);  hidden_states_3 = None
2025-03-04T22:55:57.6756823Z         
2025-03-04T22:55:57.6757453Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.6758530Z         scores: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.matmul(query_states, transpose_3);  query_states = transpose_3 = None
2025-03-04T22:55:57.6759138Z         
2025-03-04T22:55:57.6760016Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:441 in compute_bias, code: context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
2025-03-04T22:55:57.6761197Z         arange: "i64[2048][1]cuda:0" = torch.arange(2048, dtype = torch.int64, device = device(type='cuda', index=0))
2025-03-04T22:55:57.6761907Z         context_position: "i64[2048, 1][1, 1]cuda:0" = arange[(slice(None, None, None), None)];  arange = None
2025-03-04T22:55:57.6762386Z         
2025-03-04T22:55:57.6763251Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:442 in compute_bias, code: memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
2025-03-04T22:55:57.6764414Z         arange_1: "i64[2048][1]cuda:0" = torch.arange(2048, dtype = torch.int64, device = device(type='cuda', index=0))
2025-03-04T22:55:57.6765143Z         memory_position: "i64[1, 2048][2048, 1]cuda:0" = arange_1[(None, slice(None, None, None))];  arange_1 = None
2025-03-04T22:55:57.6765633Z         
2025-03-04T22:55:57.6766610Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:443 in compute_bias, code: relative_position = memory_position - context_position  # shape (query_length, key_length)
2025-03-04T22:55:57.6768076Z         relative_position: "i64[2048, 2048][2048, 1]cuda:0" = memory_position - context_position;  memory_position = context_position = None
2025-03-04T22:55:57.6768667Z         
2025-03-04T22:55:57.6769526Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:414 in _relative_position_bucket, code: relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
2025-03-04T22:55:57.6770523Z         gt: "b8[2048, 2048][2048, 1]cuda:0" = relative_position > 0
2025-03-04T22:55:57.6770964Z         to_3: "i64[2048, 2048][2048, 1]cuda:0" = gt.to(torch.int64);  gt = None
2025-03-04T22:55:57.6771423Z         mul_3: "i64[2048, 2048][2048, 1]cuda:0" = to_3 * 16;  to_3 = None
2025-03-04T22:55:57.6771908Z         relative_buckets: "i64[2048, 2048][2048, 1]cuda:0" = 0 + mul_3;  mul_3 = None
2025-03-04T22:55:57.6772321Z         
2025-03-04T22:55:57.6773113Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:415 in _relative_position_bucket, code: relative_position = torch.abs(relative_position)
2025-03-04T22:55:57.6774207Z         relative_position_1: "i64[2048, 2048][2048, 1]cuda:0" = torch.abs(relative_position);  relative_position = None
2025-03-04T22:55:57.6774720Z         
2025-03-04T22:55:57.6775469Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:422 in _relative_position_bucket, code: is_small = relative_position < max_exact
2025-03-04T22:55:57.6776394Z         is_small: "b8[2048, 2048][2048, 1]cuda:0" = relative_position_1 < 8
2025-03-04T22:55:57.6776761Z         
2025-03-04T22:55:57.6777545Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:426 in _relative_position_bucket, code: torch.log(relative_position.float() / max_exact)
2025-03-04T22:55:57.6778516Z         float_1: "f32[2048, 2048][2048, 1]cuda:0" = relative_position_1.float()
2025-03-04T22:55:57.6779010Z         truediv: "f32[2048, 2048][2048, 1]cuda:0" = float_1 / 8;  float_1 = None
2025-03-04T22:55:57.6779507Z         log: "f32[2048, 2048][2048, 1]cuda:0" = torch.log(truediv);  truediv = None
2025-03-04T22:55:57.6780028Z         truediv_1: "f32[2048, 2048][2048, 1]cuda:0" = log / 2.772588722239781;  log = None
2025-03-04T22:55:57.6780538Z         mul_4: "f32[2048, 2048][2048, 1]cuda:0" = truediv_1 * 8;  truediv_1 = None
2025-03-04T22:55:57.6780922Z         
2025-03-04T22:55:57.6781593Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:429 in _relative_position_bucket, code: ).to(torch.long)
2025-03-04T22:55:57.6782440Z         to_4: "i64[2048, 2048][2048, 1]cuda:0" = mul_4.to(torch.int64);  mul_4 = None
2025-03-04T22:55:57.6782836Z         
2025-03-04T22:55:57.6783603Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:425 in _relative_position_bucket, code: relative_position_if_large = max_exact + (
2025-03-04T22:55:57.6784592Z         relative_position_if_large: "i64[2048, 2048][2048, 1]cuda:0" = 8 + to_4;  to_4 = None
2025-03-04T22:55:57.6785027Z         
2025-03-04T22:55:57.6785957Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:431 in _relative_position_bucket, code: relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
2025-03-04T22:55:57.6787128Z         full_like: "i64[2048, 2048][2048, 1]cuda:0" = torch.full_like(relative_position_if_large, 15)
2025-03-04T22:55:57.6787590Z         
2025-03-04T22:55:57.6788344Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:430 in _relative_position_bucket, code: relative_position_if_large = torch.min(
2025-03-04T22:55:57.6789649Z         relative_position_if_large_1: "i64[2048, 2048][2048, 1]cuda:0" = torch.min(relative_position_if_large, full_like);  relative_position_if_large = full_like = None
2025-03-04T22:55:57.6790399Z         
2025-03-04T22:55:57.6791330Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:434 in _relative_position_bucket, code: relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
2025-03-04T22:55:57.6792789Z         where: "i64[2048, 2048][2048, 1]cuda:0" = torch.where(is_small, relative_position_1, relative_position_if_large_1);  is_small = relative_position_1 = relative_position_if_large_1 = None
2025-03-04T22:55:57.6793841Z         relative_buckets += where;  relative_buckets_1: "i64[2048, 2048][2048, 1]cuda:0" = relative_buckets;  relative_buckets = where = None
2025-03-04T22:55:57.6794427Z         
2025-03-04T22:55:57.6795390Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:450 in compute_bias, code: values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
2025-03-04T22:55:57.6797820Z         values: "bf16[2048, 2048, 8][16384, 8, 1]cuda:0" = torch.nn.functional.embedding(relative_buckets_1, l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_relative_attention_bias_parameters_weight_, None, None, 2.0, False, False);  relative_buckets_1 = l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_relative_attention_bias_parameters_weight_ = None
2025-03-04T22:55:57.6799542Z         
2025-03-04T22:55:57.6800413Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:451 in compute_bias, code: values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
2025-03-04T22:55:57.6801525Z         permute: "bf16[8, 2048, 2048][1, 16384, 8]cuda:0" = values.permute([2, 0, 1]);  values = None
2025-03-04T22:55:57.6802143Z         values_1: "bf16[1, 8, 2048, 2048][8, 1, 16384, 8]cuda:0" = permute.unsqueeze(0);  permute = None
2025-03-04T22:55:57.6802583Z         
2025-03-04T22:55:57.6803429Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:552 in forward, code: position_bias = position_bias + mask  # (batch_size, n_heads, seq_length, key_length)
2025-03-04T22:55:57.6804688Z         position_bias: "bf16[4, 8, 2048, 2048][33554432, 1, 16384, 8]cuda:0" = values_1 + extended_attention_mask_2;  values_1 = extended_attention_mask_2 = None
2025-03-04T22:55:57.6805309Z         
2025-03-04T22:55:57.6805962Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.6806933Z         scores += position_bias;  scores_1: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores;  scores = None
2025-03-04T22:55:57.6807459Z         
2025-03-04T22:55:57.6808362Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.6809355Z         float_2: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_1.float()
2025-03-04T22:55:57.6810042Z         softmax: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_2, dim = -1);  float_2 = None
2025-03-04T22:55:57.6810865Z         attn_weights: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = softmax.type_as(scores_1);  softmax = scores_1 = None
2025-03-04T22:55:57.6811415Z         
2025-03-04T22:55:57.6812102Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.6813378Z         attn_weights_1: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights, p = 0.1, training = False);  attn_weights = None
2025-03-04T22:55:57.6814139Z         
2025-03-04T22:55:57.6815030Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.6816325Z         matmul_1: "bf16[4, 8, 2048, 64][1048576, 131072, 64, 1]cuda:0" = torch.matmul(attn_weights_1, hidden_states_4);  attn_weights_1 = hidden_states_4 = None
2025-03-04T22:55:57.6816929Z         
2025-03-04T22:55:57.6817760Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.6818854Z         transpose_4: "bf16[4, 2048, 8, 64][1048576, 64, 131072, 1]cuda:0" = matmul_1.transpose(1, 2);  matmul_1 = None
2025-03-04T22:55:57.6819548Z         contiguous: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = transpose_4.contiguous();  transpose_4 = None
2025-03-04T22:55:57.6820232Z         attn_output: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = contiguous.view(4, -1, 512);  contiguous = None
2025-03-04T22:55:57.6820713Z         
2025-03-04T22:55:57.6821384Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.6823201Z         attn_output_1: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(attn_output, l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output = l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.6824561Z         
2025-03-04T22:55:57.6825585Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.6826774Z         dropout_2: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_1, 0.1, False, False);  attn_output_1 = None
2025-03-04T22:55:57.6827648Z         hidden_states_5: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states + dropout_2;  hidden_states = dropout_2 = None
2025-03-04T22:55:57.6828179Z         
2025-03-04T22:55:57.6828986Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.6829987Z         to_5: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_5.to(torch.float32)
2025-03-04T22:55:57.6830503Z         pow_2: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_5.pow(2);  to_5 = None
2025-03-04T22:55:57.6831056Z         variance_1: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_2.mean(-1, keepdim = True);  pow_2 = None
2025-03-04T22:55:57.6831498Z         
2025-03-04T22:55:57.6832319Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.6833332Z         add_5: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_1 + 1e-06;  variance_1 = None
2025-03-04T22:55:57.6833843Z         rsqrt_1: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_5);  add_5 = None
2025-03-04T22:55:57.6834431Z         hidden_states_6: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_5 * rsqrt_1;  rsqrt_1 = None
2025-03-04T22:55:57.6834897Z         
2025-03-04T22:55:57.6835632Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.6836946Z         hidden_states_7: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_6.to(torch.bfloat16);  hidden_states_6 = None
2025-03-04T22:55:57.6837471Z         
2025-03-04T22:55:57.6838129Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.6839802Z         forwarded_states: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_7;  l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_7 = None
2025-03-04T22:55:57.6841016Z         
2025-03-04T22:55:57.6841685Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.6843607Z         hidden_states_8: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states, l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states = l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.6845026Z         
2025-03-04T22:55:57.6845700Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.6846802Z         hidden_states_9: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_8, inplace = False);  hidden_states_8 = None
2025-03-04T22:55:57.6847421Z         
2025-03-04T22:55:57.6848200Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.6849341Z         hidden_states_10: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_9, 0.1, False, False);  hidden_states_9 = None
2025-03-04T22:55:57.6849982Z         
2025-03-04T22:55:57.6850669Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.6852536Z         hidden_states_11: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_10, l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_10 = l_self_modules_encoder_modules_block_modules_0_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.6853925Z         
2025-03-04T22:55:57.6854703Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.6855892Z         dropout_4: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_11, 0.1, False, False);  hidden_states_11 = None
2025-03-04T22:55:57.6856771Z         hidden_states_12: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_5 + dropout_4;  hidden_states_5 = dropout_4 = None
2025-03-04T22:55:57.6857311Z         
2025-03-04T22:55:57.6858117Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.6859124Z         to_7: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_12.to(torch.float32)
2025-03-04T22:55:57.6859644Z         pow_3: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_7.pow(2);  to_7 = None
2025-03-04T22:55:57.6860345Z         variance_2: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_3.mean(-1, keepdim = True);  pow_3 = None
2025-03-04T22:55:57.6860785Z         
2025-03-04T22:55:57.6861603Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.6862607Z         add_7: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_2 + 1e-06;  variance_2 = None
2025-03-04T22:55:57.6863124Z         rsqrt_2: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_7);  add_7 = None
2025-03-04T22:55:57.6863718Z         hidden_states_13: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_12 * rsqrt_2;  rsqrt_2 = None
2025-03-04T22:55:57.6864190Z         
2025-03-04T22:55:57.6864924Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.6865993Z         hidden_states_14: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_13.to(torch.bfloat16);  hidden_states_13 = None
2025-03-04T22:55:57.6866526Z         
2025-03-04T22:55:57.6867188Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.6868868Z         normed_hidden_states_1: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_14;  l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_14 = None
2025-03-04T22:55:57.6870098Z         
2025-03-04T22:55:57.6870957Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.6872948Z         linear_6: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_1, l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.6874268Z         
2025-03-04T22:55:57.6875120Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6876194Z         view_5: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_6.view(4, -1, 8, 64);  linear_6 = None
2025-03-04T22:55:57.6876822Z         query_states_1: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_5.transpose(1, 2);  view_5 = None
2025-03-04T22:55:57.6877282Z         
2025-03-04T22:55:57.6878009Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.6879846Z         linear_7: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_1, l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.6881172Z         
2025-03-04T22:55:57.6882020Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6883097Z         view_6: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_7.view(4, -1, 8, 64);  linear_7 = None
2025-03-04T22:55:57.6883919Z         hidden_states_15: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_6.transpose(1, 2);  view_6 = None
2025-03-04T22:55:57.6884389Z         
2025-03-04T22:55:57.6885111Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.6887028Z         linear_8: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_1, l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_1 = l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.6888572Z         
2025-03-04T22:55:57.6889421Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6890502Z         view_7: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_8.view(4, -1, 8, 64);  linear_8 = None
2025-03-04T22:55:57.6891141Z         hidden_states_16: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_7.transpose(1, 2);  view_7 = None
2025-03-04T22:55:57.6891607Z         
2025-03-04T22:55:57.6892300Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.6893297Z         transpose_8: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_15.transpose(3, 2);  hidden_states_15 = None
2025-03-04T22:55:57.6893806Z         
2025-03-04T22:55:57.6894436Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.6895479Z         scores_2: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.matmul(query_states_1, transpose_8);  query_states_1 = transpose_8 = None
2025-03-04T22:55:57.6896095Z         
2025-03-04T22:55:57.6896746Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.6897726Z         scores_2 += position_bias;  scores_3: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_2;  scores_2 = None
2025-03-04T22:55:57.6898310Z         
2025-03-04T22:55:57.6899099Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.6900086Z         float_3: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_3.float()
2025-03-04T22:55:57.6900777Z         softmax_1: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_3, dim = -1);  float_3 = None
2025-03-04T22:55:57.6901620Z         attn_weights_2: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = softmax_1.type_as(scores_3);  softmax_1 = scores_3 = None
2025-03-04T22:55:57.6902185Z         
2025-03-04T22:55:57.6902869Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.6904035Z         attn_weights_3: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_2, p = 0.1, training = False);  attn_weights_2 = None
2025-03-04T22:55:57.6904722Z         
2025-03-04T22:55:57.6905605Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.6912981Z         matmul_3: "bf16[4, 8, 2048, 64][1048576, 131072, 64, 1]cuda:0" = torch.matmul(attn_weights_3, hidden_states_16);  attn_weights_3 = hidden_states_16 = None
2025-03-04T22:55:57.6913606Z         
2025-03-04T22:55:57.6914452Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.6915554Z         transpose_9: "bf16[4, 2048, 8, 64][1048576, 64, 131072, 1]cuda:0" = matmul_3.transpose(1, 2);  matmul_3 = None
2025-03-04T22:55:57.6916235Z         contiguous_1: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = transpose_9.contiguous();  transpose_9 = None
2025-03-04T22:55:57.6916927Z         attn_output_2: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = contiguous_1.view(4, -1, 512);  contiguous_1 = None
2025-03-04T22:55:57.6917417Z         
2025-03-04T22:55:57.6918099Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.6919937Z         attn_output_3: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_2, l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_2 = l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.6921299Z         
2025-03-04T22:55:57.6922079Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.6923252Z         dropout_6: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_3, 0.1, False, False);  attn_output_3 = None
2025-03-04T22:55:57.6924117Z         hidden_states_17: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_12 + dropout_6;  hidden_states_12 = dropout_6 = None
2025-03-04T22:55:57.6924667Z         
2025-03-04T22:55:57.6925797Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.6926807Z         to_9: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_17.to(torch.float32)
2025-03-04T22:55:57.6927316Z         pow_4: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_9.pow(2);  to_9 = None
2025-03-04T22:55:57.6927948Z         variance_3: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_4.mean(-1, keepdim = True);  pow_4 = None
2025-03-04T22:55:57.6928391Z         
2025-03-04T22:55:57.6929214Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.6930233Z         add_9: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_3 + 1e-06;  variance_3 = None
2025-03-04T22:55:57.6930747Z         rsqrt_3: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_9);  add_9 = None
2025-03-04T22:55:57.6931336Z         hidden_states_18: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_17 * rsqrt_3;  rsqrt_3 = None
2025-03-04T22:55:57.6931800Z         
2025-03-04T22:55:57.6932536Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.6933595Z         hidden_states_19: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_18.to(torch.bfloat16);  hidden_states_18 = None
2025-03-04T22:55:57.6934125Z         
2025-03-04T22:55:57.6934791Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.6936793Z         forwarded_states_1: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_19;  l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_19 = None
2025-03-04T22:55:57.6938013Z         
2025-03-04T22:55:57.6938692Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.6940602Z         hidden_states_20: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_1, l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_1 = l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.6942034Z         
2025-03-04T22:55:57.6942722Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.6943836Z         hidden_states_21: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_20, inplace = False);  hidden_states_20 = None
2025-03-04T22:55:57.6944468Z         
2025-03-04T22:55:57.6945159Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.6946295Z         hidden_states_22: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_21, 0.1, False, False);  hidden_states_21 = None
2025-03-04T22:55:57.6946940Z         
2025-03-04T22:55:57.6947614Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.6949532Z         hidden_states_23: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_22, l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_22 = l_self_modules_encoder_modules_block_modules_1_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.6950920Z         
2025-03-04T22:55:57.6951683Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.6952862Z         dropout_8: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_23, 0.1, False, False);  hidden_states_23 = None
2025-03-04T22:55:57.6953755Z         hidden_states_24: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_17 + dropout_8;  hidden_states_17 = dropout_8 = None
2025-03-04T22:55:57.6954299Z         
2025-03-04T22:55:57.6955099Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.6956102Z         to_11: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_24.to(torch.float32)
2025-03-04T22:55:57.6956626Z         pow_5: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_11.pow(2);  to_11 = None
2025-03-04T22:55:57.6957176Z         variance_4: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_5.mean(-1, keepdim = True);  pow_5 = None
2025-03-04T22:55:57.6957620Z         
2025-03-04T22:55:57.6958448Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.6959638Z         add_11: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_4 + 1e-06;  variance_4 = None
2025-03-04T22:55:57.6960157Z         rsqrt_4: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_11);  add_11 = None
2025-03-04T22:55:57.6960752Z         hidden_states_25: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_24 * rsqrt_4;  rsqrt_4 = None
2025-03-04T22:55:57.6961219Z         
2025-03-04T22:55:57.6961956Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.6963015Z         hidden_states_26: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_25.to(torch.bfloat16);  hidden_states_25 = None
2025-03-04T22:55:57.6963550Z         
2025-03-04T22:55:57.6964217Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.6965922Z         normed_hidden_states_2: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_26;  l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_26 = None
2025-03-04T22:55:57.6967146Z         
2025-03-04T22:55:57.6968186Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.6970186Z         linear_12: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_2, l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.6971515Z         
2025-03-04T22:55:57.6972369Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6973464Z         view_9: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_12.view(4, -1, 8, 64);  linear_12 = None
2025-03-04T22:55:57.6974113Z         query_states_2: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_9.transpose(1, 2);  view_9 = None
2025-03-04T22:55:57.6974575Z         
2025-03-04T22:55:57.6975296Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.6977143Z         linear_13: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_2, l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.6978468Z         
2025-03-04T22:55:57.6979314Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6980404Z         view_10: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_13.view(4, -1, 8, 64);  linear_13 = None
2025-03-04T22:55:57.6981055Z         hidden_states_27: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_10.transpose(1, 2);  view_10 = None
2025-03-04T22:55:57.6981524Z         
2025-03-04T22:55:57.6982243Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.6984356Z         linear_14: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_2, l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_2 = l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.6985765Z         
2025-03-04T22:55:57.6986611Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.6987700Z         view_11: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_14.view(4, -1, 8, 64);  linear_14 = None
2025-03-04T22:55:57.6988400Z         hidden_states_28: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_11.transpose(1, 2);  view_11 = None
2025-03-04T22:55:57.6988874Z         
2025-03-04T22:55:57.6989572Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.6990570Z         transpose_13: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_27.transpose(3, 2);  hidden_states_27 = None
2025-03-04T22:55:57.6991081Z         
2025-03-04T22:55:57.6991707Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.6992753Z         scores_4: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.matmul(query_states_2, transpose_13);  query_states_2 = transpose_13 = None
2025-03-04T22:55:57.6993368Z         
2025-03-04T22:55:57.6994023Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.6995014Z         scores_4 += position_bias;  scores_5: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_4;  scores_4 = None
2025-03-04T22:55:57.6995546Z         
2025-03-04T22:55:57.6996341Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.6997328Z         float_4: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_5.float()
2025-03-04T22:55:57.6998032Z         softmax_2: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_4, dim = -1);  float_4 = None
2025-03-04T22:55:57.6998912Z         attn_weights_4: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = softmax_2.type_as(scores_5);  softmax_2 = scores_5 = None
2025-03-04T22:55:57.6999472Z         
2025-03-04T22:55:57.7000168Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7001338Z         attn_weights_5: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_4, p = 0.1, training = False);  attn_weights_4 = None
2025-03-04T22:55:57.7002026Z         
2025-03-04T22:55:57.7002912Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7004207Z         matmul_5: "bf16[4, 8, 2048, 64][1048576, 131072, 64, 1]cuda:0" = torch.matmul(attn_weights_5, hidden_states_28);  attn_weights_5 = hidden_states_28 = None
2025-03-04T22:55:57.7004822Z         
2025-03-04T22:55:57.7005734Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7006911Z         transpose_14: "bf16[4, 2048, 8, 64][1048576, 64, 131072, 1]cuda:0" = matmul_5.transpose(1, 2);  matmul_5 = None
2025-03-04T22:55:57.7007708Z         contiguous_2: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = transpose_14.contiguous();  transpose_14 = None
2025-03-04T22:55:57.7008410Z         attn_output_4: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = contiguous_2.view(4, -1, 512);  contiguous_2 = None
2025-03-04T22:55:57.7008900Z         
2025-03-04T22:55:57.7009563Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7011385Z         attn_output_5: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_4, l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_4 = l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7012736Z         
2025-03-04T22:55:57.7013514Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7014685Z         dropout_10: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_5, 0.1, False, False);  attn_output_5 = None
2025-03-04T22:55:57.7015560Z         hidden_states_29: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_24 + dropout_10;  hidden_states_24 = dropout_10 = None
2025-03-04T22:55:57.7016106Z         
2025-03-04T22:55:57.7016906Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7017920Z         to_13: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_29.to(torch.float32)
2025-03-04T22:55:57.7018435Z         pow_6: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_13.pow(2);  to_13 = None
2025-03-04T22:55:57.7018980Z         variance_5: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_6.mean(-1, keepdim = True);  pow_6 = None
2025-03-04T22:55:57.7019419Z         
2025-03-04T22:55:57.7020241Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7021244Z         add_13: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_5 + 1e-06;  variance_5 = None
2025-03-04T22:55:57.7021758Z         rsqrt_5: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_13);  add_13 = None
2025-03-04T22:55:57.7022348Z         hidden_states_30: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_29 * rsqrt_5;  rsqrt_5 = None
2025-03-04T22:55:57.7022819Z         
2025-03-04T22:55:57.7023560Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7024622Z         hidden_states_31: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_30.to(torch.bfloat16);  hidden_states_30 = None
2025-03-04T22:55:57.7025328Z         
2025-03-04T22:55:57.7025995Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7027682Z         forwarded_states_2: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_31;  l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_31 = None
2025-03-04T22:55:57.7029081Z         
2025-03-04T22:55:57.7029887Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7031796Z         hidden_states_32: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_2, l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_2 = l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7033222Z         
2025-03-04T22:55:57.7033903Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7035020Z         hidden_states_33: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_32, inplace = False);  hidden_states_32 = None
2025-03-04T22:55:57.7035656Z         
2025-03-04T22:55:57.7036351Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7037492Z         hidden_states_34: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_33, 0.1, False, False);  hidden_states_33 = None
2025-03-04T22:55:57.7038163Z         
2025-03-04T22:55:57.7038860Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7040726Z         hidden_states_35: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_34, l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_34 = l_self_modules_encoder_modules_block_modules_2_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7042117Z         
2025-03-04T22:55:57.7042882Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7044060Z         dropout_12: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_35, 0.1, False, False);  hidden_states_35 = None
2025-03-04T22:55:57.7044950Z         hidden_states_36: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_29 + dropout_12;  hidden_states_29 = dropout_12 = None
2025-03-04T22:55:57.7045498Z         
2025-03-04T22:55:57.7046297Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7047311Z         to_15: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_36.to(torch.float32)
2025-03-04T22:55:57.7047896Z         pow_7: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_15.pow(2);  to_15 = None
2025-03-04T22:55:57.7048439Z         variance_6: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_7.mean(-1, keepdim = True);  pow_7 = None
2025-03-04T22:55:57.7048877Z         
2025-03-04T22:55:57.7049701Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7050707Z         add_15: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_6 + 1e-06;  variance_6 = None
2025-03-04T22:55:57.7051222Z         rsqrt_6: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_15);  add_15 = None
2025-03-04T22:55:57.7051811Z         hidden_states_37: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_36 * rsqrt_6;  rsqrt_6 = None
2025-03-04T22:55:57.7052368Z         
2025-03-04T22:55:57.7053171Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7054233Z         hidden_states_38: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_37.to(torch.bfloat16);  hidden_states_37 = None
2025-03-04T22:55:57.7054762Z         
2025-03-04T22:55:57.7055425Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7057111Z         normed_hidden_states_3: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_38;  l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_38 = None
2025-03-04T22:55:57.7058392Z         
2025-03-04T22:55:57.7059261Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7061245Z         linear_18: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_3, l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7062569Z         
2025-03-04T22:55:57.7063413Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7064515Z         view_13: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_18.view(4, -1, 8, 64);  linear_18 = None
2025-03-04T22:55:57.7065163Z         query_states_3: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_13.transpose(1, 2);  view_13 = None
2025-03-04T22:55:57.7065625Z         
2025-03-04T22:55:57.7066346Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7068183Z         linear_19: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_3, l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7069500Z         
2025-03-04T22:55:57.7070351Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7071447Z         view_14: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_19.view(4, -1, 8, 64);  linear_19 = None
2025-03-04T22:55:57.7072092Z         hidden_states_39: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_14.transpose(1, 2);  view_14 = None
2025-03-04T22:55:57.7072561Z         
2025-03-04T22:55:57.7073279Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7075300Z         linear_20: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_3, l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_3 = l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7076780Z         
2025-03-04T22:55:57.7077627Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7078720Z         view_15: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_20.view(4, -1, 8, 64);  linear_20 = None
2025-03-04T22:55:57.7079366Z         hidden_states_40: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_15.transpose(1, 2);  view_15 = None
2025-03-04T22:55:57.7079831Z         
2025-03-04T22:55:57.7080522Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7081525Z         transpose_18: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_39.transpose(3, 2);  hidden_states_39 = None
2025-03-04T22:55:57.7082046Z         
2025-03-04T22:55:57.7082676Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7083719Z         scores_6: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.matmul(query_states_3, transpose_18);  query_states_3 = transpose_18 = None
2025-03-04T22:55:57.7084337Z         
2025-03-04T22:55:57.7084987Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7085964Z         scores_6 += position_bias;  scores_7: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_6;  scores_6 = None
2025-03-04T22:55:57.7086495Z         
2025-03-04T22:55:57.7087289Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7088367Z         float_5: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_7.float()
2025-03-04T22:55:57.7089057Z         softmax_3: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_5, dim = -1);  float_5 = None
2025-03-04T22:55:57.7089900Z         attn_weights_6: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = softmax_3.type_as(scores_7);  softmax_3 = scores_7 = None
2025-03-04T22:55:57.7090460Z         
2025-03-04T22:55:57.7091144Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7092312Z         attn_weights_7: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_6, p = 0.1, training = False);  attn_weights_6 = None
2025-03-04T22:55:57.7093004Z         
2025-03-04T22:55:57.7093891Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7095180Z         matmul_7: "bf16[4, 8, 2048, 64][1048576, 131072, 64, 1]cuda:0" = torch.matmul(attn_weights_7, hidden_states_40);  attn_weights_7 = hidden_states_40 = None
2025-03-04T22:55:57.7095790Z         
2025-03-04T22:55:57.7096615Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7097711Z         transpose_19: "bf16[4, 2048, 8, 64][1048576, 64, 131072, 1]cuda:0" = matmul_7.transpose(1, 2);  matmul_7 = None
2025-03-04T22:55:57.7098398Z         contiguous_3: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = transpose_19.contiguous();  transpose_19 = None
2025-03-04T22:55:57.7099253Z         attn_output_6: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = contiguous_3.view(4, -1, 512);  contiguous_3 = None
2025-03-04T22:55:57.7099745Z         
2025-03-04T22:55:57.7101841Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7103663Z         attn_output_7: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_6, l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_6 = l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7105017Z         
2025-03-04T22:55:57.7105798Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7106980Z         dropout_14: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_7, 0.1, False, False);  attn_output_7 = None
2025-03-04T22:55:57.7107856Z         hidden_states_41: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_36 + dropout_14;  hidden_states_36 = dropout_14 = None
2025-03-04T22:55:57.7108404Z         
2025-03-04T22:55:57.7109204Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7110205Z         to_17: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_41.to(torch.float32)
2025-03-04T22:55:57.7110722Z         pow_8: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_17.pow(2);  to_17 = None
2025-03-04T22:55:57.7111267Z         variance_7: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_8.mean(-1, keepdim = True);  pow_8 = None
2025-03-04T22:55:57.7111709Z         
2025-03-04T22:55:57.7112540Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7113546Z         add_17: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_7 + 1e-06;  variance_7 = None
2025-03-04T22:55:57.7114061Z         rsqrt_7: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_17);  add_17 = None
2025-03-04T22:55:57.7114648Z         hidden_states_42: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_41 * rsqrt_7;  rsqrt_7 = None
2025-03-04T22:55:57.7115116Z         
2025-03-04T22:55:57.7115850Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7116912Z         hidden_states_43: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_42.to(torch.bfloat16);  hidden_states_42 = None
2025-03-04T22:55:57.7117445Z         
2025-03-04T22:55:57.7118109Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7119796Z         forwarded_states_3: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_43;  l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_43 = None
2025-03-04T22:55:57.7121010Z         
2025-03-04T22:55:57.7121685Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7123672Z         hidden_states_44: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_3, l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_3 = l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7125319Z         
2025-03-04T22:55:57.7125998Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7127103Z         hidden_states_45: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_44, inplace = False);  hidden_states_44 = None
2025-03-04T22:55:57.7127822Z         
2025-03-04T22:55:57.7128559Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7129706Z         hidden_states_46: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_45, 0.1, False, False);  hidden_states_45 = None
2025-03-04T22:55:57.7130348Z         
2025-03-04T22:55:57.7131019Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7132876Z         hidden_states_47: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_46, l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_46 = l_self_modules_encoder_modules_block_modules_3_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7134252Z         
2025-03-04T22:55:57.7135015Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7136199Z         dropout_16: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_47, 0.1, False, False);  hidden_states_47 = None
2025-03-04T22:55:57.7137084Z         hidden_states_48: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_41 + dropout_16;  hidden_states_41 = dropout_16 = None
2025-03-04T22:55:57.7137624Z         
2025-03-04T22:55:57.7138415Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7139411Z         to_19: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_48.to(torch.float32)
2025-03-04T22:55:57.7139925Z         pow_9: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_19.pow(2);  to_19 = None
2025-03-04T22:55:57.7140467Z         variance_8: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_9.mean(-1, keepdim = True);  pow_9 = None
2025-03-04T22:55:57.7140901Z         
2025-03-04T22:55:57.7141725Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7142726Z         add_19: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_8 + 1e-06;  variance_8 = None
2025-03-04T22:55:57.7143238Z         rsqrt_8: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_19);  add_19 = None
2025-03-04T22:55:57.7143822Z         hidden_states_49: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_48 * rsqrt_8;  rsqrt_8 = None
2025-03-04T22:55:57.7144285Z         
2025-03-04T22:55:57.7145017Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7146208Z         hidden_states_50: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_49.to(torch.bfloat16);  hidden_states_49 = None
2025-03-04T22:55:57.7146841Z         
2025-03-04T22:55:57.7147503Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7149179Z         normed_hidden_states_4: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_50;  l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_50 = None
2025-03-04T22:55:57.7150398Z         
2025-03-04T22:55:57.7151252Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7153237Z         linear_24: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_4, l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7154559Z         
2025-03-04T22:55:57.7155395Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7156481Z         view_17: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_24.view(4, -1, 8, 64);  linear_24 = None
2025-03-04T22:55:57.7157117Z         query_states_4: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_17.transpose(1, 2);  view_17 = None
2025-03-04T22:55:57.7157572Z         
2025-03-04T22:55:57.7158347Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7160177Z         linear_25: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_4, l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7161491Z         
2025-03-04T22:55:57.7162328Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7163409Z         view_18: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_25.view(4, -1, 8, 64);  linear_25 = None
2025-03-04T22:55:57.7164059Z         hidden_states_51: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_18.transpose(1, 2);  view_18 = None
2025-03-04T22:55:57.7164524Z         
2025-03-04T22:55:57.7165234Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7167144Z         linear_26: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_4, l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_4 = l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7168688Z         
2025-03-04T22:55:57.7169527Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7170797Z         view_19: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_26.view(4, -1, 8, 64);  linear_26 = None
2025-03-04T22:55:57.7171440Z         hidden_states_52: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_19.transpose(1, 2);  view_19 = None
2025-03-04T22:55:57.7171901Z         
2025-03-04T22:55:57.7172588Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7173581Z         transpose_23: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_51.transpose(3, 2);  hidden_states_51 = None
2025-03-04T22:55:57.7174086Z         
2025-03-04T22:55:57.7174706Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7175754Z         scores_8: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.matmul(query_states_4, transpose_23);  query_states_4 = transpose_23 = None
2025-03-04T22:55:57.7176368Z         
2025-03-04T22:55:57.7177014Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7177992Z         scores_8 += position_bias;  scores_9: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_8;  scores_8 = None
2025-03-04T22:55:57.7178519Z         
2025-03-04T22:55:57.7179303Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7180287Z         float_6: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_9.float()
2025-03-04T22:55:57.7180971Z         softmax_4: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_6, dim = -1);  float_6 = None
2025-03-04T22:55:57.7181814Z         attn_weights_8: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = softmax_4.type_as(scores_9);  softmax_4 = scores_9 = None
2025-03-04T22:55:57.7182365Z         
2025-03-04T22:55:57.7183042Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7184203Z         attn_weights_9: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_8, p = 0.1, training = False);  attn_weights_8 = None
2025-03-04T22:55:57.7184884Z         
2025-03-04T22:55:57.7185763Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7187054Z         matmul_9: "bf16[4, 8, 2048, 64][1048576, 131072, 64, 1]cuda:0" = torch.matmul(attn_weights_9, hidden_states_52);  attn_weights_9 = hidden_states_52 = None
2025-03-04T22:55:57.7187659Z         
2025-03-04T22:55:57.7188531Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7189618Z         transpose_24: "bf16[4, 2048, 8, 64][1048576, 64, 131072, 1]cuda:0" = matmul_9.transpose(1, 2);  matmul_9 = None
2025-03-04T22:55:57.7190296Z         contiguous_4: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = transpose_24.contiguous();  transpose_24 = None
2025-03-04T22:55:57.7190988Z         attn_output_8: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = contiguous_4.view(4, -1, 512);  contiguous_4 = None
2025-03-04T22:55:57.7191474Z         
2025-03-04T22:55:57.7192133Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7194109Z         attn_output_9: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_8, l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_8 = l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7195454Z         
2025-03-04T22:55:57.7196226Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7197393Z         dropout_18: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_9, 0.1, False, False);  attn_output_9 = None
2025-03-04T22:55:57.7198262Z         hidden_states_53: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_48 + dropout_18;  hidden_states_48 = dropout_18 = None
2025-03-04T22:55:57.7198815Z         
2025-03-04T22:55:57.7199617Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7200615Z         to_21: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_53.to(torch.float32)
2025-03-04T22:55:57.7201131Z         pow_10: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_21.pow(2);  to_21 = None
2025-03-04T22:55:57.7201678Z         variance_9: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_10.mean(-1, keepdim = True);  pow_10 = None
2025-03-04T22:55:57.7202111Z         
2025-03-04T22:55:57.7202931Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7203939Z         add_21: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_9 + 1e-06;  variance_9 = None
2025-03-04T22:55:57.7204453Z         rsqrt_9: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_21);  add_21 = None
2025-03-04T22:55:57.7205037Z         hidden_states_54: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_53 * rsqrt_9;  rsqrt_9 = None
2025-03-04T22:55:57.7205500Z         
2025-03-04T22:55:57.7206229Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7207281Z         hidden_states_55: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_54.to(torch.bfloat16);  hidden_states_54 = None
2025-03-04T22:55:57.7207878Z         
2025-03-04T22:55:57.7208587Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7210271Z         forwarded_states_4: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_55;  l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_55 = None
2025-03-04T22:55:57.7211485Z         
2025-03-04T22:55:57.7212159Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7214064Z         hidden_states_56: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_4, l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_4 = l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7215573Z         
2025-03-04T22:55:57.7216320Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7217432Z         hidden_states_57: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_56, inplace = False);  hidden_states_56 = None
2025-03-04T22:55:57.7218068Z         
2025-03-04T22:55:57.7223500Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7224684Z         hidden_states_58: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_57, 0.1, False, False);  hidden_states_57 = None
2025-03-04T22:55:57.7225511Z         
2025-03-04T22:55:57.7226194Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7228125Z         hidden_states_59: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_58, l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_58 = l_self_modules_encoder_modules_block_modules_4_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7229514Z         
2025-03-04T22:55:57.7230287Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7231471Z         dropout_20: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_59, 0.1, False, False);  hidden_states_59 = None
2025-03-04T22:55:57.7232359Z         hidden_states_60: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_53 + dropout_20;  hidden_states_53 = dropout_20 = None
2025-03-04T22:55:57.7232910Z         
2025-03-04T22:55:57.7233708Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7234706Z         to_23: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_60.to(torch.float32)
2025-03-04T22:55:57.7235224Z         pow_11: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_23.pow(2);  to_23 = None
2025-03-04T22:55:57.7235775Z         variance_10: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_11.mean(-1, keepdim = True);  pow_11 = None
2025-03-04T22:55:57.7236211Z         
2025-03-04T22:55:57.7237031Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7238047Z         add_23: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_10 + 1e-06;  variance_10 = None
2025-03-04T22:55:57.7238568Z         rsqrt_10: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_23);  add_23 = None
2025-03-04T22:55:57.7239165Z         hidden_states_61: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_60 * rsqrt_10;  rsqrt_10 = None
2025-03-04T22:55:57.7239632Z         
2025-03-04T22:55:57.7240366Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7241426Z         hidden_states_62: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_61.to(torch.bfloat16);  hidden_states_61 = None
2025-03-04T22:55:57.7241954Z         
2025-03-04T22:55:57.7242615Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7244610Z         normed_hidden_states_5: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_62;  l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_62 = None
2025-03-04T22:55:57.7245840Z         
2025-03-04T22:55:57.7246695Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7248770Z         linear_30: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_5, l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7250097Z         
2025-03-04T22:55:57.7250957Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7252048Z         view_21: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_30.view(4, -1, 8, 64);  linear_30 = None
2025-03-04T22:55:57.7252686Z         query_states_5: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_21.transpose(1, 2);  view_21 = None
2025-03-04T22:55:57.7253148Z         
2025-03-04T22:55:57.7253865Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7255707Z         linear_31: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_5, l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7257028Z         
2025-03-04T22:55:57.7257870Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7258961Z         view_22: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_31.view(4, -1, 8, 64);  linear_31 = None
2025-03-04T22:55:57.7259605Z         hidden_states_63: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_22.transpose(1, 2);  view_22 = None
2025-03-04T22:55:57.7260066Z         
2025-03-04T22:55:57.7260785Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7262709Z         linear_32: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_5, l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_5 = l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7264109Z         
2025-03-04T22:55:57.7264950Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7266034Z         view_23: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_32.view(4, -1, 8, 64);  linear_32 = None
2025-03-04T22:55:57.7266675Z         hidden_states_64: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_23.transpose(1, 2);  view_23 = None
2025-03-04T22:55:57.7267223Z         
2025-03-04T22:55:57.7268013Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7269033Z         transpose_28: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_63.transpose(3, 2);  hidden_states_63 = None
2025-03-04T22:55:57.7269541Z         
2025-03-04T22:55:57.7270163Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7271203Z         scores_10: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.matmul(query_states_5, transpose_28);  query_states_5 = transpose_28 = None
2025-03-04T22:55:57.7271814Z         
2025-03-04T22:55:57.7272460Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7273496Z         scores_10 += position_bias;  scores_11: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_10;  scores_10 = position_bias = None
2025-03-04T22:55:57.7274071Z         
2025-03-04T22:55:57.7274857Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7275847Z         float_7: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = scores_11.float()
2025-03-04T22:55:57.7276540Z         softmax_5: "f32[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_7, dim = -1);  float_7 = None
2025-03-04T22:55:57.7277384Z         attn_weights_10: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = softmax_5.type_as(scores_11);  softmax_5 = scores_11 = None
2025-03-04T22:55:57.7277941Z         
2025-03-04T22:55:57.7278632Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7279815Z         attn_weights_11: "bf16[4, 8, 2048, 2048][33554432, 4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_10, p = 0.1, training = False);  attn_weights_10 = None
2025-03-04T22:55:57.7280509Z         
2025-03-04T22:55:57.7281390Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7282687Z         matmul_11: "bf16[4, 8, 2048, 64][1048576, 131072, 64, 1]cuda:0" = torch.matmul(attn_weights_11, hidden_states_64);  attn_weights_11 = hidden_states_64 = None
2025-03-04T22:55:57.7283301Z         
2025-03-04T22:55:57.7284132Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7285235Z         transpose_29: "bf16[4, 2048, 8, 64][1048576, 64, 131072, 1]cuda:0" = matmul_11.transpose(1, 2);  matmul_11 = None
2025-03-04T22:55:57.7285924Z         contiguous_5: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = transpose_29.contiguous();  transpose_29 = None
2025-03-04T22:55:57.7286616Z         attn_output_10: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = contiguous_5.view(4, -1, 512);  contiguous_5 = None
2025-03-04T22:55:57.7287102Z         
2025-03-04T22:55:57.7287847Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7289748Z         attn_output_11: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_10, l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_10 = l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7291168Z         
2025-03-04T22:55:57.7291946Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7293120Z         dropout_22: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_11, 0.1, False, False);  attn_output_11 = None
2025-03-04T22:55:57.7293993Z         hidden_states_65: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_60 + dropout_22;  hidden_states_60 = dropout_22 = None
2025-03-04T22:55:57.7294533Z         
2025-03-04T22:55:57.7295329Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7296345Z         to_25: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_65.to(torch.float32)
2025-03-04T22:55:57.7296861Z         pow_12: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_25.pow(2);  to_25 = None
2025-03-04T22:55:57.7297418Z         variance_11: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_12.mean(-1, keepdim = True);  pow_12 = None
2025-03-04T22:55:57.7297875Z         
2025-03-04T22:55:57.7298737Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7299745Z         add_25: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_11 + 1e-06;  variance_11 = None
2025-03-04T22:55:57.7300266Z         rsqrt_11: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_25);  add_25 = None
2025-03-04T22:55:57.7300862Z         hidden_states_66: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_65 * rsqrt_11;  rsqrt_11 = None
2025-03-04T22:55:57.7301340Z         
2025-03-04T22:55:57.7302071Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7303131Z         hidden_states_67: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_66.to(torch.bfloat16);  hidden_states_66 = None
2025-03-04T22:55:57.7303659Z         
2025-03-04T22:55:57.7304323Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7306011Z         forwarded_states_5: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_67;  l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_67 = None
2025-03-04T22:55:57.7307237Z         
2025-03-04T22:55:57.7307915Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7309822Z         hidden_states_68: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_5, l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_5 = l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7311249Z         
2025-03-04T22:55:57.7311931Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7313129Z         hidden_states_69: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_68, inplace = False);  hidden_states_68 = None
2025-03-04T22:55:57.7313890Z         
2025-03-04T22:55:57.7314587Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7315731Z         hidden_states_70: "bf16[4, 2048, 2048][4194304, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_69, 0.1, False, False);  hidden_states_69 = None
2025-03-04T22:55:57.7316377Z         
2025-03-04T22:55:57.7317055Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7318978Z         hidden_states_71: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_70, l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_70 = l_self_modules_encoder_modules_block_modules_5_modules_layer_modules_1_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7320366Z         
2025-03-04T22:55:57.7321133Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7322314Z         dropout_24: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_71, 0.1, False, False);  hidden_states_71 = None
2025-03-04T22:55:57.7323203Z         hidden_states_72: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_65 + dropout_24;  hidden_states_65 = dropout_24 = None
2025-03-04T22:55:57.7323748Z         
2025-03-04T22:55:57.7324545Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7325726Z         to_27: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_72.to(torch.float32)
2025-03-04T22:55:57.7326242Z         pow_13: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = to_27.pow(2);  to_27 = None
2025-03-04T22:55:57.7326798Z         variance_12: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = pow_13.mean(-1, keepdim = True);  pow_13 = None
2025-03-04T22:55:57.7327238Z         
2025-03-04T22:55:57.7328177Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7329191Z         add_27: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = variance_12 + 1e-06;  variance_12 = None
2025-03-04T22:55:57.7329713Z         rsqrt_12: "f32[4, 2048, 1][2048, 1, 1]cuda:0" = torch.rsqrt(add_27);  add_27 = None
2025-03-04T22:55:57.7330372Z         hidden_states_73: "f32[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_72 * rsqrt_12;  hidden_states_72 = rsqrt_12 = None
2025-03-04T22:55:57.7330901Z         
2025-03-04T22:55:57.7331636Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7332692Z         hidden_states_74: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = hidden_states_73.to(torch.bfloat16);  hidden_states_73 = None
2025-03-04T22:55:57.7333222Z         
2025-03-04T22:55:57.7333885Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7335274Z         hidden_states_75: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = l_self_modules_encoder_modules_final_layer_norm_parameters_weight_ * hidden_states_74;  l_self_modules_encoder_modules_final_layer_norm_parameters_weight_ = hidden_states_74 = None
2025-03-04T22:55:57.7336327Z         
2025-03-04T22:55:57.7337160Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1158 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7338350Z         hidden_states_76: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_75, 0.1, False, False);  hidden_states_75 = None
2025-03-04T22:55:57.7338990Z         
2025-03-04T22:55:57.7339933Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:554 in _prepare_decoder_input_ids_for_generation, code: torch.ones((batch_size, 1), dtype=torch.long, device=device) * decoder_start_token_id
2025-03-04T22:55:57.7341142Z         ones: "i64[4, 1][1, 1]cuda:0" = torch.ones((4, 1), dtype = torch.int64, device = device(type='cuda', index=0))
2025-03-04T22:55:57.7341739Z         decoder_input_ids_start: "i64[4, 1][1, 1]cuda:0" = ones * 0;  ones = None
2025-03-04T22:55:57.7342143Z         
2025-03-04T22:55:57.7343068Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2357 in greedy_search, code: eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
2025-03-04T22:55:57.7344095Z         tensor: "i64[1][1]cpu" = torch.tensor([1])
2025-03-04T22:55:57.7344591Z         eos_token_id_tensor: "i64[1][1]cuda:0" = tensor.to(device(type='cuda', index=0));  tensor = None
2025-03-04T22:55:57.7345053Z         
2025-03-04T22:55:57.7345952Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2386 in greedy_search, code: unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
2025-03-04T22:55:57.7347170Z         unfinished_sequences: "i64[4][1]cuda:0" = torch.ones(4, dtype = torch.int64, device = device(type='cuda', index=0))
2025-03-04T22:55:57.7347696Z         
2025-03-04T22:55:57.7348424Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1011 in forward, code: input_ids = input_ids.view(-1, input_shape[-1])
2025-03-04T22:55:57.7349325Z         input_ids_1: "i64[4, 1][1, 1]cuda:0" = decoder_input_ids_start.view(-1, 1)
2025-03-04T22:55:57.7349710Z         
2025-03-04T22:55:57.7350419Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1021 in forward, code: inputs_embeds = self.embed_tokens(input_ids)
2025-03-04T22:55:57.7351767Z         inputs_embeds_1: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.embedding(input_ids_1, l_self_modules_encoder_modules_embed_tokens_parameters_weight_, None, None, 2.0, False, False);  input_ids_1 = None
2025-03-04T22:55:57.7352619Z         
2025-03-04T22:55:57.7353469Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1037 in forward, code: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
2025-03-04T22:55:57.7354570Z         attention_mask: "f32[4, 1][1, 1]cuda:0" = torch.ones(4, 1, device = device(type='cuda', index=0))
2025-03-04T22:55:57.7355017Z         
2025-03-04T22:55:57.7355826Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:922 in create_extended_attention_mask_for_decoder, code: seq_ids = torch.arange(seq_length, device=device)
2025-03-04T22:55:57.7356846Z         seq_ids: "i64[1][1]cuda:0" = torch.arange(1, device = device(type='cuda', index=0))
2025-03-04T22:55:57.7357265Z         
2025-03-04T22:55:57.7358257Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:923 in create_extended_attention_mask_for_decoder, code: causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None]
2025-03-04T22:55:57.7359418Z         getitem_3: "i64[1, 1, 1][1, 1, 1]cuda:0" = seq_ids[(None, None, slice(None, None, None))]
2025-03-04T22:55:57.7360099Z         repeat: "i64[4, 1, 1][1, 1, 1]cuda:0" = getitem_3.repeat(4, 1, 1);  getitem_3 = None
2025-03-04T22:55:57.7360667Z         getitem_4: "i64[1, 1, 1][1, 1, 1]cuda:0" = seq_ids[(None, slice(None, None, None), None)];  seq_ids = None
2025-03-04T22:55:57.7361256Z         causal_mask: "b8[4, 1, 1][1, 1, 1]cuda:0" = repeat <= getitem_4;  repeat = getitem_4 = None
2025-03-04T22:55:57.7361668Z         
2025-03-04T22:55:57.7362485Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:926 in create_extended_attention_mask_for_decoder, code: causal_mask = causal_mask.to(attention_mask.dtype)
2025-03-04T22:55:57.7363524Z         causal_mask_1: "f32[4, 1, 1][1, 1, 1]cuda:0" = causal_mask.to(torch.float32);  causal_mask = None
2025-03-04T22:55:57.7363958Z         
2025-03-04T22:55:57.7364897Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:938 in create_extended_attention_mask_for_decoder, code: extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :]
2025-03-04T22:55:57.7366256Z         getitem_5: "f32[4, 1, 1, 1][1, 1, 1, 1]cuda:0" = causal_mask_1[(slice(None, None, None), None, slice(None, None, None), slice(None, None, None))];  causal_mask_1 = None
2025-03-04T22:55:57.7367191Z         getitem_6: "f32[4, 1, 1, 1][1, 1, 1, 1]cuda:0" = attention_mask[(slice(None, None, None), None, None, slice(None, None, None))];  attention_mask = None
2025-03-04T22:55:57.7368073Z         extended_attention_mask_3: "f32[4, 1, 1, 1][1, 1, 1, 1]cuda:0" = getitem_5 * getitem_6;  getitem_5 = getitem_6 = None
2025-03-04T22:55:57.7368587Z         
2025-03-04T22:55:57.7369490Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:989 in get_extended_attention_mask, code: extended_attention_mask = extended_attention_mask.to(dtype=dtype)  # fp16 compatibility
2025-03-04T22:55:57.7370799Z         extended_attention_mask_4: "bf16[4, 1, 1, 1][1, 1, 1, 1]cuda:0" = extended_attention_mask_3.to(dtype = torch.bfloat16);  extended_attention_mask_3 = None
2025-03-04T22:55:57.7371410Z         
2025-03-04T22:55:57.7372277Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:990 in get_extended_attention_mask, code: extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(dtype).min
2025-03-04T22:55:57.7373391Z         sub_2: "bf16[4, 1, 1, 1][1, 1, 1, 1]cuda:0" = 1.0 - extended_attention_mask_4;  extended_attention_mask_4 = None
2025-03-04T22:55:57.7374031Z         extended_attention_mask_5: "bf16[4, 1, 1, 1][1, 1, 1, 1]cuda:0" = sub_2 * -3.3895313892515355e+38;  sub_2 = None
2025-03-04T22:55:57.7374493Z         
2025-03-04T22:55:57.7375318Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:902 in invert_attention_mask, code: encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :]
2025-03-04T22:55:57.7376615Z         encoder_extended_attention_mask: "i64[4, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = l_stack0_[(slice(None, None, None), None, None, slice(None, None, None))];  l_stack0_ = None
2025-03-04T22:55:57.7377290Z         
2025-03-04T22:55:57.7378270Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:908 in invert_attention_mask, code: encoder_extended_attention_mask = encoder_extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
2025-03-04T22:55:57.7379772Z         encoder_extended_attention_mask_1: "bf16[4, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = encoder_extended_attention_mask.to(dtype = torch.bfloat16);  encoder_extended_attention_mask = None
2025-03-04T22:55:57.7380506Z         
2025-03-04T22:55:57.7381424Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/modeling_utils.py:909 in invert_attention_mask, code: encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * torch.finfo(self.dtype).min
2025-03-04T22:55:57.7382857Z         sub_3: "bf16[4, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = 1.0 - encoder_extended_attention_mask_1;  encoder_extended_attention_mask_1 = None
2025-03-04T22:55:57.7383682Z         encoder_extended_attention_mask_2: "bf16[4, 1, 1, 2048][2048, 2048, 2048, 1]cuda:0" = sub_3 * -3.3895313892515355e+38;  sub_3 = None
2025-03-04T22:55:57.7384222Z         
2025-03-04T22:55:57.7384926Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1073 in forward, code: hidden_states = self.dropout(inputs_embeds)
2025-03-04T22:55:57.7386034Z         hidden_states_77: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(inputs_embeds_1, 0.1, False, False);  inputs_embeds_1 = None
2025-03-04T22:55:57.7386638Z         
2025-03-04T22:55:57.7387443Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7388429Z         to_33: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_77.to(torch.float32)
2025-03-04T22:55:57.7388904Z         pow_14: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_33.pow(2);  to_33 = None
2025-03-04T22:55:57.7389412Z         variance_13: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_14.mean(-1, keepdim = True);  pow_14 = None
2025-03-04T22:55:57.7389824Z         
2025-03-04T22:55:57.7390647Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7391630Z         add_28: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_13 + 1e-06;  variance_13 = None
2025-03-04T22:55:57.7392105Z         rsqrt_13: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_28);  add_28 = None
2025-03-04T22:55:57.7392653Z         hidden_states_78: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_77 * rsqrt_13;  rsqrt_13 = None
2025-03-04T22:55:57.7393109Z         
2025-03-04T22:55:57.7393849Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7394882Z         hidden_states_79: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_78.to(torch.bfloat16);  hidden_states_78 = None
2025-03-04T22:55:57.7395388Z         
2025-03-04T22:55:57.7396053Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7397719Z         normed_hidden_states_6: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_79;  l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_79 = None
2025-03-04T22:55:57.7398971Z         
2025-03-04T22:55:57.7399839Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7401798Z         linear_36: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_6, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7403096Z         
2025-03-04T22:55:57.7403943Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7405008Z         view_26: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_36.view(4, -1, 8, 64);  linear_36 = None
2025-03-04T22:55:57.7405772Z         query_states_6: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_26.transpose(1, 2);  view_26 = None
2025-03-04T22:55:57.7406210Z         
2025-03-04T22:55:57.7406934Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7408867Z         linear_37: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_6, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7410163Z         
2025-03-04T22:55:57.7411016Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7412087Z         view_27: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_37.view(4, -1, 8, 64);  linear_37 = None
2025-03-04T22:55:57.7412680Z         hidden_states_80: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_27.transpose(1, 2);  view_27 = None
2025-03-04T22:55:57.7413120Z         
2025-03-04T22:55:57.7413837Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7415733Z         linear_38: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_6, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_6 = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7417114Z         
2025-03-04T22:55:57.7417960Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7419022Z         view_28: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_38.view(4, -1, 8, 64);  linear_38 = None
2025-03-04T22:55:57.7419613Z         hidden_states_81: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_28.transpose(1, 2);  view_28 = None
2025-03-04T22:55:57.7420049Z         
2025-03-04T22:55:57.7420742Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7421709Z         transpose_33: "bf16[4, 8, 64, 1][512, 64, 1, 512]cuda:0" = hidden_states_80.transpose(3, 2);  hidden_states_80 = None
2025-03-04T22:55:57.7422197Z         
2025-03-04T22:55:57.7422829Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7423778Z         scores_12: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.matmul(query_states_6, transpose_33);  query_states_6 = transpose_33 = None
2025-03-04T22:55:57.7424300Z         
2025-03-04T22:55:57.7425315Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:441 in compute_bias, code: context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
2025-03-04T22:55:57.7426472Z         arange_3: "i64[1][1]cuda:0" = torch.arange(1, dtype = torch.int64, device = device(type='cuda', index=0))
2025-03-04T22:55:57.7427151Z         context_position_1: "i64[1, 1][1, 1]cuda:0" = arange_3[(slice(None, None, None), None)];  arange_3 = None
2025-03-04T22:55:57.7427618Z         
2025-03-04T22:55:57.7429559Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:442 in compute_bias, code: memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
2025-03-04T22:55:57.7430822Z         arange_4: "i64[1][1]cuda:0" = torch.arange(1, dtype = torch.int64, device = device(type='cuda', index=0))
2025-03-04T22:55:57.7431496Z         memory_position_1: "i64[1, 1][1, 1]cuda:0" = arange_4[(None, slice(None, None, None))];  arange_4 = None
2025-03-04T22:55:57.7431962Z         
2025-03-04T22:55:57.7432850Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:443 in compute_bias, code: relative_position = memory_position - context_position  # shape (query_length, key_length)
2025-03-04T22:55:57.7434104Z         relative_position_2: "i64[1, 1][1, 1]cuda:0" = memory_position_1 - context_position_1;  memory_position_1 = context_position_1 = None
2025-03-04T22:55:57.7434677Z         
2025-03-04T22:55:57.7435605Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:417 in _relative_position_bucket, code: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
2025-03-04T22:55:57.7436711Z         zeros_like: "i64[1, 1][1, 1]cuda:0" = torch.zeros_like(relative_position_2)
2025-03-04T22:55:57.7437334Z         min_2: "i64[1, 1][1, 1]cuda:0" = torch.min(relative_position_2, zeros_like);  relative_position_2 = zeros_like = None
2025-03-04T22:55:57.7437956Z         relative_position_3: "i64[1, 1][1, 1]cuda:0" = -min_2;  min_2 = None
2025-03-04T22:55:57.7438394Z         
2025-03-04T22:55:57.7439152Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:422 in _relative_position_bucket, code: is_small = relative_position < max_exact
2025-03-04T22:55:57.7440056Z         is_small_1: "b8[1, 1][1, 1]cuda:0" = relative_position_3 < 16
2025-03-04T22:55:57.7440411Z         
2025-03-04T22:55:57.7441200Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:426 in _relative_position_bucket, code: torch.log(relative_position.float() / max_exact)
2025-03-04T22:55:57.7442144Z         float_8: "f32[1, 1][1, 1]cuda:0" = relative_position_3.float()
2025-03-04T22:55:57.7442582Z         truediv_2: "f32[1, 1][1, 1]cuda:0" = float_8 / 16;  float_8 = None
2025-03-04T22:55:57.7443029Z         log_1: "f32[1, 1][1, 1]cuda:0" = torch.log(truediv_2);  truediv_2 = None
2025-03-04T22:55:57.7443487Z         truediv_3: "f32[1, 1][1, 1]cuda:0" = log_1 / 2.0794415416798357;  log_1 = None
2025-03-04T22:55:57.7443939Z         mul_35: "f32[1, 1][1, 1]cuda:0" = truediv_3 * 16;  truediv_3 = None
2025-03-04T22:55:57.7444290Z         
2025-03-04T22:55:57.7444962Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:429 in _relative_position_bucket, code: ).to(torch.long)
2025-03-04T22:55:57.7445782Z         to_35: "i64[1, 1][1, 1]cuda:0" = mul_35.to(torch.int64);  mul_35 = None
2025-03-04T22:55:57.7446147Z         
2025-03-04T22:55:57.7446913Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:425 in _relative_position_bucket, code: relative_position_if_large = max_exact + (
2025-03-04T22:55:57.7447949Z         relative_position_if_large_2: "i64[1, 1][1, 1]cuda:0" = 16 + to_35;  to_35 = None
2025-03-04T22:55:57.7448364Z         
2025-03-04T22:55:57.7449288Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:431 in _relative_position_bucket, code: relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
2025-03-04T22:55:57.7450425Z         full_like_1: "i64[1, 1][1, 1]cuda:0" = torch.full_like(relative_position_if_large_2, 31)
2025-03-04T22:55:57.7450845Z         
2025-03-04T22:55:57.7451596Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:430 in _relative_position_bucket, code: relative_position_if_large = torch.min(
2025-03-04T22:55:57.7452959Z         relative_position_if_large_3: "i64[1, 1][1, 1]cuda:0" = torch.min(relative_position_if_large_2, full_like_1);  relative_position_if_large_2 = full_like_1 = None
2025-03-04T22:55:57.7453608Z         
2025-03-04T22:55:57.7454523Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:434 in _relative_position_bucket, code: relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
2025-03-04T22:55:57.7455949Z         where_1: "i64[1, 1][1, 1]cuda:0" = torch.where(is_small_1, relative_position_3, relative_position_if_large_3);  is_small_1 = relative_position_3 = relative_position_if_large_3 = None
2025-03-04T22:55:57.7456776Z         relative_buckets_2: "i64[1, 1][1, 1]cuda:0" = 0 + where_1;  where_1 = None
2025-03-04T22:55:57.7457168Z         
2025-03-04T22:55:57.7458132Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:450 in compute_bias, code: values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
2025-03-04T22:55:57.7460519Z         values_2: "bf16[1, 1, 8][8, 8, 1]cuda:0" = torch.nn.functional.embedding(relative_buckets_2, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_relative_attention_bias_parameters_weight_, None, None, 2.0, False, False);  relative_buckets_2 = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_relative_attention_bias_parameters_weight_ = None
2025-03-04T22:55:57.7462139Z         
2025-03-04T22:55:57.7463017Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:451 in compute_bias, code: values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
2025-03-04T22:55:57.7464091Z         permute_1: "bf16[8, 1, 1][1, 8, 8]cuda:0" = values_2.permute([2, 0, 1]);  values_2 = None
2025-03-04T22:55:57.7464636Z         values_3: "bf16[1, 8, 1, 1][8, 1, 8, 8]cuda:0" = permute_1.unsqueeze(0);  permute_1 = None
2025-03-04T22:55:57.7465035Z         
2025-03-04T22:55:57.7465873Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:552 in forward, code: position_bias = position_bias + mask  # (batch_size, n_heads, seq_length, key_length)
2025-03-04T22:55:57.7467047Z         position_bias_1: "bf16[4, 8, 1, 1][8, 1, 8, 8]cuda:0" = values_3 + extended_attention_mask_5;  values_3 = extended_attention_mask_5 = None
2025-03-04T22:55:57.7467598Z         
2025-03-04T22:55:57.7468252Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7469170Z         scores_12 += position_bias_1;  scores_13: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_12;  scores_12 = None
2025-03-04T22:55:57.7469641Z         
2025-03-04T22:55:57.7470446Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7471381Z         float_9: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_13.float()
2025-03-04T22:55:57.7471934Z         softmax_6: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.softmax(float_9, dim = -1);  float_9 = None
2025-03-04T22:55:57.7472600Z         attn_weights_12: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = softmax_6.type_as(scores_13);  softmax_6 = scores_13 = None
2025-03-04T22:55:57.7473071Z         
2025-03-04T22:55:57.7473753Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7474829Z         attn_weights_13: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_12, p = 0.1, training = False);  attn_weights_12 = None
2025-03-04T22:55:57.7475585Z         
2025-03-04T22:55:57.7476472Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7477723Z         matmul_13: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_13, hidden_states_81);  attn_weights_13 = hidden_states_81 = None
2025-03-04T22:55:57.7478290Z         
2025-03-04T22:55:57.7479117Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7480171Z         transpose_34: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_13.transpose(1, 2);  matmul_13 = None
2025-03-04T22:55:57.7480794Z         contiguous_6: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_34.contiguous();  transpose_34 = None
2025-03-04T22:55:57.7481426Z         attn_output_12: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_6.view(4, -1, 512);  contiguous_6 = None
2025-03-04T22:55:57.7481881Z         
2025-03-04T22:55:57.7482548Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7484344Z         attn_output_13: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_12, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_12 = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7485668Z         
2025-03-04T22:55:57.7486452Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7487690Z         dropout_28: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_13, 0.1, False, False);  attn_output_13 = None
2025-03-04T22:55:57.7488561Z         hidden_states_82: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_77 + dropout_28;  hidden_states_77 = dropout_28 = None
2025-03-04T22:55:57.7489078Z         
2025-03-04T22:55:57.7489878Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7490856Z         to_36: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_82.to(torch.float32)
2025-03-04T22:55:57.7491330Z         pow_15: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_36.pow(2);  to_36 = None
2025-03-04T22:55:57.7491835Z         variance_14: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_15.mean(-1, keepdim = True);  pow_15 = None
2025-03-04T22:55:57.7492263Z         
2025-03-04T22:55:57.7493086Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7494076Z         add_33: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_14 + 1e-06;  variance_14 = None
2025-03-04T22:55:57.7494547Z         rsqrt_14: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_33);  add_33 = None
2025-03-04T22:55:57.7495096Z         hidden_states_83: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_82 * rsqrt_14;  rsqrt_14 = None
2025-03-04T22:55:57.7495547Z         
2025-03-04T22:55:57.7496279Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7496594Z         hidden_states_84: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_83.to(torch.bfloat16);  hidden_states_83 = None
2025-03-04T22:55:57.7496841Z         
2025-03-04T22:55:57.7497390Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7498458Z         normed_hidden_states_7: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_84;  l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_84 = None
2025-03-04T22:55:57.7498547Z         
2025-03-04T22:55:57.7499290Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7500517Z         linear_40: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_7, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_, None);  normed_hidden_states_7 = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7500607Z         
2025-03-04T22:55:57.7501341Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7501578Z         view_30: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_40.view(4, -1, 8, 64);  linear_40 = None
2025-03-04T22:55:57.7501823Z         query_states_7: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_30.transpose(1, 2);  view_30 = None
2025-03-04T22:55:57.7501909Z         
2025-03-04T22:55:57.7502533Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7503671Z         linear_41: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7503756Z         
2025-03-04T22:55:57.7504488Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7504752Z         view_31: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_41.view(4, -1, 8, 64);  linear_41 = None
2025-03-04T22:55:57.7505044Z         hidden_states_85: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_31.transpose(1, 2);  view_31 = None
2025-03-04T22:55:57.7505128Z         
2025-03-04T22:55:57.7505743Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7506872Z         linear_42: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7506962Z         
2025-03-04T22:55:57.7507689Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7508135Z         view_32: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_42.view(4, -1, 8, 64);  linear_42 = None
2025-03-04T22:55:57.7508414Z         hidden_states_86: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_32.transpose(1, 2);  view_32 = None
2025-03-04T22:55:57.7508504Z         
2025-03-04T22:55:57.7509080Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7509410Z         transpose_38: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_85.transpose(3, 2);  hidden_states_85 = None
2025-03-04T22:55:57.7509494Z         
2025-03-04T22:55:57.7510010Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7510421Z         scores_14: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.matmul(query_states_7, transpose_38);  query_states_7 = transpose_38 = None
2025-03-04T22:55:57.7510511Z         
2025-03-04T22:55:57.7511041Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:538 in forward, code: position_bias = torch.zeros(
2025-03-04T22:55:57.7511472Z         position_bias_2: "bf16[1, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.zeros((1, 8, 1, 2048), device = device(type='cuda', index=0), dtype = torch.bfloat16)
2025-03-04T22:55:57.7511559Z         
2025-03-04T22:55:57.7512278Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:552 in forward, code: position_bias = position_bias + mask  # (batch_size, n_heads, seq_length, key_length)
2025-03-04T22:55:57.7512804Z         position_bias_3: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = position_bias_2 + encoder_extended_attention_mask_2;  position_bias_2 = encoder_extended_attention_mask_2 = None
2025-03-04T22:55:57.7512894Z         
2025-03-04T22:55:57.7513443Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7513765Z         scores_14 += position_bias_3;  scores_15: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_14;  scores_14 = None
2025-03-04T22:55:57.7513857Z         
2025-03-04T22:55:57.7514534Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7519468Z         float_10: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_15.float()
2025-03-04T22:55:57.7519828Z         softmax_7: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_10, dim = -1);  float_10 = None
2025-03-04T22:55:57.7520167Z         attn_weights_14: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = softmax_7.type_as(scores_15);  softmax_7 = scores_15 = None
2025-03-04T22:55:57.7520267Z         
2025-03-04T22:55:57.7520848Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7521321Z         attn_weights_15: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_14, p = 0.1, training = False);  attn_weights_14 = None
2025-03-04T22:55:57.7521407Z         
2025-03-04T22:55:57.7522178Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7522560Z         matmul_15: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_15, hidden_states_86);  attn_weights_15 = hidden_states_86 = None
2025-03-04T22:55:57.7522753Z         
2025-03-04T22:55:57.7523537Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7523791Z         transpose_39: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_15.transpose(1, 2);  matmul_15 = None
2025-03-04T22:55:57.7524052Z         contiguous_7: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_39.contiguous();  transpose_39 = None
2025-03-04T22:55:57.7524322Z         attn_output_14: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_7.view(4, -1, 512);  contiguous_7 = None
2025-03-04T22:55:57.7524404Z         
2025-03-04T22:55:57.7525166Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7526331Z         attn_output_15: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_14, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_, None);  attn_output_14 = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7526427Z         
2025-03-04T22:55:57.7527084Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:647 in forward, code: layer_output = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7527470Z         dropout_30: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_15, 0.1, False, False);  attn_output_15 = None
2025-03-04T22:55:57.7527867Z         layer_output: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_82 + dropout_30;  hidden_states_82 = dropout_30 = None
2025-03-04T22:55:57.7527959Z         
2025-03-04T22:55:57.7528690Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7528881Z         to_38: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output.to(torch.float32)
2025-03-04T22:55:57.7529055Z         pow_16: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_38.pow(2);  to_38 = None
2025-03-04T22:55:57.7529282Z         variance_15: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_16.mean(-1, keepdim = True);  pow_16 = None
2025-03-04T22:55:57.7529368Z         
2025-03-04T22:55:57.7530071Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7530253Z         add_36: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_15 + 1e-06;  variance_15 = None
2025-03-04T22:55:57.7530435Z         rsqrt_15: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_36);  add_36 = None
2025-03-04T22:55:57.7530692Z         hidden_states_87: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output * rsqrt_15;  rsqrt_15 = None
2025-03-04T22:55:57.7530777Z         
2025-03-04T22:55:57.7531401Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7531718Z         hidden_states_88: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_87.to(torch.bfloat16);  hidden_states_87 = None
2025-03-04T22:55:57.7531804Z         
2025-03-04T22:55:57.7532358Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7533368Z         forwarded_states_6: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_layer_norm_parameters_weight_ * hidden_states_88;  l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = hidden_states_88 = None
2025-03-04T22:55:57.7533699Z         
2025-03-04T22:55:57.7534265Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7535500Z         hidden_states_89: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_6, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_6 = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7535586Z         
2025-03-04T22:55:57.7536152Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7536582Z         hidden_states_90: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_89, inplace = False);  hidden_states_89 = None
2025-03-04T22:55:57.7536668Z         
2025-03-04T22:55:57.7537248Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7537686Z         hidden_states_91: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_90, 0.1, False, False);  hidden_states_90 = None
2025-03-04T22:55:57.7537771Z         
2025-03-04T22:55:57.7538381Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7539559Z         hidden_states_92: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_91, l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_91 = l_self_modules_decoder_modules_block_modules_0_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7539650Z         
2025-03-04T22:55:57.7540300Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7540705Z         dropout_32: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_92, 0.1, False, False);  hidden_states_92 = None
2025-03-04T22:55:57.7541018Z         hidden_states_93: "bf16[4, 1, 512][512, 512, 1]cuda:0" = layer_output + dropout_32;  layer_output = dropout_32 = None
2025-03-04T22:55:57.7541102Z         
2025-03-04T22:55:57.7541787Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7541989Z         to_40: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_93.to(torch.float32)
2025-03-04T22:55:57.7542165Z         pow_17: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_40.pow(2);  to_40 = None
2025-03-04T22:55:57.7542394Z         variance_16: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_17.mean(-1, keepdim = True);  pow_17 = None
2025-03-04T22:55:57.7542480Z         
2025-03-04T22:55:57.7543182Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7543366Z         add_38: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_16 + 1e-06;  variance_16 = None
2025-03-04T22:55:57.7543545Z         rsqrt_16: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_38);  add_38 = None
2025-03-04T22:55:57.7543805Z         hidden_states_94: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_93 * rsqrt_16;  rsqrt_16 = None
2025-03-04T22:55:57.7543974Z         
2025-03-04T22:55:57.7544694Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7545013Z         hidden_states_95: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_94.to(torch.bfloat16);  hidden_states_94 = None
2025-03-04T22:55:57.7545100Z         
2025-03-04T22:55:57.7545645Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7546655Z         normed_hidden_states_8: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_95;  l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_95 = None
2025-03-04T22:55:57.7546744Z         
2025-03-04T22:55:57.7547494Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7548617Z         linear_46: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_8, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7548702Z         
2025-03-04T22:55:57.7549434Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7549669Z         view_34: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_46.view(4, -1, 8, 64);  linear_46 = None
2025-03-04T22:55:57.7549924Z         query_states_8: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_34.transpose(1, 2);  view_34 = None
2025-03-04T22:55:57.7550011Z         
2025-03-04T22:55:57.7550618Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7551730Z         linear_47: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_8, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7551818Z         
2025-03-04T22:55:57.7552548Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7552790Z         view_35: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_47.view(4, -1, 8, 64);  linear_47 = None
2025-03-04T22:55:57.7553040Z         hidden_states_96: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_35.transpose(1, 2);  view_35 = None
2025-03-04T22:55:57.7553126Z         
2025-03-04T22:55:57.7553728Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7554924Z         linear_48: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_8, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_8 = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7555091Z         
2025-03-04T22:55:57.7555889Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7556127Z         view_36: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_48.view(4, -1, 8, 64);  linear_48 = None
2025-03-04T22:55:57.7556374Z         hidden_states_97: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_36.transpose(1, 2);  view_36 = None
2025-03-04T22:55:57.7556464Z         
2025-03-04T22:55:57.7557041Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7557340Z         transpose_43: "bf16[4, 8, 64, 1][512, 64, 1, 512]cuda:0" = hidden_states_96.transpose(3, 2);  hidden_states_96 = None
2025-03-04T22:55:57.7557430Z         
2025-03-04T22:55:57.7557949Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7558286Z         scores_16: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.matmul(query_states_8, transpose_43);  query_states_8 = transpose_43 = None
2025-03-04T22:55:57.7558374Z         
2025-03-04T22:55:57.7558909Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7559190Z         scores_16 += position_bias_1;  scores_17: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_16;  scores_16 = None
2025-03-04T22:55:57.7559274Z         
2025-03-04T22:55:57.7559951Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7560119Z         float_11: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_17.float()
2025-03-04T22:55:57.7560415Z         softmax_8: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.softmax(float_11, dim = -1);  float_11 = None
2025-03-04T22:55:57.7560700Z         attn_weights_16: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = softmax_8.type_as(scores_17);  softmax_8 = scores_17 = None
2025-03-04T22:55:57.7560786Z         
2025-03-04T22:55:57.7561350Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7561761Z         attn_weights_17: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_16, p = 0.1, training = False);  attn_weights_16 = None
2025-03-04T22:55:57.7561845Z         
2025-03-04T22:55:57.7562614Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7563006Z         matmul_17: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_17, hidden_states_97);  attn_weights_17 = hidden_states_97 = None
2025-03-04T22:55:57.7563091Z         
2025-03-04T22:55:57.7563802Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7564048Z         transpose_44: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_17.transpose(1, 2);  matmul_17 = None
2025-03-04T22:55:57.7564313Z         contiguous_8: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_44.contiguous();  transpose_44 = None
2025-03-04T22:55:57.7564577Z         attn_output_16: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_8.view(4, -1, 512);  contiguous_8 = None
2025-03-04T22:55:57.7564751Z         
2025-03-04T22:55:57.7565374Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7566517Z         attn_output_17: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_16, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_16 = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7566602Z         
2025-03-04T22:55:57.7567269Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7567731Z         dropout_34: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_17, 0.1, False, False);  attn_output_17 = None
2025-03-04T22:55:57.7568076Z         hidden_states_98: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_93 + dropout_34;  hidden_states_93 = dropout_34 = None
2025-03-04T22:55:57.7568165Z         
2025-03-04T22:55:57.7568853Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7569049Z         to_42: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_98.to(torch.float32)
2025-03-04T22:55:57.7569219Z         pow_18: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_42.pow(2);  to_42 = None
2025-03-04T22:55:57.7569449Z         variance_17: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_18.mean(-1, keepdim = True);  pow_18 = None
2025-03-04T22:55:57.7569533Z         
2025-03-04T22:55:57.7570244Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7570433Z         add_40: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_17 + 1e-06;  variance_17 = None
2025-03-04T22:55:57.7570617Z         rsqrt_17: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_40);  add_40 = None
2025-03-04T22:55:57.7570874Z         hidden_states_99: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_98 * rsqrt_17;  rsqrt_17 = None
2025-03-04T22:55:57.7570965Z         
2025-03-04T22:55:57.7571581Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7571903Z         hidden_states_100: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_99.to(torch.bfloat16);  hidden_states_99 = None
2025-03-04T22:55:57.7571987Z         
2025-03-04T22:55:57.7572535Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7573561Z         normed_hidden_states_9: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_100;  l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_100 = None
2025-03-04T22:55:57.7573649Z         
2025-03-04T22:55:57.7574391Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7575681Z         linear_50: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_9, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_, None);  normed_hidden_states_9 = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7575841Z         
2025-03-04T22:55:57.7576571Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7576811Z         view_38: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_50.view(4, -1, 8, 64);  linear_50 = None
2025-03-04T22:55:57.7577053Z         query_states_9: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_38.transpose(1, 2);  view_38 = None
2025-03-04T22:55:57.7577146Z         
2025-03-04T22:55:57.7577756Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7578949Z         linear_51: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7579039Z         
2025-03-04T22:55:57.7579770Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7580037Z         view_39: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_51.view(4, -1, 8, 64);  linear_51 = None
2025-03-04T22:55:57.7580324Z         hidden_states_101: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_39.transpose(1, 2);  view_39 = None
2025-03-04T22:55:57.7580408Z         
2025-03-04T22:55:57.7581022Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7582159Z         linear_52: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7582250Z         
2025-03-04T22:55:57.7582980Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7583241Z         view_40: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_52.view(4, -1, 8, 64);  linear_52 = None
2025-03-04T22:55:57.7583522Z         hidden_states_102: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_40.transpose(1, 2);  view_40 = None
2025-03-04T22:55:57.7583609Z         
2025-03-04T22:55:57.7584197Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7584526Z         transpose_48: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_101.transpose(3, 2);  hidden_states_101 = None
2025-03-04T22:55:57.7584612Z         
2025-03-04T22:55:57.7585123Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7585522Z         scores_18: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.matmul(query_states_9, transpose_48);  query_states_9 = transpose_48 = None
2025-03-04T22:55:57.7585607Z         
2025-03-04T22:55:57.7586143Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7586702Z         scores_18 += position_bias_3;  scores_19: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_18;  scores_18 = None
2025-03-04T22:55:57.7586795Z         
2025-03-04T22:55:57.7587470Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7587661Z         float_12: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_19.float()
2025-03-04T22:55:57.7588005Z         softmax_9: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_12, dim = -1);  float_12 = None
2025-03-04T22:55:57.7588392Z         attn_weights_18: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = softmax_9.type_as(scores_19);  softmax_9 = scores_19 = None
2025-03-04T22:55:57.7588477Z         
2025-03-04T22:55:57.7589048Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7589519Z         attn_weights_19: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_18, p = 0.1, training = False);  attn_weights_18 = None
2025-03-04T22:55:57.7589604Z         
2025-03-04T22:55:57.7590368Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7590754Z         matmul_19: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_19, hidden_states_102);  attn_weights_19 = hidden_states_102 = None
2025-03-04T22:55:57.7590840Z         
2025-03-04T22:55:57.7591544Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7591800Z         transpose_49: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_19.transpose(1, 2);  matmul_19 = None
2025-03-04T22:55:57.7592059Z         contiguous_9: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_49.contiguous();  transpose_49 = None
2025-03-04T22:55:57.7592326Z         attn_output_18: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_9.view(4, -1, 512);  contiguous_9 = None
2025-03-04T22:55:57.7592409Z         
2025-03-04T22:55:57.7592957Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7594110Z         attn_output_19: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_18, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_, None);  attn_output_18 = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7594203Z         
2025-03-04T22:55:57.7594858Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:647 in forward, code: layer_output = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7595246Z         dropout_36: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_19, 0.1, False, False);  attn_output_19 = None
2025-03-04T22:55:57.7595570Z         layer_output_1: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_98 + dropout_36;  hidden_states_98 = dropout_36 = None
2025-03-04T22:55:57.7595656Z         
2025-03-04T22:55:57.7596335Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7596612Z         to_44: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_1.to(torch.float32)
2025-03-04T22:55:57.7596858Z         pow_19: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_44.pow(2);  to_44 = None
2025-03-04T22:55:57.7597092Z         variance_18: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_19.mean(-1, keepdim = True);  pow_19 = None
2025-03-04T22:55:57.7597177Z         
2025-03-04T22:55:57.7597884Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7598093Z         add_42: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_18 + 1e-06;  variance_18 = None
2025-03-04T22:55:57.7598296Z         rsqrt_18: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_42);  add_42 = None
2025-03-04T22:55:57.7598552Z         hidden_states_103: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_1 * rsqrt_18;  rsqrt_18 = None
2025-03-04T22:55:57.7598636Z         
2025-03-04T22:55:57.7599268Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7599593Z         hidden_states_104: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_103.to(torch.bfloat16);  hidden_states_103 = None
2025-03-04T22:55:57.7599682Z         
2025-03-04T22:55:57.7600228Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7601240Z         forwarded_states_7: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_layer_norm_parameters_weight_ * hidden_states_104;  l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = hidden_states_104 = None
2025-03-04T22:55:57.7601325Z         
2025-03-04T22:55:57.7601893Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7603120Z         hidden_states_105: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_7, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_7 = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7603208Z         
2025-03-04T22:55:57.7603772Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7604202Z         hidden_states_106: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_105, inplace = False);  hidden_states_105 = None
2025-03-04T22:55:57.7604297Z         
2025-03-04T22:55:57.7604881Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7605327Z         hidden_states_107: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_106, 0.1, False, False);  hidden_states_106 = None
2025-03-04T22:55:57.7605412Z         
2025-03-04T22:55:57.7605971Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7607163Z         hidden_states_108: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_107, l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_107 = l_self_modules_decoder_modules_block_modules_1_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7607331Z         
2025-03-04T22:55:57.7608232Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7608644Z         dropout_38: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_108, 0.1, False, False);  hidden_states_108 = None
2025-03-04T22:55:57.7608967Z         hidden_states_109: "bf16[4, 1, 512][512, 512, 1]cuda:0" = layer_output_1 + dropout_38;  layer_output_1 = dropout_38 = None
2025-03-04T22:55:57.7609059Z         
2025-03-04T22:55:57.7609740Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7609940Z         to_46: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_109.to(torch.float32)
2025-03-04T22:55:57.7610127Z         pow_20: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_46.pow(2);  to_46 = None
2025-03-04T22:55:57.7610351Z         variance_19: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_20.mean(-1, keepdim = True);  pow_20 = None
2025-03-04T22:55:57.7610438Z         
2025-03-04T22:55:57.7611139Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7611327Z         add_44: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_19 + 1e-06;  variance_19 = None
2025-03-04T22:55:57.7611507Z         rsqrt_19: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_44);  add_44 = None
2025-03-04T22:55:57.7611774Z         hidden_states_110: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_109 * rsqrt_19;  rsqrt_19 = None
2025-03-04T22:55:57.7611857Z         
2025-03-04T22:55:57.7612481Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7612808Z         hidden_states_111: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_110.to(torch.bfloat16);  hidden_states_110 = None
2025-03-04T22:55:57.7612896Z         
2025-03-04T22:55:57.7613442Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7614462Z         normed_hidden_states_10: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_111;  l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_111 = None
2025-03-04T22:55:57.7614548Z         
2025-03-04T22:55:57.7615300Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7616416Z         linear_56: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_10, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7616507Z         
2025-03-04T22:55:57.7617238Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7617474Z         view_42: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_56.view(4, -1, 8, 64);  linear_56 = None
2025-03-04T22:55:57.7617721Z         query_states_10: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_42.transpose(1, 2);  view_42 = None
2025-03-04T22:55:57.7617983Z         
2025-03-04T22:55:57.7618614Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7619725Z         linear_57: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_10, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7619812Z         
2025-03-04T22:55:57.7620539Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7620785Z         view_43: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_57.view(4, -1, 8, 64);  linear_57 = None
2025-03-04T22:55:57.7621035Z         hidden_states_112: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_43.transpose(1, 2);  view_43 = None
2025-03-04T22:55:57.7621123Z         
2025-03-04T22:55:57.7621724Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7622912Z         linear_58: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_10, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_10 = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7623003Z         
2025-03-04T22:55:57.7623732Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7623975Z         view_44: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_58.view(4, -1, 8, 64);  linear_58 = None
2025-03-04T22:55:57.7624223Z         hidden_states_113: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_44.transpose(1, 2);  view_44 = None
2025-03-04T22:55:57.7624309Z         
2025-03-04T22:55:57.7625042Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7625350Z         transpose_53: "bf16[4, 8, 64, 1][512, 64, 1, 512]cuda:0" = hidden_states_112.transpose(3, 2);  hidden_states_112 = None
2025-03-04T22:55:57.7625435Z         
2025-03-04T22:55:57.7625947Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7626298Z         scores_20: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.matmul(query_states_10, transpose_53);  query_states_10 = transpose_53 = None
2025-03-04T22:55:57.7626385Z         
2025-03-04T22:55:57.7626918Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7627201Z         scores_20 += position_bias_1;  scores_21: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_20;  scores_20 = None
2025-03-04T22:55:57.7627284Z         
2025-03-04T22:55:57.7627963Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7628126Z         float_13: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_21.float()
2025-03-04T22:55:57.7628720Z         softmax_10: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.softmax(float_13, dim = -1);  float_13 = None
2025-03-04T22:55:57.7629008Z         attn_weights_20: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = softmax_10.type_as(scores_21);  softmax_10 = scores_21 = None
2025-03-04T22:55:57.7629096Z         
2025-03-04T22:55:57.7629661Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7630074Z         attn_weights_21: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_20, p = 0.1, training = False);  attn_weights_20 = None
2025-03-04T22:55:57.7630158Z         
2025-03-04T22:55:57.7630927Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7631325Z         matmul_21: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_21, hidden_states_113);  attn_weights_21 = hidden_states_113 = None
2025-03-04T22:55:57.7631413Z         
2025-03-04T22:55:57.7632118Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7632366Z         transpose_54: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_21.transpose(1, 2);  matmul_21 = None
2025-03-04T22:55:57.7632629Z         contiguous_10: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_54.contiguous();  transpose_54 = None
2025-03-04T22:55:57.7632900Z         attn_output_20: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_10.view(4, -1, 512);  contiguous_10 = None
2025-03-04T22:55:57.7632986Z         
2025-03-04T22:55:57.7633533Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7634678Z         attn_output_21: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_20, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_20 = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7634763Z         
2025-03-04T22:55:57.7635425Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7635814Z         dropout_40: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_21, 0.1, False, False);  attn_output_21 = None
2025-03-04T22:55:57.7636153Z         hidden_states_114: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_109 + dropout_40;  hidden_states_109 = dropout_40 = None
2025-03-04T22:55:57.7636240Z         
2025-03-04T22:55:57.7636926Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7637122Z         to_48: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_114.to(torch.float32)
2025-03-04T22:55:57.7637296Z         pow_21: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_48.pow(2);  to_48 = None
2025-03-04T22:55:57.7637522Z         variance_20: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_21.mean(-1, keepdim = True);  pow_21 = None
2025-03-04T22:55:57.7637611Z         
2025-03-04T22:55:57.7638312Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7638495Z         add_46: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_20 + 1e-06;  variance_20 = None
2025-03-04T22:55:57.7638825Z         rsqrt_20: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_46);  add_46 = None
2025-03-04T22:55:57.7639095Z         hidden_states_115: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_114 * rsqrt_20;  rsqrt_20 = None
2025-03-04T22:55:57.7639180Z         
2025-03-04T22:55:57.7639796Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7640122Z         hidden_states_116: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_115.to(torch.bfloat16);  hidden_states_115 = None
2025-03-04T22:55:57.7640207Z         
2025-03-04T22:55:57.7640756Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7641780Z         normed_hidden_states_11: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_116;  l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_116 = None
2025-03-04T22:55:57.7641874Z         
2025-03-04T22:55:57.7642615Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7643817Z         linear_60: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_11, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_, None);  normed_hidden_states_11 = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7643907Z         
2025-03-04T22:55:57.7644643Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7644878Z         view_46: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_60.view(4, -1, 8, 64);  linear_60 = None
2025-03-04T22:55:57.7645124Z         query_states_11: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_46.transpose(1, 2);  view_46 = None
2025-03-04T22:55:57.7645208Z         
2025-03-04T22:55:57.7645825Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7646966Z         linear_61: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7647057Z         
2025-03-04T22:55:57.7647837Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7648104Z         view_47: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_61.view(4, -1, 8, 64);  linear_61 = None
2025-03-04T22:55:57.7648393Z         hidden_states_117: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_47.transpose(1, 2);  view_47 = None
2025-03-04T22:55:57.7648478Z         
2025-03-04T22:55:57.7649091Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7650381Z         linear_62: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7650471Z         
2025-03-04T22:55:57.7651198Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7651468Z         view_48: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_62.view(4, -1, 8, 64);  linear_62 = None
2025-03-04T22:55:57.7651749Z         hidden_states_118: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_48.transpose(1, 2);  view_48 = None
2025-03-04T22:55:57.7651838Z         
2025-03-04T22:55:57.7652422Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7652756Z         transpose_58: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_117.transpose(3, 2);  hidden_states_117 = None
2025-03-04T22:55:57.7652840Z         
2025-03-04T22:55:57.7653350Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7653751Z         scores_22: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.matmul(query_states_11, transpose_58);  query_states_11 = transpose_58 = None
2025-03-04T22:55:57.7653836Z         
2025-03-04T22:55:57.7654376Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7654703Z         scores_22 += position_bias_3;  scores_23: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_22;  scores_22 = None
2025-03-04T22:55:57.7654796Z         
2025-03-04T22:55:57.7655468Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7655661Z         float_14: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_23.float()
2025-03-04T22:55:57.7656004Z         softmax_11: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_14, dim = -1);  float_14 = None
2025-03-04T22:55:57.7656345Z         attn_weights_22: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = softmax_11.type_as(scores_23);  softmax_11 = scores_23 = None
2025-03-04T22:55:57.7656430Z         
2025-03-04T22:55:57.7656995Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7657471Z         attn_weights_23: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_22, p = 0.1, training = False);  attn_weights_22 = None
2025-03-04T22:55:57.7657558Z         
2025-03-04T22:55:57.7658380Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7658774Z         matmul_23: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_23, hidden_states_118);  attn_weights_23 = hidden_states_118 = None
2025-03-04T22:55:57.7658858Z         
2025-03-04T22:55:57.7659570Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7659968Z         transpose_59: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_23.transpose(1, 2);  matmul_23 = None
2025-03-04T22:55:57.7660236Z         contiguous_11: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_59.contiguous();  transpose_59 = None
2025-03-04T22:55:57.7660509Z         attn_output_22: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_11.view(4, -1, 512);  contiguous_11 = None
2025-03-04T22:55:57.7660597Z         
2025-03-04T22:55:57.7661145Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7662297Z         attn_output_23: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_22, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_, None);  attn_output_22 = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7662389Z         
2025-03-04T22:55:57.7663051Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:647 in forward, code: layer_output = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7663440Z         dropout_42: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_23, 0.1, False, False);  attn_output_23 = None
2025-03-04T22:55:57.7663769Z         layer_output_2: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_114 + dropout_42;  hidden_states_114 = dropout_42 = None
2025-03-04T22:55:57.7663857Z         
2025-03-04T22:55:57.7664536Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7664725Z         to_50: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_2.to(torch.float32)
2025-03-04T22:55:57.7664901Z         pow_22: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_50.pow(2);  to_50 = None
2025-03-04T22:55:57.7665137Z         variance_21: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_22.mean(-1, keepdim = True);  pow_22 = None
2025-03-04T22:55:57.7665221Z         
2025-03-04T22:55:57.7665923Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7666102Z         add_48: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_21 + 1e-06;  variance_21 = None
2025-03-04T22:55:57.7666285Z         rsqrt_21: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_48);  add_48 = None
2025-03-04T22:55:57.7666538Z         hidden_states_119: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_2 * rsqrt_21;  rsqrt_21 = None
2025-03-04T22:55:57.7666626Z         
2025-03-04T22:55:57.7667240Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7667575Z         hidden_states_120: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_119.to(torch.bfloat16);  hidden_states_119 = None
2025-03-04T22:55:57.7667659Z         
2025-03-04T22:55:57.7668234Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7669271Z         forwarded_states_8: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_layer_norm_parameters_weight_ * hidden_states_120;  l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = hidden_states_120 = None
2025-03-04T22:55:57.7669356Z         
2025-03-04T22:55:57.7669918Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7671331Z         hidden_states_121: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_8, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_8 = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7671419Z         
2025-03-04T22:55:57.7671981Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7672412Z         hidden_states_122: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_121, inplace = False);  hidden_states_121 = None
2025-03-04T22:55:57.7672495Z         
2025-03-04T22:55:57.7673291Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7673743Z         hidden_states_123: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_122, 0.1, False, False);  hidden_states_122 = None
2025-03-04T22:55:57.7673830Z         
2025-03-04T22:55:57.7674385Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7675579Z         hidden_states_124: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_123, l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_123 = l_self_modules_decoder_modules_block_modules_2_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7675667Z         
2025-03-04T22:55:57.7676323Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7676733Z         dropout_44: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_124, 0.1, False, False);  hidden_states_124 = None
2025-03-04T22:55:57.7677055Z         hidden_states_125: "bf16[4, 1, 512][512, 512, 1]cuda:0" = layer_output_2 + dropout_44;  layer_output_2 = dropout_44 = None
2025-03-04T22:55:57.7677143Z         
2025-03-04T22:55:57.7677824Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7678035Z         to_52: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_125.to(torch.float32)
2025-03-04T22:55:57.7678233Z         pow_23: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_52.pow(2);  to_52 = None
2025-03-04T22:55:57.7678477Z         variance_22: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_23.mean(-1, keepdim = True);  pow_23 = None
2025-03-04T22:55:57.7678560Z         
2025-03-04T22:55:57.7679265Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7679444Z         add_50: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_22 + 1e-06;  variance_22 = None
2025-03-04T22:55:57.7679632Z         rsqrt_22: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_50);  add_50 = None
2025-03-04T22:55:57.7679894Z         hidden_states_126: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_125 * rsqrt_22;  rsqrt_22 = None
2025-03-04T22:55:57.7679981Z         
2025-03-04T22:55:57.7680597Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7681080Z         hidden_states_127: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_126.to(torch.bfloat16);  hidden_states_126 = None
2025-03-04T22:55:57.7681168Z         
2025-03-04T22:55:57.7681717Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7682733Z         normed_hidden_states_12: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_127;  l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_127 = None
2025-03-04T22:55:57.7682821Z         
2025-03-04T22:55:57.7683566Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7684693Z         linear_66: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_12, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7684779Z         
2025-03-04T22:55:57.7685509Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7685757Z         view_50: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_66.view(4, -1, 8, 64);  linear_66 = None
2025-03-04T22:55:57.7685999Z         query_states_12: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_50.transpose(1, 2);  view_50 = None
2025-03-04T22:55:57.7686091Z         
2025-03-04T22:55:57.7686698Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7687869Z         linear_67: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_12, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7687955Z         
2025-03-04T22:55:57.7688685Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7688923Z         view_51: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_67.view(4, -1, 8, 64);  linear_67 = None
2025-03-04T22:55:57.7689182Z         hidden_states_128: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_51.transpose(1, 2);  view_51 = None
2025-03-04T22:55:57.7689270Z         
2025-03-04T22:55:57.7689869Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7691054Z         linear_68: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_12, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_12 = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7691138Z         
2025-03-04T22:55:57.7691946Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7692263Z         view_52: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_68.view(4, -1, 8, 64);  linear_68 = None
2025-03-04T22:55:57.7692521Z         hidden_states_129: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_52.transpose(1, 2);  view_52 = None
2025-03-04T22:55:57.7692605Z         
2025-03-04T22:55:57.7693183Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7693486Z         transpose_63: "bf16[4, 8, 64, 1][512, 64, 1, 512]cuda:0" = hidden_states_128.transpose(3, 2);  hidden_states_128 = None
2025-03-04T22:55:57.7693574Z         
2025-03-04T22:55:57.7694084Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7694439Z         scores_24: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.matmul(query_states_12, transpose_63);  query_states_12 = transpose_63 = None
2025-03-04T22:55:57.7694523Z         
2025-03-04T22:55:57.7695058Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7695336Z         scores_24 += position_bias_1;  scores_25: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_24;  scores_24 = None
2025-03-04T22:55:57.7695422Z         
2025-03-04T22:55:57.7696096Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7696265Z         float_15: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_25.float()
2025-03-04T22:55:57.7696562Z         softmax_12: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.softmax(float_15, dim = -1);  float_15 = None
2025-03-04T22:55:57.7696855Z         attn_weights_24: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = softmax_12.type_as(scores_25);  softmax_12 = scores_25 = None
2025-03-04T22:55:57.7696943Z         
2025-03-04T22:55:57.7697507Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7697942Z         attn_weights_25: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_24, p = 0.1, training = False);  attn_weights_24 = None
2025-03-04T22:55:57.7698032Z         
2025-03-04T22:55:57.7698824Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7699210Z         matmul_25: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_25, hidden_states_129);  attn_weights_25 = hidden_states_129 = None
2025-03-04T22:55:57.7699300Z         
2025-03-04T22:55:57.7700011Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7700261Z         transpose_64: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_25.transpose(1, 2);  matmul_25 = None
2025-03-04T22:55:57.7700523Z         contiguous_12: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_64.contiguous();  transpose_64 = None
2025-03-04T22:55:57.7700798Z         attn_output_24: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_12.view(4, -1, 512);  contiguous_12 = None
2025-03-04T22:55:57.7700881Z         
2025-03-04T22:55:57.7701429Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7702740Z         attn_output_25: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_24, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_24 = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7702831Z         
2025-03-04T22:55:57.7703487Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7703877Z         dropout_46: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_25, 0.1, False, False);  attn_output_25 = None
2025-03-04T22:55:57.7704217Z         hidden_states_130: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_125 + dropout_46;  hidden_states_125 = dropout_46 = None
2025-03-04T22:55:57.7704301Z         
2025-03-04T22:55:57.7704993Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7705188Z         to_54: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_130.to(torch.float32)
2025-03-04T22:55:57.7705363Z         pow_24: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_54.pow(2);  to_54 = None
2025-03-04T22:55:57.7705589Z         variance_23: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_24.mean(-1, keepdim = True);  pow_24 = None
2025-03-04T22:55:57.7705676Z         
2025-03-04T22:55:57.7706378Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7706560Z         add_52: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_23 + 1e-06;  variance_23 = None
2025-03-04T22:55:57.7706741Z         rsqrt_23: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_52);  add_52 = None
2025-03-04T22:55:57.7707016Z         hidden_states_131: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_130 * rsqrt_23;  rsqrt_23 = None
2025-03-04T22:55:57.7707101Z         
2025-03-04T22:55:57.7707716Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7708041Z         hidden_states_132: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_131.to(torch.bfloat16);  hidden_states_131 = None
2025-03-04T22:55:57.7708128Z         
2025-03-04T22:55:57.7708674Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7709703Z         normed_hidden_states_13: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_132;  l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_132 = None
2025-03-04T22:55:57.7709790Z         
2025-03-04T22:55:57.7710534Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7711736Z         linear_70: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_13, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_, None);  normed_hidden_states_13 = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7711820Z         
2025-03-04T22:55:57.7712631Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7712940Z         view_54: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_70.view(4, -1, 8, 64);  linear_70 = None
2025-03-04T22:55:57.7713186Z         query_states_13: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_54.transpose(1, 2);  view_54 = None
2025-03-04T22:55:57.7713269Z         
2025-03-04T22:55:57.7713881Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7715014Z         linear_71: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7715109Z         
2025-03-04T22:55:57.7715836Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7716102Z         view_55: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_71.view(4, -1, 8, 64);  linear_71 = None
2025-03-04T22:55:57.7716386Z         hidden_states_133: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_55.transpose(1, 2);  view_55 = None
2025-03-04T22:55:57.7716473Z         
2025-03-04T22:55:57.7717090Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7718272Z         linear_72: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7718361Z         
2025-03-04T22:55:57.7719087Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7719357Z         view_56: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_72.view(4, -1, 8, 64);  linear_72 = None
2025-03-04T22:55:57.7719637Z         hidden_states_134: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_56.transpose(1, 2);  view_56 = None
2025-03-04T22:55:57.7719726Z         
2025-03-04T22:55:57.7720300Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7720641Z         transpose_68: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_133.transpose(3, 2);  hidden_states_133 = None
2025-03-04T22:55:57.7720724Z         
2025-03-04T22:55:57.7721241Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7721641Z         scores_26: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.matmul(query_states_13, transpose_68);  query_states_13 = transpose_68 = None
2025-03-04T22:55:57.7721729Z         
2025-03-04T22:55:57.7722262Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7722588Z         scores_26 += position_bias_3;  scores_27: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_26;  scores_26 = None
2025-03-04T22:55:57.7722760Z         
2025-03-04T22:55:57.7723511Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7723703Z         float_16: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_27.float()
2025-03-04T22:55:57.7724052Z         softmax_13: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_16, dim = -1);  float_16 = None
2025-03-04T22:55:57.7724392Z         attn_weights_26: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = softmax_13.type_as(scores_27);  softmax_13 = scores_27 = None
2025-03-04T22:55:57.7724477Z         
2025-03-04T22:55:57.7725216Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7725693Z         attn_weights_27: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_26, p = 0.1, training = False);  attn_weights_26 = None
2025-03-04T22:55:57.7725786Z         
2025-03-04T22:55:57.7726552Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7726941Z         matmul_27: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_27, hidden_states_134);  attn_weights_27 = hidden_states_134 = None
2025-03-04T22:55:57.7727024Z         
2025-03-04T22:55:57.7727778Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7728030Z         transpose_69: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_27.transpose(1, 2);  matmul_27 = None
2025-03-04T22:55:57.7728350Z         contiguous_13: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_69.contiguous();  transpose_69 = None
2025-03-04T22:55:57.7728628Z         attn_output_26: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_13.view(4, -1, 512);  contiguous_13 = None
2025-03-04T22:55:57.7728714Z         
2025-03-04T22:55:57.7729262Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7730414Z         attn_output_27: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_26, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_, None);  attn_output_26 = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7730499Z         
2025-03-04T22:55:57.7731169Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:647 in forward, code: layer_output = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7731559Z         dropout_48: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_27, 0.1, False, False);  attn_output_27 = None
2025-03-04T22:55:57.7731889Z         layer_output_3: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_130 + dropout_48;  hidden_states_130 = dropout_48 = None
2025-03-04T22:55:57.7731976Z         
2025-03-04T22:55:57.7732658Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7732850Z         to_56: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_3.to(torch.float32)
2025-03-04T22:55:57.7733022Z         pow_25: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_56.pow(2);  to_56 = None
2025-03-04T22:55:57.7733487Z         variance_24: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_25.mean(-1, keepdim = True);  pow_25 = None
2025-03-04T22:55:57.7733575Z         
2025-03-04T22:55:57.7734280Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7734460Z         add_54: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_24 + 1e-06;  variance_24 = None
2025-03-04T22:55:57.7734644Z         rsqrt_24: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_54);  add_54 = None
2025-03-04T22:55:57.7734898Z         hidden_states_135: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_3 * rsqrt_24;  rsqrt_24 = None
2025-03-04T22:55:57.7734987Z         
2025-03-04T22:55:57.7735604Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7735941Z         hidden_states_136: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_135.to(torch.bfloat16);  hidden_states_135 = None
2025-03-04T22:55:57.7736026Z         
2025-03-04T22:55:57.7736576Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7737587Z         forwarded_states_9: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_layer_norm_parameters_weight_ * hidden_states_136;  l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = hidden_states_136 = None
2025-03-04T22:55:57.7737674Z         
2025-03-04T22:55:57.7738260Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7739523Z         hidden_states_137: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_9, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_9 = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7739611Z         
2025-03-04T22:55:57.7740173Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7740602Z         hidden_states_138: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_137, inplace = False);  hidden_states_137 = None
2025-03-04T22:55:57.7740690Z         
2025-03-04T22:55:57.7741275Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7741727Z         hidden_states_139: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_138, 0.1, False, False);  hidden_states_138 = None
2025-03-04T22:55:57.7741816Z         
2025-03-04T22:55:57.7742370Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7743558Z         hidden_states_140: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_139, l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_139 = l_self_modules_decoder_modules_block_modules_3_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7743644Z         
2025-03-04T22:55:57.7744377Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7744883Z         dropout_50: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_140, 0.1, False, False);  hidden_states_140 = None
2025-03-04T22:55:57.7745214Z         hidden_states_141: "bf16[4, 1, 512][512, 512, 1]cuda:0" = layer_output_3 + dropout_50;  layer_output_3 = dropout_50 = None
2025-03-04T22:55:57.7745299Z         
2025-03-04T22:55:57.7745981Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7746179Z         to_58: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_141.to(torch.float32)
2025-03-04T22:55:57.7746351Z         pow_26: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_58.pow(2);  to_58 = None
2025-03-04T22:55:57.7746579Z         variance_25: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_26.mean(-1, keepdim = True);  pow_26 = None
2025-03-04T22:55:57.7746670Z         
2025-03-04T22:55:57.7747380Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7747560Z         add_56: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_25 + 1e-06;  variance_25 = None
2025-03-04T22:55:57.7747745Z         rsqrt_25: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_56);  add_56 = None
2025-03-04T22:55:57.7748008Z         hidden_states_142: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_141 * rsqrt_25;  rsqrt_25 = None
2025-03-04T22:55:57.7748096Z         
2025-03-04T22:55:57.7748711Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7749038Z         hidden_states_143: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_142.to(torch.bfloat16);  hidden_states_142 = None
2025-03-04T22:55:57.7749131Z         
2025-03-04T22:55:57.7749680Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7750697Z         normed_hidden_states_14: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_143;  l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_143 = None
2025-03-04T22:55:57.7750786Z         
2025-03-04T22:55:57.7751530Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7752654Z         linear_76: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_14, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7752741Z         
2025-03-04T22:55:57.7753469Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7753708Z         view_58: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_76.view(4, -1, 8, 64);  linear_76 = None
2025-03-04T22:55:57.7753951Z         query_states_14: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_58.transpose(1, 2);  view_58 = None
2025-03-04T22:55:57.7754036Z         
2025-03-04T22:55:57.7754763Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7755951Z         linear_77: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_14, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7756037Z         
2025-03-04T22:55:57.7756770Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7757004Z         view_59: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_77.view(4, -1, 8, 64);  linear_77 = None
2025-03-04T22:55:57.7757258Z         hidden_states_144: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_59.transpose(1, 2);  view_59 = None
2025-03-04T22:55:57.7757351Z         
2025-03-04T22:55:57.7757962Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7759191Z         linear_78: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_14, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_14 = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7759280Z         
2025-03-04T22:55:57.7760008Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7760249Z         view_60: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_78.view(4, -1, 8, 64);  linear_78 = None
2025-03-04T22:55:57.7760500Z         hidden_states_145: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_60.transpose(1, 2);  view_60 = None
2025-03-04T22:55:57.7760584Z         
2025-03-04T22:55:57.7761161Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7761464Z         transpose_73: "bf16[4, 8, 64, 1][512, 64, 1, 512]cuda:0" = hidden_states_144.transpose(3, 2);  hidden_states_144 = None
2025-03-04T22:55:57.7761554Z         
2025-03-04T22:55:57.7762065Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7762411Z         scores_28: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.matmul(query_states_14, transpose_73);  query_states_14 = transpose_73 = None
2025-03-04T22:55:57.7762501Z         
2025-03-04T22:55:57.7763042Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7763318Z         scores_28 += position_bias_1;  scores_29: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_28;  scores_28 = None
2025-03-04T22:55:57.7763409Z         
2025-03-04T22:55:57.7764083Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7764252Z         float_17: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_29.float()
2025-03-04T22:55:57.7764545Z         softmax_14: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.softmax(float_17, dim = -1);  float_17 = None
2025-03-04T22:55:57.7764834Z         attn_weights_28: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = softmax_14.type_as(scores_29);  softmax_14 = scores_29 = None
2025-03-04T22:55:57.7765072Z         
2025-03-04T22:55:57.7765643Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7766055Z         attn_weights_29: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_28, p = 0.1, training = False);  attn_weights_28 = None
2025-03-04T22:55:57.7766145Z         
2025-03-04T22:55:57.7766909Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7767299Z         matmul_29: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_29, hidden_states_145);  attn_weights_29 = hidden_states_145 = None
2025-03-04T22:55:57.7767390Z         
2025-03-04T22:55:57.7768193Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7768485Z         transpose_74: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_29.transpose(1, 2);  matmul_29 = None
2025-03-04T22:55:57.7768749Z         contiguous_14: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_74.contiguous();  transpose_74 = None
2025-03-04T22:55:57.7769024Z         attn_output_28: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_14.view(4, -1, 512);  contiguous_14 = None
2025-03-04T22:55:57.7769109Z         
2025-03-04T22:55:57.7769658Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7770794Z         attn_output_29: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_28, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_28 = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7770887Z         
2025-03-04T22:55:57.7771545Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7771940Z         dropout_52: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_29, 0.1, False, False);  attn_output_29 = None
2025-03-04T22:55:57.7772279Z         hidden_states_146: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_141 + dropout_52;  hidden_states_141 = dropout_52 = None
2025-03-04T22:55:57.7772367Z         
2025-03-04T22:55:57.7773055Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7773255Z         to_60: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_146.to(torch.float32)
2025-03-04T22:55:57.7773429Z         pow_27: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_60.pow(2);  to_60 = None
2025-03-04T22:55:57.7773659Z         variance_26: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_27.mean(-1, keepdim = True);  pow_27 = None
2025-03-04T22:55:57.7773743Z         
2025-03-04T22:55:57.7774446Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7774632Z         add_58: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_26 + 1e-06;  variance_26 = None
2025-03-04T22:55:57.7774812Z         rsqrt_26: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_58);  add_58 = None
2025-03-04T22:55:57.7775231Z         hidden_states_147: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_146 * rsqrt_26;  rsqrt_26 = None
2025-03-04T22:55:57.7775319Z         
2025-03-04T22:55:57.7775936Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7776261Z         hidden_states_148: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_147.to(torch.bfloat16);  hidden_states_147 = None
2025-03-04T22:55:57.7776350Z         
2025-03-04T22:55:57.7776896Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7777919Z         normed_hidden_states_15: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_148;  l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_148 = None
2025-03-04T22:55:57.7778015Z         
2025-03-04T22:55:57.7778764Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7779961Z         linear_80: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_15, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_, None);  normed_hidden_states_15 = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7780055Z         
2025-03-04T22:55:57.7780784Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7781034Z         view_62: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_80.view(4, -1, 8, 64);  linear_80 = None
2025-03-04T22:55:57.7781284Z         query_states_15: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_62.transpose(1, 2);  view_62 = None
2025-03-04T22:55:57.7781368Z         
2025-03-04T22:55:57.7781982Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7783111Z         linear_81: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7783200Z         
2025-03-04T22:55:57.7783933Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7784201Z         view_63: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_81.view(4, -1, 8, 64);  linear_81 = None
2025-03-04T22:55:57.7784485Z         hidden_states_149: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_63.transpose(1, 2);  view_63 = None
2025-03-04T22:55:57.7784573Z         
2025-03-04T22:55:57.7785183Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7786416Z         linear_82: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7786574Z         
2025-03-04T22:55:57.7787308Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7800097Z         view_64: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_82.view(4, -1, 8, 64);  linear_82 = None
2025-03-04T22:55:57.7800422Z         hidden_states_150: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_64.transpose(1, 2);  view_64 = None
2025-03-04T22:55:57.7800512Z         
2025-03-04T22:55:57.7801114Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7801466Z         transpose_78: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_149.transpose(3, 2);  hidden_states_149 = None
2025-03-04T22:55:57.7801551Z         
2025-03-04T22:55:57.7802078Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7802486Z         scores_30: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.matmul(query_states_15, transpose_78);  query_states_15 = transpose_78 = None
2025-03-04T22:55:57.7802578Z         
2025-03-04T22:55:57.7803119Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7803452Z         scores_30 += position_bias_3;  scores_31: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_30;  scores_30 = None
2025-03-04T22:55:57.7803535Z         
2025-03-04T22:55:57.7804229Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7804425Z         float_18: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_31.float()
2025-03-04T22:55:57.7804782Z         softmax_15: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_18, dim = -1);  float_18 = None
2025-03-04T22:55:57.7805127Z         attn_weights_30: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = softmax_15.type_as(scores_31);  softmax_15 = scores_31 = None
2025-03-04T22:55:57.7805217Z         
2025-03-04T22:55:57.7805790Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7806278Z         attn_weights_31: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_30, p = 0.1, training = False);  attn_weights_30 = None
2025-03-04T22:55:57.7806368Z         
2025-03-04T22:55:57.7807160Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7807558Z         matmul_31: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_31, hidden_states_150);  attn_weights_31 = hidden_states_150 = None
2025-03-04T22:55:57.7807729Z         
2025-03-04T22:55:57.7808455Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7808702Z         transpose_79: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_31.transpose(1, 2);  matmul_31 = None
2025-03-04T22:55:57.7808968Z         contiguous_15: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_79.contiguous();  transpose_79 = None
2025-03-04T22:55:57.7809451Z         attn_output_30: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_15.view(4, -1, 512);  contiguous_15 = None
2025-03-04T22:55:57.7809541Z         
2025-03-04T22:55:57.7810097Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7811273Z         attn_output_31: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_30, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_, None);  attn_output_30 = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7811357Z         
2025-03-04T22:55:57.7812028Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:647 in forward, code: layer_output = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7812430Z         dropout_54: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_31, 0.1, False, False);  attn_output_31 = None
2025-03-04T22:55:57.7812766Z         layer_output_4: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_146 + dropout_54;  hidden_states_146 = dropout_54 = None
2025-03-04T22:55:57.7812847Z         
2025-03-04T22:55:57.7813549Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7813738Z         to_62: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_4.to(torch.float32)
2025-03-04T22:55:57.7813916Z         pow_28: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_62.pow(2);  to_62 = None
2025-03-04T22:55:57.7814146Z         variance_27: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_28.mean(-1, keepdim = True);  pow_28 = None
2025-03-04T22:55:57.7814237Z         
2025-03-04T22:55:57.7814953Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7815140Z         add_60: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_27 + 1e-06;  variance_27 = None
2025-03-04T22:55:57.7815326Z         rsqrt_27: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_60);  add_60 = None
2025-03-04T22:55:57.7815583Z         hidden_states_151: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_4 * rsqrt_27;  rsqrt_27 = None
2025-03-04T22:55:57.7815672Z         
2025-03-04T22:55:57.7816299Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7816631Z         hidden_states_152: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_151.to(torch.bfloat16);  hidden_states_151 = None
2025-03-04T22:55:57.7816718Z         
2025-03-04T22:55:57.7817280Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7818369Z         forwarded_states_10: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_layer_norm_parameters_weight_ * hidden_states_152;  l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = hidden_states_152 = None
2025-03-04T22:55:57.7818457Z         
2025-03-04T22:55:57.7819019Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7820357Z         hidden_states_153: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_10, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_10 = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7820517Z         
2025-03-04T22:55:57.7821089Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7821522Z         hidden_states_154: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_153, inplace = False);  hidden_states_153 = None
2025-03-04T22:55:57.7821609Z         
2025-03-04T22:55:57.7822193Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7822653Z         hidden_states_155: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_154, 0.1, False, False);  hidden_states_154 = None
2025-03-04T22:55:57.7822744Z         
2025-03-04T22:55:57.7823307Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7824521Z         hidden_states_156: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_155, l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_155 = l_self_modules_decoder_modules_block_modules_4_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7824604Z         
2025-03-04T22:55:57.7825428Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7825854Z         dropout_56: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_156, 0.1, False, False);  hidden_states_156 = None
2025-03-04T22:55:57.7826184Z         hidden_states_157: "bf16[4, 1, 512][512, 512, 1]cuda:0" = layer_output_4 + dropout_56;  layer_output_4 = dropout_56 = None
2025-03-04T22:55:57.7826272Z         
2025-03-04T22:55:57.7826969Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7827161Z         to_64: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_157.to(torch.float32)
2025-03-04T22:55:57.7827337Z         pow_29: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_64.pow(2);  to_64 = None
2025-03-04T22:55:57.7827563Z         variance_28: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_29.mean(-1, keepdim = True);  pow_29 = None
2025-03-04T22:55:57.7827659Z         
2025-03-04T22:55:57.7828428Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7828608Z         add_62: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_28 + 1e-06;  variance_28 = None
2025-03-04T22:55:57.7828788Z         rsqrt_28: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_62);  add_62 = None
2025-03-04T22:55:57.7829052Z         hidden_states_158: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_157 * rsqrt_28;  rsqrt_28 = None
2025-03-04T22:55:57.7829133Z         
2025-03-04T22:55:57.7829765Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7830095Z         hidden_states_159: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_158.to(torch.bfloat16);  hidden_states_158 = None
2025-03-04T22:55:57.7830323Z         
2025-03-04T22:55:57.7830995Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7832034Z         normed_hidden_states_16: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_ * hidden_states_159;  l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_layer_norm_parameters_weight_ = hidden_states_159 = None
2025-03-04T22:55:57.7832127Z         
2025-03-04T22:55:57.7832881Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7834021Z         linear_86: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_16, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7834110Z         
2025-03-04T22:55:57.7834854Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7835089Z         view_66: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_86.view(4, -1, 8, 64);  linear_86 = None
2025-03-04T22:55:57.7835339Z         query_states_16: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_66.transpose(1, 2);  view_66 = None
2025-03-04T22:55:57.7835421Z         
2025-03-04T22:55:57.7836037Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7837182Z         linear_87: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_16, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7837268Z         
2025-03-04T22:55:57.7838019Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7838253Z         view_67: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_87.view(4, -1, 8, 64);  linear_87 = None
2025-03-04T22:55:57.7838508Z         hidden_states_160: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_67.transpose(1, 2);  view_67 = None
2025-03-04T22:55:57.7838600Z         
2025-03-04T22:55:57.7839218Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:498 in project, code: hidden_states = shape(proj_layer(hidden_states))
2025-03-04T22:55:57.7840422Z         linear_88: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_16, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_, None);  normed_hidden_states_16 = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7840510Z         
2025-03-04T22:55:57.7841248Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7841484Z         view_68: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_88.view(4, -1, 8, 64);  linear_88 = None
2025-03-04T22:55:57.7841895Z         hidden_states_161: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_68.transpose(1, 2);  view_68 = None
2025-03-04T22:55:57.7841985Z         
2025-03-04T22:55:57.7842564Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7842869Z         transpose_83: "bf16[4, 8, 64, 1][512, 64, 1, 512]cuda:0" = hidden_states_160.transpose(3, 2);  hidden_states_160 = None
2025-03-04T22:55:57.7842953Z         
2025-03-04T22:55:57.7843464Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7843826Z         scores_32: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.matmul(query_states_16, transpose_83);  query_states_16 = transpose_83 = None
2025-03-04T22:55:57.7843910Z         
2025-03-04T22:55:57.7844463Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7844785Z         scores_32 += position_bias_1;  scores_33: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_32;  scores_32 = position_bias_1 = None
2025-03-04T22:55:57.7844869Z         
2025-03-04T22:55:57.7845553Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7845723Z         float_19: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = scores_33.float()
2025-03-04T22:55:57.7846017Z         softmax_16: "f32[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.softmax(float_19, dim = -1);  float_19 = None
2025-03-04T22:55:57.7846308Z         attn_weights_32: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = softmax_16.type_as(scores_33);  softmax_16 = scores_33 = None
2025-03-04T22:55:57.7846393Z         
2025-03-04T22:55:57.7846977Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7847390Z         attn_weights_33: "bf16[4, 8, 1, 1][8, 1, 1, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_32, p = 0.1, training = False);  attn_weights_32 = None
2025-03-04T22:55:57.7847479Z         
2025-03-04T22:55:57.7848395Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7848787Z         matmul_33: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_33, hidden_states_161);  attn_weights_33 = hidden_states_161 = None
2025-03-04T22:55:57.7848869Z         
2025-03-04T22:55:57.7849598Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7849851Z         transpose_84: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_33.transpose(1, 2);  matmul_33 = None
2025-03-04T22:55:57.7850120Z         contiguous_16: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_84.contiguous();  transpose_84 = None
2025-03-04T22:55:57.7850397Z         attn_output_32: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_16.view(4, -1, 512);  contiguous_16 = None
2025-03-04T22:55:57.7850485Z         
2025-03-04T22:55:57.7851039Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7852303Z         attn_output_33: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_32, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_, None);  attn_output_32 = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_0_modules_self_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7852465Z         
2025-03-04T22:55:57.7853133Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:611 in forward, code: hidden_states = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7853531Z         dropout_58: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_33, 0.1, False, False);  attn_output_33 = None
2025-03-04T22:55:57.7853873Z         hidden_states_162: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_157 + dropout_58;  hidden_states_157 = dropout_58 = None
2025-03-04T22:55:57.7853961Z         
2025-03-04T22:55:57.7854653Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7854861Z         to_66: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_162.to(torch.float32)
2025-03-04T22:55:57.7855034Z         pow_30: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_66.pow(2);  to_66 = None
2025-03-04T22:55:57.7855265Z         variance_29: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_30.mean(-1, keepdim = True);  pow_30 = None
2025-03-04T22:55:57.7855347Z         
2025-03-04T22:55:57.7856061Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7856242Z         add_64: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_29 + 1e-06;  variance_29 = None
2025-03-04T22:55:57.7856425Z         rsqrt_29: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_64);  add_64 = None
2025-03-04T22:55:57.7856687Z         hidden_states_163: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_162 * rsqrt_29;  rsqrt_29 = None
2025-03-04T22:55:57.7856778Z         
2025-03-04T22:55:57.7857403Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7857733Z         hidden_states_164: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_163.to(torch.bfloat16);  hidden_states_163 = None
2025-03-04T22:55:57.7857816Z         
2025-03-04T22:55:57.7858426Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7859474Z         normed_hidden_states_17: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_ * hidden_states_164;  l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_layer_norm_parameters_weight_ = hidden_states_164 = None
2025-03-04T22:55:57.7859561Z         
2025-03-04T22:55:57.7860327Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:521 in forward, code: query_states = shape(self.q(hidden_states))  # (batch_size, n_heads, seq_length, dim_per_head)
2025-03-04T22:55:57.7861550Z         linear_90: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(normed_hidden_states_17, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_, None);  normed_hidden_states_17 = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_q_parameters_weight_ = None
2025-03-04T22:55:57.7861640Z         
2025-03-04T22:55:57.7862377Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7862770Z         view_70: "bf16[4, 1, 8, 64][512, 512, 64, 1]cuda:0" = linear_90.view(4, -1, 8, 64);  linear_90 = None
2025-03-04T22:55:57.7863015Z         query_states_17: "bf16[4, 8, 1, 64][512, 64, 512, 1]cuda:0" = view_70.transpose(1, 2);  view_70 = None
2025-03-04T22:55:57.7863103Z         
2025-03-04T22:55:57.7863720Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7864881Z         linear_91: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_, None);  l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_k_parameters_weight_ = None
2025-03-04T22:55:57.7864968Z         
2025-03-04T22:55:57.7865709Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7865979Z         view_71: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_91.view(4, -1, 8, 64);  linear_91 = None
2025-03-04T22:55:57.7866262Z         hidden_states_165: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_71.transpose(1, 2);  view_71 = None
2025-03-04T22:55:57.7866351Z         
2025-03-04T22:55:57.7866967Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:502 in project, code: hidden_states = shape(proj_layer(key_value_states))
2025-03-04T22:55:57.7868223Z         linear_92: "bf16[4, 2048, 512][1048576, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_76, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_, None);  hidden_states_76 = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_v_parameters_weight_ = None
2025-03-04T22:55:57.7868311Z         
2025-03-04T22:55:57.7869052Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:487 in shape, code: return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
2025-03-04T22:55:57.7869314Z         view_72: "bf16[4, 2048, 8, 64][1048576, 512, 64, 1]cuda:0" = linear_92.view(4, -1, 8, 64);  linear_92 = None
2025-03-04T22:55:57.7869596Z         hidden_states_166: "bf16[4, 8, 2048, 64][1048576, 64, 512, 1]cuda:0" = view_72.transpose(1, 2);  view_72 = None
2025-03-04T22:55:57.7869678Z         
2025-03-04T22:55:57.7870265Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:533 in forward, code: query_states, key_states.transpose(3, 2)
2025-03-04T22:55:57.7870595Z         transpose_88: "bf16[4, 8, 64, 2048][1048576, 64, 1, 512]cuda:0" = hidden_states_165.transpose(3, 2);  hidden_states_165 = None
2025-03-04T22:55:57.7870690Z         
2025-03-04T22:55:57.7871203Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:532 in forward, code: scores = torch.matmul(
2025-03-04T22:55:57.7871616Z         scores_34: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.matmul(query_states_17, transpose_88);  query_states_17 = transpose_88 = None
2025-03-04T22:55:57.7871704Z         
2025-03-04T22:55:57.7872245Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:561 in forward, code: scores += position_bias_masked
2025-03-04T22:55:57.7872620Z         scores_34 += position_bias_3;  scores_35: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_34;  scores_34 = position_bias_3 = None
2025-03-04T22:55:57.7872702Z         
2025-03-04T22:55:57.7873520Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:562 in forward, code: attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T22:55:57.7873781Z         float_20: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = scores_35.float()
2025-03-04T22:55:57.7874134Z         softmax_17: "f32[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.softmax(float_20, dim = -1);  float_20 = None
2025-03-04T22:55:57.7874472Z         attn_weights_34: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = softmax_17.type_as(scores_35);  softmax_17 = scores_35 = None
2025-03-04T22:55:57.7874559Z         
2025-03-04T22:55:57.7875129Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:565 in forward, code: attn_weights = nn.functional.dropout(
2025-03-04T22:55:57.7875607Z         attn_weights_35: "bf16[4, 8, 1, 2048][16384, 2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(attn_weights_34, p = 0.1, training = False);  attn_weights_34 = None
2025-03-04T22:55:57.7875696Z         
2025-03-04T22:55:57.7876485Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:573 in forward, code: attn_output = unshape(torch.matmul(attn_weights, value_states))  # (batch_size, seq_length, dim)
2025-03-04T22:55:57.7876874Z         matmul_35: "bf16[4, 8, 1, 64][512, 64, 64, 1]cuda:0" = torch.matmul(attn_weights_35, hidden_states_166);  attn_weights_35 = hidden_states_166 = None
2025-03-04T22:55:57.7876964Z         
2025-03-04T22:55:57.7877682Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:491 in unshape, code: return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
2025-03-04T22:55:57.7877932Z         transpose_89: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = matmul_35.transpose(1, 2);  matmul_35 = None
2025-03-04T22:55:57.7878197Z         contiguous_17: "bf16[4, 1, 8, 64][512, 64, 64, 1]cuda:0" = transpose_89.contiguous();  transpose_89 = None
2025-03-04T22:55:57.7878483Z         attn_output_34: "bf16[4, 1, 512][512, 64, 1]cuda:0" = contiguous_17.view(4, -1, 512);  contiguous_17 = None
2025-03-04T22:55:57.7878569Z         
2025-03-04T22:55:57.7879129Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:574 in forward, code: attn_output = self.o(attn_output)
2025-03-04T22:55:57.7880306Z         attn_output_35: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(attn_output_34, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_, None);  attn_output_34 = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_1_modules_enc_dec_attention_modules_o_parameters_weight_ = None
2025-03-04T22:55:57.7880389Z         
2025-03-04T22:55:57.7881056Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:647 in forward, code: layer_output = hidden_states + self.dropout(attention_output[0])
2025-03-04T22:55:57.7881459Z         dropout_60: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(attn_output_35, 0.1, False, False);  attn_output_35 = None
2025-03-04T22:55:57.7881797Z         layer_output_5: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_162 + dropout_60;  hidden_states_162 = dropout_60 = None
2025-03-04T22:55:57.7881879Z         
2025-03-04T22:55:57.7882570Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7882756Z         to_68: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_5.to(torch.float32)
2025-03-04T22:55:57.7882938Z         pow_31: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_68.pow(2);  to_68 = None
2025-03-04T22:55:57.7883164Z         variance_30: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_31.mean(-1, keepdim = True);  pow_31 = None
2025-03-04T22:55:57.7883330Z         
2025-03-04T22:55:57.7884116Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7884304Z         add_66: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_30 + 1e-06;  variance_30 = None
2025-03-04T22:55:57.7884482Z         rsqrt_30: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_66);  add_66 = None
2025-03-04T22:55:57.7884741Z         hidden_states_167: "f32[4, 1, 512][512, 512, 1]cuda:0" = layer_output_5 * rsqrt_30;  rsqrt_30 = None
2025-03-04T22:55:57.7884823Z         
2025-03-04T22:55:57.7885453Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7885780Z         hidden_states_168: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_167.to(torch.bfloat16);  hidden_states_167 = None
2025-03-04T22:55:57.7885869Z         
2025-03-04T22:55:57.7886432Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7887466Z         forwarded_states_11: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_layer_norm_parameters_weight_ * hidden_states_168;  l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_layer_norm_parameters_weight_ = hidden_states_168 = None
2025-03-04T22:55:57.7887553Z         
2025-03-04T22:55:57.7888240Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:289 in forward, code: hidden_states = self.wi(hidden_states)
2025-03-04T22:55:57.7889506Z         hidden_states_169: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch._C._nn.linear(forwarded_states_11, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_, None);  forwarded_states_11 = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_dense_relu_dense_modules_wi_parameters_weight_ = None
2025-03-04T22:55:57.7889594Z         
2025-03-04T22:55:57.7890164Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:290 in forward, code: hidden_states = self.act(hidden_states)
2025-03-04T22:55:57.7890596Z         hidden_states_170: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.relu(hidden_states_169, inplace = False);  hidden_states_169 = None
2025-03-04T22:55:57.7890680Z         
2025-03-04T22:55:57.7891264Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:291 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7891723Z         hidden_states_171: "bf16[4, 1, 2048][2048, 2048, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_170, 0.1, False, False);  hidden_states_170 = None
2025-03-04T22:55:57.7891816Z         
2025-03-04T22:55:57.7892378Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:298 in forward, code: hidden_states = self.wo(hidden_states)
2025-03-04T22:55:57.7893595Z         hidden_states_172: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch._C._nn.linear(hidden_states_171, l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_, None);  hidden_states_171 = l_self_modules_decoder_modules_block_modules_5_modules_layer_modules_2_modules_dense_relu_dense_modules_wo_parameters_weight_ = None
2025-03-04T22:55:57.7893679Z         
2025-03-04T22:55:57.7894337Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:345 in forward, code: hidden_states = hidden_states + self.dropout(forwarded_states)
2025-03-04T22:55:57.7894928Z         dropout_62: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_172, 0.1, False, False);  hidden_states_172 = None
2025-03-04T22:55:57.7895260Z         hidden_states_173: "bf16[4, 1, 512][512, 512, 1]cuda:0" = layer_output_5 + dropout_62;  layer_output_5 = dropout_62 = None
2025-03-04T22:55:57.7895343Z         
2025-03-04T22:55:57.7896037Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:254 in forward, code: variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
2025-03-04T22:55:57.7896231Z         to_70: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_173.to(torch.float32)
2025-03-04T22:55:57.7896408Z         pow_32: "f32[4, 1, 512][512, 512, 1]cuda:0" = to_70.pow(2);  to_70 = None
2025-03-04T22:55:57.7896633Z         variance_31: "f32[4, 1, 1][1, 1, 1]cuda:0" = pow_32.mean(-1, keepdim = True);  pow_32 = None
2025-03-04T22:55:57.7896719Z         
2025-03-04T22:55:57.7897433Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:255 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
2025-03-04T22:55:57.7897618Z         add_68: "f32[4, 1, 1][1, 1, 1]cuda:0" = variance_31 + 1e-06;  variance_31 = None
2025-03-04T22:55:57.7897799Z         rsqrt_31: "f32[4, 1, 1][1, 1, 1]cuda:0" = torch.rsqrt(add_68);  add_68 = None
2025-03-04T22:55:57.7898149Z         hidden_states_174: "f32[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_173 * rsqrt_31;  hidden_states_173 = rsqrt_31 = None
2025-03-04T22:55:57.7898235Z         
2025-03-04T22:55:57.7898885Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:259 in forward, code: hidden_states = hidden_states.to(self.weight.dtype)
2025-03-04T22:55:57.7899207Z         hidden_states_175: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_174.to(torch.bfloat16);  hidden_states_174 = None
2025-03-04T22:55:57.7899297Z         
2025-03-04T22:55:57.7899862Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:261 in forward, code: return self.weight * hidden_states
2025-03-04T22:55:57.7900589Z         hidden_states_176: "bf16[4, 1, 512][512, 512, 1]cuda:0" = l_self_modules_decoder_modules_final_layer_norm_parameters_weight_ * hidden_states_175;  l_self_modules_decoder_modules_final_layer_norm_parameters_weight_ = hidden_states_175 = None
2025-03-04T22:55:57.7900676Z         
2025-03-04T22:55:57.7901265Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1158 in forward, code: hidden_states = self.dropout(hidden_states)
2025-03-04T22:55:57.7901700Z         hidden_states_177: "bf16[4, 1, 512][512, 512, 1]cuda:0" = torch.nn.functional.dropout(hidden_states_176, 0.1, False, False);  hidden_states_176 = None
2025-03-04T22:55:57.7901785Z         
2025-03-04T22:55:57.7902440Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1774 in forward, code: sequence_output = sequence_output * (self.model_dim**-0.5)
2025-03-04T22:55:57.7902768Z         sequence_output: "bf16[4, 1, 512][512, 512, 1]cuda:0" = hidden_states_177 * 0.04419417382415922;  hidden_states_177 = None
2025-03-04T22:55:57.7902855Z         
2025-03-04T22:55:57.7903436Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py:1776 in forward, code: lm_logits = self.lm_head(sequence_output)
2025-03-04T22:55:57.7904230Z         lm_logits: "bf16[4, 1, 32128][32128, 32128, 1]cuda:0" = torch._C._nn.linear(sequence_output, l_self_modules_encoder_modules_embed_tokens_parameters_weight_, None);  sequence_output = l_self_modules_encoder_modules_embed_tokens_parameters_weight_ = None
2025-03-04T22:55:57.7904315Z         
2025-03-04T22:55:57.7904922Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2414 in greedy_search, code: next_token_logits = outputs.logits[:, -1, :]
2025-03-04T22:55:57.7905472Z         next_token_logits: "bf16[4, 32128][32128, 1]cuda:0" = lm_logits[(slice(None, None, None), -1, slice(None, None, None))];  lm_logits = None
2025-03-04T22:55:57.7905560Z         
2025-03-04T22:55:57.7906198Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2440 in greedy_search, code: next_tokens = torch.argmax(next_tokens_scores, dim=-1)
2025-03-04T22:55:57.7906497Z         next_tokens: "i64[4][1]cuda:0" = torch.argmax(next_token_logits, dim = -1);  next_token_logits = None
2025-03-04T22:55:57.7906580Z         
2025-03-04T22:55:57.7907353Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2446 in greedy_search, code: next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
2025-03-04T22:55:57.7907576Z         mul_73: "i64[4][1]cuda:0" = next_tokens * unfinished_sequences;  next_tokens = None
2025-03-04T22:55:57.7907729Z         sub_5: "i64[4][1]cuda:0" = 1 - unfinished_sequences
2025-03-04T22:55:57.7907866Z         mul_74: "i64[4][1]cuda:0" = 0 * sub_5;  sub_5 = None
2025-03-04T22:55:57.7908056Z         next_tokens_1: "i64[4][1]cuda:0" = mul_73 + mul_74;  mul_73 = mul_74 = None
2025-03-04T22:55:57.7908146Z         
2025-03-04T22:55:57.7908814Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2449 in greedy_search, code: input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
2025-03-04T22:55:57.7909032Z         getitem_11: "i64[4, 1][1, 1]cuda:0" = next_tokens_1[(slice(None, None, None), None)]
2025-03-04T22:55:57.7909461Z         input_ids_2: "i64[4, 2][2, 1]cuda:0" = torch.cat([decoder_input_ids_start, getitem_11], dim = -1);  decoder_input_ids_start = getitem_11 = input_ids_2 = None
2025-03-04T22:55:57.7909548Z         
2025-03-04T22:55:57.7910335Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2459 in greedy_search, code: next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
2025-03-04T22:55:57.7910534Z         tile: "i64[1, 4][4, 1]cuda:0" = next_tokens_1.tile(1, 1);  next_tokens_1 = None
2025-03-04T22:55:57.7910805Z         unsqueeze_2: "i64[1, 1][1, 1]cuda:0" = eos_token_id_tensor.unsqueeze(1);  eos_token_id_tensor = None
2025-03-04T22:55:57.7910991Z         ne: "b8[1, 4][4, 1]cuda:0" = tile.ne(unsqueeze_2);  tile = unsqueeze_2 = None
2025-03-04T22:55:57.7911139Z         prod: "i64[4][1]cuda:0" = ne.prod(dim = 0);  ne = None
2025-03-04T22:55:57.7911223Z         
2025-03-04T22:55:57.7911850Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2458 in greedy_search, code: unfinished_sequences = unfinished_sequences.mul(
2025-03-04T22:55:57.7912196Z         unfinished_sequences_1: "i64[4][1]cuda:0" = unfinished_sequences.mul(prod);  unfinished_sequences = prod = None
2025-03-04T22:55:57.7912281Z         
2025-03-04T22:55:57.7912860Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/generation/utils.py:2463 in greedy_search, code: if unfinished_sequences.max() == 0:
2025-03-04T22:55:57.7913100Z         max_1: "i64[][]cuda:0" = unfinished_sequences_1.max();  unfinished_sequences_1 = None
2025-03-04T22:55:57.7913236Z         eq: "b8[][]cuda:0" = max_1 == 0;  max_1 = eq = None
2025-03-04T22:55:57.7913318Z         
2025-03-04T22:56:45.6843230Z CUDAGraph supports dynamic shapes by recording a new graph for each distinct input size. Recording too many CUDAGraphs may lead to extra overhead. We have observed 51 distinct sizes. Please consider the following options for better performance: a) padding inputs to a few fixed number of shapes; or b) set torch._inductor.config.triton.cudagraph_skip_dynamic_graphs=True. Set torch._inductor.config.triton.cudagraph_dynamic_shape_warn_limit=None to silence this warning.
2025-03-04T23:08:26.6961114Z Compilation time (from dynamo_timed): 51.040347705
2025-03-04T23:08:26.6962060Z pass
2025-03-04T23:08:26.6994221Z TIMING: entire_frame_compile:51.04035 gc:0.00593 _recursive_pre_grad_passes:0.02649 pad_mm_benchmark:0.56858 _recursive_joint_graph_passes:2.19904 _recursive_post_grad_passes:0.27413 async_compile.wait:1.42865 code_gen:6.97951 inductor_compile:15.76831 backend_compile:35.37582 total_wall_time:51.04035
2025-03-04T23:08:26.6996258Z STATS: call_* op count: 1815 | FakeTensorMode.__torch_dispatch__:51400 | ProxyTorchDispatchMode.__torch_dispatch__:17788 | FakeTensor.__torch_dispatch__:4557 | attempt fast:109 | fast is_contiguous:79 | slow both tensors nontrivially broadcast:12 | slow no contiguity match:18
2025-03-04T23:08:26.6997504Z Dynamo produced 4 graphs covering 1815 ops with 5 graph breaks (3 unique)
2025-03-04T23:08:41.5255584Z 
2025-03-04T23:08:53.6873081Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:08:53.6873591Z loading model: 0it [00:12, ?it/s]
2025-03-04T23:08:53.7239439Z cuda eval  hf_T5_large                        
2025-03-04T23:08:53.7501709Z Compilation time (from dynamo_timed): 0
2025-03-04T23:08:53.7502225Z pass_due_to_skip
2025-03-04T23:08:54.0380604Z TIMING: total_wall_time:0
2025-03-04T23:08:54.0381055Z STATS: call_* op count: 0
2025-03-04T23:08:54.0381598Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-04T23:08:58.4903585Z 
2025-03-04T23:08:59.8981944Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:08:59.8982469Z loading model: 0it [00:01, ?it/s]
2025-03-04T23:08:59.8999891Z cuda eval  hf_Whisper                         
2025-03-04T23:09:08.4595689Z Compilation time (from dynamo_timed): 5.965549593
2025-03-04T23:09:08.4596222Z pass
2025-03-04T23:09:08.4605347Z TIMING: _recursive_pre_grad_passes:0.00489 pad_mm_benchmark:0.08808 _recursive_joint_graph_passes:0.31891 _recursive_post_grad_passes:0.15281 async_compile.wait:1.2392 code_gen:2.22442 inductor_compile:3.12053 backend_compile:4.95207 entire_frame_compile:5.96555 gc:0.00058 total_wall_time:5.96555
2025-03-04T23:09:08.4607685Z STATS: call_* op count: 115 | FakeTensorMode.__torch_dispatch__:5171 | FakeTensor.__torch_dispatch__:674 | ProxyTorchDispatchMode.__torch_dispatch__:2050
2025-03-04T23:09:08.4608537Z Dynamo produced 1 graphs covering 115 ops with 0 graph breaks (0 unique)
2025-03-04T23:09:13.8971923Z 
2025-03-04T23:09:19.3026223Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:09:19.3026586Z loading model: 0it [00:05, ?it/s]
2025-03-04T23:09:19.3115993Z cuda eval  hf_distil_whisper                  
2025-03-04T23:10:04.6250675Z Compilation time (from dynamo_timed): 20.105864454
2025-03-04T23:10:04.6251214Z pass
2025-03-04T23:10:04.6403770Z TIMING: _recursive_pre_grad_passes:0.01222 pad_mm_benchmark:0.08548 _recursive_joint_graph_passes:0.77645 _recursive_post_grad_passes:0.82735 async_compile.wait:1.14881 code_gen:4.67231 inductor_compile:8.91422 backend_compile:15.6147 entire_frame_compile:20.10586 gc:0.00039 total_wall_time:20.10586
2025-03-04T23:10:04.6405631Z STATS: call_* op count: 635 | FakeTensorMode.__torch_dispatch__:25989 | FakeTensor.__torch_dispatch__:3554 | ProxyTorchDispatchMode.__torch_dispatch__:11250
2025-03-04T23:10:04.6406603Z Dynamo produced 1 graphs covering 635 ops with 0 graph breaks (0 unique)
2025-03-04T23:10:10.6050706Z 
2025-03-04T23:10:11.1549286Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:10:11.1549761Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:10:11.1552957Z cuda eval  lennard_jones                      
2025-03-04T23:10:14.2281143Z Compilation time (from dynamo_timed): 1.598268801
2025-03-04T23:10:14.2281678Z pass
2025-03-04T23:10:14.2283450Z TIMING: _recursive_pre_grad_passes:0.0034 pad_mm_benchmark:0.23308 _recursive_joint_graph_passes:0.37002 _recursive_post_grad_passes:0.03363 async_compile.precompile:0.09282 async_compile.wait:0.00018 code_gen:0.15663 inductor_compile:0.28295 backend_compile:1.4189 entire_frame_compile:1.59827 gc:0.00021 total_wall_time:1.59827
2025-03-04T23:10:14.2285965Z STATS: call_* op count: 9 | FakeTensorMode.__torch_dispatch__:819 | ProxyTorchDispatchMode.__torch_dispatch__:219 | FakeTensor.__torch_dispatch__:49
2025-03-04T23:10:14.2288070Z Dynamo produced 1 graphs covering 9 ops with 0 graph breaks (0 unique)
2025-03-04T23:10:16.1281925Z accuracy            pass_rate=66.67%
2025-03-04T23:10:16.1285631Z calls_captured      gmean=0.00x mean=291.104x
2025-03-04T23:10:16.1288572Z unique_graphs       gmean=0.00x mean=1.729x
2025-03-04T23:10:16.1292530Z graph_breaks        gmean=0.00x mean=1.000x
2025-03-04T23:10:16.1296789Z unique_graph_breaks gmean=0.00x mean=0.312x
2025-03-04T23:10:16.1301204Z autograd_captures   gmean=0.00x mean=0.000x
2025-03-04T23:10:16.1304910Z autograd_compiles   gmean=0.00x mean=0.000x
2025-03-04T23:10:16.1308860Z cudagraph_skips     gmean=0.00x mean=0.292x
2025-03-04T23:10:16.1309769Z compilation_latency mean=9.191 seconds
2025-03-04T23:10:17.2364078Z + python benchmarks/dynamo/check_accuracy.py --actual /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv --expected benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_inference.csv
2025-03-04T23:10:17.5718876Z torchrec_dlrm                       XFAIL
2025-03-04T23:10:17.5722916Z BERT_pytorch                        PASS
2025-03-04T23:10:17.5728570Z Background_Matting                  XFAIL
2025-03-04T23:10:17.5734799Z LearningToPaint                     PASS
2025-03-04T23:10:17.5739784Z Super_SloMo                         PASS
2025-03-04T23:10:17.5745230Z alexnet                             PASS
2025-03-04T23:10:17.5750780Z basic_gnn_edgecnn                   PASS
2025-03-04T23:10:17.5756094Z basic_gnn_gcn                       PASS
2025-03-04T23:10:17.5761677Z basic_gnn_gin                       PASS
2025-03-04T23:10:17.5766978Z basic_gnn_sage                      PASS
2025-03-04T23:10:17.5772790Z dcgan                               PASS
2025-03-04T23:10:17.5777885Z demucs                              PASS
2025-03-04T23:10:17.5783187Z densenet121                         PASS
2025-03-04T23:10:17.5788494Z detectron2_fasterrcnn_r_101_c4      XFAIL
2025-03-04T23:10:17.5793764Z detectron2_fasterrcnn_r_101_dc5     XFAIL
2025-03-04T23:10:17.5799194Z detectron2_fasterrcnn_r_101_fpn     XFAIL
2025-03-04T23:10:17.5804600Z detectron2_fasterrcnn_r_50_c4       XFAIL
2025-03-04T23:10:17.5810198Z detectron2_fasterrcnn_r_50_dc5      XFAIL
2025-03-04T23:10:17.5815335Z detectron2_fasterrcnn_r_50_fpn      XFAIL
2025-03-04T23:10:17.5820601Z detectron2_fcos_r_50_fpn            PASS
2025-03-04T23:10:17.5826312Z detectron2_maskrcnn_r_101_c4        XFAIL
2025-03-04T23:10:17.5831611Z detectron2_maskrcnn_r_101_fpn       XFAIL
2025-03-04T23:10:17.5836651Z detectron2_maskrcnn_r_50_c4         XFAIL
2025-03-04T23:10:17.5841790Z detectron2_maskrcnn_r_50_fpn        XFAIL
2025-03-04T23:10:17.5846933Z dlrm                                PASS
2025-03-04T23:10:17.5852407Z doctr_det_predictor                 PASS
2025-03-04T23:10:17.5857454Z doctr_reco_predictor                PASS
2025-03-04T23:10:17.5862531Z drq                                 PASS
2025-03-04T23:10:17.5867685Z fastNLP_Bert                        PASS
2025-03-04T23:10:17.5872790Z functorch_dp_cifar10                PASS
2025-03-04T23:10:17.5878033Z functorch_maml_omniglot             PASS
2025-03-04T23:10:17.5883042Z hf_Albert                           PASS
2025-03-04T23:10:17.5888309Z hf_Bart                             PASS
2025-03-04T23:10:17.5893495Z hf_Bert                             PASS
2025-03-04T23:10:17.5898843Z hf_Bert_large                       PASS
2025-03-04T23:10:17.5903878Z hf_BigBird                          XFAIL
2025-03-04T23:10:17.5909199Z hf_DistilBert                       PASS
2025-03-04T23:10:17.5914398Z hf_GPT2                             PASS
2025-03-04T23:10:17.5919604Z hf_GPT2_large                       XFAIL
2025-03-04T23:10:17.5924509Z hf_Reformer                         PASS
2025-03-04T23:10:17.5931149Z hf_Roberta_base                     PASS
2025-03-04T23:10:17.5936113Z hf_T5                               PASS
2025-03-04T23:10:17.5941118Z hf_T5_base                          XFAIL
2025-03-04T23:10:17.5946276Z hf_T5_generate                      PASS
2025-03-04T23:10:17.5951515Z hf_T5_large                         XFAIL
2025-03-04T23:10:17.5956487Z hf_Whisper                          PASS
2025-03-04T23:10:17.5961534Z hf_distil_whisper                   PASS
2025-03-04T23:10:17.5966905Z lennard_jones                       PASS
2025-03-04T23:10:17.6530507Z + python benchmarks/dynamo/check_graph_breaks.py --actual /var/lib/jenkins/workspace/test/test-reports/inference_torchbench.csv --expected benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_inference.csv
2025-03-04T23:10:17.9825590Z torchrec_dlrm                       PASS
2025-03-04T23:10:17.9830208Z BERT_pytorch                        PASS
2025-03-04T23:10:17.9835599Z Background_Matting                  PASS
2025-03-04T23:10:17.9841226Z LearningToPaint                     PASS
2025-03-04T23:10:17.9846673Z Super_SloMo                         PASS
2025-03-04T23:10:17.9852213Z alexnet                             PASS
2025-03-04T23:10:17.9857596Z basic_gnn_edgecnn                   PASS
2025-03-04T23:10:17.9863237Z basic_gnn_gcn                       PASS
2025-03-04T23:10:17.9868755Z basic_gnn_gin                       PASS
2025-03-04T23:10:17.9874227Z basic_gnn_sage                      PASS
2025-03-04T23:10:17.9879645Z dcgan                               PASS
2025-03-04T23:10:17.9885101Z demucs                              PASS
2025-03-04T23:10:17.9890791Z densenet121                         PASS
2025-03-04T23:10:17.9896211Z detectron2_fasterrcnn_r_101_c4      PASS
2025-03-04T23:10:17.9901714Z detectron2_fasterrcnn_r_101_dc5     PASS
2025-03-04T23:10:17.9907231Z detectron2_fasterrcnn_r_101_fpn     PASS
2025-03-04T23:10:17.9912603Z detectron2_fasterrcnn_r_50_c4       PASS
2025-03-04T23:10:17.9918068Z detectron2_fasterrcnn_r_50_dc5      PASS
2025-03-04T23:10:17.9923505Z detectron2_fasterrcnn_r_50_fpn      PASS
2025-03-04T23:10:17.9929897Z detectron2_fcos_r_50_fpn            PASS
2025-03-04T23:10:17.9935408Z detectron2_maskrcnn_r_101_c4        PASS_BUT_FLAKY
2025-03-04T23:10:17.9940812Z detectron2_maskrcnn_r_101_fpn       PASS
2025-03-04T23:10:17.9946269Z detectron2_maskrcnn_r_50_c4         PASS
2025-03-04T23:10:17.9951769Z detectron2_maskrcnn_r_50_fpn        PASS
2025-03-04T23:10:17.9957157Z dlrm                                PASS
2025-03-04T23:10:17.9962658Z doctr_det_predictor                 PASS
2025-03-04T23:10:17.9968212Z doctr_reco_predictor                PASS
2025-03-04T23:10:17.9973809Z drq                                 PASS
2025-03-04T23:10:17.9979275Z fastNLP_Bert                        PASS
2025-03-04T23:10:17.9984655Z functorch_dp_cifar10                PASS
2025-03-04T23:10:17.9990061Z functorch_maml_omniglot             PASS
2025-03-04T23:10:17.9995482Z hf_Albert                           PASS
2025-03-04T23:10:18.0000857Z hf_Bart                             PASS
2025-03-04T23:10:18.0006377Z hf_Bert                             PASS
2025-03-04T23:10:18.0011999Z hf_Bert_large                       PASS
2025-03-04T23:10:18.0017399Z hf_BigBird                          PASS
2025-03-04T23:10:18.0022827Z hf_DistilBert                       PASS
2025-03-04T23:10:18.0028801Z hf_GPT2                             PASS
2025-03-04T23:10:18.0034252Z hf_GPT2_large                       PASS
2025-03-04T23:10:18.0039681Z hf_Reformer                         PASS
2025-03-04T23:10:18.0045048Z hf_Roberta_base                     PASS
2025-03-04T23:10:18.0050883Z hf_T5                               PASS
2025-03-04T23:10:18.0056214Z hf_T5_base                          PASS
2025-03-04T23:10:18.0061628Z hf_T5_generate                      PASS
2025-03-04T23:10:18.0067046Z hf_T5_large                         PASS
2025-03-04T23:10:18.0072552Z hf_Whisper                          PASS
2025-03-04T23:10:18.0077907Z hf_distil_whisper                   PASS
2025-03-04T23:10:18.0083561Z lennard_jones                       PASS
2025-03-04T23:10:18.0626774Z + test_single_dynamo_benchmark training torchbench 0 --training --amp
2025-03-04T23:10:18.0629255Z ++ pwd
2025-03-04T23:10:18.0632478Z + TEST_REPORTS_DIR=/var/lib/jenkins/workspace/test/test-reports
2025-03-04T23:10:18.0633128Z + mkdir -p /var/lib/jenkins/workspace/test/test-reports
2025-03-04T23:10:18.0656155Z + local name=training
2025-03-04T23:10:18.0656495Z + shift
2025-03-04T23:10:18.0656829Z + local suite=torchbench
2025-03-04T23:10:18.0657210Z + shift
2025-03-04T23:10:18.0657481Z + local shard_id=0
2025-03-04T23:10:18.0657719Z + shift
2025-03-04T23:10:18.0657981Z + partition_flags=()
2025-03-04T23:10:18.0658363Z + local partition_flags
2025-03-04T23:10:18.0658736Z + [[ -n 2 ]]
2025-03-04T23:10:18.0659044Z + [[ -n 0 ]]
2025-03-04T23:10:18.0659583Z + partition_flags=(--total-partitions "$NUM_TEST_SHARDS" --partition-id "$shard_id")
2025-03-04T23:10:18.0660113Z + [[ inductor_torchbench == *perf_compare* ]]
2025-03-04T23:10:18.0660474Z + [[ inductor_torchbench == *perf* ]]
2025-03-04T23:10:18.0660805Z + [[ inductor_torchbench == *_avx2* ]]
2025-03-04T23:10:18.0661141Z + [[ inductor_torchbench == *_avx512* ]]
2025-03-04T23:10:18.0662316Z + python benchmarks/dynamo/torchbench.py --ci --accuracy --timing --explain --print-compilation-time --inductor --device cuda --training --amp --total-partitions 2 --partition-id 0 --output /var/lib/jenkins/workspace/test/test-reports/training_torchbench.csv
2025-03-04T23:10:24.8705652Z 
2025-03-04T23:10:26.0284473Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:10:26.0284841Z loading model: 0it [00:01, ?it/s]
2025-03-04T23:10:26.0285172Z cuda train torchrec_dlrm                      
2025-03-04T23:10:39.9390590Z Compilation time (from dynamo_timed): 11.568526112999999
2025-03-04T23:10:39.9396035Z pass
2025-03-04T23:10:40.0502815Z TIMING: entire_frame_compile:9.43017 gc:0.00247 _recursive_pre_grad_passes:0.00551 pad_mm_benchmark:1.65046 _recursive_joint_graph_passes:1.86313 _recursive_post_grad_passes:0.10325 async_compile.wait:1.68324 code_gen:3.41516 inductor_compile:4.95724 backend_compile:6.95294 entire_backward_compile:2.13835 total_wall_time:11.56853
2025-03-04T23:10:40.0505536Z STATS: call_* op count: 123 | FakeTensorMode.__torch_dispatch__:8236 | FakeTensor.__torch_dispatch__:1431 | ProxyTorchDispatchMode.__torch_dispatch__:3082
2025-03-04T23:10:40.0506455Z Dynamo produced 2 graphs covering 123 ops with 6 graph breaks (5 unique)
2025-03-04T23:10:41.7373314Z [rank0]:[W304 23:10:41.724239219 ProcessGroupNCCL.cpp:1477] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
2025-03-04T23:10:45.6040120Z 
2025-03-04T23:10:47.9139537Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:10:47.9139912Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:10:47.9140240Z cuda train BERT_pytorch                       
2025-03-04T23:11:23.8919998Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:11:23.8920852Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 464, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:11:23.8921604Z     pred = mod(*cloned_inputs)
2025-03-04T23:11:23.8922353Z   File "/var/lib/jenkins/workspace/torchbench/torchbenchmark/models/BERT_pytorch/bert_pytorch/model/language_model.py", line 24, in forward
2025-03-04T23:11:23.8923121Z     x = self.bert(x, segment_label)
2025-03-04T23:11:23.8923818Z   File "/var/lib/jenkins/workspace/torchbench/torchbenchmark/models/BERT_pytorch/bert_pytorch/model/bert.py", line 47, in forward
2025-03-04T23:11:23.8924537Z     x = self.embedding(x, segment_info)
2025-03-04T23:11:23.8925541Z   File "/var/lib/jenkins/workspace/torchbench/torchbenchmark/models/BERT_pytorch/bert_pytorch/model/embedding/bert.py", line 32, in forward
2025-03-04T23:11:23.8926434Z     x = self.token(sequence) + self.position(sequence) + self.segment(segment_label)
2025-03-04T23:11:23.8926796Z 
2025-03-04T23:11:24.3333021Z Compilation time (from dynamo_timed): 32.35475193
2025-03-04T23:11:24.3356818Z pass
2025-03-04T23:11:24.4112375Z TIMING: entire_frame_compile:22.43487 gc:0.00337 _recursive_pre_grad_passes:0.01004 pad_mm_benchmark:0.59032 _recursive_joint_graph_passes:1.78194 _recursive_post_grad_passes:0.82617 async_compile.wait:2.59536 code_gen:10.59921 inductor_compile:17.3429 backend_compile:17.41776 entire_backward_compile:9.91988 total_wall_time:32.35475
2025-03-04T23:11:24.4114318Z STATS: call_* op count: 547 | FakeTensorMode.__torch_dispatch__:42843 | ProxyTorchDispatchMode.__torch_dispatch__:21110 | FakeTensor.__torch_dispatch__:5520
2025-03-04T23:11:24.4115161Z Dynamo produced 2 graphs covering 547 ops with 6 graph breaks (5 unique)
2025-03-04T23:11:30.9197178Z 
2025-03-04T23:11:35.8607209Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:11:35.8607576Z loading model: 0it [00:04, ?it/s]
2025-03-04T23:11:35.8608014Z cuda train Background_Matting                 
2025-03-04T23:11:35.8874829Z Compilation time (from dynamo_timed): 0
2025-03-04T23:11:35.8875282Z pass_due_to_skip
2025-03-04T23:11:35.9118248Z TIMING: total_wall_time:0
2025-03-04T23:11:35.9118591Z STATS: call_* op count: 0
2025-03-04T23:11:35.9118996Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-04T23:11:40.2440610Z 
2025-03-04T23:11:43.6586159Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:11:43.6586585Z loading model: 0it [00:03, ?it/s]
2025-03-04T23:11:43.6586922Z cuda train LearningToPaint                    
2025-03-04T23:11:58.7562081Z Compilation time (from dynamo_timed): 12.079869853000002
2025-03-04T23:11:58.7569783Z pass
2025-03-04T23:11:58.7643017Z TIMING: entire_frame_compile:7.86883 gc:0.00274 _recursive_pre_grad_passes:0.00631 pad_mm_benchmark:0.21163 _recursive_joint_graph_passes:0.41185 _recursive_post_grad_passes:0.15337 async_compile.wait:1.17185 code_gen:4.39555 inductor_compile:6.63295 backend_compile:5.83885 entire_backward_compile:4.21104 total_wall_time:12.07987
2025-03-04T23:11:58.7644723Z STATS: call_* op count: 75 | FakeTensorMode.__torch_dispatch__:9316 | ProxyTorchDispatchMode.__torch_dispatch__:4406 | FakeTensor.__torch_dispatch__:1936
2025-03-04T23:11:58.7645589Z Dynamo produced 2 graphs covering 75 ops with 6 graph breaks (5 unique)
2025-03-04T23:12:04.6703386Z 
2025-03-04T23:12:07.3763855Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:12:07.3764239Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:12:07.3764568Z cuda train Super_SloMo                        
2025-03-04T23:13:11.3098711Z W0304 23:13:11.308000 53709 site-packages/torch/_logging/_internal.py:1134] [6/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:13:34.9547592Z Compilation time (from dynamo_timed): 61.483664404
2025-03-04T23:13:34.9561313Z pass
2025-03-04T23:13:34.9826683Z TIMING: entire_frame_compile:47.11207 gc:0.00418 _recursive_pre_grad_passes:0.0252 _recursive_joint_graph_passes:0.71471 _recursive_post_grad_passes:0.5091 async_compile.wait:6.38266 code_gen:30.1936 inductor_compile:40.45721 backend_compile:38.05468 entire_backward_compile:14.3716 total_wall_time:61.48366
2025-03-04T23:13:34.9829950Z STATS: call_* op count: 857 | FakeTensorMode.__torch_dispatch__:39804 | ProxyTorchDispatchMode.__torch_dispatch__:17939 | FakeTensor.__torch_dispatch__:8745
2025-03-04T23:13:34.9831031Z Dynamo produced 3 graphs covering 857 ops with 7 graph breaks (5 unique)
2025-03-04T23:13:42.3327957Z 
2025-03-04T23:13:44.1014080Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:13:44.1014554Z loading model: 0it [00:01, ?it/s]
2025-03-04T23:13:44.1014978Z cuda train alexnet                            
2025-03-04T23:13:59.4576036Z Compilation time (from dynamo_timed): 11.510377345
2025-03-04T23:13:59.4576428Z pass
2025-03-04T23:13:59.4780635Z TIMING: entire_frame_compile:4.2156 gc:0.00157 _recursive_pre_grad_passes:0.00414 pad_mm_benchmark:0.55378 _recursive_joint_graph_passes:0.70986 _recursive_post_grad_passes:0.04471 async_compile.wait:1.45917 code_gen:8.42047 inductor_compile:9.05541 backend_compile:3.65116 entire_backward_compile:7.29478 total_wall_time:11.51038
2025-03-04T23:13:59.4782898Z STATS: call_* op count: 26 | FakeTensorMode.__torch_dispatch__:2640 | ProxyTorchDispatchMode.__torch_dispatch__:950 | FakeTensor.__torch_dispatch__:270
2025-03-04T23:13:59.4783917Z Dynamo produced 2 graphs covering 26 ops with 6 graph breaks (5 unique)
2025-03-04T23:14:04.9090319Z 
2025-03-04T23:14:07.0584804Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:14:07.0585181Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:14:07.0585516Z cuda train basic_gnn_edgecnn                  
2025-03-04T23:14:10.4980407Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_reduce.two Found from   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 98, in torch_dynamo_resume_in_scatter_at_93
2025-03-04T23:14:10.4981601Z     return src.new_zeros(size).scatter_reduce_(
2025-03-04T23:14:10.4981853Z 
2025-03-04T23:14:10.4981858Z 
2025-03-04T23:14:12.4821536Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:14:12.4822104Z    File "/tmp/jenkins_pyg/tmpvjbgcd8n.py", line 190, in propagate
2025-03-04T23:14:12.4822673Z     kwargs = self._collect(edge_index, the_size, in_kwargs)
2025-03-04T23:14:12.4823143Z   File "/tmp/jenkins_pyg/tmpvjbgcd8n.py", line 128, in _collect
2025-03-04T23:14:12.4823555Z     x_i = self._lift(x_i, edge_def, i)
2025-03-04T23:14:12.4823945Z   File "/tmp/jenkins_pyg/tmpvjbgcd8n.py", line 88, in _lift
2025-03-04T23:14:12.4824369Z     return src.index_select(self.node_dim, index)
2025-03-04T23:14:12.4824623Z 
2025-03-04T23:14:12.6417719Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:14:12.6418276Z    File "/tmp/jenkins_pyg/tmpo0l5f2di.py", line 190, in propagate
2025-03-04T23:14:12.6418752Z     kwargs = self._collect(edge_index, the_size, in_kwargs)
2025-03-04T23:14:12.6419228Z   File "/tmp/jenkins_pyg/tmpo0l5f2di.py", line 128, in _collect
2025-03-04T23:14:12.6419637Z     x_i = self._lift(x_i, edge_def, i)
2025-03-04T23:14:12.6420032Z   File "/tmp/jenkins_pyg/tmpo0l5f2di.py", line 88, in _lift
2025-03-04T23:14:12.6420475Z     return src.index_select(self.node_dim, index)
2025-03-04T23:14:12.6420729Z 
2025-03-04T23:14:12.8793502Z W0304 23:14:12.878000 55469 site-packages/torch/_logging/_internal.py:1134] [24/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:14:17.7709751Z Compilation time (from dynamo_timed): 8.355027405
2025-03-04T23:14:17.7710184Z pass
2025-03-04T23:14:17.7722319Z TIMING: entire_frame_compile:7.53665 gc:0.01034 _recursive_pre_grad_passes:0.01062 _recursive_joint_graph_passes:0.19578 inductor_compile:4.55093 backend_compile:5.67536 pad_mm_benchmark:0.01023 _recursive_post_grad_passes:0.05412 async_compile.wait:2.89393 code_gen:3.59717 entire_backward_compile:0.81838 total_wall_time:8.35503
2025-03-04T23:14:17.7724027Z STATS: call_* op count: 128 | FakeTensorMode.__torch_dispatch__:4842 | ProxyTorchDispatchMode.__torch_dispatch__:1878 | FakeTensor.__torch_dispatch__:905
2025-03-04T23:14:17.7725129Z Dynamo produced 8 graphs covering 128 ops with 20 graph breaks (6 unique)
2025-03-04T23:14:23.3353592Z 
2025-03-04T23:14:25.5235808Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:14:25.5236183Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:14:25.5236527Z cuda train basic_gnn_gcn                      
2025-03-04T23:14:28.3131098Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/gcn_conv.py", line 100, in torch_dynamo_resume_in_gcn_norm_at_91
2025-03-04T23:14:28.3132523Z     deg = scatter(edge_weight, idx, dim=0, dim_size=num_nodes, reduce='sum')
2025-03-04T23:14:28.3133252Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T23:14:28.3133934Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T23:14:28.3134212Z 
2025-03-04T23:14:28.3134227Z 
2025-03-04T23:14:28.7912643Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/tmp/jenkins_pyg/tmphvwp_d72.py", line 243, in torch_dynamo_resume_in_forward_at_221
2025-03-04T23:14:28.7913872Z     out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
2025-03-04T23:14:28.7914371Z   File "/tmp/jenkins_pyg/tmphvwp_d72.py", line 188, in propagate
2025-03-04T23:14:28.7914942Z     out = self.aggregate(out, ptr=kwargs.ptr, dim_size=kwargs.dim_size, index=kwargs.index)
2025-03-04T23:14:28.7915790Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T23:14:28.7916605Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T23:14:28.7917313Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T23:14:28.7918000Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T23:14:28.7918691Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 21, in forward
2025-03-04T23:14:28.7919391Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='sum')
2025-03-04T23:14:28.7920070Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T23:14:28.7920702Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T23:14:28.7921327Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T23:14:28.7921994Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T23:14:28.7922276Z 
2025-03-04T23:14:28.7922280Z 
2025-03-04T23:14:29.2407912Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/tmp/jenkins_pyg/tmpv973d8hy.py", line 243, in torch_dynamo_resume_in_forward_at_221
2025-03-04T23:14:29.2408959Z     out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
2025-03-04T23:14:29.2409494Z   File "/tmp/jenkins_pyg/tmpv973d8hy.py", line 188, in propagate
2025-03-04T23:14:29.2410071Z     out = self.aggregate(out, ptr=kwargs.ptr, dim_size=kwargs.dim_size, index=kwargs.index)
2025-03-04T23:14:29.2410914Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T23:14:29.2411687Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T23:14:29.2412385Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T23:14:29.2413065Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T23:14:29.2413742Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 21, in forward
2025-03-04T23:14:29.2414424Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='sum')
2025-03-04T23:14:29.2415095Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T23:14:29.2415731Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T23:14:29.2416357Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T23:14:29.2417021Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T23:14:29.2417305Z 
2025-03-04T23:14:29.2417310Z 
2025-03-04T23:14:29.5075717Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/tmp/jenkins_pyg/tmpv973d8hy.py", line 243, in torch_dynamo_resume_in_forward_at_221
2025-03-04T23:14:29.5076805Z     out = self.propagate(edge_index, x=x, edge_weight=edge_weight,
2025-03-04T23:14:29.5077297Z   File "/tmp/jenkins_pyg/tmpv973d8hy.py", line 188, in propagate
2025-03-04T23:14:29.5077862Z     out = self.aggregate(out, ptr=kwargs.ptr, dim_size=kwargs.dim_size, index=kwargs.index)
2025-03-04T23:14:29.5079230Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T23:14:29.5079999Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T23:14:29.5080699Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T23:14:29.5081381Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T23:14:29.5082062Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 21, in forward
2025-03-04T23:14:29.5082745Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='sum')
2025-03-04T23:14:29.5083419Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T23:14:29.5084051Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T23:14:29.5084675Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T23:14:29.5085350Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T23:14:29.5085631Z 
2025-03-04T23:14:29.5085636Z 
2025-03-04T23:14:30.1318572Z W0304 23:14:30.129000 55736 site-packages/torch/_logging/_internal.py:1134] [18/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:14:33.6151957Z Compilation time (from dynamo_timed): 5.830800702
2025-03-04T23:14:33.6152345Z pass
2025-03-04T23:14:33.6159160Z TIMING: entire_frame_compile:5.34913 gc:0.00923 _recursive_pre_grad_passes:0.01108 _recursive_joint_graph_passes:0.17557 inductor_compile:3.25476 backend_compile:4.37609 _recursive_post_grad_passes:0.03057 async_compile.wait:2.15326 code_gen:2.67946 pad_mm_benchmark:0.00507 entire_backward_compile:0.48167 total_wall_time:5.8308
2025-03-04T23:14:33.6160874Z STATS: call_* op count: 112 | FakeTensorMode.__torch_dispatch__:2921 | FakeTensor.__torch_dispatch__:463 | ProxyTorchDispatchMode.__torch_dispatch__:1051
2025-03-04T23:14:33.6161746Z Dynamo produced 10 graphs covering 112 ops with 13 graph breaks (6 unique)
2025-03-04T23:14:39.0166554Z 
2025-03-04T23:14:41.1469771Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:14:41.1470136Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:14:41.1470473Z cuda train basic_gnn_gin                      
2025-03-04T23:14:43.9638470Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 464, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:14:43.9639624Z     pred = mod(*cloned_inputs)
2025-03-04T23:14:43.9640246Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/models/basic_gnn.py", line 226, in forward
2025-03-04T23:14:43.9640883Z     x = self.convs[i](x, edge_index)
2025-03-04T23:14:43.9641276Z   File "/tmp/jenkins_pyg/tmpfo1q2w_c.py", line 225, in forward
2025-03-04T23:14:43.9641732Z     out = self.propagate(edge_index, x=x, size=size)
2025-03-04T23:14:43.9642181Z   File "/tmp/jenkins_pyg/tmpfo1q2w_c.py", line 182, in propagate
2025-03-04T23:14:43.9642745Z     out = self.aggregate(out, index=kwargs.index, ptr=kwargs.ptr, dim_size=kwargs.dim_size)
2025-03-04T23:14:43.9643569Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T23:14:43.9644320Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T23:14:43.9645020Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T23:14:43.9645697Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T23:14:43.9646371Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 21, in forward
2025-03-04T23:14:43.9647052Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='sum')
2025-03-04T23:14:43.9648336Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T23:14:43.9648965Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T23:14:43.9649587Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 74, in scatter
2025-03-04T23:14:43.9650266Z     return src.new_zeros(size).scatter_add_(dim, index, src)
2025-03-04T23:14:43.9650541Z 
2025-03-04T23:14:43.9650545Z 
2025-03-04T23:14:44.6822830Z W0304 23:14:44.681000 55986 site-packages/torch/_logging/_internal.py:1134] [6/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:14:48.7224546Z Compilation time (from dynamo_timed): 6.483058539
2025-03-04T23:14:48.7225099Z pass
2025-03-04T23:14:48.7253172Z TIMING: entire_frame_compile:5.95603 gc:0.00317 _recursive_pre_grad_passes:0.00918 pad_mm_benchmark:0.01032 _recursive_joint_graph_passes:0.19094 _recursive_post_grad_passes:0.07632 async_compile.wait:1.71142 code_gen:2.52836 inductor_compile:3.32676 backend_compile:4.61718 entire_backward_compile:0.52703 total_wall_time:6.48306
2025-03-04T23:14:48.7254887Z STATS: call_* op count: 135 | FakeTensorMode.__torch_dispatch__:4234 | ProxyTorchDispatchMode.__torch_dispatch__:1842 | FakeTensor.__torch_dispatch__:774
2025-03-04T23:14:48.7255733Z Dynamo produced 3 graphs covering 135 ops with 7 graph breaks (5 unique)
2025-03-04T23:14:54.1844561Z 
2025-03-04T23:14:56.3068837Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:14:56.3069203Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:14:56.3069537Z cuda train basic_gnn_sage                     
2025-03-04T23:14:58.9359583Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter_add.default Found from   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 464, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:14:58.9360753Z     pred = mod(*cloned_inputs)
2025-03-04T23:14:58.9361425Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/models/basic_gnn.py", line 226, in forward
2025-03-04T23:14:58.9362061Z     x = self.convs[i](x, edge_index)
2025-03-04T23:14:58.9362451Z   File "/tmp/jenkins_pyg/tmp99118ukr.py", line 228, in forward
2025-03-04T23:14:58.9362884Z     out = self.propagate(edge_index, x=x, size=size)
2025-03-04T23:14:58.9363323Z   File "/tmp/jenkins_pyg/tmp99118ukr.py", line 182, in propagate
2025-03-04T23:14:58.9363883Z     out = self.aggregate(out, ptr=kwargs.ptr, index=kwargs.index, dim_size=kwargs.dim_size)
2025-03-04T23:14:58.9364704Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/conv/message_passing.py", line 604, in aggregate
2025-03-04T23:14:58.9365459Z     return self.aggr_module(inputs, index, ptr=ptr, dim_size=dim_size,
2025-03-04T23:14:58.9366159Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 109, in __call__
2025-03-04T23:14:58.9366849Z     return super().__call__(x, index, ptr, dim_size, dim, **kwargs)
2025-03-04T23:14:58.9367520Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/basic.py", line 34, in forward
2025-03-04T23:14:58.9368320Z     return self.reduce(x, index, ptr, dim_size, dim, reduce='mean')
2025-03-04T23:14:58.9368997Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/nn/aggr/base.py", line 155, in reduce
2025-03-04T23:14:58.9369626Z     return scatter(x, index, dim, dim_size, reduce)
2025-03-04T23:14:58.9370253Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch_geometric/utils/scatter.py", line 78, in scatter
2025-03-04T23:14:58.9370927Z     count.scatter_add_(0, index, src.new_ones(src.size(dim)))
2025-03-04T23:14:58.9371217Z 
2025-03-04T23:14:58.9371221Z 
2025-03-04T23:14:59.5234256Z W0304 23:14:59.522000 56172 site-packages/torch/_logging/_internal.py:1134] [6/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:15:03.0395933Z Compilation time (from dynamo_timed): 5.669243961
2025-03-04T23:15:03.0396742Z pass
2025-03-04T23:15:03.0426711Z TIMING: entire_frame_compile:5.25257 gc:0.00349 _recursive_pre_grad_passes:0.00857 pad_mm_benchmark:0.00961 _recursive_joint_graph_passes:0.18317 _recursive_post_grad_passes:0.05501 async_compile.wait:1.5327 code_gen:2.2513 inductor_compile:2.88671 backend_compile:4.29335 entire_backward_compile:0.41668 total_wall_time:5.66924
2025-03-04T23:15:03.0428412Z STATS: call_* op count: 126 | FakeTensorMode.__torch_dispatch__:3571 | ProxyTorchDispatchMode.__torch_dispatch__:1467 | FakeTensor.__torch_dispatch__:591
2025-03-04T23:15:03.0429253Z Dynamo produced 3 graphs covering 126 ops with 7 graph breaks (5 unique)
2025-03-04T23:15:08.4453470Z 
2025-03-04T23:15:09.1343594Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:15:09.1343953Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:15:09.1344275Z cuda train dcgan                              
2025-03-04T23:15:14.2593309Z Compilation time (from dynamo_timed): 3.3105713469999998
2025-03-04T23:15:14.2593703Z pass
2025-03-04T23:15:14.2613136Z TIMING: entire_frame_compile:2.64432 gc:0.00259 _recursive_pre_grad_passes:0.00388 _recursive_joint_graph_passes:0.14134 _recursive_post_grad_passes:0.03997 async_compile.wait:0.29148 code_gen:0.84499 inductor_compile:1.47522 backend_compile:2.09122 entire_backward_compile:0.66625 total_wall_time:3.31057
2025-03-04T23:15:14.2614730Z STATS: call_* op count: 17 | FakeTensorMode.__torch_dispatch__:2045 | FakeTensor.__torch_dispatch__:382 | ProxyTorchDispatchMode.__torch_dispatch__:830
2025-03-04T23:15:14.2615548Z Dynamo produced 2 graphs covering 17 ops with 6 graph breaks (5 unique)
2025-03-04T23:15:19.5718902Z 
2025-03-04T23:15:23.3462232Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:15:23.3462632Z loading model: 0it [00:03, ?it/s]
2025-03-04T23:15:23.3462965Z cuda train demucs                             
2025-03-04T23:16:32.0288840Z Compilation time (from dynamo_timed): 11.683912408
2025-03-04T23:16:32.0294493Z pass
2025-03-04T23:16:32.0546595Z TIMING: entire_frame_compile:7.22657 gc:0.00463 _recursive_pre_grad_passes:0.00855 _recursive_joint_graph_passes:0.17564 inductor_compile:7.56806 backend_compile:5.21211 _recursive_post_grad_passes:0.11827 async_compile.precompile:0.4282 async_compile.wait:0.67438 code_gen:5.76811 entire_backward_compile:4.45734 total_wall_time:11.68391
2025-03-04T23:16:32.0548337Z STATS: call_* op count: 88 | FakeTensorMode.__torch_dispatch__:6250 | ProxyTorchDispatchMode.__torch_dispatch__:2363 | FakeTensor.__torch_dispatch__:1045
2025-03-04T23:16:32.0549173Z Dynamo produced 6 graphs covering 88 ops with 9 graph breaks (6 unique)
2025-03-04T23:16:37.7938793Z 
2025-03-04T23:16:40.2823909Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:16:40.2824367Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:16:40.2824690Z cuda train densenet121                        
2025-03-04T23:18:26.2385637Z Compilation time (from dynamo_timed): 96.065599521
2025-03-04T23:18:26.2435318Z pass
2025-03-04T23:18:26.2520147Z TIMING: entire_frame_compile:50.22889 gc:0.00341 _recursive_pre_grad_passes:0.01649 pad_mm_benchmark:0.21654 _recursive_joint_graph_passes:0.77154 _recursive_post_grad_passes:0.95354 async_compile.wait:14.6399 code_gen:49.31597 inductor_compile:63.46496 backend_compile:36.8931 entire_backward_compile:45.83671 total_wall_time:96.0656
2025-03-04T23:18:26.2523518Z STATS: call_* op count: 435 | FakeTensorMode.__torch_dispatch__:59623 | ProxyTorchDispatchMode.__torch_dispatch__:30351 | FakeTensor.__torch_dispatch__:13117
2025-03-04T23:18:26.2524380Z Dynamo produced 2 graphs covering 435 ops with 6 graph breaks (5 unique)
2025-03-04T23:18:34.7711064Z 
2025-03-04T23:18:37.3837242Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:18:37.3837812Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:18:37.3838298Z cuda train detectron2_maskrcnn_r_50_c4        
2025-03-04T23:18:37.3850825Z Traceback (most recent call last):
2025-03-04T23:18:37.3851699Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1909, in validate_model
2025-03-04T23:18:37.3853233Z     self.model_iter_fn(model, example_inputs)
2025-03-04T23:18:37.3854201Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 464, in forward_and_backward_pass
2025-03-04T23:18:37.3855176Z     pred = mod(*cloned_inputs)
2025-03-04T23:18:37.3856132Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:18:37.3857181Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:18:37.3858167Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:18:37.3859105Z     return forward_call(*args, **kwargs)
2025-03-04T23:18:37.3860149Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 150, in forward
2025-03-04T23:18:37.3861197Z     return self.inference(batched_inputs)
2025-03-04T23:18:37.3862178Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/meta_arch/rcnn.py", line 213, in inference
2025-03-04T23:18:37.3863268Z     results, _ = self.roi_heads(images, features, proposals, None)
2025-03-04T23:18:37.3864369Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:18:37.3865388Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:18:37.3866331Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:18:37.3867332Z     return forward_call(*args, **kwargs)
2025-03-04T23:18:37.3868348Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 477, in forward
2025-03-04T23:18:37.3869408Z     box_features = self._shared_roi_transform(
2025-03-04T23:18:37.3870536Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/roi_heads/roi_heads.py", line 456, in _shared_roi_transform
2025-03-04T23:18:37.3871662Z     x = self.pooler(features, boxes)
2025-03-04T23:18:37.3872662Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:18:37.3873714Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:18:37.3874708Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:18:37.3875701Z     return forward_call(*args, **kwargs)
2025-03-04T23:18:37.3876696Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/modeling/poolers.py", line 246, in forward
2025-03-04T23:18:37.3877738Z     return self.level_poolers[0](x[0], pooler_fmt_boxes)
2025-03-04T23:18:37.3878810Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:18:37.3879846Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:18:37.3880870Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:18:37.3881886Z     return forward_call(*args, **kwargs)
2025-03-04T23:18:37.3882911Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/detectron2/layers/roi_align.py", line 58, in forward
2025-03-04T23:18:37.3883889Z     return roi_align(
2025-03-04T23:18:37.3884801Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 236, in roi_align
2025-03-04T23:18:37.3886185Z     return _roi_align(input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio, aligned)
2025-03-04T23:18:37.3887578Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 168, in _roi_align
2025-03-04T23:18:37.3889108Z     val = _bilinear_interpolate(input, roi_batch_ind, y, x, ymask, xmask)  # [K, C, PH, PW, IY, IX]
2025-03-04T23:18:37.3898933Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 62, in _bilinear_interpolate
2025-03-04T23:18:37.3900332Z     v1 = masked_index(y_low, x_low)
2025-03-04T23:18:37.3901352Z   File "/var/lib/jenkins/.local/lib/python3.10/site-packages/torchvision/ops/roi_align.py", line 55, in masked_index
2025-03-04T23:18:37.3902312Z     return input[
2025-03-04T23:18:37.3905384Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 21890.87 GiB. GPU 0 has a total capacity of 21.98 GiB of which 20.21 GiB is free. Process 118691 has 1.76 GiB memory in use. Of the allocated memory 1.38 GiB is allocated by PyTorch, and 86.31 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)
2025-03-04T23:18:37.3908487Z 
2025-03-04T23:18:37.3908850Z The above exception was the direct cause of the following exception:
2025-03-04T23:18:37.3909410Z 
2025-03-04T23:18:37.3909606Z Traceback (most recent call last):
2025-03-04T23:18:37.3910422Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T23:18:37.3911224Z     ) = runner.load_model(
2025-03-04T23:18:37.3912041Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 376, in load_model
2025-03-04T23:18:37.3912992Z     self.validate_model(model, example_inputs)
2025-03-04T23:18:37.3913936Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1911, in validate_model
2025-03-04T23:18:37.3914893Z     raise RuntimeError("Eager run failed") from e
2025-03-04T23:18:37.3915487Z RuntimeError: Eager run failed
2025-03-04T23:18:37.3915802Z 
2025-03-04T23:18:37.3915968Z eager_fail_to_run
2025-03-04T23:18:41.8259318Z 
2025-03-04T23:18:53.3603744Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:18:53.3604264Z loading model: 0it [00:11, ?it/s]
2025-03-04T23:18:53.3604709Z cuda train dlrm                               
2025-03-04T23:18:59.5251473Z skipping cudagraphs due to sparsity not handled. Please file issue for sparse inference weights. Found from : 
2025-03-04T23:19:00.0710894Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 464, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:19:00.0711649Z     pred = mod(*cloned_inputs)
2025-03-04T23:19:00.0712299Z   File "/var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dlrm/dlrm_s_pytorch.py", line 354, in forward
2025-03-04T23:19:00.0713032Z     return self.sequential_forward(dense_x, lS_o, lS_i)
2025-03-04T23:19:00.0713822Z   File "/var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dlrm/dlrm_s_pytorch.py", line 366, in sequential_forward
2025-03-04T23:19:00.0714546Z     ly = self.apply_emb(lS_o, lS_i, self.emb_l)
2025-03-04T23:19:00.0715222Z   File "/var/lib/jenkins/workspace/torchbench/torchbenchmark/models/dlrm/dlrm_s_pytorch.py", line 307, in apply_emb
2025-03-04T23:19:00.0715950Z     V = E(sparse_index_group_batch, sparse_offset_group_batch)
2025-03-04T23:19:00.0716258Z 
2025-03-04T23:19:00.0716421Z Compilation time (from dynamo_timed): 4.702295427
2025-03-04T23:19:00.0716776Z pass
2025-03-04T23:19:00.1118584Z TIMING: entire_frame_compile:4.0285 gc:0.00223 _recursive_pre_grad_passes:0.00476 pad_mm_benchmark:0.91754 _recursive_joint_graph_passes:1.09074 _recursive_post_grad_passes:0.07088 async_compile.wait:0.1984 code_gen:0.80705 inductor_compile:1.59457 backend_compile:3.29035 entire_backward_compile:0.6738 total_wall_time:4.7023
2025-03-04T23:19:00.1121930Z STATS: call_* op count: 40 | FakeTensorMode.__torch_dispatch__:3521 | ProxyTorchDispatchMode.__torch_dispatch__:1379 | FakeTensor.__torch_dispatch__:316
2025-03-04T23:19:00.1123288Z Dynamo produced 2 graphs covering 40 ops with 6 graph breaks (5 unique)
2025-03-04T23:19:05.4859764Z 
2025-03-04T23:19:07.2503560Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:19:07.2503928Z loading model: 0it [00:01, ?it/s]
2025-03-04T23:19:07.2504253Z cuda train drq                                
2025-03-04T23:19:13.1950286Z W0304 23:19:13.193000 59661 site-packages/torch/_logging/_internal.py:1134] [6/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:19:19.8589668Z Compilation time (from dynamo_timed): 11.223331506
2025-03-04T23:19:19.8590790Z pass
2025-03-04T23:19:19.8637512Z TIMING: entire_frame_compile:10.2604 gc:0.00369 _recursive_pre_grad_passes:0.01132 pad_mm_benchmark:0.57441 _recursive_joint_graph_passes:0.75729 _recursive_post_grad_passes:0.07204 async_compile.wait:4.19367 code_gen:5.37586 inductor_compile:6.49151 backend_compile:8.45488 entire_backward_compile:0.96293 total_wall_time:11.22333
2025-03-04T23:19:19.8640266Z STATS: call_* op count: 174 | FakeTensorMode.__torch_dispatch__:5632 | FakeTensor.__torch_dispatch__:1277 | ProxyTorchDispatchMode.__torch_dispatch__:2241
2025-03-04T23:19:19.8641533Z Dynamo produced 5 graphs covering 174 ops with 7 graph breaks (5 unique)
2025-03-04T23:19:25.4558837Z 
2025-03-04T23:19:27.5549984Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:19:27.5550381Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:19:27.5550718Z cuda train fastNLP_Bert                       
2025-03-04T23:19:34.3344689Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] Graph break from `Tensor.item()`, consider setting:
2025-03-04T23:19:34.3345761Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]     torch._dynamo.config.capture_scalar_outputs = True
2025-03-04T23:19:34.3346598Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] or:
2025-03-04T23:19:34.3347390Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]     env TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1
2025-03-04T23:19:34.3348344Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] to include these operations in the captured graph.
2025-03-04T23:19:34.3349141Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] 
2025-03-04T23:19:34.3349921Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] Graph break: from user code at:
2025-03-04T23:19:34.3351196Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 464, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:19:34.3352450Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]     pred = mod(*cloned_inputs)
2025-03-04T23:19:34.3353573Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/fastNLP/models/bert.py", line 265, in forward
2025-03-04T23:19:34.3354709Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]     sequence_output = self.bert(words)
2025-03-04T23:19:34.3355922Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/fastNLP/embeddings/bert_embedding.py", line 137, in forward
2025-03-04T23:19:34.3357106Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]     outputs = self.model(words)
2025-03-04T23:19:34.3358285Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/fastNLP/embeddings/bert_embedding.py", line 445, in forward
2025-03-04T23:19:34.3360120Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0]     max_word_piece_length = batch_word_pieces_length.sum(dim=-1).max().item()  # 表示word piece的长度(包括padding)
2025-03-04T23:19:34.3363268Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] 
2025-03-04T23:19:34.3363899Z W0304 23:19:34.333000 60013 site-packages/torch/_dynamo/variables/tensor.py:913] [2/0] 
2025-03-04T23:20:03.1985242Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:20:03.1986329Z    File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/fastNLP/modules/encoder/bert.py", line 512, in forward
2025-03-04T23:20:03.1987194Z     embedding_output = self.embeddings(input_ids, token_type_ids)
2025-03-04T23:20:03.1987902Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/fastNLP/modules/encoder/bert.py", line 236, in forward
2025-03-04T23:20:03.1988562Z     words_embeddings = self.word_embeddings(input_ids)
2025-03-04T23:20:03.1988834Z 
2025-03-04T23:20:06.0564362Z Compilation time (from dynamo_timed): 28.715170019
2025-03-04T23:20:06.0588117Z pass
2025-03-04T23:20:06.1237846Z TIMING: entire_frame_compile:20.00607 gc:0.00633 _recursive_pre_grad_passes:0.01157 _recursive_joint_graph_passes:2.06536 inductor_compile:15.48956 backend_compile:16.71055 _recursive_post_grad_passes:0.74444 async_compile.precompile:0.22779 async_compile.wait:1.13962 code_gen:9.66741 pad_mm_benchmark:0.92783 entire_backward_compile:8.7091 total_wall_time:28.71517
2025-03-04T23:20:06.1239715Z STATS: call_* op count: 448 | FakeTensorMode.__torch_dispatch__:36734 | ProxyTorchDispatchMode.__torch_dispatch__:18987 | FakeTensor.__torch_dispatch__:4829
2025-03-04T23:20:06.1240565Z Dynamo produced 7 graphs covering 448 ops with 10 graph breaks (6 unique)
2025-03-04T23:20:12.5436675Z 
2025-03-04T23:20:13.6498547Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:20:13.6499070Z loading model: 0it [00:01, ?it/s]
2025-03-04T23:20:13.6499463Z cuda train functorch_dp_cifar10               
2025-03-04T23:20:33.5009503Z W0304 23:20:33.499000 60352 site-packages/torch/_logging/_internal.py:1134] [6/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:20:48.8344820Z Compilation time (from dynamo_timed): 31.99590491
2025-03-04T23:20:48.8350080Z pass
2025-03-04T23:20:48.8414533Z TIMING: entire_frame_compile:22.71372 gc:0.00397 _recursive_pre_grad_passes:0.01604 pad_mm_benchmark:0.22119 _recursive_joint_graph_passes:0.70021 _recursive_post_grad_passes:0.27086 async_compile.wait:4.49031 code_gen:14.49399 inductor_compile:20.42388 backend_compile:18.43656 entire_backward_compile:9.28218 total_wall_time:31.9959
2025-03-04T23:20:48.8416284Z STATS: call_* op count: 402 | FakeTensorMode.__torch_dispatch__:19028 | ProxyTorchDispatchMode.__torch_dispatch__:8433 | FakeTensor.__torch_dispatch__:5816
2025-03-04T23:20:48.8417138Z Dynamo produced 3 graphs covering 402 ops with 7 graph breaks (5 unique)
2025-03-04T23:20:55.2182308Z 
2025-03-04T23:20:55.9818597Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:20:55.9818952Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:20:55.9819294Z cuda train functorch_maml_omniglot            
2025-03-04T23:21:03.2258249Z W0304 23:21:03.224000 60935 site-packages/torch/_logging/_internal.py:1134] [6/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:21:08.4576503Z Compilation time (from dynamo_timed): 10.314905186
2025-03-04T23:21:08.4577141Z pass
2025-03-04T23:21:08.4579311Z TIMING: entire_frame_compile:7.69102 gc:0.00327 _recursive_pre_grad_passes:0.00887 pad_mm_benchmark:0.21794 _recursive_joint_graph_passes:0.38163 _recursive_post_grad_passes:0.04869 async_compile.wait:2.69303 code_gen:5.91144 inductor_compile:7.07372 backend_compile:6.63123 entire_backward_compile:2.62388 total_wall_time:10.31491
2025-03-04T23:21:08.4582144Z STATS: call_* op count: 107 | FakeTensorMode.__torch_dispatch__:4022 | ProxyTorchDispatchMode.__torch_dispatch__:1544 | FakeTensor.__torch_dispatch__:1035
2025-03-04T23:21:08.4583464Z Dynamo produced 3 graphs covering 107 ops with 7 graph breaks (5 unique)
2025-03-04T23:21:13.9444791Z 
2025-03-04T23:21:15.6957292Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:21:15.6957804Z loading model: 0it [00:01, ?it/s]
2025-03-04T23:21:15.6958253Z cuda train hf_Albert                          
2025-03-04T23:21:47.7445633Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:21:47.7448506Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:21:47.7449958Z     pred = mod(**cloned_inputs)
2025-03-04T23:21:47.7450980Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/albert/modeling_albert.py", line 986, in forward
2025-03-04T23:21:47.7451661Z     outputs = self.albert(
2025-03-04T23:21:47.7452305Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/albert/modeling_albert.py", line 729, in forward
2025-03-04T23:21:47.7452996Z     embedding_output = self.embeddings(
2025-03-04T23:21:47.7453673Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/albert/modeling_albert.py", line 250, in forward
2025-03-04T23:21:47.7454389Z     inputs_embeds = self.word_embeddings(input_ids)
2025-03-04T23:21:47.7454643Z 
2025-03-04T23:21:47.9775492Z W0304 23:21:47.976000 61235 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:21:56.0194924Z Compilation time (from dynamo_timed): 31.192332182
2025-03-04T23:21:56.0202483Z pass
2025-03-04T23:21:56.0418596Z TIMING: entire_frame_compile:23.19062 gc:0.00395 _recursive_pre_grad_passes:0.01583 pad_mm_benchmark:0.16008 _recursive_joint_graph_passes:2.11229 _recursive_post_grad_passes:0.56032 async_compile.wait:3.56437 code_gen:10.86841 inductor_compile:16.93279 backend_compile:18.87961 entire_backward_compile:8.00171 total_wall_time:31.19233
2025-03-04T23:21:56.0420321Z STATS: call_* op count: 602 | FakeTensorMode.__torch_dispatch__:43077 | FakeTensor.__torch_dispatch__:5995 | ProxyTorchDispatchMode.__torch_dispatch__:21891
2025-03-04T23:21:56.0421165Z Dynamo produced 3 graphs covering 602 ops with 6 graph breaks (5 unique)
2025-03-04T23:22:02.5813716Z 
2025-03-04T23:22:06.9875944Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:22:06.9876818Z loading model: 0it [00:04, ?it/s]
2025-03-04T23:22:06.9877229Z cuda train hf_Bart                            
2025-03-04T23:22:49.3247063Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:22:49.3248499Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:22:49.3249679Z     pred = mod(**cloned_inputs)
2025-03-04T23:22:49.3250739Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py", line 1731, in forward
2025-03-04T23:22:49.3251762Z     outputs = self.model(
2025-03-04T23:22:49.3252726Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py", line 1599, in forward
2025-03-04T23:22:49.3253434Z     encoder_outputs = self.encoder(
2025-03-04T23:22:49.3254089Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bart/modeling_bart.py", line 1152, in forward
2025-03-04T23:22:49.3254883Z     inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale
2025-03-04T23:22:49.3255188Z 
2025-03-04T23:22:49.4919368Z W0304 23:22:49.491000 61620 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:23:39.1014895Z Compilation time (from dynamo_timed): 78.168625208
2025-03-04T23:23:39.1044215Z pass
2025-03-04T23:23:39.1532021Z TIMING: entire_frame_compile:68.81636 gc:0.00548 _recursive_pre_grad_passes:0.04876 pad_mm_benchmark:0.50238 _recursive_joint_graph_passes:1.85797 _recursive_post_grad_passes:1.16265 async_compile.wait:4.80376 code_gen:25.12054 inductor_compile:40.43862 backend_compile:53.95671 entire_backward_compile:9.35226 total_wall_time:78.16863
2025-03-04T23:23:39.1533794Z STATS: call_* op count: 1780 | FakeTensorMode.__torch_dispatch__:74149 | FakeTensor.__torch_dispatch__:17204 | ProxyTorchDispatchMode.__torch_dispatch__:34381
2025-03-04T23:23:39.1534988Z Dynamo produced 3 graphs covering 1780 ops with 6 graph breaks (5 unique)
2025-03-04T23:23:47.9185795Z 
2025-03-04T23:23:50.9875114Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:23:50.9875735Z loading model: 0it [00:03, ?it/s]
2025-03-04T23:23:50.9876061Z cuda train hf_Bert                            
2025-03-04T23:24:26.3248099Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:24:26.3249146Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:24:26.3249872Z     pred = mod(**cloned_inputs)
2025-03-04T23:24:26.3250526Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1360, in forward
2025-03-04T23:24:26.3251189Z     outputs = self.bert(
2025-03-04T23:24:26.3251812Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1006, in forward
2025-03-04T23:24:26.3252525Z     embedding_output = self.embeddings(
2025-03-04T23:24:26.3253202Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 232, in forward
2025-03-04T23:24:26.3253903Z     inputs_embeds = self.word_embeddings(input_ids)
2025-03-04T23:24:26.3254158Z 
2025-03-04T23:24:26.5007807Z W0304 23:24:26.499000 61997 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:25:05.7698761Z Compilation time (from dynamo_timed): 63.535016049
2025-03-04T23:25:05.7723497Z pass
2025-03-04T23:25:05.8155134Z TIMING: entire_frame_compile:57.00928 gc:0.00468 _recursive_pre_grad_passes:0.03959 pad_mm_benchmark:0.42904 _recursive_joint_graph_passes:2.29007 _recursive_post_grad_passes:0.6574 async_compile.wait:4.19329 code_gen:19.08282 inductor_compile:30.59368 backend_compile:43.68948 entire_backward_compile:6.52574 total_wall_time:63.53502
2025-03-04T23:25:05.8156874Z STATS: call_* op count: 1403 | FakeTensorMode.__torch_dispatch__:63530 | FakeTensor.__torch_dispatch__:13415 | ProxyTorchDispatchMode.__torch_dispatch__:29541
2025-03-04T23:25:13.7553204Z Dynamo produced 3 graphs covering 1403 ops with 6 graph breaks (5 unique)
2025-03-04T23:25:13.7553590Z 
2025-03-04T23:25:19.0415028Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:25:19.0415410Z loading model: 0it [00:05, ?it/s]
2025-03-04T23:25:19.0415740Z cuda train hf_Bert_large                      
2025-03-04T23:26:30.3816407Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:26:30.3817284Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:26:30.3818018Z     pred = mod(**cloned_inputs)
2025-03-04T23:26:30.3818682Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1360, in forward
2025-03-04T23:26:30.3819352Z     outputs = self.bert(
2025-03-04T23:26:30.3820128Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 1006, in forward
2025-03-04T23:26:30.3821073Z     embedding_output = self.embeddings(
2025-03-04T23:26:30.3821751Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/bert/modeling_bert.py", line 232, in forward
2025-03-04T23:26:30.3822452Z     inputs_embeds = self.word_embeddings(input_ids)
2025-03-04T23:26:30.3822704Z 
2025-03-04T23:26:30.6207400Z W0304 23:26:30.619000 62310 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:27:50.2666354Z Compilation time (from dynamo_timed): 125.307187035
2025-03-04T23:27:50.2710339Z pass
2025-03-04T23:27:50.3858891Z TIMING: entire_frame_compile:112.26004 gc:0.00589 _recursive_pre_grad_passes:0.0782 pad_mm_benchmark:0.60383 _recursive_joint_graph_passes:4.53223 _recursive_post_grad_passes:1.29573 async_compile.wait:7.44481 code_gen:39.40246 inductor_compile:61.83415 backend_compile:86.18927 entire_backward_compile:13.04715 total_wall_time:125.30719
2025-03-04T23:27:50.3862628Z STATS: call_* op count: 2711 | FakeTensorMode.__torch_dispatch__:123518 | FakeTensor.__torch_dispatch__:26015 | ProxyTorchDispatchMode.__torch_dispatch__:57717
2025-03-04T23:27:50.3864015Z Dynamo produced 3 graphs covering 2711 ops with 6 graph breaks (5 unique)
2025-03-04T23:28:01.1471290Z 
2025-03-04T23:28:05.2634622Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:28:05.2634986Z loading model: 0it [00:04, ?it/s]
2025-03-04T23:28:05.2635316Z cuda train hf_BigBird                         
2025-03-04T23:28:08.0827609Z WARNING:common:fp64 golden ref were not generated for hf_BigBird. Setting accuracy check to cosine
2025-03-04T23:29:03.7206466Z W0304 23:29:03.719000 62631 site-packages/torch/_inductor/utils.py:1714] [2/0_1] DeviceCopy in input program
2025-03-04T23:29:28.7333767Z skipping cudagraphs due to skipping cudagraphs due to cpu device (cat_1). Found from : 
2025-03-04T23:29:28.7334765Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:29:28.7335492Z     pred = mod(**cloned_inputs)
2025-03-04T23:29:28.7336189Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 2450, in forward
2025-03-04T23:29:28.7336882Z     outputs = self.bert(
2025-03-04T23:29:28.7337534Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 2133, in forward
2025-03-04T23:29:28.7338228Z     encoder_outputs = self.encoder(
2025-03-04T23:29:28.7338908Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 1631, in forward
2025-03-04T23:29:28.7339604Z     layer_outputs = layer_module(
2025-03-04T23:29:28.7340274Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 1488, in forward
2025-03-04T23:29:28.7340997Z     self_attention_outputs = self.attention(
2025-03-04T23:29:28.7341703Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 1401, in forward
2025-03-04T23:29:28.7342391Z     self_outputs = self.self(
2025-03-04T23:29:28.7343052Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 472, in forward
2025-03-04T23:29:28.7343852Z     context_layer, attention_probs = self.bigbird_block_sparse_attention(
2025-03-04T23:29:28.7344743Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/big_bird/modeling_big_bird.py", line 593, in bigbird_block_sparse_attention
2025-03-04T23:29:28.7345535Z     rand_attn = np.stack(rand_attn, axis=0)
2025-03-04T23:29:28.7345762Z 
2025-03-04T23:29:56.0508566Z W0304 23:29:56.049000 62631 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:30:35.9629213Z Compilation time (from dynamo_timed): 139.57371699700002
2025-03-04T23:30:35.9662558Z pass
2025-03-04T23:30:36.0033672Z TIMING: entire_frame_compile:113.49649 gc:0.00427 _recursive_pre_grad_passes:0.07629 pad_mm_benchmark:1.39157 _recursive_joint_graph_passes:5.7967 _recursive_post_grad_passes:2.37249 async_compile.wait:4.87455 code_gen:47.75697 inductor_compile:72.93856 backend_compile:92.07823 entire_backward_compile:26.07722 total_wall_time:139.57372
2025-03-04T23:30:36.0035617Z STATS: call_* op count: 4383 | FakeTensorMode.__torch_dispatch__:189920 | FakeTensor.__torch_dispatch__:22501 | ProxyTorchDispatchMode.__torch_dispatch__:85812
2025-03-04T23:30:36.0036552Z Dynamo produced 3 graphs covering 4383 ops with 6 graph breaks (5 unique)
2025-03-04T23:30:46.8338677Z 
2025-03-04T23:30:49.2712455Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:30:49.2712837Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:30:49.2713747Z cuda train hf_DistilBert                      
2025-03-04T23:31:10.6481711Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:31:10.6482606Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:31:10.6483331Z     pred = mod(**cloned_inputs)
2025-03-04T23:31:10.6484048Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 905, in forward
2025-03-04T23:31:10.6484781Z     dlbrt_output = self.distilbert(
2025-03-04T23:31:10.6485494Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 814, in forward
2025-03-04T23:31:10.6486356Z     embeddings = self.embeddings(input_ids, inputs_embeds)  # (bs, seq_length, dim)
2025-03-04T23:31:10.6487219Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/distilbert/modeling_distilbert.py", line 141, in forward
2025-03-04T23:31:10.6488210Z     input_embeds = self.word_embeddings(input_ids)  # (bs, max_seq_length, dim)
2025-03-04T23:31:10.6488552Z 
2025-03-04T23:31:10.8011704Z W0304 23:31:10.800000 63168 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:31:31.8383643Z Compilation time (from dynamo_timed): 35.444275146
2025-03-04T23:31:31.8395364Z pass
2025-03-04T23:31:31.8648238Z TIMING: entire_frame_compile:31.02078 gc:0.00445 _recursive_pre_grad_passes:0.02416 pad_mm_benchmark:0.11612 _recursive_joint_graph_passes:0.86227 _recursive_post_grad_passes:0.53972 async_compile.wait:3.75963 code_gen:11.52711 inductor_compile:18.3722 backend_compile:24.15755 entire_backward_compile:4.4235 total_wall_time:35.44428
2025-03-04T23:31:31.8651661Z STATS: call_* op count: 754 | FakeTensorMode.__torch_dispatch__:32979 | FakeTensor.__torch_dispatch__:7395 | ProxyTorchDispatchMode.__torch_dispatch__:15147
2025-03-04T23:31:31.8653398Z Dynamo produced 3 graphs covering 754 ops with 6 graph breaks (5 unique)
2025-03-04T23:31:38.5664714Z 
2025-03-04T23:31:42.1426315Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:31:42.1426899Z loading model: 0it [00:03, ?it/s]
2025-03-04T23:31:42.1427420Z cuda train hf_GPT2                            
2025-03-04T23:32:15.1463988Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:32:15.1465403Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:32:15.1466543Z     pred = mod(**cloned_inputs)
2025-03-04T23:32:15.1467565Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 1074, in forward
2025-03-04T23:32:15.1468645Z     transformer_outputs = self.transformer(
2025-03-04T23:32:15.1469385Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py", line 837, in forward
2025-03-04T23:32:15.1470120Z     inputs_embeds = self.wte(input_ids)
2025-03-04T23:32:15.1470348Z 
2025-03-04T23:32:15.2945681Z W0304 23:32:15.293000 63499 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:32:44.0783270Z Compilation time (from dynamo_timed): 54.11841719
2025-03-04T23:32:44.0806265Z pass
2025-03-04T23:32:44.1586020Z TIMING: entire_frame_compile:45.7224 gc:0.00468 _recursive_pre_grad_passes:0.03533 pad_mm_benchmark:0.40783 _recursive_joint_graph_passes:1.72625 _recursive_post_grad_passes:0.64592 async_compile.wait:4.05402 code_gen:18.36791 inductor_compile:28.31682 backend_compile:35.64133 entire_backward_compile:8.39601 total_wall_time:54.11842
2025-03-04T23:32:44.1587769Z STATS: call_* op count: 1393 | FakeTensorMode.__torch_dispatch__:51771 | FakeTensor.__torch_dispatch__:11405 | ProxyTorchDispatchMode.__torch_dispatch__:23299
2025-03-04T23:32:44.1589009Z Dynamo produced 3 graphs covering 1393 ops with 6 graph breaks (5 unique)
2025-03-04T23:32:51.5417076Z 
2025-03-04T23:33:07.3613721Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:33:07.3614115Z loading model: 0it [00:15, ?it/s]
2025-03-04T23:33:07.3614475Z cuda train hf_GPT2_large                      
2025-03-04T23:33:07.3873386Z Compilation time (from dynamo_timed): 0
2025-03-04T23:33:07.3873735Z pass_due_to_skip
2025-03-04T23:33:07.6502746Z TIMING: total_wall_time:0
2025-03-04T23:33:07.6503191Z STATS: call_* op count: 0
2025-03-04T23:33:07.6503801Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-04T23:33:11.9449187Z 
2025-03-04T23:33:14.0316156Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:33:14.0316520Z loading model: 0it [00:02, ?it/s]
2025-03-04T23:33:14.0316843Z cuda train hf_Reformer                        
2025-03-04T23:33:22.1573183Z 
2025-03-04T23:33:22.1573785Z class GraphModule(torch.nn.Module):
2025-03-04T23:33:22.1574276Z     def forward(self, L_cloned_inputs_input_ids_: "i64[4, 4096][4096, 1]cuda:0"):
2025-03-04T23:33:22.1574838Z         l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_
2025-03-04T23:33:22.1575200Z         
2025-03-04T23:33:22.1575458Z         # No stacktrace found for following nodes
2025-03-04T23:33:22.1576042Z         _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None);  _enter_autocast = None
2025-03-04T23:33:22.1576585Z         
2025-03-04T23:33:22.1577642Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-04T23:33:22.1579282Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-04T23:33:22.1579740Z         
2025-03-04T23:33:22.1580549Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-04T23:33:22.1581761Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(4096, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-04T23:33:22.1582589Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-04T23:33:22.1583088Z         
2025-03-04T23:33:22.1583234Z 
2025-03-04T23:33:22.1583354Z class GraphModule(torch.nn.Module):
2025-03-04T23:33:22.1583786Z     def forward(self, L_cloned_inputs_input_ids_: "i64[4, 4096][4096, 1]cuda:0"):
2025-03-04T23:33:22.1584283Z         l_cloned_inputs_input_ids_ = L_cloned_inputs_input_ids_
2025-03-04T23:33:22.1584638Z         
2025-03-04T23:33:22.1584888Z         # No stacktrace found for following nodes
2025-03-04T23:33:22.1585464Z         _enter_autocast = torch.amp.autocast_mode._enter_autocast('cuda', None, True, None);  _enter_autocast = None
2025-03-04T23:33:22.1586003Z         
2025-03-04T23:33:22.1586954Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-04T23:33:22.1588136Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-04T23:33:22.1588590Z         
2025-03-04T23:33:22.1589393Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-04T23:33:22.1590591Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(4096, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-04T23:33:22.1591416Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-04T23:33:22.1592330Z         
2025-03-04T23:33:23.0021595Z 
2025-03-04T23:33:23.0022215Z class GraphModule(torch.nn.Module):
2025-03-04T23:33:23.0023029Z     def forward(self, L_input_ids_: "i64[4, 4096][4096, 1]cuda:0"):
2025-03-04T23:33:23.0023435Z         l_input_ids_ = L_input_ids_
2025-03-04T23:33:23.0023718Z         
2025-03-04T23:33:23.0024700Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:91 in _get_least_common_mult_chunk_len, code: return np.lcm(config.lsh_attn_chunk_length, config.local_attn_chunk_length)
2025-03-04T23:33:23.0026156Z         least_common_mult_chunk_length: "i64[][]cpu" = torch__dynamo_utils_wrapped_lcm(64, 64)
2025-03-04T23:33:23.0026611Z         
2025-03-04T23:33:23.0027421Z          # File: /opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py:2061 in forward, code: input_shape[-1] % least_common_mult_chunk_length != 0
2025-03-04T23:33:23.0028629Z         wrapped_mod: "i64[][]cpu" = torch__dynamo_utils_wrapped_mod(4096, least_common_mult_chunk_length);  least_common_mult_chunk_length = None
2025-03-04T23:33:23.0029464Z         wrapped_ne: "b8[][]cpu" = torch__dynamo_utils_wrapped_ne(wrapped_mod, 0);  wrapped_mod = wrapped_ne = None
2025-03-04T23:33:23.0029963Z         
2025-03-04T23:33:32.2043468Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter.src Found from   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1480, in torch_dynamo_resume_in_forward_at_1478
2025-03-04T23:33:32.2044725Z     attn_outputs = self.attention(
2025-03-04T23:33:32.2045417Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1313, in forward
2025-03-04T23:33:32.2046149Z     self_attention_outputs = self.self_attention(
2025-03-04T23:33:32.2046876Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 482, in forward
2025-03-04T23:33:32.2047909Z     sorted_bucket_idx, undo_sorted_bucket_idx = self._get_sorted_bucket_idx_and_undo_sorted_bucket_idx(
2025-03-04T23:33:32.2048954Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 702, in _get_sorted_bucket_idx_and_undo_sorted_bucket_idx
2025-03-04T23:33:32.2049881Z     undo_sorted_bucket_idx.scatter_(-1, sorted_bucket_idx, indices)
2025-03-04T23:33:32.2050178Z 
2025-03-04T23:33:32.2050183Z 
2025-03-04T23:33:34.9977451Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter.src Found from   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1480, in torch_dynamo_resume_in_forward_at_1478
2025-03-04T23:33:34.9978694Z     attn_outputs = self.attention(
2025-03-04T23:33:34.9979388Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1313, in forward
2025-03-04T23:33:34.9980169Z     self_attention_outputs = self.self_attention(
2025-03-04T23:33:34.9980890Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 482, in forward
2025-03-04T23:33:34.9981779Z     sorted_bucket_idx, undo_sorted_bucket_idx = self._get_sorted_bucket_idx_and_undo_sorted_bucket_idx(
2025-03-04T23:33:34.9982822Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 702, in _get_sorted_bucket_idx_and_undo_sorted_bucket_idx
2025-03-04T23:33:34.9983751Z     undo_sorted_bucket_idx.scatter_(-1, sorted_bucket_idx, indices)
2025-03-04T23:33:34.9984048Z 
2025-03-04T23:33:34.9984053Z 
2025-03-04T23:33:36.4835496Z skipping cudagraphs due to disabling cudagraphs due to incompatible op aten.scatter.src Found from   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1480, in torch_dynamo_resume_in_forward_at_1478
2025-03-04T23:33:36.4837825Z     attn_outputs = self.attention(
2025-03-04T23:33:36.4839088Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 1313, in forward
2025-03-04T23:33:36.4840246Z     self_attention_outputs = self.self_attention(
2025-03-04T23:33:36.4841336Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 482, in forward
2025-03-04T23:33:36.4842703Z     sorted_bucket_idx, undo_sorted_bucket_idx = self._get_sorted_bucket_idx_and_undo_sorted_bucket_idx(
2025-03-04T23:33:36.4844369Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 702, in _get_sorted_bucket_idx_and_undo_sorted_bucket_idx
2025-03-04T23:33:36.4845896Z     undo_sorted_bucket_idx.scatter_(-1, sorted_bucket_idx, indices)
2025-03-04T23:33:36.4846408Z 
2025-03-04T23:33:36.4846414Z 
2025-03-04T23:33:39.6680622Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:33:39.6681628Z    File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 2094, in torch_dynamo_resume_in_forward_at_2066
2025-03-04T23:33:39.6682471Z     embedding_output = self.embeddings(
2025-03-04T23:33:39.6683169Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/reformer/modeling_reformer.py", line 254, in forward
2025-03-04T23:33:39.6683957Z     inputs_embeds = self.word_embeddings(input_ids)
2025-03-04T23:33:39.6684216Z 
2025-03-04T23:33:39.6812940Z W0304 23:33:39.680000 63965 site-packages/torch/_logging/_internal.py:1134] [32/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:34:01.6818985Z Compilation time (from dynamo_timed): 32.014001782
2025-03-04T23:34:01.6831102Z pass
2025-03-04T23:34:01.6982363Z TIMING: entire_frame_compile:30.68233 gc:0.01725 _recursive_pre_grad_passes:0.02986 _recursive_joint_graph_passes:0.48694 inductor_compile:17.97325 backend_compile:23.91887 _recursive_post_grad_passes:0.13367 async_compile.wait:6.85782 code_gen:13.60303 pad_mm_benchmark:0.02577 entire_backward_compile:1.33167 total_wall_time:32.014
2025-03-04T23:34:01.6984254Z STATS: call_* op count: 844 | FakeTensorMode.__torch_dispatch__:23644 | ProxyTorchDispatchMode.__torch_dispatch__:9106 | FakeTensor.__torch_dispatch__:4372 | attempt fast:21 | fast is_contiguous:21
2025-03-04T23:34:01.6985254Z Dynamo produced 17 graphs covering 844 ops with 23 graph breaks (8 unique)
2025-03-04T23:34:08.0586459Z 
2025-03-04T23:34:15.7058604Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:34:15.7059136Z loading model: 0it [00:07, ?it/s]
2025-03-04T23:34:15.7059648Z cuda train hf_Roberta_base                    
2025-03-04T23:34:20.2178828Z WARNING:common:fp64 golden ref were not generated for hf_Roberta_base. Setting accuracy check to cosine
2025-03-04T23:34:49.9643343Z skipping cudagraphs due to deterministic index put. Found from : 
2025-03-04T23:34:49.9644425Z    File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in torch_dynamo_resume_in_forward_and_backward_pass_at_459
2025-03-04T23:34:49.9645297Z     pred = mod(**cloned_inputs)
2025-03-04T23:34:49.9646278Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py", line 1092, in forward
2025-03-04T23:34:49.9647113Z     outputs = self.roberta(
2025-03-04T23:34:49.9647948Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py", line 830, in forward
2025-03-04T23:34:49.9648676Z     embedding_output = self.embeddings(
2025-03-04T23:34:49.9649393Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/xlm_roberta/modeling_xlm_roberta.py", line 126, in forward
2025-03-04T23:34:49.9650141Z     inputs_embeds = self.word_embeddings(input_ids)
2025-03-04T23:34:49.9650396Z 
2025-03-04T23:34:50.1700419Z W0304 23:34:50.169000 64446 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:35:28.1357936Z Compilation time (from dynamo_timed): 62.788141748
2025-03-04T23:35:28.1371166Z fail_accuracy
2025-03-04T23:35:28.1569475Z TIMING: entire_frame_compile:56.34854 gc:0.00486 _recursive_pre_grad_passes:0.03942 pad_mm_benchmark:1.22249 _recursive_joint_graph_passes:3.06815 _recursive_post_grad_passes:0.65867 async_compile.wait:3.05399 code_gen:17.82479 inductor_compile:29.27681 backend_compile:43.06654 entire_backward_compile:6.4396 total_wall_time:62.78814
2025-03-04T23:35:28.1572913Z STATS: call_* op count: 1411 | FakeTensorMode.__torch_dispatch__:63635 | FakeTensor.__torch_dispatch__:13431 | ProxyTorchDispatchMode.__torch_dispatch__:29575
2025-03-04T23:35:28.1574220Z Dynamo produced 3 graphs covering 1411 ops with 6 graph breaks (5 unique)
2025-03-04T23:35:36.1404694Z 
2025-03-04T23:35:41.4228466Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:35:41.4228878Z loading model: 0it [00:05, ?it/s]
2025-03-04T23:35:41.4229220Z cuda train hf_T5_base                         
2025-03-04T23:35:44.5673490Z WARNING:common:fp64 golden ref were not generated for hf_T5_base. Setting accuracy check to cosine
2025-03-04T23:35:46.5621953Z ERROR:common:
2025-03-04T23:35:46.5622311Z Traceback (most recent call last):
2025-03-04T23:35:46.5622875Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 2171, in check_accuracy
2025-03-04T23:35:46.5623468Z     correct_rerun_result = self.run_n_iterations(
2025-03-04T23:35:46.5624054Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 1947, in run_n_iterations
2025-03-04T23:35:46.5624653Z     model_iter_fn(mod, inputs, collect_outputs=False)
2025-03-04T23:35:46.5625754Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 462, in forward_and_backward_pass
2025-03-04T23:35:46.5626371Z     pred = mod(**cloned_inputs)
2025-03-04T23:35:46.5627050Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:35:46.5627711Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:35:46.5628321Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:35:46.5628931Z     return forward_call(*args, **kwargs)
2025-03-04T23:35:46.5629575Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1748, in forward
2025-03-04T23:35:46.5630225Z     decoder_outputs = self.decoder(
2025-03-04T23:35:46.5630858Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:35:46.5631812Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:35:46.5632428Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:35:46.5633059Z     return forward_call(*args, **kwargs)
2025-03-04T23:35:46.5640067Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 1115, in forward
2025-03-04T23:35:46.5640725Z     layer_outputs = layer_module(
2025-03-04T23:35:46.5641355Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:35:46.5642012Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:35:46.5642619Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:35:46.5643228Z     return forward_call(*args, **kwargs)
2025-03-04T23:35:46.5643863Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 695, in forward
2025-03-04T23:35:46.5644517Z     self_attention_outputs = self.layer[0](
2025-03-04T23:35:46.5645161Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:35:46.5646405Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:35:46.5647012Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:35:46.5647618Z     return forward_call(*args, **kwargs)
2025-03-04T23:35:46.5648378Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 602, in forward
2025-03-04T23:35:46.5649034Z     attention_output = self.SelfAttention(
2025-03-04T23:35:46.5649682Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
2025-03-04T23:35:46.5650330Z     return self._call_impl(*args, **kwargs)
2025-03-04T23:35:46.5650934Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
2025-03-04T23:35:46.5651540Z     return forward_call(*args, **kwargs)
2025-03-04T23:35:46.5652185Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/transformers/models/t5/modeling_t5.py", line 562, in forward
2025-03-04T23:35:46.5652928Z     attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
2025-03-04T23:35:46.5653617Z   File "/opt/conda/envs/py_3.10/lib/python3.10/site-packages/torch/nn/functional.py", line 2140, in softmax
2025-03-04T23:35:46.5654190Z     ret = input.softmax(dim)
2025-03-04T23:35:46.5656194Z torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 192.00 MiB. GPU 0 has a total capacity of 21.98 GiB of which 30.44 MiB is free. Process 124540 has 21.94 GiB memory in use. Of the allocated memory 21.35 GiB is allocated by PyTorch, and 240.72 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)
2025-03-04T23:35:46.5884485Z Compilation time (from dynamo_timed): 0
2025-03-04T23:35:46.7485389Z eager_2nd_run_OOM
2025-03-04T23:35:46.7564284Z TIMING: total_wall_time:0
2025-03-04T23:35:46.7565081Z STATS: call_* op count: 0
2025-03-04T23:35:46.7566072Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-04T23:35:51.1739334Z 
2025-03-04T23:36:01.8582516Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:36:01.8582937Z loading model: 0it [00:10, ?it/s]
2025-03-04T23:36:01.8583269Z cuda train hf_T5_large                        
2025-03-04T23:36:01.8848355Z Compilation time (from dynamo_timed): 0
2025-03-04T23:36:01.8848823Z pass_due_to_skip
2025-03-04T23:36:02.3334940Z TIMING: total_wall_time:0
2025-03-04T23:36:02.3335323Z STATS: call_* op count: 0
2025-03-04T23:36:02.3335799Z Dynamo produced 0 graphs covering 0 ops with 0 graph breaks (0 unique)
2025-03-04T23:36:06.6682809Z 
2025-03-04T23:36:08.1714811Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:36:08.1715192Z loading model: 0it [00:01, ?it/s]
2025-03-04T23:36:08.1715570Z cuda train hf_Whisper                         
2025-03-04T23:36:26.7792371Z W0304 23:36:26.778000 64751 site-packages/torch/_logging/_internal.py:1134] [5/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:36:42.7163978Z Compilation time (from dynamo_timed): 28.913607510000002
2025-03-04T23:36:42.7168935Z pass
2025-03-04T23:36:42.7236492Z TIMING: entire_frame_compile:24.06846 gc:0.00433 _recursive_pre_grad_passes:0.01771 pad_mm_benchmark:0.25955 _recursive_joint_graph_passes:0.89584 _recursive_post_grad_passes:0.28423 async_compile.wait:6.56627 code_gen:13.21097 inductor_compile:17.56247 backend_compile:19.58464 entire_backward_compile:4.84515 total_wall_time:28.91361
2025-03-04T23:36:42.7238222Z STATS: call_* op count: 488 | FakeTensorMode.__torch_dispatch__:20461 | FakeTensor.__torch_dispatch__:5035 | ProxyTorchDispatchMode.__torch_dispatch__:9166
2025-03-04T23:36:42.7239065Z Dynamo produced 3 graphs covering 488 ops with 6 graph breaks (5 unique)
2025-03-04T23:36:49.0877936Z 
2025-03-04T23:36:49.2562140Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:36:49.2563213Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:36:49.2563738Z cuda train hf_distil_whisper                  
2025-03-04T23:36:49.2568996Z Traceback (most recent call last):
2025-03-04T23:36:49.2569758Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/common.py", line 3995, in run
2025-03-04T23:36:49.2570520Z     ) = runner.load_model(
2025-03-04T23:36:49.2571276Z   File "/var/lib/jenkins/workspace/benchmarks/dynamo/torchbench.py", line 316, in load_model
2025-03-04T23:36:49.2572162Z     benchmark = benchmark_cls(
2025-03-04T23:36:49.2573003Z   File "/var/lib/jenkins/workspace/torchbench/torchbenchmark/util/model.py", line 43, in __call__
2025-03-04T23:36:49.2573942Z     obj = type.__call__(cls, *args, **kwargs)
2025-03-04T23:36:49.2575050Z   File "/var/lib/jenkins/workspace/torchbench/torchbenchmark/models/hf_distil_whisper/__init__.py", line 13, in __init__
2025-03-04T23:36:49.2576262Z     raise NotImplementedError("Training is not implemented.")
2025-03-04T23:36:49.2577013Z NotImplementedError: Training is not implemented.
2025-03-04T23:36:49.2577460Z 
2025-03-04T23:36:49.2577621Z model_fail_to_load
2025-03-04T23:36:53.4118795Z 
2025-03-04T23:36:54.0165325Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:36:54.0166017Z loading model: 0it [00:00, ?it/s]
2025-03-04T23:36:54.0166676Z cuda train lennard_jones                      
2025-03-04T23:36:58.0998841Z W0304 23:36:58.098000 65232 site-packages/torch/_logging/_internal.py:1134] [6/0] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored
2025-03-04T23:37:01.3729496Z Compilation time (from dynamo_timed): 5.387971038
2025-03-04T23:37:01.3730369Z pass
2025-03-04T23:37:01.3732078Z TIMING: entire_frame_compile:5.03273 gc:0.00423 _recursive_pre_grad_passes:0.00848 pad_mm_benchmark:0.52834 _recursive_joint_graph_passes:0.69459 _recursive_post_grad_passes:0.06047 async_compile.precompile:0.08058 async_compile.wait:1.37081 code_gen:1.98895 inductor_compile:2.57993 backend_compile:4.31091 entire_backward_compile:0.35524 total_wall_time:5.38797
2025-03-04T23:37:01.3733920Z STATS: call_* op count: 82 | FakeTensorMode.__torch_dispatch__:3047 | ProxyTorchDispatchMode.__torch_dispatch__:1300 | FakeTensor.__torch_dispatch__:671
2025-03-04T23:37:01.3734766Z Dynamo produced 3 graphs covering 82 ops with 7 graph breaks (5 unique)
2025-03-04T23:37:03.3442560Z accuracy            pass_rate=79.41%
2025-03-04T23:37:03.3448114Z calls_captured      gmean=0.00x mean=579.147x
2025-03-04T23:37:03.3452097Z unique_graphs       gmean=0.00x mean=3.294x
2025-03-04T23:37:03.3455509Z graph_breaks        gmean=0.00x mean=6.471x
2025-03-04T23:37:03.3459627Z unique_graph_breaks gmean=0.00x mean=4.324x
2025-03-04T23:37:03.3463989Z autograd_captures   gmean=0.00x mean=0.000x
2025-03-04T23:37:03.3467965Z autograd_compiles   gmean=0.00x mean=0.000x
2025-03-04T23:37:03.3472423Z cudagraph_skips     gmean=0.00x mean=0.735x
2025-03-04T23:37:03.3473334Z compilation_latency mean=29.700 seconds
2025-03-04T23:37:04.9865202Z + python benchmarks/dynamo/check_accuracy.py --actual /var/lib/jenkins/workspace/test/test-reports/training_torchbench.csv --expected benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_training.csv
2025-03-04T23:37:05.3139552Z torchrec_dlrm                       PASS
2025-03-04T23:37:05.3144094Z BERT_pytorch                        PASS
2025-03-04T23:37:05.3149018Z Background_Matting                  XFAIL
2025-03-04T23:37:05.3153987Z LearningToPaint                     PASS
2025-03-04T23:37:05.3158961Z Super_SloMo                         PASS
2025-03-04T23:37:05.3163595Z alexnet                             PASS
2025-03-04T23:37:05.3168500Z basic_gnn_edgecnn                   PASS
2025-03-04T23:37:05.3173234Z basic_gnn_gcn                       PASS
2025-03-04T23:37:05.3178039Z basic_gnn_gin                       PASS
2025-03-04T23:37:05.3182830Z basic_gnn_sage                      PASS
2025-03-04T23:37:05.3187569Z dcgan                               PASS
2025-03-04T23:37:05.3192366Z demucs                              PASS
2025-03-04T23:37:05.3197364Z densenet121                         PASS
2025-03-04T23:37:05.3201790Z detectron2_maskrcnn_r_50_c4         XFAIL
2025-03-04T23:37:05.3206517Z dlrm                                PASS
2025-03-04T23:37:05.3211531Z drq                                 PASS
2025-03-04T23:37:05.3216197Z fastNLP_Bert                        PASS
2025-03-04T23:37:05.3220906Z functorch_dp_cifar10                PASS
2025-03-04T23:37:05.3226385Z functorch_maml_omniglot             PASS
2025-03-04T23:37:05.3231533Z hf_Albert                           PASS
2025-03-04T23:37:05.3236064Z hf_Bart                             PASS
2025-03-04T23:37:05.3240623Z hf_Bert                             PASS
2025-03-04T23:37:05.3245255Z hf_Bert_large                       PASS
2025-03-04T23:37:05.3250391Z hf_BigBird                          PASS
2025-03-04T23:37:05.3254925Z hf_DistilBert                       PASS
2025-03-04T23:37:05.3259317Z hf_GPT2                             PASS
2025-03-04T23:37:05.3264179Z hf_GPT2_large                       XFAIL
2025-03-04T23:37:05.3268630Z hf_Reformer                         PASS
2025-03-04T23:37:05.3273210Z hf_Roberta_base                     XFAIL
2025-03-04T23:37:05.3277976Z hf_T5_base                          XFAIL
2025-03-04T23:37:05.3282479Z hf_T5_large                         XFAIL
2025-03-04T23:37:05.3287233Z hf_Whisper                          PASS
2025-03-04T23:37:05.3292167Z hf_distil_whisper                   XFAIL
2025-03-04T23:37:05.3296579Z lennard_jones                       PASS
2025-03-04T23:37:05.3834360Z + python benchmarks/dynamo/check_graph_breaks.py --actual /var/lib/jenkins/workspace/test/test-reports/training_torchbench.csv --expected benchmarks/dynamo/ci_expected_accuracy/inductor_torchbench_training.csv
2025-03-04T23:37:05.7132459Z torchrec_dlrm                       PASS
2025-03-04T23:37:05.7136969Z BERT_pytorch                        PASS
2025-03-04T23:37:05.7141578Z Background_Matting                  PASS
2025-03-04T23:37:05.7146936Z LearningToPaint                     PASS
2025-03-04T23:37:05.7151569Z Super_SloMo                         PASS
2025-03-04T23:37:05.7156336Z alexnet                             PASS
2025-03-04T23:37:05.7161021Z basic_gnn_edgecnn                   PASS
2025-03-04T23:37:05.7165796Z basic_gnn_gcn                       PASS
2025-03-04T23:37:05.7170502Z basic_gnn_gin                       PASS
2025-03-04T23:37:05.7175258Z basic_gnn_sage                      PASS
2025-03-04T23:37:05.7179725Z dcgan                               PASS
2025-03-04T23:37:05.7184650Z demucs                              PASS
2025-03-04T23:37:05.7189065Z densenet121                         PASS
2025-03-04T23:37:05.7193890Z detectron2_maskrcnn_r_50_c4         PASS
2025-03-04T23:37:05.7198374Z dlrm                                PASS
2025-03-04T23:37:05.7203229Z drq                                 PASS
2025-03-04T23:37:05.7207826Z fastNLP_Bert                        PASS
2025-03-04T23:37:05.7212675Z functorch_dp_cifar10                PASS
2025-03-04T23:37:05.7217041Z functorch_maml_omniglot             PASS
2025-03-04T23:37:05.7221947Z hf_Albert                           PASS
2025-03-04T23:37:05.7226586Z hf_Bart                             PASS
2025-03-04T23:37:05.7232033Z hf_Bert                             PASS
2025-03-04T23:37:05.7236330Z hf_Bert_large                       PASS
2025-03-04T23:37:05.7241189Z hf_BigBird                          PASS
2025-03-04T23:37:05.7245543Z hf_DistilBert                       PASS
2025-03-04T23:37:05.7250721Z hf_GPT2                             PASS
2025-03-04T23:37:05.7254909Z hf_GPT2_large                       PASS
2025-03-04T23:37:05.7259836Z hf_Reformer                         PASS
2025-03-04T23:37:05.7266039Z hf_Roberta_base                     PASS
2025-03-04T23:37:05.7270346Z hf_T5_base                          PASS
2025-03-04T23:37:05.7274635Z hf_T5_large                         PASS
2025-03-04T23:37:05.7279578Z hf_Whisper                          PASS
2025-03-04T23:37:05.7283934Z hf_distil_whisper                   PASS
2025-03-04T23:37:05.7288926Z lennard_jones                       PASS
2025-03-04T23:37:05.7872945Z + cleanup_workspace
2025-03-04T23:37:05.7873577Z + echo 'sudo may print the following warning message that can be ignored. The chown command will still run.'
2025-03-04T23:37:05.7874351Z sudo may print the following warning message that can be ignored. The chown command will still run.
2025-03-04T23:37:05.7874970Z + echo '    sudo: setrlimit(RLIMIT_STACK): Operation not permitted'
2025-03-04T23:37:05.7875440Z     sudo: setrlimit(RLIMIT_STACK): Operation not permitted
2025-03-04T23:37:05.7875975Z + echo 'For more details refer to https://github.com/sudo-project/sudo/issues/42'
2025-03-04T23:37:05.7876567Z For more details refer to https://github.com/sudo-project/sudo/issues/42
2025-03-04T23:37:05.7877045Z + sudo chown -R 1000 /var/lib/jenkins/workspace
2025-03-04T23:37:06.6501385Z ##[group]Run pytorch/test-infra/.github/actions/upload-benchmark-results@main
2025-03-04T23:37:06.6501870Z with:
2025-03-04T23:37:06.6502136Z   benchmark-results-dir: test/test-reports
2025-03-04T23:37:06.6502466Z   dry-run: false
2025-03-04T23:37:06.6502715Z   schema-version: v3
2025-03-04T23:37:06.6503165Z   github-token: ***
2025-03-04T23:37:06.6503407Z env:
2025-03-04T23:37:06.6503632Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:06.6503973Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:06.6504534Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:06.6505036Z ##[endgroup]
2025-03-04T23:37:06.6534058Z ##[group]Run set -eux
2025-03-04T23:37:06.6534331Z set -eux
2025-03-04T23:37:06.6534611Z python3 -mpip install boto3==1.35.33
2025-03-04T23:37:06.6548650Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:06.6549042Z env:
2025-03-04T23:37:06.6549301Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:06.6549646Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:06.6550211Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:06.6550710Z ##[endgroup]
2025-03-04T23:37:06.6597914Z + python3 -mpip install boto3==1.35.33
2025-03-04T23:37:07.0765802Z Defaulting to user installation because normal site-packages is not writeable
2025-03-04T23:37:08.2132840Z Collecting boto3==1.35.33
2025-03-04T23:37:08.2389461Z   Downloading boto3-1.35.33-py3-none-any.whl (139 kB)
2025-03-04T23:37:08.2584718Z 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)
2025-03-04T23:37:08.3003950Z Collecting s3transfer<0.11.0,>=0.10.0
2025-03-04T23:37:08.3036351Z   Downloading s3transfer-0.10.4-py3-none-any.whl (83 kB)
2025-03-04T23:37:09.5359937Z Collecting botocore<1.36.0,>=1.35.33
2025-03-04T23:37:09.5433398Z   Downloading botocore-1.35.99-py3-none-any.whl (13.3 MB)
2025-03-04T23:37:09.7532513Z 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)
2025-03-04T23:37:09.7536032Z 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)
2025-03-04T23:37:09.8802223Z 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)
2025-03-04T23:37:09.9705207Z Installing collected packages: botocore, s3transfer, boto3
2025-03-04T23:37:10.5658329Z Successfully installed boto3-1.35.33 botocore-1.35.99 s3transfer-0.10.4
2025-03-04T23:37:10.6816771Z ##[group]Run set -eux
2025-03-04T23:37:10.6817068Z set -eux
2025-03-04T23:37:10.6817307Z 
2025-03-04T23:37:10.6817566Z if [[ -z "${GITHUB_TOKEN}" ]]; then
2025-03-04T23:37:10.6817946Z   echo "Missing github-token input"
2025-03-04T23:37:10.6818272Z   exit 1
2025-03-04T23:37:10.6818685Z fi
2025-03-04T23:37:10.6829123Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:10.6829509Z env:
2025-03-04T23:37:10.6829738Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:10.6830082Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:10.6830641Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:10.6831496Z   GITHUB_TOKEN: ***
2025-03-04T23:37:10.6831754Z ##[endgroup]
2025-03-04T23:37:10.6863345Z + [[ -z *** ]]
2025-03-04T23:37:10.6915400Z ##[group]Run pytorch/test-infra/.github/actions/get-workflow-job-id@main
2025-03-04T23:37:10.6915841Z with:
2025-03-04T23:37:10.6916208Z   github-token: ***
2025-03-04T23:37:10.6916456Z env:
2025-03-04T23:37:10.6916686Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:10.6917037Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:10.6917813Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:10.6918337Z ##[endgroup]
2025-03-04T23:37:10.6943601Z ##[group]Run set -eux
2025-03-04T23:37:10.6943880Z set -eux
2025-03-04T23:37:10.6944119Z 
2025-03-04T23:37:10.6944589Z python3 "${GITHUB_ACTION_PATH}/../../scripts/get_workflow_job_id.py" "${GITHUB_RUN_ID}" "${RUNNER_NAME}"
2025-03-04T23:37:10.6952799Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:10.6953179Z env:
2025-03-04T23:37:10.6953407Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:10.6953746Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:10.6954296Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:10.6955130Z   GITHUB_TOKEN: ***
2025-03-04T23:37:10.6955383Z ##[endgroup]
2025-03-04T23:37:10.6983513Z + 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-05d5204902c63dc95
2025-03-04T23:37:11.8824569Z setting job-id=38197725386
2025-03-04T23:37:11.8825942Z setting job-name=cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T23:37:11.8946593Z ##[group]Run set -eux
2025-03-04T23:37:11.8946926Z set -eux
2025-03-04T23:37:11.8947162Z 
2025-03-04T23:37:11.8947558Z python3 "${GITHUB_ACTION_PATH}/../../scripts/benchmarks/gather_metadata.py" \
2025-03-04T23:37:11.8948077Z   --schema-version "${SCHEMA_VERSION}" \
2025-03-04T23:37:11.8948436Z   --repo "${REPO}" \
2025-03-04T23:37:11.8948756Z   --head-branch "${HEAD_BRANCH}" \
2025-03-04T23:37:11.8949095Z   --head-sha "${HEAD_SHA}" \
2025-03-04T23:37:11.8949437Z   --workflow-id "${WORKFLOW_RUN_ID}" \
2025-03-04T23:37:11.8949800Z   --run-attempt "${RUN_ATTEMPT}" \
2025-03-04T23:37:11.8950148Z   --job-id "${JOB_ID}" \
2025-03-04T23:37:11.8950454Z   --job-name "${JOB_NAME}"
2025-03-04T23:37:11.8959250Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:11.8959630Z env:
2025-03-04T23:37:11.8959860Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:11.8960201Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:11.8960757Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:11.8961255Z   SCHEMA_VERSION: v3
2025-03-04T23:37:11.8961521Z   REPO: pytorch/pytorch
2025-03-04T23:37:11.8961829Z   HEAD_BRANCH: refs/tags/ciflow/inductor/148205
2025-03-04T23:37:11.8962215Z   HEAD_SHA: 1b7498080987913ecb3aff6253c5e88f3540d911
2025-03-04T23:37:11.8962570Z   WORKFLOW_RUN_ID: 13661696663
2025-03-04T23:37:11.8962857Z   RUN_ATTEMPT: 1
2025-03-04T23:37:11.8963097Z   JOB_ID: 38197725386
2025-03-04T23:37:11.8963573Z   JOB_NAME: cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T23:37:11.8964109Z ##[endgroup]
2025-03-04T23:37:11.8993842Z + python3 /home/ec2-user/actions-runner/_work/_actions/pytorch/test-infra/main/.github/actions/upload-benchmark-results/../../scripts/benchmarks/gather_metadata.py --schema-version v3 --repo pytorch/pytorch --head-branch refs/tags/ciflow/inductor/148205 --head-sha 1b7498080987913ecb3aff6253c5e88f3540d911 --workflow-id 13661696663 --run-attempt 1 --job-id 38197725386 --job-name 'cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)'
2025-03-04T23:37:11.9331524Z ##[group]Run set -eux
2025-03-04T23:37:11.9331808Z set -eux
2025-03-04T23:37:11.9332043Z 
2025-03-04T23:37:11.9332314Z # TODO (huydhn): Implement this part
2025-03-04T23:37:11.9332685Z echo "runners=[]" >> "${GITHUB_OUTPUT}"
2025-03-04T23:37:11.9341465Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:11.9341853Z env:
2025-03-04T23:37:11.9342087Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:11.9342643Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:11.9343222Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:11.9343725Z ##[endgroup]
2025-03-04T23:37:11.9373456Z + echo 'runners=[]'
2025-03-04T23:37:11.9402880Z ##[group]Run set -eux
2025-03-04T23:37:11.9403155Z set -eux
2025-03-04T23:37:11.9403391Z 
2025-03-04T23:37:11.9403652Z # TODO (huydhn): Implement this part
2025-03-04T23:37:11.9404042Z echo "dependencies={}" >> "${GITHUB_OUTPUT}"
2025-03-04T23:37:11.9412121Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:11.9412510Z env:
2025-03-04T23:37:11.9412747Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:11.9413098Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:11.9413661Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:11.9414167Z ##[endgroup]
2025-03-04T23:37:11.9440303Z + echo 'dependencies={}'
2025-03-04T23:37:11.9472024Z ##[group]Run set -eux
2025-03-04T23:37:11.9472324Z set -eux
2025-03-04T23:37:11.9472562Z 
2025-03-04T23:37:11.9472843Z if [[ ! -d "${BENCHMARK_RESULTS_DIR}" ]]; then
2025-03-04T23:37:11.9473287Z   echo "${BENCHMARK_RESULTS_DIR} does not exist, skipping"
2025-03-04T23:37:11.9473772Z   # We don't want the job to fail if the directory doesn't exist
2025-03-04T23:37:11.9474166Z   exit 0
2025-03-04T23:37:11.9474399Z fi
2025-03-04T23:37:11.9474621Z 
2025-03-04T23:37:11.9474869Z if [[ "${DRY_RUN}" == "true" ]]; then
2025-03-04T23:37:11.9475387Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-04T23:37:11.9475937Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-04T23:37:11.9476372Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-04T23:37:11.9476744Z     --runners "${RUNNER_INFO}" \
2025-03-04T23:37:11.9477102Z     --dependencies "${DEPENDENCIES}" \
2025-03-04T23:37:11.9477449Z     --dry-run
2025-03-04T23:37:11.9477704Z else
2025-03-04T23:37:11.9478085Z   python3 "${GITHUB_ACTION_PATH}/../../scripts/upload_benchmark_results.py" \
2025-03-04T23:37:11.9478624Z     --benchmark-results-dir "${BENCHMARK_RESULTS_DIR}" \
2025-03-04T23:37:11.9479049Z     --metadata "${BENCHMARK_METADATA}" \
2025-03-04T23:37:11.9479402Z     --runners "${RUNNER_INFO}" \
2025-03-04T23:37:11.9479757Z     --dependencies "${DEPENDENCIES}"
2025-03-04T23:37:11.9480081Z fi
2025-03-04T23:37:11.9488114Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:11.9488490Z env:
2025-03-04T23:37:11.9488725Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:11.9489079Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:11.9489646Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:11.9490378Z   BENCHMARK_RESULTS_DIR: test/test-reports
2025-03-04T23:37:11.9490701Z   DRY_RUN: false
2025-03-04T23:37:11.9492069Z   BENCHMARK_METADATA: {"timestamp": 1741131431, "schema_version": "v3", "name": "cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, 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": 38197725386}
2025-03-04T23:37:11.9493556Z   RUNNER_INFO: []
2025-03-04T23:37:11.9493811Z   DEPENDENCIES: {}
2025-03-04T23:37:11.9494059Z ##[endgroup]
2025-03-04T23:37:11.9519971Z + [[ ! -d test/test-reports ]]
2025-03-04T23:37:11.9520379Z + [[ false == \t\r\u\e ]]
2025-03-04T23:37:11.9523145Z + 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": 1741131431, "schema_version": "v3", "name": "cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, 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": 38197725386}' --runners '[]' --dependencies '{}'
2025-03-04T23:37:12.1272959Z INFO:root:Upload test/test-reports/inference_torchbench.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38197725386/inference_torchbench.json
2025-03-04T23:37:12.1660438Z INFO:botocore.credentials:Found credentials from IAM Role: gh-ci-github-action-runners-runner-role
2025-03-04T23:37:12.3478711Z INFO:root:Upload test/test-reports/inference_torchbench_graph_breaks.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38197725386/inference_torchbench_graph_breaks.json
2025-03-04T23:37:12.4520086Z INFO:root:Upload test/test-reports/training_torchbench.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38197725386/training_torchbench.json
2025-03-04T23:37:12.6162063Z INFO:root:Upload test/test-reports/training_torchbench_graph_breaks.json to s3://ossci-benchmarks/v3/pytorch/pytorch/13661696663/38197725386/training_torchbench_graph_breaks.json
2025-03-04T23:37:12.7875013Z ##[group]Run cat test/**/*_toprint.log || true
2025-03-04T23:37:12.7875415Z cat test/**/*_toprint.log || true
2025-03-04T23:37:12.7884070Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:12.7884451Z env:
2025-03-04T23:37:12.7884681Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:12.7885019Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:12.7885577Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:12.7886078Z ##[endgroup]
2025-03-04T23:37:12.7972927Z cat: 'test/**/*_toprint.log': No such file or directory
2025-03-04T23:37:12.8019641Z ##[group]Run kill "$MONITOR_SCRIPT_PID"
2025-03-04T23:37:12.8020003Z kill "$MONITOR_SCRIPT_PID"
2025-03-04T23:37:12.8028584Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:12.8028961Z env:
2025-03-04T23:37:12.8029179Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:12.8029524Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:12.8030076Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:12.8030591Z   MONITOR_SCRIPT_PID: 54149
2025-03-04T23:37:12.8030864Z ##[endgroup]
2025-03-04T23:37:12.8182748Z Prepare all required actions
2025-03-04T23:37:12.8183141Z Getting action download info
2025-03-04T23:37:12.9635421Z Download action repository 'actions/upload-artifact@v4' (SHA:4cec3d8aa04e39d1a68397de0c4cd6fb9dce8ec1)
2025-03-04T23:37:13.4106784Z ##[group]Run ./.github/actions/upload-test-artifacts
2025-03-04T23:37:13.4107140Z with:
2025-03-04T23:37:13.4107553Z   file-suffix: test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386
2025-03-04T23:37:13.4108060Z   s3-bucket: gha-artifacts
2025-03-04T23:37:13.4108518Z env:
2025-03-04T23:37:13.4108755Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:13.4109102Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:13.4109665Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:13.4110168Z ##[endgroup]
2025-03-04T23:37:13.4143994Z ##[group]Run # Remove any previous test jsons if they exist
2025-03-04T23:37:13.4144496Z # Remove any previous test jsons if they exist
2025-03-04T23:37:13.4144874Z rm -f test-jsons-*.zip
2025-03-04T23:37:13.4145305Z zip -r "test-jsons-${FILE_SUFFIX}.zip" test/test-reports -i '*.json'
2025-03-04T23:37:13.4154114Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:13.4154496Z env:
2025-03-04T23:37:13.4154730Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:13.4155077Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:13.4155634Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:13.4156327Z   FILE_SUFFIX: test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386
2025-03-04T23:37:13.4156815Z ##[endgroup]
2025-03-04T23:37:13.4358632Z   adding: test/test-reports/inference_torchbench.json (deflated 99%)
2025-03-04T23:37:13.4387608Z   adding: test/test-reports/inference_torchbench_graph_breaks.json (deflated 98%)
2025-03-04T23:37:13.4434812Z   adding: test/test-reports/training_torchbench.json (deflated 99%)
2025-03-04T23:37:13.4563988Z   adding: test/test-reports/training_torchbench_graph_breaks.json (deflated 98%)
2025-03-04T23:37:13.4615141Z ##[group]Run # Remove any previous test reports if they exist
2025-03-04T23:37:13.4615622Z # Remove any previous test reports if they exist
2025-03-04T23:37:13.4616019Z rm -f test-reports-*.zip
2025-03-04T23:37:13.4616500Z zip -r "test-reports-${FILE_SUFFIX}.zip" test/test-reports -i '*.xml' -i '*.csv'
2025-03-04T23:37:13.4625448Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:13.4625848Z env:
2025-03-04T23:37:13.4626084Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:13.4626429Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:13.4626987Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:13.4627668Z   FILE_SUFFIX: test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386
2025-03-04T23:37:13.4628155Z ##[endgroup]
2025-03-04T23:37:13.4679938Z   adding: test/test-reports/inference_torchbench.csv (deflated 69%)
2025-03-04T23:37:13.4683762Z   adding: test/test-reports/inference_torchbench_graph_breaks.csv (deflated 93%)
2025-03-04T23:37:13.4684902Z   adding: test/test-reports/inference_torchbench_graph_break_deduped.csv (deflated 78%)
2025-03-04T23:37:13.4685654Z   adding: test/test-reports/training_torchbench.csv (deflated 59%)
2025-03-04T23:37:13.4696566Z   adding: test/test-reports/training_torchbench_graph_breaks.csv (deflated 97%)
2025-03-04T23:37:13.4697971Z   adding: test/test-reports/training_torchbench_graph_break_deduped.csv (deflated 79%)
2025-03-04T23:37:13.4735514Z ##[group]Run # Remove any previous usage logs if they exist
2025-03-04T23:37:13.4735988Z # Remove any previous usage logs if they exist
2025-03-04T23:37:13.4736358Z rm -f logs-*.zip
2025-03-04T23:37:13.4736825Z # this workflow is also run in bazel build test, but we dont generate usage reports for it
2025-03-04T23:37:13.4737587Z # so check to see if the file exists first
2025-03-04T23:37:13.4737960Z if [ -f 'usage_log.txt' ]; then
2025-03-04T23:37:13.4738338Z     zip "logs-${FILE_SUFFIX}.zip" 'usage_log.txt'
2025-03-04T23:37:13.4738689Z fi
2025-03-04T23:37:13.4739064Z if find "test/test-reports" -name "*.log" 2>/dev/null | grep -q .; then
2025-03-04T23:37:13.4739621Z     zip -r "logs-${FILE_SUFFIX}.zip" test/test-reports -i '*.log'
2025-03-04T23:37:13.4740016Z fi
2025-03-04T23:37:13.4749011Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:13.4749390Z env:
2025-03-04T23:37:13.4749614Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:13.4749956Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:13.4750512Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:13.4751207Z   FILE_SUFFIX: test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386
2025-03-04T23:37:13.4751695Z ##[endgroup]
2025-03-04T23:37:13.4868677Z   adding: usage_log.txt (deflated 97%)
2025-03-04T23:37:13.4929109Z ##[group]Run # Remove any previous debugging artifacts if they exist
2025-03-04T23:37:13.4929641Z # Remove any previous debugging artifacts if they exist
2025-03-04T23:37:13.4930048Z rm -f debug-*.zip
2025-03-04T23:37:13.4930348Z if [ -d 'test/debug' ]; then
2025-03-04T23:37:13.4930710Z   zip -r "debug-${FILE_SUFFIX}.zip" test/debug
2025-03-04T23:37:13.4931074Z fi
2025-03-04T23:37:13.4939744Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:13.4940124Z env:
2025-03-04T23:37:13.4940355Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:13.4940699Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:13.4941261Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:13.4941962Z   FILE_SUFFIX: test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386
2025-03-04T23:37:13.4942452Z ##[endgroup]
2025-03-04T23:37:13.5032144Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-04T23:37:13.5032483Z with:
2025-03-04T23:37:13.5032721Z   s3-bucket: gha-artifacts
2025-03-04T23:37:13.5033057Z   s3-prefix: pytorch/pytorch/13661696663/1/artifact

2025-03-04T23:37:13.5033416Z   retention-days: 14
2025-03-04T23:37:13.5033698Z   if-no-files-found: warn
2025-03-04T23:37:13.5033981Z   path: test-jsons-*.zip
2025-03-04T23:37:13.5034257Z   name: artifact
2025-03-04T23:37:13.5034518Z   region: us-east-1
2025-03-04T23:37:13.5034751Z env:
2025-03-04T23:37:13.5034985Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:13.5035332Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:13.5035896Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:13.5036402Z ##[endgroup]
2025-03-04T23:37:13.8776502Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-04T23:37:13.8777201Z With the provided path, there will be 1 file uploaded
2025-03-04T23:37:13.8777809Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact
2025-03-04T23:37:13.8846071Z Starting upload of test-jsons-test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386.zip
2025-03-04T23:37:14.0439024Z Finished upload of test-jsons-test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386.zip
2025-03-04T23:37:14.0755795Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-04T23:37:14.0756329Z with:
2025-03-04T23:37:14.0756718Z   s3-bucket: gha-artifacts
2025-03-04T23:37:14.0757246Z   s3-prefix: pytorch/pytorch/13661696663/1/artifact

2025-03-04T23:37:14.0757817Z   retention-days: 14
2025-03-04T23:37:14.0758242Z   if-no-files-found: error
2025-03-04T23:37:14.0758705Z   path: test-reports-*.zip
2025-03-04T23:37:14.0759148Z   name: artifact
2025-03-04T23:37:14.0759538Z   region: us-east-1
2025-03-04T23:37:14.0759922Z env:
2025-03-04T23:37:14.0760273Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:14.0761117Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:14.0762016Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:14.0762812Z ##[endgroup]
2025-03-04T23:37:14.4049483Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-04T23:37:14.4049934Z With the provided path, there will be 1 file uploaded
2025-03-04T23:37:14.4050390Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact
2025-03-04T23:37:14.4104761Z Starting upload of test-reports-test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386.zip
2025-03-04T23:37:14.5814138Z Finished upload of test-reports-test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386.zip
2025-03-04T23:37:14.6106178Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-04T23:37:14.6106523Z with:
2025-03-04T23:37:14.6106759Z   s3-bucket: gha-artifacts
2025-03-04T23:37:14.6107090Z   s3-prefix: pytorch/pytorch/13661696663/1/artifact

2025-03-04T23:37:14.6107462Z   retention-days: 14
2025-03-04T23:37:14.6107736Z   if-no-files-found: ignore
2025-03-04T23:37:14.6108021Z   path: logs-*.zip
2025-03-04T23:37:14.6108267Z   name: artifact
2025-03-04T23:37:14.6108509Z   region: us-east-1
2025-03-04T23:37:14.6108751Z env:
2025-03-04T23:37:14.6108975Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:14.6109323Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:14.6110025Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:14.6110659Z ##[endgroup]
2025-03-04T23:37:14.9403315Z NOTE: s3-prefix specified, ignoring name parameter
2025-03-04T23:37:14.9404062Z With the provided path, there will be 1 file uploaded
2025-03-04T23:37:14.9404988Z Uploading to s3 prefix: pytorch/pytorch/13661696663/1/artifact
2025-03-04T23:37:14.9460588Z Starting upload of logs-test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386.zip
2025-03-04T23:37:15.0783572Z Finished upload of logs-test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386.zip
2025-03-04T23:37:15.1086483Z ##[group]Run seemethere/upload-artifact-s3@v5
2025-03-04T23:37:15.1086974Z with:
2025-03-04T23:37:15.1087319Z   s3-bucket: gha-artifacts
2025-03-04T23:37:15.1087874Z   s3-prefix: pytorch/pytorch/13661696663/1/artifact

2025-03-04T23:37:15.1088596Z   retention-days: 14
2025-03-04T23:37:15.1088981Z   if-no-files-found: ignore
2025-03-04T23:37:15.1089344Z   path: debug-*.zip
2025-03-04T23:37:15.1089757Z   name: artifact
2025-03-04T23:37:15.1090116Z   region: us-east-1
2025-03-04T23:37:15.1090500Z env:
2025-03-04T23:37:15.1090860Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:15.1091297Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:15.1092009Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:15.1092627Z ##[endgroup]
2025-03-04T23:37:15.4314106Z No files were found with the provided path: debug-*.zip. No artifacts will be uploaded.
2025-03-04T23:37:15.4634141Z ##[group]Run # shellcheck disable=SC2156
2025-03-04T23:37:15.4634524Z # shellcheck disable=SC2156
2025-03-04T23:37:15.4635086Z find . -iname "core.[1-9]*" -exec docker exec "${DOCKER_CONTAINER_ID}" sh -c "gdb python {} -ex 'bt' -ex 'q'" \;
2025-03-04T23:37:15.4644225Z shell: /usr/bin/bash -e {0}
2025-03-04T23:37:15.4644497Z env:
2025-03-04T23:37:15.4644723Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:15.4645076Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:15.4645636Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:15.4646153Z ##[endgroup]
2025-03-04T23:37:15.7547789Z Prepare all required actions
2025-03-04T23:37:15.7548171Z Getting action download info
2025-03-04T23:37:15.8864791Z ##[group]Run ./.github/actions/upload-utilization-stats
2025-03-04T23:37:15.8865156Z with:
2025-03-04T23:37:15.8865385Z   job_id: 38197725386
2025-03-04T23:37:15.8865866Z   job_name: cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T23:37:15.8866405Z   workflow_name: inductor
2025-03-04T23:37:15.8866689Z   workflow_run_id: 13661696663
2025-03-04T23:37:15.8866976Z   workflow_attempt: 1
2025-03-04T23:37:15.8867224Z env:
2025-03-04T23:37:15.8867455Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:15.8867800Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:15.8868365Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:15.8869052Z ##[endgroup]
2025-03-04T23:37:15.8895005Z ##[group]Run echo "workflow_id: 13661696663"
2025-03-04T23:37:15.8895407Z echo "workflow_id: 13661696663"
2025-03-04T23:37:15.8895750Z echo "workflow_attempt: 1"
2025-03-04T23:37:15.8896081Z echo "workflow_Name: inductor"
2025-03-04T23:37:15.8896411Z echo "job_id: 38197725386"
2025-03-04T23:37:15.8896984Z echo "job_name:  cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)"
2025-03-04T23:37:15.8906036Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:15.8906420Z env:
2025-03-04T23:37:15.8906646Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:15.8906993Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:15.8907596Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:15.8908100Z ##[endgroup]
2025-03-04T23:37:15.8937130Z workflow_id: 13661696663
2025-03-04T23:37:15.8937644Z workflow_attempt: 1
2025-03-04T23:37:15.8938021Z workflow_Name: inductor
2025-03-04T23:37:15.8938399Z job_id: 38197725386
2025-03-04T23:37:15.8939125Z job_name:  cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)
2025-03-04T23:37:15.8983889Z ##[group]Run nick-fields/retry@v3.0.0
2025-03-04T23:37:15.8984202Z with:
2025-03-04T23:37:15.8995487Z   shell: bash
2025-03-04T23:37:15.8995736Z   timeout_minutes: 5
2025-03-04T23:37:15.8995991Z   max_attempts: 5
2025-03-04T23:37:15.8996237Z   retry_wait_seconds: 30
2025-03-04T23:37:15.8996705Z   command: set -eu
python3 -m pip install python-dateutil==2.8.2 boto3==1.35.42 pandas==2.1.3

2025-03-04T23:37:15.8997219Z   polling_interval_seconds: 1
2025-03-04T23:37:15.8997503Z   warning_on_retry: true
2025-03-04T23:37:15.8997774Z   continue_on_error: false
2025-03-04T23:37:15.8998036Z env:
2025-03-04T23:37:15.8998257Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:15.8998595Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:15.8999164Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:15.8999662Z ##[endgroup]
2025-03-04T23:37:16.2335700Z Defaulting to user installation because normal site-packages is not writeable
2025-03-04T23:37:16.3189476Z Collecting python-dateutil==2.8.2
2025-03-04T23:37:16.3429880Z   Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)
2025-03-04T23:37:17.4013364Z Collecting boto3==1.35.42
2025-03-04T23:37:17.4057216Z   Downloading boto3-1.35.42-py3-none-any.whl (139 kB)
2025-03-04T23:37:17.9630565Z Collecting pandas==2.1.3
2025-03-04T23:37:17.9671528Z   Downloading pandas-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)
2025-03-04T23:37:18.0929714Z Requirement already satisfied: six>=1.5 in /usr/lib/python3.9/site-packages (from python-dateutil==2.8.2) (1.15.0)
2025-03-04T23:37:18.0974871Z 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)
2025-03-04T23:37:18.0978731Z 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)
2025-03-04T23:37:18.0983111Z 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)
2025-03-04T23:37:18.1638486Z Requirement already satisfied: pytz>=2020.1 in /usr/lib/python3.9/site-packages (from pandas==2.1.3) (2022.7.1)
2025-03-04T23:37:18.1962597Z Collecting tzdata>=2022.1
2025-03-04T23:37:18.1996956Z   Downloading tzdata-2025.1-py2.py3-none-any.whl (346 kB)
2025-03-04T23:37:19.0990310Z Collecting numpy<2,>=1.22.4
2025-03-04T23:37:19.1035239Z   Downloading numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)
2025-03-04T23:37:19.2892566Z 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)
2025-03-04T23:37:19.4591345Z Installing collected packages: python-dateutil, tzdata, numpy, pandas, boto3
2025-03-04T23:37:24.3868082Z   Attempting uninstall: boto3
2025-03-04T23:37:24.3868481Z     Found existing installation: boto3 1.35.33
2025-03-04T23:37:24.3992459Z     Uninstalling boto3-1.35.33:
2025-03-04T23:37:24.4007113Z       Successfully uninstalled boto3-1.35.33
2025-03-04T23:37:24.4579687Z Successfully installed boto3-1.35.42 numpy-1.26.4 pandas-2.1.3 python-dateutil-2.8.2 tzdata-2025.1
2025-03-04T23:37:24.9846216Z Command completed after 1 attempt(s).
2025-03-04T23:37:24.9922547Z ##[group]Run python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-04T23:37:24.9923239Z python3 -m tools.stats.upload_utilization_stats.upload_utilization_stats \
2025-03-04T23:37:24.9923749Z   --workflow-run-id "13661696663" \
2025-03-04T23:37:24.9924106Z   --workflow-name "inductor" \
2025-03-04T23:37:24.9924456Z   --workflow-run-attempt "1" \
2025-03-04T23:37:24.9925054Z   --job-id "38197725386" \
2025-03-04T23:37:24.9925644Z   --job-name "cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)"
2025-03-04T23:37:24.9934776Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:24.9935167Z env:
2025-03-04T23:37:24.9935403Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:24.9935752Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:24.9936318Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:24.9936845Z ##[endgroup]
2025-03-04T23:37:27.5853994Z repo: pytorch/pytorch
2025-03-04T23:37:27.5854535Z Downloading logs-test-inductor_torchbench-1-2-linux.g5.4xlarge.nvidia.gpu_38197725386.zip
2025-03-04T23:37:27.5855131Z Converted Log Model: UtilizationMetadata:
2025-03-04T23:37:27.5856526Z  UtilizationMetadata(level='metadata', workflow_id='13661696663', job_id='38197725386', workflow_name='inductor', job_name='cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)', usage_collect_interval=1.0, data_model_version=1.0, start_at=1741122704, gpu_count=1, cpu_count=16, gpu_type='pynvml', error=None)
2025-03-04T23:37:27.5857963Z [Db Segments] detected pytest cmd: 24, generated segments: 24
2025-03-04T23:37:27.5858379Z [db model] Peek db timeseries 
2025-03-04T23:37:27.5858676Z :{
2025-03-04T23:37:27.5858906Z     "created_at": 1741131447,
2025-03-04T23:37:27.5859204Z     "type": "utilization",
2025-03-04T23:37:27.5859478Z     "tags": [
2025-03-04T23:37:27.5859709Z         "record"
2025-03-04T23:37:27.5859937Z     ],
2025-03-04T23:37:27.5860164Z     "time_stamp": 1741122704,
2025-03-04T23:37:27.5860463Z     "repo": "pytorch/pytorch",
2025-03-04T23:37:27.5860756Z     "workflow_id": 13661696663,
2025-03-04T23:37:27.5861043Z     "run_attempt": 1,
2025-03-04T23:37:27.5861301Z     "job_id": 38197725386,
2025-03-04T23:37:27.5861580Z     "workflow_name": "inductor",
2025-03-04T23:37:27.5862104Z     "job_name": "cuda12.4-py3.10-gcc9-sm86 / test (inductor_torchbench, 1, 2, linux.g5.4xlarge.nvidia.gpu)",
2025-03-04T23:37:27.5862649Z     "json_data": "{}"
2025-03-04T23:37:27.5863128Z }
2025-03-04T23:37:27.5863626Z Writing 1 documents to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661696663/1/38197725386/metadata
2025-03-04T23:37:27.5864508Z Done! Finish writing document to S3 ossci-utilization/util_metadata/v_1.0/pytorch/pytorch/13661696663/1/38197725386/metadata 
2025-03-04T23:37:27.5865422Z Writing 1744 documents to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661696663/1/38197725386/time_series
2025-03-04T23:37:27.5866361Z Done! Finish writing document to S3 ossci-utilization/util_timeseries/v_1.0/pytorch/pytorch/13661696663/1/38197725386/time_series 
2025-03-04T23:37:27.7051336Z ##[group]Run pytorch/test-infra/.github/actions/teardown-linux@main
2025-03-04T23:37:27.7051772Z with:
2025-03-04T23:37:27.7051989Z env:
2025-03-04T23:37:27.7052214Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:27.7052559Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:27.7053300Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:27.7053799Z ##[endgroup]
2025-03-04T23:37:27.7074597Z ##[group]Run set -eou pipefail
2025-03-04T23:37:27.7074933Z set -eou pipefail
2025-03-04T23:37:27.7075212Z 
2025-03-04T23:37:27.7075584Z echo "Holding runner for 2 hours until all ssh sessions have logged out"
2025-03-04T23:37:27.7076047Z for _ in $(seq 1440); do
2025-03-04T23:37:27.7076392Z     # Break if no ssh session exists anymore
2025-03-04T23:37:27.7076750Z     if [ "$(who)" = "" ]; then
2025-03-04T23:37:27.7077059Z       break
2025-03-04T23:37:27.7077341Z     fi
2025-03-04T23:37:27.7077580Z     echo "."
2025-03-04T23:37:27.7077833Z     sleep 5
2025-03-04T23:37:27.7078085Z done
2025-03-04T23:37:27.7087008Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:27.7087399Z env:
2025-03-04T23:37:27.7087809Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:27.7088164Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:27.7088729Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:27.7089238Z ##[endgroup]
2025-03-04T23:37:27.7131145Z Holding runner for 2 hours until all ssh sessions have logged out
2025-03-04T23:37:27.7229664Z ##[group]Run # ignore expansion of "docker ps -q" since it could be empty
2025-03-04T23:37:27.7230237Z # ignore expansion of "docker ps -q" since it could be empty
2025-03-04T23:37:27.7230671Z # shellcheck disable=SC2046
2025-03-04T23:37:27.7231033Z docker stop $(docker ps -q) || true
2025-03-04T23:37:27.7231392Z # Prune all of the docker images
2025-03-04T23:37:27.7231730Z docker system prune -af
2025-03-04T23:37:27.7240473Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:27.7240877Z env:
2025-03-04T23:37:27.7241108Z   GIT_DEFAULT_BRANCH: main
2025-03-04T23:37:27.7241460Z   GPU_FLAG: --gpus all -e NVIDIA_DRIVER_CAPABILITIES=all
2025-03-04T23:37:27.7242025Z   DOCKER_CONTAINER_ID: 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:27.7242530Z ##[endgroup]
2025-03-04T23:37:28.7804553Z 135038d589b4
2025-03-04T23:37:32.5789314Z Deleted Containers:
2025-03-04T23:37:32.5789818Z 135038d589b4a7e6ecaca235fc6cbbcd481fecedd28e8b0664ead13edd9491de
2025-03-04T23:37:32.5790162Z 
2025-03-04T23:37:42.1078634Z Deleted Images:
2025-03-04T23:37:42.1079901Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks:e4800fd93ba7d48bf4197a488fd32c12de647b0e
2025-03-04T23:37:42.1081614Z untagged: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks@sha256:10bcf94a2effce3256f6633d4b69f9e7f22aa569173ddbb4eab262e4cf671bbc
2025-03-04T23:37:42.1082821Z deleted: sha256:c79be20b310e0a73ffb7cab4b71fb60706b307c1344d8b5f619c83b5644451a4
2025-03-04T23:37:42.1083474Z deleted: sha256:e3da5cbb04a261eab672715131206966656057afe989583e469877d774cdaf8d
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2025-03-04T23:37:42.1136877Z Total reclaimed space: 55.56GB
2025-03-04T23:37:42.1218911Z Post job cleanup.
2025-03-04T23:37:42.1285174Z Post job cleanup.
2025-03-04T23:37:42.2356244Z [command]/usr/bin/git version
2025-03-04T23:37:42.2426045Z git version 2.47.1
2025-03-04T23:37:42.2464964Z Copying '/home/ec2-user/.gitconfig' to '/home/ec2-user/actions-runner/_work/_temp/1603696d-3fd7-409f-9f98-f1a9d1c6e548/.gitconfig'
2025-03-04T23:37:42.2474302Z Temporarily overriding HOME='/home/ec2-user/actions-runner/_work/_temp/1603696d-3fd7-409f-9f98-f1a9d1c6e548' before making global git config changes
2025-03-04T23:37:42.2475211Z Adding repository directory to the temporary git global config as a safe directory
2025-03-04T23:37:42.2479950Z [command]/usr/bin/git config --global --add safe.directory /home/ec2-user/actions-runner/_work/pytorch/pytorch
2025-03-04T23:37:42.2524613Z [command]/usr/bin/git config --local --name-only --get-regexp core\.sshCommand
2025-03-04T23:37:42.2571380Z [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' || :"
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2025-03-04T23:37:42.6897282Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-04T23:37:42.6962478Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
2025-03-04T23:37:42.7036421Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
2025-03-04T23:37:42.7107446Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-04T23:37:42.7196520Z Entering 'third_party/pocketfft'
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2025-03-04T23:37:42.7337216Z Entering 'third_party/protobuf/third_party/benchmark'
2025-03-04T23:37:42.7404133Z Entering 'third_party/protobuf/third_party/googletest'
2025-03-04T23:37:42.7475844Z Entering 'third_party/psimd'
2025-03-04T23:37:42.7543177Z Entering 'third_party/pthreadpool'
2025-03-04T23:37:42.7611806Z Entering 'third_party/pybind11'
2025-03-04T23:37:42.7681326Z Entering 'third_party/python-peachpy'
2025-03-04T23:37:42.7754492Z Entering 'third_party/sleef'
2025-03-04T23:37:42.7825071Z Entering 'third_party/tensorpipe'
2025-03-04T23:37:42.7891933Z Entering 'third_party/tensorpipe/third_party/googletest'
2025-03-04T23:37:42.7959664Z Entering 'third_party/tensorpipe/third_party/libnop'
2025-03-04T23:37:42.8026676Z Entering 'third_party/tensorpipe/third_party/libuv'
2025-03-04T23:37:42.8093648Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T23:37:42.8158043Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T23:37:42.8252975Z [command]/usr/bin/git config --local --name-only --get-regexp http\.https\:\/\/github\.com\/\.extraheader
2025-03-04T23:37:42.8278023Z http.https://github.com/.extraheader
2025-03-04T23:37:42.8291455Z [command]/usr/bin/git config --local --unset-all http.https://github.com/.extraheader
2025-03-04T23:37:42.8328270Z [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' || :"
2025-03-04T23:37:42.8689637Z Entering 'android/libs/fbjni'
2025-03-04T23:37:42.8735751Z http.https://github.com/.extraheader
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2025-03-04T23:37:42.9980084Z Entering 'third_party/fbgemm'
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2025-03-04T23:37:43.1371527Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.1475498Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.1658599Z http.https://github.com/.extraheader
2025-03-04T23:37:43.1700556Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/DCGM'
2025-03-04T23:37:43.1747124Z http.https://github.com/.extraheader
2025-03-04T23:37:43.1793398Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/cpr'
2025-03-04T23:37:43.1836840Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.1924316Z http.https://github.com/.extraheader
2025-03-04T23:37:43.1969192Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags'
2025-03-04T23:37:43.2012780Z http.https://github.com/.extraheader
2025-03-04T23:37:43.2056218Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/gflags/doc'
2025-03-04T23:37:43.2100018Z http.https://github.com/.extraheader
2025-03-04T23:37:43.2149916Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/glog'
2025-03-04T23:37:43.2192660Z http.https://github.com/.extraheader
2025-03-04T23:37:43.2237174Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/googletest'
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2025-03-04T23:37:43.2324636Z Entering 'third_party/kineto/libkineto/third_party/dynolog/third_party/json'
2025-03-04T23:37:43.2368553Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.2456548Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.2634938Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.2816690Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.3098327Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.3281849Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.3372544Z http.https://github.com/.extraheader
2025-03-04T23:37:43.3415465Z Entering 'third_party/opentelemetry-cpp/third_party/ms-gsl'
2025-03-04T23:37:43.3459194Z http.https://github.com/.extraheader
2025-03-04T23:37:43.3501344Z Entering 'third_party/opentelemetry-cpp/third_party/nlohmann-json'
2025-03-04T23:37:43.3545931Z http.https://github.com/.extraheader
2025-03-04T23:37:43.3589955Z Entering 'third_party/opentelemetry-cpp/third_party/opentelemetry-proto'
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2025-03-04T23:37:43.3675484Z Entering 'third_party/opentelemetry-cpp/third_party/opentracing-cpp'
2025-03-04T23:37:43.3718671Z http.https://github.com/.extraheader
2025-03-04T23:37:43.3762156Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp'
2025-03-04T23:37:43.3813512Z http.https://github.com/.extraheader
2025-03-04T23:37:43.3857384Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/civetweb'
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2025-03-04T23:37:43.3947840Z Entering 'third_party/opentelemetry-cpp/third_party/prometheus-cpp/3rdparty/googletest'
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2025-03-04T23:37:43.4039358Z Entering 'third_party/opentelemetry-cpp/tools/vcpkg'
2025-03-04T23:37:43.4082973Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.4192985Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.4371722Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.4732278Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.4820621Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.4909133Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.4998995Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.5086185Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.5172687Z http.https://github.com/.extraheader
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2025-03-04T23:37:43.5258749Z http.https://github.com/.extraheader
2025-03-04T23:37:43.5302810Z Entering 'third_party/tensorpipe/third_party/pybind11'
2025-03-04T23:37:43.5346219Z http.https://github.com/.extraheader
2025-03-04T23:37:43.5386530Z Entering 'third_party/tensorpipe/third_party/pybind11/tools/clang'
2025-03-04T23:37:43.5432060Z http.https://github.com/.extraheader
2025-03-04T23:37:43.5606185Z A job completed hook has been configured by the self-hosted runner administrator
2025-03-04T23:37:43.5637171Z ##[group]Run '/home/ec2-user/runner-scripts/after_job.sh'
2025-03-04T23:37:43.5645099Z shell: /usr/bin/bash --noprofile --norc -e -o pipefail {0}
2025-03-04T23:37:43.5645494Z ##[endgroup]
2025-03-04T23:37:50.1446726Z Cleaning up orphan processes