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Update base for Update on "Trace attention inference patterns with p=0, cleanup"
When dropout is traced in inference, it creates a clone() instead of training pattern of rand() etc. This was partially addressed by manually #108141, however that did not cover all of the patterns that included dropout, and there is no reason we should have to specify them manually. This updates the inference patterns generated to trace with dropout_p = 0.0. cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng Xia-Weiwen wenzhe-nrv jiayisunx peterbell10 ipiszy ngimel yf225 chenyang78 kadeng muchulee8 aakhundov [ghstack-poisoned]
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4.27.4
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6c26faa159b79a42d7fa46cb66e2d21523351987

.ci/docker/common/install_inductor_benchmark_deps.sh

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function install_huggingface() {
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local version
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version=$(get_pinned_commit huggingface)
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commit=$(get_pinned_commit huggingface)
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pip_install pandas==2.0.3
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pip_install "transformers==${version}"
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pip_install "git+https://github.com/huggingface/transformers@${commit}"
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}
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function install_timm() {

.ci/docker/common/install_onnx.sh

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transformers==4.32.1
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pip_install coloredlogs packaging
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retry pip_install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ --no-cache-dir --no-input ort-nightly==1.16.0.dev20230824005
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retry pip_install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ --no-cache-dir --no-input ort-nightly==1.16.0.dev20230908001
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# Using 1.15dev branch for the following not yet released features and fixes.
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# - Segfault fix for shape inference.
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# - Inliner to workaround ORT segfault.
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pip_install -i https://test.pypi.org/simple/ onnx==1.14.1rc2
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# TODO: change this when onnx-script is on testPypi
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pip_install onnx==1.14.1
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pip_install onnxscript-preview==0.1.0.dev20230828 --no-deps
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# Cache the transformers model to be used later by ONNX tests. We need to run the transformers

.ci/pytorch/win-test-helpers/test_libtorch.bat

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:: Skip LibTorch tests when building a GPU binary and testing on a CPU machine
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:: because LibTorch tests are not well designed for this use case.
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if "%USE_CUDA%" == "0" IF NOT "%CUDA_VERSION%" == "cpu" exit /b 0
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::if "%USE_CUDA%" == "0" IF NOT "%CUDA_VERSION%" == "cpu" exit /b 0
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call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
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if errorlevel 1 exit /b 1
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:: Skip verify_api_visibility as it a compile level test
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if "%~1" == "verify_api_visibility" goto :eof
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:: See https://github.com/pytorch/pytorch/issues/25161
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if "%~1" == "c10_metaprogramming_test" goto :eof
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if "%~1" == "module_test" goto :eof
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:: See https://github.com/pytorch/pytorch/issues/25312
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if "%~1" == "converter_nomigraph_test" goto :eof
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:: See https://github.com/pytorch/pytorch/issues/35636
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if "%~1" == "generate_proposals_op_gpu_test" goto :eof
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:: See https://github.com/pytorch/pytorch/issues/35648
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if "%~1" == "reshape_op_gpu_test" goto :eof
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:: See https://github.com/pytorch/pytorch/issues/35651
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if "%~1" == "utility_ops_gpu_test" goto :eof
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echo Running "%~2"
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if "%~1" == "c10_intrusive_ptr_benchmark" (

.clang-tidy

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performance-*,
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readability-container-size-empty,
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'
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HeaderFilterRegex: '^(c10/(?!test)|torch/csrc/(?!deploy/interpreter/cpython)).*$'
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HeaderFilterRegex: '^(c10/(?!test)|torch/csrc/).*$'
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AnalyzeTemporaryDtors: false
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WarningsAsErrors: '*'
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...

.github/workflows/inductor-perf-compare.yml

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uses: ./.github/workflows/_linux-build.yml
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with:
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build-environment: linux-focal-cuda12.1-py3.10-gcc9-sm80
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docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks:9dd361d1c04129f8eaa9d6b43335917800dd6d24
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docker-image-name: pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks
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cuda-arch-list: '8.0'
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test-matrix: |
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{ include: [

.github/workflows/inductor-perf-test-nightly.yml

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uses: ./.github/workflows/_linux-build.yml
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with:
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docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks:9dd361d1c04129f8eaa9d6b43335917800dd6d24
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docker-image-name: pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks
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cuda-arch-list: '8.0'
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test-matrix: |
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{ include: [

.github/workflows/inductor-periodic.yml

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uses: ./.github/workflows/_linux-build.yml
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with:
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build-environment: linux-focal-cuda12.1-py3.10-gcc9-sm86
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docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks:9dd361d1c04129f8eaa9d6b43335917800dd6d24
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docker-image-name: pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks
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cuda-arch-list: '8.6'
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{ include: [

.github/workflows/inductor.yml

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uses: ./.github/workflows/_linux-build.yml
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with:
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docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks:9dd361d1c04129f8eaa9d6b43335917800dd6d24
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docker-image-name: pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks
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cuda-arch-list: '8.6'
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{ include: [
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uses: ./.github/workflows/_linux-build.yml
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with:
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docker-image-name: 308535385114.dkr.ecr.us-east-1.amazonaws.com/pytorch/pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks:9dd361d1c04129f8eaa9d6b43335917800dd6d24
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docker-image-name: pytorch-linux-focal-cuda12.1-cudnn8-py3-gcc9-inductor-benchmarks
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cuda-arch-list: '8.0'
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test-matrix: |
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{ include: [

.github/workflows/lint.yml

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lintrunner:
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with:
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timeout: 120
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runner: linux.2xlarge
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docker-image: pytorch-linux-focal-linter
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fetch-depth: 0

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