Add M1 Py3.10 build definitions (#74018)

This change is specific to release-1.11 as on master those builds are
already unified

.azure_pipelines/build-pipeline.yml

@@ -0,0 +1,63 @@
+# PyTorch CI Builds Pipeline on Azure DevOps
+#
+# This pipeline:
+#   1) builds PyTorch on select configurations
+#   2) runs only TestTorch unit tests.
+
+stages:
+- stage: 'Build'
+  displayName: 'Build PyTorch'
+  jobs:
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_CPU_docker
+      pool: 'PyTorch-Linux-CPU'
+      container_endpoint: pytorchms.azurecr.io
+      build_stage: True
+      is_ci_build: True
+      os: ubuntu
+      cuda: cpu
+      customMatrixes:
+        Py_38:
+          configuration: ubuntu_1804_py_38_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
+
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_GPU_docker
+      pool: 'PyTorch-Linux-GPU'
+      container_endpoint: pytorchms.azurecr.io
+      build_stage: True
+      is_ci_build: True
+      os: ubuntu
+      cuda: gpu
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
+          CUDA_VERSION: 112
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_CPU
+      pool: 'PyTorch-Win-CPU'
+      build_stage: True
+      is_ci_build: True
+      os: windows
+      cuda: cpu
+      customMatrixes:
+        Py_37:
+          configuration: windows_2019_py_37_cpu
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_GPU
+      pool: 'PyTorch-Win-GPU'
+      build_stage: True
+      is_ci_build: True
+      os: windows
+      cuda: gpu
+      customMatrixes:
+        Py_38_CUDA_102_cuDNN_765:
+          configuration: windows_2019_py_38_cuda_102_cudnn_765
+          CUDA_VERSION: 102

.azure_pipelines/daily-pipeline.yml

@@ -0,0 +1,82 @@
+# PyTorch Daily Builds Pipeline on Azure DevOps
+#
+# This pipeline:
+#   1) builds PyTorch on all available configurations
+#   2) runs all PyTorch unit tests
+
+stages:
+- stage: 'BuildTest'
+  displayName: 'Build and Test PyTorch'
+  jobs:
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_CPU_docker
+      pool: 'PyTorch-Linux-CPU'
+      container_endpoint: pytorchms.azurecr.io
+      build_stage: True
+      is_daily_build: True
+      os: ubuntu
+      cuda: cpu
+      customMatrixes:
+        Py_38:
+          configuration: ubuntu_1804_py_38_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
+        Py_37:
+          configuration: ubuntu_1804_py_37_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cpu_dev
+
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_GPU_docker
+      pool: 'PyTorch-Linux-GPU'
+      container_endpoint: pytorchms.azurecr.io
+      build_stage: True
+      is_daily_build: True
+      os: ubuntu
+      cuda: gpu
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
+          CUDA_VERSION: 112
+        Py_38_CUDA_102_cuDNN_810:
+          configuration: ubuntu_1804_py_38_cuda_102_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cuda_102_cudnn_8_dev
+          CUDA_VERSION: 102
+        Py_37_CUDA_101_cuDNN_765:
+          configuration: ubuntu_1804_py_37_cuda_101_cudnn_765
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cuda_101_cudnn_7_dev
+          CUDA_VERSION: 101
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_CPU
+      pool: 'PyTorch-Win-CPU'
+      build_stage: True
+      is_daily_build: True
+      os: windows
+      cuda: cpu
+      customMatrixes:
+        Py_38:
+          configuration: windows_2019_py_38_cpu
+        Py_37:
+          configuration: windows_2019_py_37_cpu
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_GPU
+      pool: 'PyTorch-Win-GPU'
+      build_stage: True
+      is_daily_build: True
+      os: windows
+      cuda: gpu
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: windows_2019_py_39_cuda_112_cudnn_810
+          CUDA_VERSION: 112
+        Py_38_CUDA_102_cuDNN_765:
+          configuration: windows_2019_py_38_cuda_102_cudnn_765
+          CUDA_VERSION: 102
+        Py_37_CUDA_101_cuDNN_764:
+          configuration: windows_2019_py_37_cuda_101_cudnn_764
+          CUDA_VERSION: 101

.azure_pipelines/job_templates/build-verify-publish-template-unix.yml

@@ -0,0 +1,134 @@
+# PyTorch build steps template with Unix images Azure DevOps Instances
+#
+# This build depends on 3 parameters set as environment variables in the pipeline:
+#   - AZURE_DEVOPS_CLI_PAT: Secret var for authenticating to Azure DevOps
+#   - AZURE_DEVOPS_ARTIFACTS_ORGANIZATION: Azure Artifacts Organization name to publish artifacts
+#   - AZURE_DEVOPS_ARTIFACTS_PROJECT: Azure Artifacts Project name to publish artifacts
+
+parameters:
+  name: ''
+  pool: ''
+  container_endpoint: ''
+  os: ''
+  cuda: ''
+  is_ci_build: False
+  is_official_build: False
+  is_daily_build: False
+  build_stage: False
+  verify_stage: False
+  publish_stage: False
+  customMatrixes: ''
+
+jobs:
+- job: ${{parameters.name}}
+  timeoutInMinutes: 300
+  strategy:
+    matrix:
+      ${{ insert }}: ${{parameters.customMatrixes}}
+  pool:
+    name: ${{ parameters.pool}}
+  variables:
+    DECODE_PERCENTS: false
+  container:
+    image: $[variables['container_image']]
+    endpoint: ${{parameters.container_endpoint}}
+
+  steps:
+  # Build stage
+  - ${{ if eq(parameters.build_stage, 'True') }}:
+    # Set up environment variables for specific pipeline build
+    - template: set-environment-variables.yml
+      parameters:
+        os: ${{ parameters.os}}
+        cuda: ${{ parameters.cuda}}
+        is_official_build: ${{ parameters.is_official_build}}
+
+    # Sync and update PyTorch submodules
+    - bash: git submodule update --init --recursive --jobs 0
+      displayName: Update PyTorch submodules
+
+    # Build PyTorch and run unit tests - no packaging
+    - ${{ if or(eq(parameters.is_ci_build, 'True'), eq(parameters.is_daily_build, 'True')) }}:
+      # Build PyTorch from source in develop mode
+      - bash: python setup.py develop
+        displayName: Build PyTorch from source
+
+      - ${{ if eq(parameters.is_ci_build, 'True') }}:
+        # Run TestTorch unit tests to demonstrate successful PyTorch build
+        - bash: python test/test_torch.py TestTorch
+          displayName: Run TestTorch unit tests
+
+      - ${{ if eq(parameters.is_daily_build, 'True') }}:
+        # Run all unit tests to demonstrate successful PyTorch build
+        - bash: python test/run_test.py --continue-through-error --exclude-jit-executor --verbose
+          displayName: Run all unit tests
+
+      # Run ComponentGovernance
+      - task: ComponentGovernanceComponentDetection@0
+        inputs:
+          scanType: 'Register'
+          verbosity: 'Verbose'
+          alertWarningLevel: 'High'
+
+    # Build PyTorch and produce artifacts for verification stage
+    - ${{ if eq(parameters.is_official_build, 'True') }}:
+      # Build PyTorch from source in install mode and exclude test binaries
+      - bash: python setup.py install
+        displayName: Build PyTorch from source without test binaries
+
+      # Package PyTorch Wheel
+      - bash: python setup.py bdist_wheel
+        displayName: Package PyTorch Wheel
+
+      # Publish PyTorch Wheel
+      - task: PublishPipelineArtifact@1
+        inputs:
+          targetPath: $(Build.SourcesDirectory)/dist/
+          artifactName: Build_$(Build.BuildNumber)_$(configuration)
+        displayName: Publish PyTorch Wheel to Pipeline Artifacts
+
+  # Verification stage
+  - ${{ if eq(parameters.verify_stage, 'True') }}:
+    # Download PyTorch Wheel
+    - task: DownloadPipelineArtifact@2
+      inputs:
+        artifact: Build_$(Build.BuildNumber)_$(configuration)
+        path: $(Build.SourcesDirectory)/verify
+      displayName: Download PyTorch Wheel
+
+    # Install PyTorch Wheel on Windows
+    - bash: python -m pip install $(Build.SourcesDirectory)/verify/torch*linux*.whl
+      displayName: Install PyTorch Wheel
+
+    # Ensure PyTorch installed correctly from produced wheel
+    - bash: |
+        cd $(Build.SourcesDirectory)/verify
+        python -c "import torch; print('Installed Torch version: ' + torch.__version__)"
+      displayName: Check PyTorch correctly installed from wheel
+
+  # Publishing stage
+  - ${{ if eq(parameters.publish_stage, 'True') }}:
+    # Download PyTorch Wheel
+    - task: DownloadPipelineArtifact@2
+      inputs:
+        artifact: Build_$(Build.BuildNumber)_$(configuration)
+        path: $(Build.SourcesDirectory)/publish
+      displayName: Download PyTorch Wheel
+
+    # Publish wheel to Azure Artifacts
+    # The flag continueOnError=true is needed as the artifact to be published
+    # may already exist, because the artifact is differentiated based on the
+    # last commit date.
+    - bash: |
+        export TORCH_VERSION=$(head -c 5 ./version.txt)
+        export LAST_COMMIT=$(git rev-parse --short HEAD)
+        export LAST_COMMIT_DATE=$(git log -1 --pretty=%ad --date=format:%Y%m%d)
+        cd $(Build.SourcesDirectory)/publish
+        export TORCH_WHEEL=$(echo torch*linux*whl)
+        az extension add -n azure-devops
+        echo $ADOTOKEN | az devops login
+        az artifacts universal publish --organization $AZURE_DEVOPS_ARTIFACTS_ORGANIZATION --project $AZURE_DEVOPS_ARTIFACTS_PROJECT --scope project --feed "PyTorch" --name $TORCH_WHEEL --description "PyTorch Official Build Artifact" --version $TORCH_VERSION-$LAST_COMMIT_DATE-$LAST_COMMIT --path .
+      env:
+        ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
+      continueOnError: true
+      displayName: Upload PyTorch Official Build package to Azure Artifacts

.azure_pipelines/job_templates/build-verify-publish-template-win.yml

@@ -0,0 +1,150 @@
+# PyTorch build steps template with Windows images Azure DevOps Instances
+#
+# This build depends on 3 parameters set as environment variables in the pipeline:
+#   - AZURE_DEVOPS_CLI_PAT: Secret var for authenticating to Azure DevOps
+#   - AZURE_DEVOPS_ARTIFACTS_ORGANIZATION: Azure Artifacts Organization name to publish artifacts
+#   - AZURE_DEVOPS_ARTIFACTS_PROJECT: Azure Artifacts Project name to publish artifacts
+
+parameters:
+  name: ''
+  pool: ''
+  os: ''
+  cuda: ''
+  is_ci_build: False
+  is_official_build: False
+  is_daily_build: False
+  build_stage: False
+  verify_stage: False
+  publish_stage: False
+  customMatrixes: ''
+
+jobs:
+- job: ${{parameters.name}}
+  timeoutInMinutes: 300
+  strategy:
+    matrix:
+      ${{ insert }}: ${{parameters.customMatrixes}}
+  pool:
+    name: ${{ parameters.pool}}
+  variables:
+    CMAKE_GENERATOR: Ninja
+    PACKAGE_PDBS: 0
+
+  steps:
+  # Prepare for PyTorch build on Windows
+  - template: prepare-build-template.yml
+    parameters:
+      configuration: $(configuration)
+      build_stage: ${{ parameters.build_stage}}
+
+  # Build Stage
+  - ${{ if eq(parameters.build_stage, 'True') }}:
+    # Set up environment variables for specific pipeline build
+    - template: set-environment-variables.yml
+      parameters:
+        os: ${{ parameters.os}}
+        cuda: ${{ parameters.cuda}}
+        is_official_build: ${{ parameters.is_official_build}}
+
+    # Sync and update PyTorch submodules
+    - script: git submodule update --init --recursive --jobs 0
+      displayName: Update PyTorch submodules
+
+    # Build PyTorch and run unit tests - no packaging
+    - ${{ if or(eq(parameters.is_ci_build, 'True'), eq(parameters.is_daily_build, 'True')) }}:
+      # Build PyTorch from source in develop mode with Ninja
+      - script: call activate $(configuration) && python setup.py develop
+        displayName: Build PyTorch from source
+
+      - ${{ if eq(parameters.is_ci_build, 'True') }}:
+        # Run TestTorch unit tests to demonstrate successful PyTorch build
+        - script: call activate $(configuration) && python test\test_torch.py TestTorch
+          displayName: Run TestTorch unit tests
+
+      - ${{ if eq(parameters.is_daily_build, 'True') }}:
+        # Run all unit tests to demonstrate successful PyTorch build
+        - script: call activate $(configuration) && python test/run_test.py --continue-through-error --exclude-jit-executor --verbose
+          displayName: Run all unit tests
+
+      # Run ComponentGovernance
+      - task: ComponentGovernanceComponentDetection@0
+        inputs:
+          scanType: 'Register'
+          verbosity: 'Verbose'
+          alertWarningLevel: 'High'
+
+    # Build PyTorch and produce artifacts for verification stage
+    - ${{ if eq(parameters.is_official_build, 'True') }}:
+      # Build PyTorch from source in install mode with Ninja and exclude test binaries
+      - script: call activate $(configuration) && python setup.py install
+        displayName: Build PyTorch from source without test binaries
+
+      # Package PyTorch Wheel
+      - script: call activate $(configuration) && python setup.py bdist_wheel
+        displayName: Package PyTorch Wheel
+
+      # Publish PyTorch Wheel
+      - task: PublishPipelineArtifact@1
+        inputs:
+          targetPath: $(Build.SourcesDirectory)\dist\
+          artifactName: Build_$(Build.BuildNumber)_$(configuration)
+        displayName: Publish PyTorch Wheel to Pipeline Artifacts
+
+  # Verification Stage
+  - ${{ if eq(parameters.verify_stage, 'True') }}:
+    # Download PyTorch Wheel
+    - task: DownloadPipelineArtifact@2
+      inputs:
+        artifact: Build_$(Build.BuildNumber)_$(configuration)
+        path: $(Build.SourcesDirectory)\verify
+      displayName: Download PyTorch Wheel
+
+    # Install PyTorch Wheel on Windows
+    - script: |
+        call activate $(configuration)
+        cd $(Build.SourcesDirectory)\verify
+        dir torch*win*.whl /b > whl.txt
+        set /p whl= < whl.txt
+        python -m pip install %whl%
+      displayName: Install PyTorch Wheel
+
+    # Ensure PyTorch installed correctly from produced wheel
+    - script: |
+        call activate $(configuration)
+        cd $(Build.SourcesDirectory)\verify
+        python -c "import torch; print('Installed Torch version: ' + torch.__version__)"
+      displayName: Check PyTorch correctly installed from wheel
+
+  # Publishing stage
+  - ${{ if eq(parameters.publish_stage, 'True') }}:
+    # Download PyTorch Wheel
+    - task: DownloadPipelineArtifact@2
+      inputs:
+        artifact: Build_$(Build.BuildNumber)_$(configuration)
+        path: $(Build.SourcesDirectory)\publish
+      displayName: Download PyTorch Wheel
+
+    # Set up Azure Artifacts for Windows
+    # The pip install --upgrade command is a bug fix for Azure CLI on Windows
+    # More info: https://github.com/Azure/azure-cli/issues/16858
+    - script: |
+        pip install --upgrade pip --target \opt\az\lib\python3.6\site-packages\
+        az extension add -n azure-devops
+      displayName: Set up Azure Artifacts download on Windows
+
+    # Publish wheel to Azure Artifacts
+    # The flag continueOnError=true is needed as the artifact to be published
+    # may already exist, because the artifact is differentiated based on the
+    # last commit date.
+    - script: |
+        set /p TORCH_VERSION= < version.txt
+        cd $(Build.SourcesDirectory)\publish
+        git rev-parse --short HEAD > last_commit.txt && set /p LAST_COMMIT= < last_commit.txt
+        git log -1 --pretty=%ad --date=format:%Y%m%d > last_commit_date.txt && set /p LAST_COMMIT_DATE= < last_commit_date.txt
+        dir torch*win*.whl /b > whl.txt && set /p TORCH_WHEEL= < whl.txt
+        echo %ADOTOKEN% | az devops login
+        az artifacts universal publish --organization %AZURE_DEVOPS_ARTIFACTS_ORGANIZATION% --project %AZURE_DEVOPS_ARTIFACTS_PROJECT% --scope project --feed "PyTorch" --name %TORCH_WHEEL% --description "PyTorch Official Build Artifact" --version %TORCH_VERSION:~0,5%-%LAST_COMMIT_DATE%-%LAST_COMMIT% --path .
+      env:
+        ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
+      continueOnError: true
+      displayName: Upload PyTorch nigthly package to Azure Artifacts

.azure_pipelines/job_templates/common-packages.yml

@@ -0,0 +1,17 @@
+dependencies:
+  - python=PYTHON_VERSION
+  - numpy
+  - ninja
+  - pyyaml
+  - mkl
+  - mkl-include
+  - setuptools
+  - cmake
+  - cffi
+  - typing_extensions
+  - future
+  - six
+  - requests
+  - dataclasses
+  - pip:
+    - -r ../../requirements.txt

.azure_pipelines/job_templates/notify-webapp-template.yml

@@ -0,0 +1,26 @@
+parameters:
+  name: ''
+  pool: ''
+  customMatrixes: ''
+
+jobs:
+- job: ${{parameters.name}}
+  timeoutInMinutes: 600
+  strategy:
+    matrix:
+      ${{ insert }}: ${{parameters.customMatrixes}}
+  pool:
+    name: ${{ parameters.pool}}
+  steps:
+  # Clone PyTorch Tests repository
+  - bash: |
+      B64_PAT=$(echo -n ":$_ADOTOKEN" | base64)
+      git -c http.extraHeader="Authorization: Basic ${B64_PAT}" clone $(AZURE_DEVOPS_PYTORCH_TESTS_REPO_URL)
+      cd pytorch_tests
+      git checkout $(PYTORCH_TESTS_CHECKOUT_BRANCH)
+    env:
+      _ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
+    displayName: Clone PyTorch Tests repo
+  - bash: |
+      bash $(Build.SourcesDirectory)/pytorch_tests/webapp/notify_webapp.sh
+    displayName: Notify Webapp

.azure_pipelines/job_templates/prepare-build-template.yml

@@ -0,0 +1,62 @@
+# Build prepare steps for PyTorch on Azure DevOps to build from source.
+# These steps share between normal build process and semmle security scan tasks
+
+parameters:
+  build_stage: False
+  configuration: ''
+
+steps:
+# End Python tasks that may be lingering over from previous runs
+# Note: If python.exe isn't currently running, exit code becomes 128,
+# which fails the run. Here exit code is set to 0 to avoid failed run.
+- script: |
+    taskkill /f /im python.exe
+    IF %ERRORLEVEL% EQU 128 exit 0
+  displayName: End previous Python processes
+
+# Clean up env directory in conda for fresh builds and set up conda environment YAML
+- powershell: |
+    Remove-Item 'C:\Miniconda\envs' -Recurse -ErrorAction Ignore
+    $env:PYTHON_VERSION = $env:SYSTEM_JOBNAME.Substring(3,1) + '.' + $env:SYSTEM_JOBNAME.Substring(4,1)
+    (Get-Content .azure_pipelines\job_templates\common-packages.yml) -replace 'PYTHON_VERSION', $env:PYTHON_VERSION | Out-File -encoding ASCII .azure_pipelines\job_templates\common-packages.yml
+  displayName: Clean up previous environments and Set up conda environment YAML
+
+# Make conda environment and install required packages
+- script: |
+    call conda clean --all -y
+    call conda env create -n $(configuration) --file .azure_pipelines\job_templates\common-packages.yml
+    call activate $(configuration)
+    call conda install -c conda-forge libuv=1.39
+  displayName: Set up conda environment for building from source
+
+- ${{ if eq(parameters.build_stage, 'True') }}:
+  # Install MKL
+  - script: |
+      rmdir /s /q mkl
+      del mkl_2020.2.254.7z
+      curl https://s3.amazonaws.com/ossci-windows/mkl_2020.2.254.7z -k -O
+      7z x -aoa mkl_2020.2.254.7z -omkl
+    displayName: Install MKL
+
+  # Install sccache and randomtemp
+  # Related PyTorch GitHub issue: https://github.com/pytorch/pytorch/issues/25393
+  # Related fix: https://github.com/pytorch/builder/pull/448/
+  - script: |
+      mkdir .\tmp_bin
+      curl -k https://s3.amazonaws.com/ossci-windows/sccache.exe --output .\tmp_bin\sccache.exe
+      curl -k https://s3.amazonaws.com/ossci-windows/sccache-cl.exe --output .\tmp_bin\sccache-cl.exe
+      copy .\tmp_bin\sccache.exe .\tmp_bin\nvcc.exe
+      curl -kL https://github.com/peterjc123/randomtemp-rust/releases/download/v0.4/randomtemp.exe --output .\tmp_bin\randomtemp.exe
+    displayName: Install sccache and randomtemp
+    condition: not(eq(variables.CUDA_VERSION, ''))
+
+  # CUDA 11.2's CUB directory conflicts with CUDA 10.2 and 10.1
+  # builds, where CUDA 11.2's CUB is injected into non-CUDA
+  # 11.2 builds.
+  - powershell: Remove-Item "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include\cub" -Recurse -ErrorAction Ignore
+    displayName: Remove conflicting CUB from CUDA installation
+    condition: not(eq(variables.CUDA_VERSION, ''))
+
+  - powershell: Copy-Item -Path "F:\cuda_11_2\cub\" -Destination "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include" -Recurse
+    displayName: Copy CUDA CUB for CUDA 11.2 build
+    condition: eq(variables.CUDA_VERSION, '112')

.azure_pipelines/job_templates/pytorch-template-unix.yml

@@ -0,0 +1,61 @@
+# PyTorch build steps template with Unix images Azure DevOps Instances
+#
+# This build depends on 5 parameters set as an environment variables in the pipeline:
+#   - AZURE_DEVOPS_CLI_PAT: Secret var for authenticating to Azure DevOps
+#   - AZURE_STORAGE_KEY: Secret var for authenticating to Azure Storage
+#   - _TS_CLONE_P, _TS_P, _TS_SM_P: Secret vars for specific unit tests
+
+parameters:
+  name: ''
+  pool: ''
+  container_endpoint: ''
+  customMatrixes: ''
+
+jobs:
+- job: ${{parameters.name}}
+  timeoutInMinutes: 600
+  strategy:
+    matrix:
+      ${{ insert }}: ${{parameters.customMatrixes}}
+  pool:
+    name: ${{ parameters.pool}}
+  variables:
+    DECODE_PERCENTS: false
+
+  steps:
+  # Don't checkout repo contents to save time and CPU compute. Environment variables
+  # related to checkout branch such as $(BUILD_SOURCEBRANCH) are still available.
+  - checkout: none
+
+  # Delete pytorch_tests repo from previous builds if exists
+  - bash: rm -rf pytorch_tests/
+    displayName: Delete pytorch_tests repo from previous builds if exists
+
+  # Clone PyTorch Tests repository
+  - bash: |
+      B64_PAT=$(echo -n ":$_ADOTOKEN" | base64)
+      git -c http.extraHeader="Authorization: Basic ${B64_PAT}" clone $(AZURE_DEVOPS_PYTORCH_TESTS_REPO_URL)
+      cd pytorch_tests
+      git checkout $(PYTORCH_TESTS_CHECKOUT_BRANCH)
+    env:
+      _ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
+    displayName: Clone PyTorch Tests repo
+
+  # Run PyTorch Unit Tests
+  - bash: bash $(Build.SourcesDirectory)/pytorch_tests/scripts/linux/run.sh
+    env:
+      _AZURE_STORAGE_KEY: $(AZURE_STORAGE_KEY)
+      _TS_CLONE_P: $(TS_CLONE_PASSWORD)
+      _TS_P: $(TS_PAT)
+      _TS_SM_P: $(TS_SM_PAT)
+      _AZUREML_CLONE_PASSWORD: $(AZUREML_CLONE_PASSWORD)
+      _SPPASSWORD: $(SPPASSWORD)
+    displayName: Run PyTorch Unit Tests
+
+  # Tests results are available outside the docker container since
+  # the current directory is mounted as a volume of the container.
+  - task: PublishTestResults@2
+    condition: always()
+    inputs:
+      testResultsFiles: '**/test-*.xml'
+      testRunTitle: 'Publish test results for Python'

.azure_pipelines/job_templates/pytorch-template-win.yml

@@ -0,0 +1,57 @@
+# PyTorch build steps template with Windows images Azure DevOps Instances
+#
+# This build depends on 5 parameters set as an environment variables in the pipeline:
+#   - AZURE_DEVOPS_CLI_PAT: Secret var for authenticating to Azure DevOps
+#   - AZURE_STORAGE_KEY: Secret var for authenticating to Azure Storage
+#   - _TS_CLONE_P, _TS_P, _TS_SM_P: Secret vars for specific unit tests
+
+parameters:
+  name: ''
+  pool: ''
+  customMatrixes: ''
+
+jobs:
+- job: ${{parameters.name}}
+  timeoutInMinutes: 600
+  strategy:
+    matrix:
+      ${{ insert }}: ${{parameters.customMatrixes}}
+  pool:
+    name: ${{ parameters.pool}}
+
+  steps:
+  # Don't checkout repo contents to save time and CPU compute. Environment variables
+  # related to checkout branch such as $(BUILD_SOURCEBRANCH) are still available.
+  - checkout: none
+
+  # Delete pytorch_tests repo from previous builds if exists
+  - script: if exist "pytorch_tests/" rmdir "pytorch_tests/" /q /s
+    displayName: Delete pytorch_tests repo from previous builds if exists
+
+  # Clone PyTorch Tests repository
+  - powershell: |
+      $env:B64Pat = [Convert]::ToBase64String([System.Text.Encoding]::UTF8.GetBytes(":$env:_ADOTOKEN"))
+      git -c http.extraHeader="Authorization: Basic $env:B64Pat" clone $env:AZURE_DEVOPS_pytorch_tests_REPO_URL
+      cd pytorch_tests
+      git checkout $(PYTORCH_TESTS_CHECKOUT_BRANCH)
+    env:
+      _ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
+    displayName: Clone PyTorch Tests repo
+
+  # Run PyTorch Unit Tests
+  - script: call $(Build.SourcesDirectory)\pytorch_tests\scripts\windows\run.bat
+    env:
+      _ADOTOKEN: $(AZURE_DEVOPS_CLI_PAT)
+      _AZURE_STORAGE_KEY: $(AZURE_STORAGE_KEY)
+      _TS_CLONE_P: $(TS_CLONE_PASSWORD)
+      _TS_P: $(TS_PAT)
+      _TS_SM_P: $(TS_SM_PAT)
+    displayName: Run PyTorch Unit Tests
+
+  # Tests results are available outside the docker container since
+  # the current directory is mounted as a volume of the container.
+  - task: PublishTestResults@2
+    condition: always()
+    inputs:
+      testResultsFiles: '**\test-*.xml'
+      testRunTitle: 'Publish test results for Python'

.azure_pipelines/job_templates/set-environment-variables.yml

@@ -0,0 +1,129 @@
+# Set environment variables for specific configurations
+
+parameters:
+  is_official_build: False
+  os: ''
+  cuda: ''
+
+steps:
+  # Environment configuration steps for Ubuntu builds
+  - ${{ if contains(parameters.os, 'ubuntu') }}:
+    # Set configuration specific build flags
+    - ${{ if eq(parameters.is_official_build, True) }}:
+      - bash: |
+          echo "##vso[task.setvariable variable=INSTALL_TEST;]0"
+          echo "##vso[task.setvariable variable=PYTORCH_BUILD_NUMBER;]1"
+          export PYTORCH_VERSION=$(head -c 5 ./version.txt)
+          echo "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$PYTORCH_VERSION.dev"
+        displayName: Set configuration-specific build flags
+
+      # Set PyTorch CPU/GPU build flags.
+      - ${{ if contains(parameters.cuda, 'cpu') }}:
+        - bash: |
+            echo "##vso[task.setvariable variable=USE_CUDA;]0"
+            echo "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$(PYTORCH_BUILD_VERSION).cpu"
+          displayName: Set CUDA-specific build flag for CPU builds
+
+      - ${{ if contains(parameters.cuda, 'gpu') }}:
+        - bash: |
+            echo "##vso[task.setvariable variable=USE_CUDA;]1"
+            echo "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$(PYTORCH_BUILD_VERSION).cu$(CUDA_VERSION)"
+          displayName: Set CUDA-specific build flag for GPU builds
+
+    # Set MKL environment variables
+    - bash: |
+        echo "##vso[task.setvariable variable=CMAKE_LIBRARY_PATH;]/opt/intel/lib:$CMAKE_LIBRARY_PATH"
+        echo "##vso[task.setvariable variable=CMAKE_INCLUDE_PATH;]/opt/intel/include:$CMAKE_INCLUDE_PATH"
+      displayName: Set MKL paths
+
+    # View current environment variables
+    - bash:
+        printenv
+      displayName: Show environment variables
+
+  # Environment configuration steps for Windows builds
+  - ${{ if contains(parameters.os, 'windows') }}:
+    # Set Conda Lib Path
+    - powershell: Write-Host "##vso[task.setvariable variable=CONDA_LIB_PATH;]C:\Miniconda\envs\$(configuration)\Library\bin"
+      displayName: Set Conda Lib Path
+
+    # Set configuration specific build flags
+    - ${{ if eq(parameters.is_official_build, True) }}:
+      - powershell: |
+          Write-Host "##vso[task.setvariable variable=INSTALL_TEST;]0"
+          Write-Host "##vso[task.setvariable variable=PYTORCH_BUILD_NUMBER;]1"
+          Set-Variable -Name PYTORCH_VERSION -Value (Get-Content .\version.txt).Substring(0,5)
+          Write-Host "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$PYTORCH_VERSION.dev"
+        displayName: Set configuration-specific build flags
+
+      # Set PyTorch CPU/GPU build flags..
+      - ${{ if contains(parameters.cuda, 'cpu') }}:
+        - powershell: |
+            Write-Host "##vso[task.setvariable variable=USE_CUDA;]0"
+            Write-Host "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$(PYTORCH_BUILD_VERSION).cpu"
+          displayName: Set CUDA-specific build flag for CPU build
+
+      - ${{ if contains(parameters.cuda, 'gpu') }}:
+        - powershell: |
+            Write-Host "##vso[task.setvariable variable=USE_CUDA;]1"
+            Write-Host "##vso[task.setvariable variable=PYTORCH_BUILD_VERSION;]$(PYTORCH_BUILD_VERSION).cu$(CUDA_VERSION)"
+          displayName: Set CUDA-specific build flag for GPU build
+
+    # Set CUDA 11.2, 10.2 or 10.1 specific build flags
+    - ${{ if eq(parameters.cuda, 'gpu') }}:
+      - powershell: |
+          Write-Host "##vso[task.setvariable variable=TORCH_CUDA_ARCH_LIST;]3.7+PTX;5.0;6.0;6.1;7.0;7.5;8.0;8.6"
+          Write-Host "##vso[task.setvariable variable=CUDA_PATH;]C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\"
+        displayName: Set CUDA 11.2 specific build flags
+        condition: eq(variables.CUDA_VERSION, '112')
+
+      - powershell: |
+          Write-Host "##vso[task.setvariable variable=TORCH_CUDA_ARCH_LIST;]3.7+PTX;5.0;6.0;6.1;7.0;7.5"
+          Write-Host "##vso[task.setvariable variable=CUDA_PATH;]C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.2\"
+        displayName: Set CUDA 10.2 specific build flags
+        condition: eq(variables.CUDA_VERSION, '102')
+
+      - powershell: |
+          Write-Host "##vso[task.setvariable variable=TORCH_CUDA_ARCH_LIST;]3.7+PTX;5.0;6.0;6.1;7.0;7.5"
+          Write-Host "##vso[task.setvariable variable=CUDA_PATH;]C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\"
+        displayName: Set CUDA 10.1 specific build flags
+        condition: eq(variables.CUDA_VERSION, '101')
+
+      - powershell: |
+          Write-Host "##vso[task.setvariable variable=CUDA_BIN_PATH;]$env:CUDA_PATH\bin\"
+          Write-Host "##vso[task.setvariable variable=CUDNN_ROOT;]$env:CUDA_PATH"
+          Write-Host "##vso[task.setvariable variable=CUDNN_INCLUDE_DIR;]$env:CUDA_PATH\include\"
+          Write-Host "##vso[task.setvariable variable=CUDNN_LIBRARY;]$env:CUDA_PATH\lib\x64\"
+          Write-Host "##vso[task.prependpath]$env:CUDA_PATH\bin"
+          Write-Host "##vso[task.setvariable variable=TORCH_NVCC_FLAGS;]-Xfatbin -compress-all --no-host-device-move-forward"
+          Write-Host "##vso[task.setvariable variable=THRUST_IGNORE_CUB_VERSION_CHECK;]1"
+          Write-Host "##vso[task.setvariable variable=NVTOOLSEXT_PATH;]C:\Program Files\NVIDIA Corporation\NvToolsExt\"
+        displayName: Set CUDA environment variables
+
+      - powershell: |
+          copy "$(CUDA_BIN_PATH)\cusparse*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CUDA_BIN_PATH)\cublas*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CUDA_BIN_PATH)\cudart*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CUDA_BIN_PATH)\curand*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CUDA_BIN_PATH)\cufft*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CUDA_BIN_PATH)\cusolver*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CUDA_BIN_PATH)\cudnn*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CUDA_BIN_PATH)\nvrtc*64_*.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64\nvToolsExt64_1.dll*" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CONDA_LIB_PATH)\libiomp*5md.dll" $(Build.SourcesDirectory)\torch\lib
+          copy "$(CONDA_LIB_PATH)\uv.dll" $(Build.SourcesDirectory)\torch\lib
+        displayName: Copy CUDA/cuDNN/libomp/libuv dlls to torch\lib
+
+    # Set MKL, sccache and randomtemp environment variables
+    - powershell: |
+        Write-Host "##vso[task.setvariable variable=CMAKE_INCLUDE_PATH;]$(Build.SourcesDirectory)\mkl\include"
+        Write-Host "##vso[task.setvariable variable=CMAKE_LIBRARY_PATH;]$(Build.SourcesDirectory)\mkl\lib;$env:CMAKE_LIBRARY_PATH"
+        Write-Host "##vso[task.setvariable variable=ADDITIONAL_PATH;]$(Build.SourcesDirectory)\tmp_bin"
+        Write-Host "##vso[task.setvariable variable=SCCACHE_IDLE_TIMEOUT;]1500"
+        Write-Host "##vso[task.setvariable variable=CMAKE_CUDA_COMPILER_LAUNCHER;]$(Build.SourcesDirectory)/tmp_bin/randomtemp.exe;$(Build.SourcesDirectory)/tmp_bin/sccache.exe"
+      displayName: Set MKL, sccache and randomtemp environment variables
+
+    # View current environment variables
+    - script:
+        set
+      displayName: Show environment variables

.azure_pipelines/job_templates/wheel-wait-job-template.yml

@@ -0,0 +1,14 @@
+# Main logic to initiate wait for PR artifact to be ready
+
+steps:
+- task: InvokeRESTAPI@1
+  displayName: 'Wait for job success and wheel ready'
+  timeoutInMinutes: 60
+  inputs:
+    connectionType: 'connectedServiceName'
+    serviceConnection: circleciconn
+    method: 'POST'
+    headers: '{"Content-Type":"application/json", "BranchName":"$(_TARGET_BRANCH_TO_CHECK)", "JobName":"$(TARGET_CIRCLECI_BUILD_PR)", "PRNumber":"$(_TARGET_PR_NUMBER)", "TargetCommit":"$(_TARGET_COMMIT)", "PlanUrl":"$(System.CollectionUri)", "ProjectId":"$(System.TeamProjectId)", "HubName":"$(System.HostType)", "PlanId":"$(System.PlanId)", "JobId":"$(System.JobId)", "TimelineId":"$(System.TimelineId)", "TaskInstanceId":"$(System.TaskInstanceId)", "AuthToken":"$(System.AccessToken)"}'
+    body: ''
+    urlSuffix: 'api/JobStatus'
+    waitForCompletion: true

.azure_pipelines/job_templates/wheel-wait-template.yml

@@ -0,0 +1,92 @@
+# Initiate 5 agentless-server waiting jobs to check on the
+# status of PR artifact builds, for a maximum wait time of
+# 11*60 min=660 mins. These jobs will pass immediately
+# once targeted CircleCI build is ready.
+
+jobs:
+- job: checkjob1
+  pool: server
+  timeoutInMinutes: 60
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob2
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob1
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob3
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob2
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob4
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob3
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob5
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob4
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob6
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob5
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob7
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob6
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob8
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob7
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob9
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob8
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob10
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob9
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml
+
+- job: checkjob11
+  pool: server
+  timeoutInMinutes: 60
+  dependsOn: checkjob10
+  continueOnError: true
+  steps:
+  - template: wheel-wait-job-template.yml

.azure_pipelines/nightly-pytorch-tests-pipeline.yml

@@ -0,0 +1,60 @@
+# PyTorch Nightly PyTorch Tests Builds Pipeline on Azure DevOps
+#
+# This pipeline runs custom PyTorch unit-tests on nightly
+# PyTorch wheels.
+
+stages:
+- stage: 'NightlyCustomTests'
+  displayName: 'Run custom unit tests on PyTorch wheels'
+  jobs:
+  - template: job_templates/pytorch-template-unix.yml
+    parameters:
+      name: ubuntu_1804_CPU_docker
+      pool: $(BUILD_POOL_LIN_1)
+      customMatrixes:
+        Nightly_Custom_Tests:
+          _DOCKER_IMAGE: $(DOCKER_IMAGE_LIN_1)
+          _PYTHON_VERSION: $(PYTHON_VERSION_LIN_1)
+          _CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_LIN_1)
+          _RUN_TESTS: $(RUN_TESTS_LIN)
+
+  - template: job_templates/pytorch-template-unix.yml
+    parameters:
+      name: ubuntu_1804_GPU_docker
+      pool: $(BUILD_POOL_LIN_2)
+      customMatrixes:
+        Nightly_Custom_Tests:
+          _DOCKER_IMAGE: $(DOCKER_IMAGE_LIN_2)
+          _PYTHON_VERSION: $(PYTHON_VERSION_LIN_2)
+          _CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_LIN_2)
+          _RUN_TESTS: $(RUN_TESTS_LIN)
+
+  - template: job_templates/pytorch-template-win.yml
+    parameters:
+      name: windows_2019_CPU
+      pool: $(BUILD_POOL_WIN_1)
+      customMatrixes:
+        Nightly_Custom_Tests:
+          _PYTHON_VERSION: $(PYTHON_VERSION_WIN_1)
+          _CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_WIN_1)
+          _RUN_TESTS: $(RUN_TESTS_WIN)
+
+  - template: job_templates/pytorch-template-win.yml
+    parameters:
+      name: windows_2019_GPU
+      pool: $(BUILD_POOL_WIN_2)
+      customMatrixes:
+        Nightly_Custom_Tests:
+          _PYTHON_VERSION: $(PYTHON_VERSION_WIN_2)
+          _CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_WIN_2)
+          _RUN_TESTS: $(RUN_TESTS_WIN)
+
+- stage: 'NotifyWebapp'
+  displayName: 'Notify Webapp that pipeline is finished'
+  dependsOn: NightlyCustomTests
+  condition: succeededOrFailed()
+  jobs:
+  - template: job_templates/notify-webapp-template.yml
+    parameters:
+      name: ubuntu_1804_CPU
+      pool: $(BUILD_POOL_LIN_1)

.azure_pipelines/pytorch-tests-pipeline.yml

@@ -0,0 +1,62 @@
+# PyTorch PR PyTorch Tests Builds Pipeline on Azure DevOps
+#
+# This pipeline:
+#   1) ensures that CircleCI builds for a given PR
+#      have finished, and that its artifacts are
+#      ready for download
+#   2) runs custom PyTorch unit-tests on PyTorch
+#      wheels generated during PR builds.
+
+resources:
+  webhooks:
+    - webhook: GitHubPyTorchPRTrigger
+      connection: GitHubPyTorchPRTriggerConnection
+      filters:
+        - path: repositoryName
+          value: pytorch_tests
+
+stages:
+- stage: 'EnsureArtifactsReady'
+  displayName: 'Ensure PyTorch PR Artifacts are ready'
+  jobs:
+  - template: job_templates/wheel-wait-template.yml
+  variables:
+    _TARGET_BRANCH_TO_CHECK: ${{parameters.GitHubPyTorchPRTrigger.TARGET_BRANCH_TO_CHECK_AZ_DEVOPS_PR}}
+    _TARGET_PR_NUMBER: ${{parameters.GitHubPyTorchPRTrigger.PR_NUMBER}}
+    _TARGET_COMMIT: ${{parameters.GitHubPyTorchPRTrigger.TARGET_COMMIT}}
+
+- stage: 'PRCustomTests'
+  displayName: 'Run custom unit tests on PyTorch wheels'
+  dependsOn: EnsureArtifactsReady
+  condition: succeeded()
+  jobs:
+  - template: job_templates/pytorch-template-unix.yml
+    parameters:
+      name: ubuntu_1804_GPU_docker
+      pool: $(BUILD_POOL_PR)
+      customMatrixes:
+        PR_Custom_Tests:
+          _PYTHON_VERSION: $(PYTHON_VERSION_PR)
+          _CUDA_BUILD_VERSION: $(CUDA_BUILD_VERSION_PR)
+          _TARGET_CIRCLECI_BUILD: $(TARGET_CIRCLECI_BUILD_PR)
+          _TARGET_BRANCH_TO_CHECK: ${{parameters.GitHubPyTorchPRTrigger.TARGET_BRANCH_TO_CHECK_AZ_DEVOPS_PR}}
+          _TARGET_PR_NUMBER: ${{parameters.GitHubPyTorchPRTrigger.PR_NUMBER}}
+          _TARGET_COMMIT: ${{parameters.GitHubPyTorchPRTrigger.TARGET_COMMIT}}
+          _DOCKER_IMAGE: $(DOCKER_IMAGE_PR)
+          _RUN_TESTS: $(RUN_TESTS_PR)
+
+- stage: 'NotifyWebapp'
+  displayName: 'Notify Webapp that pipeline is finished'
+  dependsOn: PRCustomTests
+  condition: succeededOrFailed()
+  jobs:
+  - template: job_templates/notify-webapp-template.yml
+    parameters:
+      name: ubuntu_1804_CPU
+      pool: $(BUILD_POOL_LIN_1)
+      customMatrixes:
+        PR_Notify_WebApp:
+          _TARGET_CIRCLECI_BUILD: $(TARGET_CIRCLECI_BUILD_PR)
+          _TARGET_BRANCH_TO_CHECK: ${{parameters.GitHubPyTorchPRTrigger.TARGET_BRANCH_TO_CHECK_AZ_DEVOPS_PR}}
+          _TARGET_PR_NUMBER: ${{parameters.GitHubPyTorchPRTrigger.PR_NUMBER}}
+          _TARGET_COMMIT: ${{parameters.GitHubPyTorchPRTrigger.TARGET_COMMIT}}

.azure_pipelines/verify-pipeline.yml

@@ -0,0 +1,224 @@
+# PyTorch Official Builds Pipeline on Azure DevOps
+#
+# This pipeline:
+#   1) builds PyTorch on all available configurations
+#   2) verifies PyTorch artifacts by installing them in a clean environment
+#      and checking torch.__version_
+#   3) publishes official PyTorch artifacts to Azure DevOps Artifacts for consumption
+
+stages:
+- stage: 'Build'
+  displayName: 'Build PyTorch'
+  jobs:
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_CPU_docker
+      pool: 'PyTorch-Linux-CPU'
+      container_endpoint: pytorchms.azurecr.io
+      build_stage: True
+      is_official_build: True
+      os: ubuntu
+      cuda: cpu
+      customMatrixes:
+        Py_38:
+          configuration: ubuntu_1804_py_38_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
+        Py_37:
+          configuration: ubuntu_1804_py_37_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cpu_dev
+
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_GPU_docker
+      pool: 'PyTorch-Linux-GPU'
+      container_endpoint: pytorchms.azurecr.io
+      build_stage: True
+      is_official_build: True
+      os: ubuntu
+      cuda: gpu
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
+          CUDA_VERSION: 112
+        Py_38_CUDA_102_cuDNN_810:
+          configuration: ubuntu_1804_py_38_cuda_102_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cuda_102_cudnn_8_dev
+          CUDA_VERSION: 102
+        Py_37_CUDA_101_cuDNN_765:
+          configuration: ubuntu_1804_py_37_cuda_101_cudnn_765
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cuda_101_cudnn_7_dev
+          CUDA_VERSION: 101
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_CPU
+      pool: 'PyTorch-Win-CPU'
+      build_stage: True
+      is_official_build: True
+      os: windows
+      cuda: cpu
+      customMatrixes:
+        Py_38:
+          configuration: windows_2019_py_38_cpu
+        Py_37:
+          configuration: windows_2019_py_37_cpu
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_GPU
+      pool: 'PyTorch-Win-GPU'
+      build_stage: True
+      is_official_build: True
+      os: windows
+      cuda: gpu
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: windows_2019_py_39_cuda_112_cudnn_810
+          CUDA_VERSION: 112
+        Py_38_CUDA_102_cuDNN_765:
+          configuration: windows_2019_py_38_cuda_102_cudnn_765
+          CUDA_VERSION: 102
+        Py_37_CUDA_101_cuDNN_764:
+          configuration: windows_2019_py_37_cuda_101_cudnn_764
+          CUDA_VERSION: 101
+
+- stage: 'Verify'
+  displayName: 'Verify PyTorch wheels'
+  dependsOn: Build
+  condition: succeeded()
+  jobs:
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_CPU_docker
+      pool: 'PyTorch-Linux-CPU'
+      container_endpoint: pytorchms.azurecr.io
+      verify_stage: True
+      is_official_build: True
+      customMatrixes:
+        Py_38:
+          configuration: ubuntu_1804_py_38_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
+        Py_37:
+          configuration: ubuntu_1804_py_37_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cpu_dev
+
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_GPU_docker
+      pool: 'PyTorch-Linux-GPU'
+      container_endpoint: pytorchms.azurecr.io
+      verify_stage: True
+      is_official_build: True
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
+          CUDA_VERSION: 112
+        Py_38_CUDA_102_cuDNN_810:
+          configuration: ubuntu_1804_py_38_cuda_102_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cuda_102_cudnn_8_dev
+          CUDA_VERSION: 102
+        Py_37_CUDA_101_cuDNN_765:
+          configuration: ubuntu_1804_py_37_cuda_101_cudnn_765
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cuda_101_cudnn_7_dev
+          CUDA_VERSION: 101
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_CPU
+      pool: 'PyTorch-Win-CPU'
+      verify_stage: True
+      is_official_build: True
+      customMatrixes:
+        Py_38:
+          configuration: windows_2019_py_38_cpu
+        Py_37:
+          configuration: windows_2019_py_37_cpu
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_GPU
+      pool: 'PyTorch-Win-GPU'
+      verify_stage: True
+      is_official_build: True
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: windows_2019_py_39_cuda_112_cudnn_810
+          CUDA_VERSION: 112
+        Py_38_CUDA_102_cuDNN_765:
+          configuration: windows_2019_py_38_cuda_102_cudnn_765
+          CUDA_VERSION: 102
+        Py_37_CUDA_101_cuDNN_764:
+          configuration: windows_2019_py_37_cuda_101_cudnn_764
+          CUDA_VERSION: 101
+
+- stage: 'Publish'
+  displayName: 'Publish PyTorch wheels'
+  dependsOn: Verify
+  condition: succeeded()
+  jobs:
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_CPU_docker
+      pool: 'PyTorch-Linux-CPU'
+      container_endpoint: pytorchms.azurecr.io
+      publish_stage: True
+      is_official_build: True
+      customMatrixes:
+        Py_38:
+          configuration: ubuntu_1804_py_38_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cpu_dev
+        Py_37:
+          configuration: ubuntu_1804_py_37_cpu
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cpu_dev
+
+  - template: job_templates/build-verify-publish-template-unix.yml
+    parameters:
+      name: ubuntu_1804_GPU_docker
+      pool: 'PyTorch-Linux-GPU'
+      container_endpoint: pytorchms.azurecr.io
+      publish_stage: True
+      is_official_build: True
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: ubuntu_1804_py_39_cuda_112_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_39_cuda_112_cudnn_8_dev
+          CUDA_VERSION: 112
+        Py_38_CUDA_102_cuDNN_810:
+          configuration: ubuntu_1804_py_38_cuda_102_cudnn_810
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_38_cuda_102_cudnn_8_dev
+          CUDA_VERSION: 102
+        Py_37_CUDA_101_cuDNN_765:
+          configuration: ubuntu_1804_py_37_cuda_101_cudnn_765
+          container_image: pytorchms.azurecr.io/ubuntu_1804_py_37_cuda_101_cudnn_7_dev
+          CUDA_VERSION: 101
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_CPU
+      pool: 'PyTorch-Win-CPU'
+      publish_stage: True
+      is_official_build: True
+      customMatrixes:
+        Py_38:
+          configuration: windows_2019_py_38_cpu
+        Py_37:
+          configuration: windows_2019_py_37_cpu
+
+  - template: job_templates/build-verify-publish-template-win.yml
+    parameters:
+      name: windows_2019_GPU
+      pool: 'PyTorch-Win-GPU'
+      publish_stage: True
+      is_official_build: True
+      customMatrixes:
+        Py_39_CUDA_112_cuDNN_810:
+          configuration: windows_2019_py_39_cuda_112_cudnn_810
+          CUDA_VERSION: 112
+        Py_38_CUDA_102_cuDNN_765:
+          configuration: windows_2019_py_38_cuda_102_cudnn_765
+          CUDA_VERSION: 102
+        Py_37_CUDA_101_cuDNN_764:
+          configuration: windows_2019_py_37_cuda_101_cudnn_764
+          CUDA_VERSION: 101

.bazelignore

@@ -1,4 +0,0 @@
-# We do not use this library in our Bazel build. It contains an
-# infinitely recursing symlink that makes Bazel very unhappy.
-third_party/ittapi/
-third_party/opentelemetry-cpp

.bazelrc

@@ -1,11 +1,10 @@
-build --cxxopt=--std=c++17
+build --copt=--std=c++14
 build --copt=-I.
 # Bazel does not support including its cc_library targets as system
 # headers. We work around this for generated code
 # (e.g. c10/macros/cmake_macros.h) by making the generated directory a
 # system include path.
 build --copt=-isystem --copt bazel-out/k8-fastbuild/bin
-build --copt=-isystem --copt bazel-out/darwin-fastbuild/bin
 build --experimental_ui_max_stdouterr_bytes=2048576
 
 # Configuration to disable tty features for environments like CI
@@ -13,102 +12,15 @@ build:no-tty --curses no
 build:no-tty --progress_report_interval 10
 build:no-tty --show_progress_rate_limit 10
 
-# Build with GPU support by default.
-build --define=cuda=true
-# rules_cuda configuration
-build --@rules_cuda//cuda:enable_cuda
-build --@rules_cuda//cuda:cuda_targets=sm_52
-build --@rules_cuda//cuda:compiler=nvcc
-build --repo_env=CUDA_PATH=/usr/local/cuda
-
-# Configuration to build without GPU support
-build:cpu-only --define=cuda=false
+# Configuration to build with GPU support
+build:gpu --define=cuda=true
 # define a separate build folder for faster switching between configs
-build:cpu-only --platform_suffix=-cpu-only
+build:gpu --platform_suffix=-gpu
 # See the note on the config-less build for details about why we are
-# doing this. We must also do it for the "-cpu-only" platform suffix.
-build --copt=-isystem --copt=bazel-out/k8-fastbuild-cpu-only/bin
+# doing this. We must also do it for the "-gpu" platform suffix.
+build --copt=-isystem --copt=bazel-out/k8-fastbuild-gpu/bin
 # rules_cuda configuration
-build:cpu-only --@rules_cuda//cuda:enable_cuda=False
-
-# Definition of --config=shell
-# interactive shell immediately before execution
-build:shell --run_under="//tools/bazel_tools:shellwrap"
-
-# Disable all warnings for external repositories. We don't care about
-# their warnings.
-build --per_file_copt=^external/@-w
-
-# Set additional warnings to error level.
-#
-# Implementation notes:
-#  * we use file extensions to determine if we are using the C++
-#    compiler or the cuda compiler
-#  * we use ^// at the start of the regex to only permit matching
-#    PyTorch files. This excludes external repos.
-#
-# Note that because this is logically a command-line flag, it is
-# considered the word on what warnings are enabled. This has the
-# unfortunate consequence of preventing us from disabling an error at
-# the target level because those flags will come before these flags in
-# the action invocation. Instead we provide per-file exceptions after
-# this.
-#
-# On the bright side, this means we don't have to more broadly apply
-# the exceptions to an entire target.
-#
-# Looking for CUDA flags? We have a cu_library macro that we can edit
-# directly. Look in //tools/rules:cu.bzl for details. Editing the
-# macro over this has the following advantages:
-#  * making changes does not require discarding the Bazel analysis
-#    cache
-#  * it allows for selective overrides on individual targets since the
-#    macro-level opts will come earlier than target level overrides
-
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=all
-# The following warnings come from -Wall. We downgrade them from error
-# to warnings here.
-#
-# We intentionally use #pragma unroll, which is compiler specific.
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-error=unknown-pragmas
-
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Werror=extra
-# The following warnings come from -Wextra. We downgrade them from error
-# to warnings here.
-#
-# unused-parameter-compare has a tremendous amount of violations in the
-# codebase. It will be a lot of work to fix them, just disable it for
-# now.
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-parameter
-# missing-field-parameters has both a large number of violations in
-# the codebase, but it also is used pervasively in the Python C
-# API. There are a couple of catches though:
-# * we use multiple versions of the Python API and hence have
-#   potentially multiple different versions of each relevant
-#   struct. They may have different numbers of fields. It will be
-#   unwieldy to support multiple versions in the same source file.
-# * Python itself for many of these structs recommends only
-#   initializing a subset of the fields. We should respect the API
-#   usage conventions of our dependencies.
-#
-# Hence, we just disable this warning altogether. We may want to clean
-# up some of the clear-cut cases that could be risky, but we still
-# likely want to have this disabled for the most part.
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-missing-field-initializers
-
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-function
-build --per_file_copt='^//.*\.(cpp|cc)$'@-Wno-unused-variable
-
-build --per_file_copt='//:aten/src/ATen/RegisterCompositeExplicitAutograd\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterCompositeImplicitAutograd\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterMkldnnCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterNestedTensorCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterQuantizedCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterSparseCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterSparseCsrCPU\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterNestedTensorMeta\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterSparseMeta\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterQuantizedMeta\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:aten/src/ATen/RegisterZeroTensor\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:torch/csrc/lazy/generated/RegisterAutogradLazy\.cpp$'@-Wno-error=unused-function
-build --per_file_copt='//:torch/csrc/lazy/generated/RegisterLazy\.cpp$'@-Wno-error=unused-function
+build:gpu --@rules_cuda//cuda:enable_cuda
+build:gpu --@rules_cuda//cuda:cuda_targets=sm_52
+build:gpu --@rules_cuda//cuda:compiler=nvcc
+build:gpu --repo_env=CUDA_PATH=/usr/local/cuda

.bazelversion

@@ -1 +1 @@
-6.1.1
+4.2.1

.buckconfig.oss

@@ -1,26 +0,0 @@
-[pt]
-  is_oss=1
-
-[buildfile]
-  name = BUCK.oss
-  includes = //tools/build_defs/select.bzl
-
-[repositories]
-  bazel_skylib = third_party/bazel-skylib/
-  ovr_config = .
-
-[download]
-  in_build = true
-
-[cxx]
-  cxxflags = -std=c++17
-  ldflags = -Wl,--no-undefined
-  should_remap_host_platform = true
-  cpp = /usr/bin/clang
-  cc = /usr/bin/clang
-  cxx = /usr/bin/clang++
-  cxxpp = /usr/bin/clang++
-  ld = /usr/bin/clang++
-
-[project]
-  default_flavors_mode=all

.ci/caffe2/common.sh

@@ -1,36 +0,0 @@
-set -ex
-
-LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
-ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
-TEST_DIR="$ROOT_DIR/test"
-gtest_reports_dir="${TEST_DIR}/test-reports/cpp"
-pytest_reports_dir="${TEST_DIR}/test-reports/python"
-
-# Figure out which Python to use
-PYTHON="$(which python)"
-if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
-  PYTHON=$(which "python${BASH_REMATCH[1]}")
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
-    # HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
-    unset HIP_PLATFORM
-    if which sccache > /dev/null; then
-        # Save sccache logs to file
-        sccache --stop-server || true
-        rm -f ~/sccache_error.log || true
-        SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 sccache --start-server
-
-        # Report sccache stats for easier debugging
-        sccache --zero-stats
-    fi
-fi
-
-# /usr/local/caffe2 is where the cpp bits are installed to in cmake-only
-# builds. In +python builds the cpp tests are copied to /usr/local/caffe2 so
-# that the test code in .ci/test.sh is the same
-INSTALL_PREFIX="/usr/local/caffe2"
-
-mkdir -p "$gtest_reports_dir" || true
-mkdir -p "$pytest_reports_dir" || true
-mkdir -p "$INSTALL_PREFIX" || true

.ci/caffe2/test.sh

@@ -1,172 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-if [[ ${BUILD_ENVIRONMENT} == *onnx* ]]; then
-  pip install click mock tabulate networkx==2.0
-  pip -q install --user "file:///var/lib/jenkins/workspace/third_party/onnx#egg=onnx"
-fi
-
-# Skip tests in environments where they are not built/applicable
-if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
-  echo 'Skipping tests'
-  exit 0
-fi
-if [[ "${BUILD_ENVIRONMENT}" == *-rocm* ]]; then
-  # temporary to locate some kernel issues on the CI nodes
-  export HSAKMT_DEBUG_LEVEL=4
-fi
-# These additional packages are needed for circleci ROCm builds.
-if [[ $BUILD_ENVIRONMENT == *rocm* ]]; then
-    # Need networkx 2.0 because bellmand_ford was moved in 2.1 . Scikit-image by
-    # defaults installs the most recent networkx version, so we install this lower
-    # version explicitly before scikit-image pulls it in as a dependency
-    pip install networkx==2.0
-    # click - onnx
-    pip install --progress-bar off click protobuf tabulate virtualenv mock typing-extensions
-fi
-
-# Find where cpp tests and Caffe2 itself are installed
-if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-  # For cmake only build we install everything into /usr/local
-  cpp_test_dir="$INSTALL_PREFIX/cpp_test"
-  ld_library_path="$INSTALL_PREFIX/lib"
-else
-  # For Python builds we install into python
-  # cd to /usr first so the python import doesn't get confused by any 'caffe2'
-  # directory in cwd
-  python_installation="$(dirname $(dirname $(cd /usr && $PYTHON -c 'import os; import caffe2; print(os.path.realpath(caffe2.__file__))')))"
-  caffe2_pypath="$python_installation/caffe2"
-  cpp_test_dir="$python_installation/torch/test"
-  ld_library_path="$python_installation/torch/lib"
-fi
-
-################################################################################
-# C++ tests #
-################################################################################
-# Only run cpp tests in the first shard, don't run cpp tests a second time in the second shard
-if [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
-  echo "Running C++ tests.."
-  for test in $(find "$cpp_test_dir" -executable -type f); do
-    case "$test" in
-      # skip tests we know are hanging or bad
-      */mkl_utils_test|*/aten/integer_divider_test)
-        continue
-        ;;
-      */scalar_tensor_test|*/basic|*/native_test)
-        if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-          continue
-        else
-          LD_LIBRARY_PATH="$ld_library_path" "$test"
-        fi
-        ;;
-      */*_benchmark)
-        LD_LIBRARY_PATH="$ld_library_path" "$test" --benchmark_color=false
-        ;;
-      *)
-        # Currently, we use a mixture of gtest (caffe2) and Catch2 (ATen). While
-        # planning to migrate to gtest as the common PyTorch c++ test suite, we
-        # currently do NOT use the xml test reporter, because Catch doesn't
-        # support multiple reporters
-        # c.f. https://github.com/catchorg/Catch2/blob/master/docs/release-notes.md#223
-        # which means that enabling XML output means you lose useful stdout
-        # output for Jenkins.  It's more important to have useful console
-        # output than it is to have XML output for Jenkins.
-        # Note: in the future, if we want to use xml test reporter once we switch
-        # to all gtest, one can simply do:
-        LD_LIBRARY_PATH="$ld_library_path" \
-            "$test" --gtest_output=xml:"$gtest_reports_dir/$(basename $test).xml"
-        ;;
-    esac
-  done
-fi
-
-################################################################################
-# Python tests #
-################################################################################
-if [[ "$BUILD_ENVIRONMENT" == *cmake* ]]; then
-  exit 0
-fi
-
-# If pip is installed as root, we must use sudo.
-# CircleCI docker images could install conda as jenkins user, or use the OS's python package.
-PIP=$(which pip)
-PIP_USER=$(stat --format '%U' $PIP)
-CURRENT_USER=$(id -u -n)
-if [[ "$PIP_USER" = root && "$CURRENT_USER" != root ]]; then
-  MAYBE_SUDO=sudo
-fi
-
-# Uninstall pre-installed hypothesis and coverage to use an older version as newer
-# versions remove the timeout parameter from settings which ideep/conv_transpose_test.py uses
-$MAYBE_SUDO pip -q uninstall -y hypothesis
-$MAYBE_SUDO pip -q uninstall -y coverage
-
-# "pip install hypothesis==3.44.6" from official server is unreliable on
-# CircleCI, so we host a copy on S3 instead
-$MAYBE_SUDO pip -q install attrs==18.1.0 -f https://s3.amazonaws.com/ossci-linux/wheels/attrs-18.1.0-py2.py3-none-any.whl
-$MAYBE_SUDO pip -q install coverage==4.5.1 -f https://s3.amazonaws.com/ossci-linux/wheels/coverage-4.5.1-cp36-cp36m-macosx_10_12_x86_64.whl
-$MAYBE_SUDO pip -q install hypothesis==3.44.6 -f https://s3.amazonaws.com/ossci-linux/wheels/hypothesis-3.44.6-py3-none-any.whl
-
-# Collect additional tests to run (outside caffe2/python)
-EXTRA_TESTS=()
-
-# CUDA builds always include NCCL support
-if [[ "$BUILD_ENVIRONMENT" == *-cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *-rocm* ]]; then
-  EXTRA_TESTS+=("$caffe2_pypath/contrib/nccl")
-fi
-
-rocm_ignore_test=()
-if [[ $BUILD_ENVIRONMENT == *-rocm* ]]; then
-  # Currently these tests are failing on ROCM platform:
-
-  # On ROCm, RCCL (distributed) development isn't complete.
-  # https://github.com/ROCmSoftwarePlatform/rccl
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/data_parallel_model_test.py")
-
-  # This test has been flaky in ROCm CI (but note the tests are
-  # cpu-only so should be unrelated to ROCm)
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/operator_test/blobs_queue_db_test.py")
-  # This test is skipped on Jenkins(compiled without MKL) and otherwise known flaky
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/ideep/convfusion_op_test.py")
-  # This test is skipped on Jenkins(compiled without MKL) and causing segfault on Circle
-  rocm_ignore_test+=("--ignore $caffe2_pypath/python/ideep/pool_op_test.py")
-fi
-
-echo "Running Python tests.."
-# locale setting is required by click package
-for loc in "en_US.utf8" "C.UTF-8"; do
-  if locale -a | grep "$loc" >/dev/null 2>&1; then
-    export LC_ALL="$loc"
-    export LANG="$loc"
-    break;
-  fi
-done
-
-# Some Caffe2 tests fail when run using AVX512 ISA, see https://github.com/pytorch/pytorch/issues/66111
-export DNNL_MAX_CPU_ISA=AVX2
-
-# Should still run even in the absence of SHARD_NUMBER
-if [[ "${SHARD_NUMBER:-1}" == "1" ]]; then
-  # TODO(sdym@meta.com) remove this when the linked issue resolved.
-  # py is temporary until https://github.com/Teemu/pytest-sugar/issues/241 is fixed
-  pip install --user py==1.11.0
-  pip install --user pytest-sugar
-  # NB: Warnings are disabled because they make it harder to see what
-  # the actual erroring test is
-  "$PYTHON" \
-    -m pytest \
-    -x \
-    -v \
-    --disable-warnings \
-    --junit-xml="$pytest_reports_dir/result.xml" \
-    --ignore "$caffe2_pypath/python/test/executor_test.py" \
-    --ignore "$caffe2_pypath/python/operator_test/matmul_op_test.py" \
-    --ignore "$caffe2_pypath/python/operator_test/pack_ops_test.py" \
-    --ignore "$caffe2_pypath/python/mkl/mkl_sbn_speed_test.py" \
-    --ignore "$caffe2_pypath/python/trt/test_pt_onnx_trt.py" \
-    ${rocm_ignore_test[@]} \
-    "$caffe2_pypath/python" \
-    "${EXTRA_TESTS[@]}"
-fi

.ci/docker/README.md

@@ -1,32 +0,0 @@
-# Docker images for GitHub CI
-
-This directory contains everything needed to build the Docker images
-that are used in our CI.
-
-The Dockerfiles located in subdirectories are parameterized to
-conditionally run build stages depending on build arguments passed to
-`docker build`. This lets us use only a few Dockerfiles for many
-images. The different configurations are identified by a freeform
-string that we call a _build environment_. This string is persisted in
-each image as the `BUILD_ENVIRONMENT` environment variable.
-
-See `build.sh` for valid build environments (it's the giant switch).
-
-## Contents
-
-* `build.sh` -- dispatch script to launch all builds
-* `common` -- scripts used to execute individual Docker build stages
-* `ubuntu` -- Dockerfile for Ubuntu image for CPU build and test jobs
-* `ubuntu-cuda` -- Dockerfile for Ubuntu image with CUDA support for nvidia-docker
-* `ubuntu-rocm` -- Dockerfile for Ubuntu image with ROCm support
-* `ubuntu-xpu` -- Dockerfile for Ubuntu image with XPU support
-
-## Usage
-
-```bash
-# Build a specific image
-./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
-
-# Set flags (see build.sh) and build image
-sudo bash -c 'PROTOBUF=1 ./build.sh pytorch-linux-bionic-py3.8-gcc9 -t myimage:latest
-```

.ci/docker/aotriton_version.txt

@@ -1,5 +0,0 @@
-0.6b
-manylinux_2_17
-rocm6
-04b5df8c8123f90cba3ede7e971e6fbc6040d506
-3db6ecbc915893ff967abd6e1b43bd5f54949868873be60dc802086c3863e648

.ci/docker/build.sh

@@ -1,558 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-image="$1"
-shift
-
-if [ -z "${image}" ]; then
-  echo "Usage: $0 IMAGE"
-  exit 1
-fi
-
-function extract_version_from_image_name() {
-  eval export $2=$(echo "${image}" | perl -n -e"/$1(\d+(\.\d+)?(\.\d+)?)/ && print \$1")
-  if [ "x${!2}" = x ]; then
-    echo "variable '$2' not correctly parsed from image='$image'"
-    exit 1
-  fi
-}
-
-function extract_all_from_image_name() {
-  # parts $image into array, splitting on '-'
-  keep_IFS="$IFS"
-  IFS="-"
-  declare -a parts=($image)
-  IFS="$keep_IFS"
-  unset keep_IFS
-
-  for part in "${parts[@]}"; do
-    name=$(echo "${part}" | perl -n -e"/([a-zA-Z]+)\d+(\.\d+)?(\.\d+)?/ && print \$1")
-    vername="${name^^}_VERSION"
-    # "py" is the odd one out, needs this special case
-    if [ "x${name}" = xpy ]; then
-      vername=ANACONDA_PYTHON_VERSION
-    fi
-    # skip non-conforming fields such as "pytorch", "linux" or "bionic" without version string
-    if [ -n "${name}" ]; then
-      extract_version_from_image_name "${name}" "${vername}"
-    fi
-  done
-}
-
-# Use the same pre-built XLA test image from PyTorch/XLA
-if [[ "$image" == *xla* ]]; then
-  echo "Using pre-built XLA test image..."
-  exit 0
-fi
-
-if [[ "$image" == *-focal* ]]; then
-  UBUNTU_VERSION=20.04
-elif [[ "$image" == *-jammy* ]]; then
-  UBUNTU_VERSION=22.04
-elif [[ "$image" == *ubuntu* ]]; then
-  extract_version_from_image_name ubuntu UBUNTU_VERSION
-elif [[ "$image" == *centos* ]]; then
-  extract_version_from_image_name centos CENTOS_VERSION
-fi
-
-if [ -n "${UBUNTU_VERSION}" ]; then
-  OS="ubuntu"
-elif [ -n "${CENTOS_VERSION}" ]; then
-  OS="centos"
-else
-  echo "Unable to derive operating system base..."
-  exit 1
-fi
-
-DOCKERFILE="${OS}/Dockerfile"
-# When using ubuntu - 22.04, start from Ubuntu docker image, instead of nvidia/cuda docker image.
-if [[ "$image" == *cuda* && "$UBUNTU_VERSION" != "22.04" ]]; then
-  DOCKERFILE="${OS}-cuda/Dockerfile"
-elif [[ "$image" == *rocm* ]]; then
-  DOCKERFILE="${OS}-rocm/Dockerfile"
-elif [[ "$image" == *xpu* ]]; then
-  DOCKERFILE="${OS}-xpu/Dockerfile"
-elif [[ "$image" == *cuda*linter* ]]; then
-  # Use a separate Dockerfile for linter to keep a small image size
-  DOCKERFILE="linter-cuda/Dockerfile"
-elif [[ "$image" == *linter* ]]; then
-  # Use a separate Dockerfile for linter to keep a small image size
-  DOCKERFILE="linter/Dockerfile"
-fi
-
-# CMake 3.18 is needed to support CUDA17 language variant
-CMAKE_VERSION=3.18.5
-
-_UCX_COMMIT=7bb2722ff2187a0cad557ae4a6afa090569f83fb
-_UCC_COMMIT=20eae37090a4ce1b32bcce6144ccad0b49943e0b
-
-# It's annoying to rename jobs every time you want to rewrite a
-# configuration, so we hardcode everything here rather than do it
-# from scratch
-case "$image" in
-  pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn9-py3.12-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.12
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda12.4-cudnn9-py3.12-gcc9-inductor-benchmarks)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.12
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-focal-cuda11.8-cudnn9-py3-gcc9)
-    CUDA_VERSION=11.8.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.1-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.1.1
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-cuda12.4-cudnn9-py3-gcc9)
-    CUDA_VERSION=12.4.0
-    CUDNN_VERSION=9
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    UCX_COMMIT=${_UCX_COMMIT}
-    UCC_COMMIT=${_UCC_COMMIT}
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-py3-clang10-onnx)
-    ANACONDA_PYTHON_VERSION=3.8
-    CLANG_VERSION=10
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    ONNX=yes
-    ;;
-  pytorch-linux-focal-py3-clang9-android-ndk-r21e)
-    ANACONDA_PYTHON_VERSION=3.8
-    CLANG_VERSION=9
-    LLVMDEV=yes
-    PROTOBUF=yes
-    ANDROID=yes
-    ANDROID_NDK_VERSION=r21e
-    GRADLE_VERSION=6.8.3
-    NINJA_VERSION=1.9.0
-    ;;
-  pytorch-linux-focal-py3.8-clang10)
-    ANACONDA_PYTHON_VERSION=3.8
-    CLANG_VERSION=10
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    VULKAN_SDK_VERSION=1.2.162.1
-    SWIFTSHADER=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-py3.11-clang10)
-    ANACONDA_PYTHON_VERSION=3.11
-    CLANG_VERSION=10
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    VULKAN_SDK_VERSION=1.2.162.1
-    SWIFTSHADER=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-py3.8-gcc9)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-rocm-n-1-py3)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ROCM_VERSION=6.0
-    NINJA_VERSION=1.9.0
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-focal-rocm-n-py3)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ROCM_VERSION=6.1
-    NINJA_VERSION=1.9.0
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-xpu-2024.0-py3)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    XPU_VERSION=0.5
-    NINJA_VERSION=1.9.0
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-    pytorch-linux-jammy-py3.8-gcc11-inductor-benchmarks)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    DOCS=yes
-    INDUCTOR_BENCHMARKS=yes
-    ;;
-  pytorch-linux-jammy-cuda11.8-cudnn9-py3.8-clang12)
-    ANACONDA_PYTHON_VERSION=3.8
-    CUDA_VERSION=11.8
-    CUDNN_VERSION=9
-    CLANG_VERSION=12
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-py3-clang12-asan)
-    ANACONDA_PYTHON_VERSION=3.9
-    CLANG_VERSION=12
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    ;;
-  pytorch-linux-jammy-py3-clang15-asan)
-    ANACONDA_PYTHON_VERSION=3.10
-    CLANG_VERSION=15
-    CONDA_CMAKE=yes
-    VISION=yes
-    ;;
-  pytorch-linux-jammy-py3.8-gcc11)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=11
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    CONDA_CMAKE=yes
-    TRITON=yes
-    DOCS=yes
-    UNINSTALL_DILL=yes
-    ;;
-  pytorch-linux-jammy-py3-clang12-executorch)
-    ANACONDA_PYTHON_VERSION=3.10
-    CLANG_VERSION=12
-    CONDA_CMAKE=yes
-    EXECUTORCH=yes
-    ;;
-  pytorch-linux-focal-linter)
-    # TODO: Use 3.9 here because of this issue https://github.com/python/mypy/issues/13627.
-    # We will need to update mypy version eventually, but that's for another day. The task
-    # would be to upgrade mypy to 1.0.0 with Python 3.11
-    ANACONDA_PYTHON_VERSION=3.9
-    CONDA_CMAKE=yes
-    ;;
-  pytorch-linux-jammy-cuda11.8-cudnn9-py3.9-linter)
-    ANACONDA_PYTHON_VERSION=3.9
-    CUDA_VERSION=11.8
-    CONDA_CMAKE=yes
-    ;;
-  pytorch-linux-jammy-aarch64-py3.10-gcc11)
-    ANACONDA_PYTHON_VERSION=3.10
-    GCC_VERSION=11
-    ACL=yes
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    CONDA_CMAKE=yes
-    # snadampal: skipping sccache due to the following issue
-    # https://github.com/pytorch/pytorch/issues/121559
-    SKIP_SCCACHE_INSTALL=yes
-    # snadampal: skipping llvm src build install because the current version
-    # from pytorch/llvm:9.0.1 is x86 specific
-    SKIP_LLVM_SRC_BUILD_INSTALL=yes
-    ;;
-  *)
-    # Catch-all for builds that are not hardcoded.
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    echo "image '$image' did not match an existing build configuration"
-    if [[ "$image" == *py* ]]; then
-      extract_version_from_image_name py ANACONDA_PYTHON_VERSION
-    fi
-    if [[ "$image" == *cuda* ]]; then
-      extract_version_from_image_name cuda CUDA_VERSION
-      extract_version_from_image_name cudnn CUDNN_VERSION
-    fi
-    if [[ "$image" == *rocm* ]]; then
-      extract_version_from_image_name rocm ROCM_VERSION
-      NINJA_VERSION=1.9.0
-      TRITON=yes
-      # To ensure that any ROCm config will build using conda cmake
-      # and thus have LAPACK/MKL enabled
-      CONDA_CMAKE=yes
-    fi
-    if [[ "$image" == *centos7* ]]; then
-      NINJA_VERSION=1.10.2
-    fi
-    if [[ "$image" == *gcc* ]]; then
-      extract_version_from_image_name gcc GCC_VERSION
-    fi
-    if [[ "$image" == *clang* ]]; then
-      extract_version_from_image_name clang CLANG_VERSION
-    fi
-    if [[ "$image" == *devtoolset* ]]; then
-      extract_version_from_image_name devtoolset DEVTOOLSET_VERSION
-    fi
-    if [[ "$image" == *glibc* ]]; then
-      extract_version_from_image_name glibc GLIBC_VERSION
-    fi
-    if [[ "$image" == *cmake* ]]; then
-      extract_version_from_image_name cmake CMAKE_VERSION
-    fi
-  ;;
-esac
-
-tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
-
-#when using cudnn version 8 install it separately from cuda
-if [[ "$image" == *cuda*  && ${OS} == "ubuntu" ]]; then
-  IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
-  if [[ ${CUDNN_VERSION} == 9 ]]; then
-    IMAGE_NAME="nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}"
-  fi
-fi
-
-# Build image
-docker build \
-       --no-cache \
-       --progress=plain \
-       --build-arg "BUILD_ENVIRONMENT=${image}" \
-       --build-arg "PROTOBUF=${PROTOBUF:-}" \
-       --build-arg "LLVMDEV=${LLVMDEV:-}" \
-       --build-arg "DB=${DB:-}" \
-       --build-arg "VISION=${VISION:-}" \
-       --build-arg "UBUNTU_VERSION=${UBUNTU_VERSION}" \
-       --build-arg "CENTOS_VERSION=${CENTOS_VERSION}" \
-       --build-arg "DEVTOOLSET_VERSION=${DEVTOOLSET_VERSION}" \
-       --build-arg "GLIBC_VERSION=${GLIBC_VERSION}" \
-       --build-arg "CLANG_VERSION=${CLANG_VERSION}" \
-       --build-arg "ANACONDA_PYTHON_VERSION=${ANACONDA_PYTHON_VERSION}" \
-       --build-arg "GCC_VERSION=${GCC_VERSION}" \
-       --build-arg "CUDA_VERSION=${CUDA_VERSION}" \
-       --build-arg "CUDNN_VERSION=${CUDNN_VERSION}" \
-       --build-arg "TENSORRT_VERSION=${TENSORRT_VERSION}" \
-       --build-arg "ANDROID=${ANDROID}" \
-       --build-arg "ANDROID_NDK=${ANDROID_NDK_VERSION}" \
-       --build-arg "GRADLE_VERSION=${GRADLE_VERSION}" \
-       --build-arg "VULKAN_SDK_VERSION=${VULKAN_SDK_VERSION}" \
-       --build-arg "SWIFTSHADER=${SWIFTSHADER}" \
-       --build-arg "CMAKE_VERSION=${CMAKE_VERSION:-}" \
-       --build-arg "NINJA_VERSION=${NINJA_VERSION:-}" \
-       --build-arg "KATEX=${KATEX:-}" \
-       --build-arg "ROCM_VERSION=${ROCM_VERSION:-}" \
-       --build-arg "PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH:-gfx906;gfx90a}" \
-       --build-arg "IMAGE_NAME=${IMAGE_NAME}" \
-       --build-arg "UCX_COMMIT=${UCX_COMMIT}" \
-       --build-arg "UCC_COMMIT=${UCC_COMMIT}" \
-       --build-arg "CONDA_CMAKE=${CONDA_CMAKE}" \
-       --build-arg "TRITON=${TRITON}" \
-       --build-arg "ONNX=${ONNX}" \
-       --build-arg "DOCS=${DOCS}" \
-       --build-arg "INDUCTOR_BENCHMARKS=${INDUCTOR_BENCHMARKS}" \
-       --build-arg "EXECUTORCH=${EXECUTORCH}" \
-       --build-arg "XPU_VERSION=${XPU_VERSION}" \
-       --build-arg "ACL=${ACL:-}" \
-       --build-arg "SKIP_SCCACHE_INSTALL=${SKIP_SCCACHE_INSTALL:-}" \
-       --build-arg "SKIP_LLVM_SRC_BUILD_INSTALL=${SKIP_LLVM_SRC_BUILD_INSTALL:-}" \
-       -f $(dirname ${DOCKERFILE})/Dockerfile \
-       -t "$tmp_tag" \
-       "$@" \
-       .
-
-# NVIDIA dockers for RC releases use tag names like `11.0-cudnn9-devel-ubuntu18.04-rc`,
-# for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
-# find the correct image. As a result, here we have to replace the
-#   "$UBUNTU_VERSION" == "18.04-rc"
-# with
-#   "$UBUNTU_VERSION" == "18.04"
-UBUNTU_VERSION=$(echo ${UBUNTU_VERSION} | sed 's/-rc$//')
-
-function drun() {
-  docker run --rm "$tmp_tag" $*
-}
-
-if [[ "$OS" == "ubuntu" ]]; then
-
-  if !(drun lsb_release -a 2>&1 | grep -qF Ubuntu); then
-    echo "OS=ubuntu, but:"
-    drun lsb_release -a
-    exit 1
-  fi
-  if !(drun lsb_release -a 2>&1 | grep -qF "$UBUNTU_VERSION"); then
-    echo "UBUNTU_VERSION=$UBUNTU_VERSION, but:"
-    drun lsb_release -a
-    exit 1
-  fi
-fi
-
-if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
-  if !(drun python --version 2>&1 | grep -qF "Python $ANACONDA_PYTHON_VERSION"); then
-    echo "ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION, but:"
-    drun python --version
-    exit 1
-  fi
-fi
-
-if [ -n "$GCC_VERSION" ]; then
-  if !(drun gcc --version 2>&1 | grep -q " $GCC_VERSION\\W"); then
-    echo "GCC_VERSION=$GCC_VERSION, but:"
-    drun gcc --version
-    exit 1
-  fi
-fi
-
-if [ -n "$CLANG_VERSION" ]; then
-  if !(drun clang --version 2>&1 | grep -qF "clang version $CLANG_VERSION"); then
-    echo "CLANG_VERSION=$CLANG_VERSION, but:"
-    drun clang --version
-    exit 1
-  fi
-fi
-
-if [ -n "$KATEX" ]; then
-  if !(drun katex --version); then
-    echo "KATEX=$KATEX, but:"
-    drun katex --version
-    exit 1
-  fi
-fi

.ci/docker/centos-rocm/Dockerfile

@@ -1,133 +0,0 @@
-ARG CENTOS_VERSION
-
-FROM centos:${CENTOS_VERSION}
-
-ARG CENTOS_VERSION
-
-# Set AMD gpu targets to build for
-ARG PYTORCH_ROCM_ARCH
-ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
-
-# Install required packages to build Caffe2
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Update CentOS git version
-RUN yum -y remove git
-RUN yum -y remove git-*
-RUN yum -y install https://packages.endpoint.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm || \
-    (yum -y install https://packages.endpointdev.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm && \
-    sed -i "s/packages.endpoint/packages.endpointdev/" /etc/yum.repos.d/endpoint.repo)
-RUN yum install -y git
-
-# Install devtoolset
-ARG DEVTOOLSET_VERSION
-COPY ./common/install_devtoolset.sh install_devtoolset.sh
-RUN bash ./install_devtoolset.sh && rm install_devtoolset.sh
-ENV BASH_ENV "/etc/profile"
-
-# (optional) Install non-default glibc version
-ARG GLIBC_VERSION
-COPY ./common/install_glibc.sh install_glibc.sh
-RUN if [ -n "${GLIBC_VERSION}" ]; then bash ./install_glibc.sh; fi
-RUN rm install_glibc.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-COPY ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# Install rocm
-ARG ROCM_VERSION
-COPY ./common/install_rocm.sh install_rocm.sh
-RUN bash ./install_rocm.sh
-RUN rm install_rocm.sh
-COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
-RUN bash ./install_rocm_magma.sh
-RUN rm install_rocm_magma.sh
-COPY ./common/install_amdsmi.sh install_amdsmi.sh
-RUN bash ./install_amdsmi.sh
-RUN rm install_amdsmi.sh
-ENV PATH /opt/rocm/bin:$PATH
-ENV PATH /opt/rocm/hcc/bin:$PATH
-ENV PATH /opt/rocm/hip/bin:$PATH
-ENV PATH /opt/rocm/opencl/bin:$PATH
-ENV PATH /opt/rocm/llvm/bin:$PATH
-ENV MAGMA_HOME /opt/rocm/magma
-ENV LANG en_US.utf8
-ENV LC_ALL en_US.utf8
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-ENV CMAKE_C_COMPILER cc
-ENV CMAKE_CXX_COMPILER c++
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
-
-# Install AOTriton (Early fail)
-COPY ./aotriton_version.txt aotriton_version.txt
-COPY ./common/common_utils.sh common_utils.sh
-COPY ./common/install_aotriton.sh install_aotriton.sh
-RUN ["/bin/bash", "-c", "./install_aotriton.sh /opt/rocm && rm -rf install_aotriton.sh aotriton_version.txt common_utils.sh"]
-ENV AOTRITON_INSTALLED_PREFIX /opt/rocm/aotriton
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ci_commit_pins/executorch.txt

@@ -1 +0,0 @@
-d4b3e5cc607e97afdba79dc90f8ef968142f347c

.ci/docker/ci_commit_pins/huggingface.txt

@@ -1 +0,0 @@
-243e186efbf7fb93328dd6b34927a4e8c8f24395

.ci/docker/ci_commit_pins/timm.txt

@@ -1 +0,0 @@
-730b907b4d45a4713cbc425cbf224c46089fd514

.ci/docker/ci_commit_pins/triton-rocm.txt

@@ -1 +0,0 @@
-01cbe5045a6898c9a925f01435c8277b2fe6afcc

.ci/docker/ci_commit_pins/triton-xpu.txt

@@ -1 +0,0 @@
-b8c64f64c18d8cac598b3adb355c21e7439c21de

.ci/docker/ci_commit_pins/triton.txt

@@ -1 +0,0 @@
-45fff310c891f5a92d55445adf8cc9d29df5841e

.ci/docker/common/cache_vision_models.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-# Cache the test models at ~/.cache/torch/hub/
-IMPORT_SCRIPT_FILENAME="/tmp/torchvision_import_script.py"
-as_jenkins echo 'import torchvision; torchvision.models.mobilenet_v2(pretrained=True); torchvision.models.mobilenet_v3_large(pretrained=True);' > "${IMPORT_SCRIPT_FILENAME}"
-
-pip_install --pre torch torchvision --index-url https://download.pytorch.org/whl/nightly/cpu
-# Very weird quoting behavior here https://github.com/conda/conda/issues/10972,
-# so echo the command to a file and run the file instead
-conda_run python "${IMPORT_SCRIPT_FILENAME}"
-
-# Cleaning up
-conda_run pip uninstall -y torch torchvision
-rm "${IMPORT_SCRIPT_FILENAME}" || true

.ci/docker/common/common_utils.sh

@@ -1,36 +0,0 @@
-#!/bin/bash
-
-# Work around bug where devtoolset replaces sudo and breaks it.
-if [ -n "$DEVTOOLSET_VERSION" ]; then
-  export SUDO=/bin/sudo
-else
-  export SUDO=sudo
-fi
-
-as_jenkins() {
-  # NB: unsetting the environment variables works around a conda bug
-  # https://github.com/conda/conda/issues/6576
-  # NB: Pass on PATH and LD_LIBRARY_PATH to sudo invocation
-  # NB: This must be run from a directory that jenkins has access to,
-  # works around https://github.com/conda/conda-package-handling/pull/34
-  $SUDO -E -H -u jenkins env -u SUDO_UID -u SUDO_GID -u SUDO_COMMAND -u SUDO_USER env "PATH=$PATH" "LD_LIBRARY_PATH=$LD_LIBRARY_PATH" $*
-}
-
-conda_install() {
-  # Ensure that the install command don't upgrade/downgrade Python
-  # This should be called as
-  #   conda_install pkg1 pkg2 ... [-c channel]
-  as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y python="$ANACONDA_PYTHON_VERSION" $*
-}
-
-conda_run() {
-  as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION --no-capture-output $*
-}
-
-pip_install() {
-  as_jenkins conda run -n py_$ANACONDA_PYTHON_VERSION pip install --progress-bar off $*
-}
-
-get_pinned_commit() {
-  cat "${1}".txt
-}

.ci/docker/common/install_acl.sh

@@ -1,16 +0,0 @@
-set -euo pipefail
-
-readonly version=v24.04
-readonly src_host=https://review.mlplatform.org/ml
-readonly src_repo=ComputeLibrary
-
-# Clone ACL
-[[ ! -d ${src_repo} ]] && git clone ${src_host}/${src_repo}.git
-cd ${src_repo}
-
-git checkout $version
-
-# Build with scons
-scons -j8  Werror=0 debug=0 neon=1 opencl=0 embed_kernels=0 \
-  os=linux arch=armv8a build=native multi_isa=1 \
-  fixed_format_kernels=1 openmp=1 cppthreads=0

.ci/docker/common/install_amdsmi.sh

@@ -1,5 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-cd /opt/rocm/share/amd_smi && pip install .

.ci/docker/common/install_aotriton.sh

@@ -1,23 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-TARBALL='aotriton.tar.bz2'
-# This read command alwasy returns with exit code 1
-read -d "\n" VER MANYLINUX ROCMBASE PINNED_COMMIT SHA256 < aotriton_version.txt || true
-ARCH=$(uname -m)
-AOTRITON_INSTALL_PREFIX="$1"
-AOTRITON_URL="https://github.com/ROCm/aotriton/releases/download/${VER}/aotriton-${VER}-${MANYLINUX}_${ARCH}-${ROCMBASE}.tar.bz2"
-
-cd "${AOTRITON_INSTALL_PREFIX}"
-# Must use -L to follow redirects
-curl -L --retry 3 -o "${TARBALL}" "${AOTRITON_URL}"
-ACTUAL_SHA256=$(sha256sum "${TARBALL}" | cut -d " " -f 1)
-if [ "${SHA256}" != "${ACTUAL_SHA256}" ]; then
-  echo -n "Error: The SHA256 of downloaded tarball is ${ACTUAL_SHA256},"
-  echo " which does not match the expected value ${SHA256}."
-  exit
-fi
-tar xf "${TARBALL}" && rm -rf "${TARBALL}"

.ci/docker/common/install_base.sh

@@ -1,159 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-install_ubuntu() {
-  # NVIDIA dockers for RC releases use tag names like `11.0-cudnn9-devel-ubuntu18.04-rc`,
-  # for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
-  # find the correct image. As a result, here we have to check for
-  #   "$UBUNTU_VERSION" == "18.04"*
-  # instead of
-  #   "$UBUNTU_VERSION" == "18.04"
-  if [[ "$UBUNTU_VERSION" == "20.04"* ]]; then
-    cmake3="cmake=3.16*"
-    maybe_libiomp_dev=""
-  elif [[ "$UBUNTU_VERSION" == "22.04"* ]]; then
-    cmake3="cmake=3.22*"
-    maybe_libiomp_dev=""
-  else
-    cmake3="cmake=3.5*"
-    maybe_libiomp_dev="libiomp-dev"
-  fi
-
-  if [[ "$CLANG_VERSION" == 15 ]]; then
-    maybe_libomp_dev="libomp-15-dev"
-  elif [[ "$CLANG_VERSION" == 12 ]]; then
-    maybe_libomp_dev="libomp-12-dev"
-  elif [[ "$CLANG_VERSION" == 10 ]]; then
-    maybe_libomp_dev="libomp-10-dev"
-  else
-    maybe_libomp_dev=""
-  fi
-
-  # HACK: UCC testing relies on libnccl library from NVIDIA repo, and version 2.16 crashes
-  # See https://github.com/pytorch/pytorch/pull/105260#issuecomment-1673399729
-  if [[ "$UBUNTU_VERSION" == "20.04"* && "$CUDA_VERSION" == "11.8"* ]]; then
-    maybe_libnccl_dev="libnccl2=2.15.5-1+cuda11.8 libnccl-dev=2.15.5-1+cuda11.8 --allow-downgrades --allow-change-held-packages"
-  else
-    maybe_libnccl_dev=""
-  fi
-
-  # Install common dependencies
-  apt-get update
-  # TODO: Some of these may not be necessary
-  ccache_deps="asciidoc docbook-xml docbook-xsl xsltproc"
-  deploy_deps="libffi-dev libbz2-dev libreadline-dev libncurses5-dev libncursesw5-dev libgdbm-dev libsqlite3-dev uuid-dev tk-dev"
-  numpy_deps="gfortran"
-  apt-get install -y --no-install-recommends \
-    $ccache_deps \
-    $numpy_deps \
-    ${deploy_deps} \
-    ${cmake3} \
-    apt-transport-https \
-    autoconf \
-    automake \
-    build-essential \
-    ca-certificates \
-    curl \
-    git \
-    libatlas-base-dev \
-    libc6-dbg \
-    ${maybe_libiomp_dev} \
-    libyaml-dev \
-    libz-dev \
-    libjemalloc2 \
-    libjpeg-dev \
-    libasound2-dev \
-    libsndfile-dev \
-    ${maybe_libomp_dev} \
-    ${maybe_libnccl_dev} \
-    software-properties-common \
-    wget \
-    sudo \
-    vim \
-    jq \
-    libtool \
-    vim \
-    unzip \
-    gpg-agent \
-    gdb
-
-  # Should resolve issues related to various apt package repository cert issues
-  # see: https://github.com/pytorch/pytorch/issues/65931
-  apt-get install -y libgnutls30
-
-  # Cleanup package manager
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-}
-
-install_centos() {
-  # Need EPEL for many packages we depend on.
-  # See http://fedoraproject.org/wiki/EPEL
-  yum --enablerepo=extras install -y epel-release
-
-  ccache_deps="asciidoc docbook-dtds docbook-style-xsl libxslt"
-  numpy_deps="gcc-gfortran"
-  # Note: protobuf-c-{compiler,devel} on CentOS are too old to be used
-  # for Caffe2. That said, we still install them to make sure the build
-  # system opts to build/use protoc and libprotobuf from third-party.
-  yum install -y \
-    $ccache_deps \
-    $numpy_deps \
-    autoconf \
-    automake \
-    bzip2 \
-    cmake \
-    cmake3 \
-    curl \
-    gcc \
-    gcc-c++ \
-    gflags-devel \
-    git \
-    glibc-devel \
-    glibc-headers \
-    glog-devel \
-    libstdc++-devel \
-    libsndfile-devel \
-    make \
-    opencv-devel \
-    sudo \
-    wget \
-    vim \
-    unzip \
-    gdb
-
-  # Cleanup
-  yum clean all
-  rm -rf /var/cache/yum
-  rm -rf /var/lib/yum/yumdb
-  rm -rf /var/lib/yum/history
-}
-
-# Install base packages depending on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-  ubuntu)
-    install_ubuntu
-    ;;
-  centos)
-    install_centos
-    ;;
-  *)
-    echo "Unable to determine OS..."
-    exit 1
-    ;;
-esac
-
-# Install Valgrind separately since the apt-get version is too old.
-mkdir valgrind_build && cd valgrind_build
-VALGRIND_VERSION=3.20.0
-wget https://ossci-linux.s3.amazonaws.com/valgrind-${VALGRIND_VERSION}.tar.bz2
-tar -xjf valgrind-${VALGRIND_VERSION}.tar.bz2
-cd valgrind-${VALGRIND_VERSION}
-./configure --prefix=/usr/local
-make -j$[$(nproc) - 2]
-sudo make install
-cd ../../
-rm -rf valgrind_build
-alias valgrind="/usr/local/bin/valgrind"

.ci/docker/common/install_clang.sh

@@ -1,44 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$CLANG_VERSION" ]; then
-
-  if [[ $CLANG_VERSION == 9 && $UBUNTU_VERSION == 18.04 ]]; then
-    sudo apt-get update
-    # gpg-agent is not available by default on 18.04
-    sudo apt-get install  -y --no-install-recommends gpg-agent
-    wget --no-check-certificate -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add  -
-    apt-add-repository "deb http://apt.llvm.org/bionic/ llvm-toolchain-bionic-${CLANG_VERSION} main"
-  elif [[ $UBUNTU_VERSION == 22.04 ]]; then
-    # work around ubuntu apt-get conflicts
-    sudo apt-get -y -f install
-  fi
-
-  sudo apt-get update
-  apt-get install -y --no-install-recommends clang-"$CLANG_VERSION"
-  apt-get install -y --no-install-recommends llvm-"$CLANG_VERSION"
-
-  # Install dev version of LLVM.
-  if [ -n "$LLVMDEV" ]; then
-    sudo apt-get install -y --no-install-recommends llvm-"$CLANG_VERSION"-dev
-  fi
-
-  # Use update-alternatives to make this version the default
-  update-alternatives --install /usr/bin/clang clang /usr/bin/clang-"$CLANG_VERSION" 50
-  update-alternatives --install /usr/bin/clang++ clang++ /usr/bin/clang++-"$CLANG_VERSION" 50
-  # Override cc/c++ to clang as well
-  update-alternatives --install /usr/bin/cc cc /usr/bin/clang 50
-  update-alternatives --install /usr/bin/c++ c++ /usr/bin/clang++ 50
-
-  # clang's packaging is a little messed up (the runtime libs aren't
-  # added into the linker path), so give it a little help
-  clang_lib=("/usr/lib/llvm-$CLANG_VERSION/lib/clang/"*"/lib/linux")
-  echo "$clang_lib" > /etc/ld.so.conf.d/clang.conf
-  ldconfig
-
-  # Cleanup package manager
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-fi

.ci/docker/common/install_cmake.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "$CMAKE_VERSION" ]
-
-# Remove system cmake install so it won't get used instead
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-  ubuntu)
-    apt-get remove cmake -y
-    ;;
-  centos)
-    yum remove cmake -y
-    ;;
-  *)
-    echo "Unable to determine OS..."
-    exit 1
-    ;;
-esac
-
-# Turn 3.6.3 into v3.6
-path=$(echo "${CMAKE_VERSION}" | sed -e 's/\([0-9].[0-9]\+\).*/v\1/')
-file="cmake-${CMAKE_VERSION}-Linux-x86_64.tar.gz"
-
-# Download and install specific CMake version in /usr/local
-pushd /tmp
-curl -Os --retry 3 "https://cmake.org/files/${path}/${file}"
-tar -C /usr/local --strip-components 1 --no-same-owner -zxf cmake-*.tar.gz
-rm -f cmake-*.tar.gz
-popd

.ci/docker/common/install_conda.sh

@@ -1,127 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Optionally install conda
-if [ -n "$ANACONDA_PYTHON_VERSION" ]; then
-  BASE_URL="https://repo.anaconda.com/miniconda"
-
-  MAJOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
-  MINOR_PYTHON_VERSION=$(echo "$ANACONDA_PYTHON_VERSION" | cut -d . -f 2)
-
-if [[ $(uname -m) == "aarch64" ]]; then
-  BASE_URL="https://github.com/conda-forge/miniforge/releases/latest/download"
-  case "$MAJOR_PYTHON_VERSION" in
-    3)
-      CONDA_FILE="Miniforge3-Linux-aarch64.sh"
-    ;;
-    *)
-      echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
-      exit 1
-      ;;
-  esac
-else
-  case "$MAJOR_PYTHON_VERSION" in
-    3)
-      CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
-    ;;
-    *)
-      echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
-      exit 1
-      ;;
-  esac
-fi
-
-  mkdir -p /opt/conda
-  chown jenkins:jenkins /opt/conda
-
-  source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-  pushd /tmp
-  wget -q "${BASE_URL}/${CONDA_FILE}"
-  # NB: Manually invoke bash per https://github.com/conda/conda/issues/10431
-  as_jenkins bash "${CONDA_FILE}" -b -f -p "/opt/conda"
-  popd
-
-  # NB: Don't do this, rely on the rpath to get it right
-  #echo "/opt/conda/lib" > /etc/ld.so.conf.d/conda-python.conf
-  #ldconfig
-  sed -e 's|PATH="\(.*\)"|PATH="/opt/conda/bin:\1"|g' -i /etc/environment
-  export PATH="/opt/conda/bin:$PATH"
-
-  # Ensure we run conda in a directory that jenkins has write access to
-  pushd /opt/conda
-
-  # Prevent conda from updating to 4.14.0, which causes docker build failures
-  # See https://hud.pytorch.org/pytorch/pytorch/commit/754d7f05b6841e555cea5a4b2c505dd9e0baec1d
-  # Uncomment the below when resolved to track the latest conda update
-  # as_jenkins conda update -y -n base conda
-
-  if [[ $(uname -m) == "aarch64" ]]; then
-    export SYSROOT_DEP="sysroot_linux-aarch64=2.17"
-  else
-    export SYSROOT_DEP="sysroot_linux-64=2.17"
-  fi
-
-  # Install correct Python version
-  # Also ensure sysroot is using a modern GLIBC to match system compilers
-  as_jenkins conda create -n py_$ANACONDA_PYTHON_VERSION -y\
-             python="$ANACONDA_PYTHON_VERSION" \
-             ${SYSROOT_DEP}
-
-  # libstdcxx from conda default channels are too old, we need GLIBCXX_3.4.30
-  # which is provided in libstdcxx 12 and up.
-  conda_install libstdcxx-ng=12.3.0 -c conda-forge
-
-  # Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
-  if [[ $(uname -m) == "aarch64" ]]; then
-    CONDA_COMMON_DEPS="astunparse pyyaml setuptools openblas==0.3.25=*openmp* ninja==1.11.1 scons==4.5.2"
-
-    if [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
-      conda_install numpy=1.24.4 ${CONDA_COMMON_DEPS}
-    else
-      conda_install numpy=1.26.2 ${CONDA_COMMON_DEPS}
-    fi
-  else
-    CONDA_COMMON_DEPS="astunparse pyyaml mkl=2021.4.0 mkl-include=2021.4.0 setuptools"
-
-    if [ "$ANACONDA_PYTHON_VERSION" = "3.11" ] || [ "$ANACONDA_PYTHON_VERSION" = "3.12" ]; then
-      conda_install numpy=1.26.0 ${CONDA_COMMON_DEPS}
-    else
-      conda_install numpy=1.21.2 ${CONDA_COMMON_DEPS}
-    fi
-  fi
-
-  # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
-  # and libpython-static for torch deploy
-  conda_install llvmdev=8.0.0 "libpython-static=${ANACONDA_PYTHON_VERSION}"
-
-  # Use conda cmake in some cases. Conda cmake will be newer than our supported
-  # min version (3.5 for xenial and 3.10 for bionic), so we only do it in those
-  # following builds that we know should use conda. Specifically, Ubuntu bionic
-  # and focal cannot find conda mkl with stock cmake, so we need a cmake from conda
-  if [ -n "${CONDA_CMAKE}" ]; then
-    conda_install cmake
-  fi
-
-  # Magma package names are concatenation of CUDA major and minor ignoring revision
-  # I.e. magma-cuda102 package corresponds to CUDA_VERSION=10.2 and CUDA_VERSION=10.2.89
-  if [ -n "$CUDA_VERSION" ]; then
-    conda_install magma-cuda$(TMP=${CUDA_VERSION/./};echo ${TMP%.*[0-9]}) -c pytorch
-  fi
-
-  # Install some other packages, including those needed for Python test reporting
-  pip_install -r /opt/conda/requirements-ci.txt
-
-  pip_install -U scikit-learn
-
-  if [ -n "$DOCS" ]; then
-    apt-get update
-    apt-get -y install expect-dev
-
-    # We are currently building docs with python 3.8 (min support version)
-    pip_install -r /opt/conda/requirements-docs.txt
-  fi
-
-  popd
-fi

.ci/docker/common/install_cudnn.sh

@@ -1,22 +0,0 @@
-#!/bin/bash
-
-if [[ -n "${CUDNN_VERSION}" ]]; then
-    # cuDNN license: https://developer.nvidia.com/cudnn/license_agreement
-    mkdir tmp_cudnn
-    pushd tmp_cudnn
-    if [[ ${CUDA_VERSION:0:2} == "12" ]]; then
-        CUDNN_NAME="cudnn-linux-x86_64-9.1.0.70_cuda12-archive"
-    elif [[ ${CUDA_VERSION:0:2} == "11" ]]; then
-        CUDNN_NAME="cudnn-linux-x86_64-9.1.0.70_cuda11-archive"
-    else
-        print "Unsupported CUDA version ${CUDA_VERSION}"
-        exit 1
-    fi
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/${CUDNN_NAME}.tar.xz
-    tar xf ${CUDNN_NAME}.tar.xz
-    cp -a ${CUDNN_NAME}/include/* /usr/local/cuda/include/
-    cp -a ${CUDNN_NAME}/lib/* /usr/local/cuda/lib64/
-    popd
-    rm -rf tmp_cudnn
-    ldconfig
-fi

.ci/docker/common/install_cusparselt.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# cuSPARSELt license: https://docs.nvidia.com/cuda/cusparselt/license.html
-mkdir tmp_cusparselt && cd tmp_cusparselt
-
-if [[ ${CUDA_VERSION:0:4} =~ ^12\.[1-4]$ ]]; then
-    arch_path='sbsa'
-    export TARGETARCH=${TARGETARCH:-$(uname -m)}
-    if [ ${TARGETARCH} = 'amd64' ] || [ "${TARGETARCH}" = 'x86_64' ]; then
-        arch_path='x86_64'
-    fi
-    CUSPARSELT_NAME="libcusparse_lt-linux-${arch_path}-0.5.2.1-archive"
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-${arch_path}/${CUSPARSELT_NAME}.tar.xz
-elif [[ ${CUDA_VERSION:0:4} == "11.8" ]]; then
-    CUSPARSELT_NAME="libcusparse_lt-linux-x86_64-0.4.0.7-archive"
-    curl --retry 3 -OLs https://developer.download.nvidia.com/compute/cusparselt/redist/libcusparse_lt/linux-x86_64/${CUSPARSELT_NAME}.tar.xz
-fi
-
-tar xf ${CUSPARSELT_NAME}.tar.xz
-cp -a ${CUSPARSELT_NAME}/include/* /usr/local/cuda/include/
-cp -a ${CUSPARSELT_NAME}/lib/* /usr/local/cuda/lib64/
-cd ..
-rm -rf tmp_cusparselt
-ldconfig

.ci/docker/common/install_db.sh

@@ -1,38 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-install_ubuntu() {
-  apt-get update
-
-  # Cleanup
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-}
-
-install_centos() {
-  # Need EPEL for many packages we depend on.
-  # See http://fedoraproject.org/wiki/EPEL
-  yum --enablerepo=extras install -y epel-release
-
-  # Cleanup
-  yum clean all
-  rm -rf /var/cache/yum
-  rm -rf /var/lib/yum/yumdb
-  rm -rf /var/lib/yum/history
-}
-
-# Install base packages depending on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-  ubuntu)
-    install_ubuntu
-    ;;
-  centos)
-    install_centos
-    ;;
-  *)
-    echo "Unable to determine OS..."
-    exit 1
-    ;;
-esac

.ci/docker/common/install_docs_reqs.sh

@@ -1,25 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$KATEX" ]; then
-  apt-get update
-  # Ignore error if gpg-agent doesn't exist (for Ubuntu 16.04)
-  apt-get install -y gpg-agent || :
-
-  curl --retry 3 -sL https://deb.nodesource.com/setup_16.x | sudo -E bash -
-  sudo apt-get install -y nodejs
-
-  curl --retry 3 -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -
-  echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list
-
-  apt-get update
-  apt-get install -y --no-install-recommends yarn
-  yarn global add katex --prefix /usr/local
-
-  sudo apt-get -y install doxygen
-
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-fi

.ci/docker/common/install_executorch.sh

@@ -1,61 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-clone_executorch() {
-  EXECUTORCH_PINNED_COMMIT=$(get_pinned_commit executorch)
-
-  # Clone the Executorch
-  git clone https://github.com/pytorch/executorch.git
-
-  # and fetch the target commit
-  pushd executorch
-  git checkout "${EXECUTORCH_PINNED_COMMIT}"
-  git submodule update --init
-  popd
-
-  chown -R jenkins executorch
-}
-
-install_buck2() {
-  pushd executorch/.ci/docker
-
-  BUCK2_VERSION=$(cat ci_commit_pins/buck2.txt)
-  source common/install_buck.sh
-
-  popd
-}
-
-install_conda_dependencies() {
-  pushd executorch/.ci/docker
-  # Install conda dependencies like flatbuffer
-  conda_install --file conda-env-ci.txt
-  popd
-}
-
-install_pip_dependencies() {
-  pushd executorch/.ci/docker
-  # Install all Python dependencies
-  pip_install -r requirements-ci.txt
-  popd
-}
-
-setup_executorch() {
-  pushd executorch
-  source .ci/scripts/utils.sh
-
-  install_flatc_from_source
-  pip_install .
-
-  # Make sure that all the newly generate files are owned by Jenkins
-  chown -R jenkins .
-  popd
-}
-
-clone_executorch
-install_buck2
-install_conda_dependencies
-install_pip_dependencies
-setup_executorch

.ci/docker/common/install_gcc.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$GCC_VERSION" ]; then
-
-  # Need the official toolchain repo to get alternate packages
-  add-apt-repository ppa:ubuntu-toolchain-r/test
-  apt-get update
-  apt-get install -y g++-$GCC_VERSION
-  update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-"$GCC_VERSION" 50
-  update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-"$GCC_VERSION" 50
-  update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-"$GCC_VERSION" 50
-
-
-  # Cleanup package manager
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-fi

.ci/docker/common/install_inductor_benchmark_deps.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-function install_huggingface() {
-  local version
-  commit=$(get_pinned_commit huggingface)
-  pip_install pandas==2.0.3
-  pip_install "git+https://github.com/huggingface/transformers@${commit}"
-}
-
-function install_timm() {
-  local commit
-  commit=$(get_pinned_commit timm)
-  pip_install pandas==2.0.3
-  pip_install "git+https://github.com/huggingface/pytorch-image-models@${commit}"
-  # Clean up
-  conda_run pip uninstall -y cmake torch torchvision triton
-}
-
-# Pango is needed for weasyprint which is needed for doctr
-conda_install pango
-install_huggingface
-install_timm

.ci/docker/common/install_linter.sh

@@ -1,29 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-if [ -n "${UBUNTU_VERSION}" ]; then
-  apt update
-  apt-get install -y clang doxygen git graphviz nodejs npm libtinfo5
-fi
-
-# Do shallow clone of PyTorch so that we can init lintrunner in Docker build context
-git clone https://github.com/pytorch/pytorch.git --depth 1
-chown -R jenkins pytorch
-
-pushd pytorch
-# Install all linter dependencies
-pip_install -r requirements.txt
-conda_run lintrunner init
-
-# Cache .lintbin directory as part of the Docker image
-cp -r .lintbin /tmp
-popd
-
-# Node dependencies required by toc linter job
-npm install -g markdown-toc
-
-# Cleaning up
-rm -rf pytorch

.ci/docker/common/install_onnx.sh

@@ -1,51 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-retry () {
-    "$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
-}
-
-# A bunch of custom pip dependencies for ONNX
-pip_install \
-  beartype==0.15.0 \
-  filelock==3.9.0 \
-  flatbuffers==2.0 \
-  mock==5.0.1 \
-  ninja==1.10.2 \
-  networkx==2.0 \
-  numpy==1.24.2
-
-# ONNXRuntime should be installed before installing
-# onnx-weekly. Otherwise, onnx-weekly could be
-# overwritten by onnx.
-pip_install \
-  parameterized==0.8.1 \
-  pytest-cov==4.0.0 \
-  pytest-subtests==0.10.0 \
-  tabulate==0.9.0 \
-  transformers==4.36.2
-
-pip_install coloredlogs packaging
-
-pip_install onnxruntime==1.18
-pip_install onnx==1.16.0
-# pip_install "onnxscript@git+https://github.com/microsoft/onnxscript@3e869ef8ccf19b5ebd21c10d3e9c267c9a9fa729" --no-deps
-pip_install onnxscript==0.1.0.dev20240523 --no-deps
-
-# Cache the transformers model to be used later by ONNX tests. We need to run the transformers
-# package to download the model. By default, the model is cached at ~/.cache/huggingface/hub/
-IMPORT_SCRIPT_FILENAME="/tmp/onnx_import_script.py"
-as_jenkins echo 'import transformers; transformers.AutoModel.from_pretrained("sshleifer/tiny-gpt2"); transformers.AutoTokenizer.from_pretrained("sshleifer/tiny-gpt2"); transformers.AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-large-v3");' > "${IMPORT_SCRIPT_FILENAME}"
-
-# Need a PyTorch version for transformers to work
-pip_install --pre torch --index-url https://download.pytorch.org/whl/nightly/cpu
-# Very weird quoting behavior here https://github.com/conda/conda/issues/10972,
-# so echo the command to a file and run the file instead
-conda_run python "${IMPORT_SCRIPT_FILENAME}"
-
-# Cleaning up
-conda_run pip uninstall -y torch
-rm "${IMPORT_SCRIPT_FILENAME}" || true

.ci/docker/common/install_openssl.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-OPENSSL=openssl-1.1.1k
-
-wget -q -O "${OPENSSL}.tar.gz" "https://ossci-linux.s3.amazonaws.com/${OPENSSL}.tar.gz"
-tar xf "${OPENSSL}.tar.gz"
-cd "${OPENSSL}"
-./config --prefix=/opt/openssl -d '-Wl,--enable-new-dtags,-rpath,$(LIBRPATH)'
-# NOTE: openssl install errors out when built with the -j option
-NPROC=$[$(nproc) - 2]
-make -j${NPROC}; make install_sw
-# Link the ssl libraries to the /usr/lib folder.
-sudo ln -s /opt/openssl/lib/lib* /usr/lib
-cd ..
-rm -rf "${OPENSSL}"

.ci/docker/common/install_protobuf.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-pb_dir="/usr/temp_pb_install_dir"
-mkdir -p $pb_dir
-
-# On the nvidia/cuda:9-cudnn7-devel-centos7 image we need this symlink or
-# else it will fail with
-#   g++: error: ./../lib64/crti.o: No such file or directory
-ln -s /usr/lib64 "$pb_dir/lib64"
-
-curl -LO "https://github.com/protocolbuffers/protobuf/releases/download/v3.17.3/protobuf-all-3.17.3.tar.gz" --retry 3
-
-tar -xvz --no-same-owner -C "$pb_dir" --strip-components 1 -f protobuf-all-3.17.3.tar.gz
-NPROC=$[$(nproc) - 2]
-pushd "$pb_dir" && ./configure && make -j${NPROC} && make -j${NPROC} check && sudo make -j${NRPOC} install && sudo ldconfig
-popd
-rm -rf $pb_dir

.ci/docker/common/install_rocm.sh

@@ -1,148 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-ver() {
-    printf "%3d%03d%03d%03d" $(echo "$1" | tr '.' ' ');
-}
-
-install_ubuntu() {
-    apt-get update
-    if [[ $UBUNTU_VERSION == 18.04 ]]; then
-      # gpg-agent is not available by default on 18.04
-      apt-get install -y --no-install-recommends gpg-agent
-    fi
-    if [[ $UBUNTU_VERSION == 20.04 ]]; then
-      # gpg-agent is not available by default on 20.04
-      apt-get install -y --no-install-recommends gpg-agent
-    fi
-    apt-get install -y kmod
-    apt-get install -y wget
-
-    # Need the libc++1 and libc++abi1 libraries to allow torch._C to load at runtime
-    apt-get install -y libc++1
-    apt-get install -y libc++abi1
-
-    # Add amdgpu repository
-    UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
-    echo "deb [arch=amd64] https://repo.radeon.com/amdgpu/${ROCM_VERSION}/ubuntu ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
-
-    # Add rocm repository
-    wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
-    local rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
-    echo "deb [arch=amd64] ${rocm_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/rocm.list
-    apt-get update --allow-insecure-repositories
-
-    DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
-                   rocm-dev \
-                   rocm-utils \
-                   rocm-libs \
-                   rccl \
-                   rocprofiler-dev \
-                   roctracer-dev \
-                   amd-smi-lib
-
-    if [[ $(ver $ROCM_VERSION) -ge $(ver 6.1) ]]; then
-        DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated rocm-llvm-dev
-    fi
-
-    # precompiled miopen kernels added in ROCm 3.5, renamed in ROCm 5.5
-    # search for all unversioned packages
-    # if search fails it will abort this script; use true to avoid case where search fails
-    MIOPENHIPGFX=$(apt-cache search --names-only miopen-hip-gfx | awk '{print $1}' | grep -F -v . || true)
-    if [[ "x${MIOPENHIPGFX}" = x ]]; then
-      echo "miopen-hip-gfx package not available" && exit 1
-    else
-      DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENHIPGFX}
-    fi
-
-    # ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime
-    for kdb in /opt/rocm/share/miopen/db/*.kdb
-    do
-        sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
-    done
-
-    # Cleanup
-    apt-get autoclean && apt-get clean
-    rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-}
-
-install_centos() {
-
-  yum update -y
-  yum install -y kmod
-  yum install -y wget
-  yum install -y openblas-devel
-
-  yum install -y epel-release
-  yum install -y dkms kernel-headers-`uname -r` kernel-devel-`uname -r`
-
-  # Add amdgpu repository
-  local amdgpu_baseurl
-  if [[ $OS_VERSION == 9 ]]; then
-      amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/9.0/main/x86_64"
-  else
-      amdgpu_baseurl="https://repo.radeon.com/amdgpu/${ROCM_VERSION}/rhel/7.9/main/x86_64"
-  fi
-  echo "[AMDGPU]" > /etc/yum.repos.d/amdgpu.repo
-  echo "name=AMDGPU" >> /etc/yum.repos.d/amdgpu.repo
-  echo "baseurl=${amdgpu_baseurl}" >> /etc/yum.repos.d/amdgpu.repo
-  echo "enabled=1" >> /etc/yum.repos.d/amdgpu.repo
-  echo "gpgcheck=1" >> /etc/yum.repos.d/amdgpu.repo
-  echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/amdgpu.repo
-
-  local rocm_baseurl="http://repo.radeon.com/rocm/yum/${ROCM_VERSION}"
-  echo "[ROCm]" > /etc/yum.repos.d/rocm.repo
-  echo "name=ROCm" >> /etc/yum.repos.d/rocm.repo
-  echo "baseurl=${rocm_baseurl}" >> /etc/yum.repos.d/rocm.repo
-  echo "enabled=1" >> /etc/yum.repos.d/rocm.repo
-  echo "gpgcheck=1" >> /etc/yum.repos.d/rocm.repo
-  echo "gpgkey=http://repo.radeon.com/rocm/rocm.gpg.key" >> /etc/yum.repos.d/rocm.repo
-
-  yum update -y
-
-  yum install -y \
-                   rocm-dev \
-                   rocm-utils \
-                   rocm-libs \
-                   rccl \
-                   rocprofiler-dev \
-                   roctracer-dev \
-                   amd-smi-lib
-
-  # precompiled miopen kernels; search for all unversioned packages
-  # if search fails it will abort this script; use true to avoid case where search fails
-  MIOPENHIPGFX=$(yum -q search miopen-hip-gfx | grep miopen-hip-gfx | awk '{print $1}'| grep -F kdb. || true)
-  if [[ "x${MIOPENHIPGFX}" = x ]]; then
-    echo "miopen-hip-gfx package not available" && exit 1
-  else
-    yum install -y ${MIOPENHIPGFX}
-  fi
-
-  # ROCm 6.0 had a regression where journal_mode was enabled on the kdb files resulting in permission errors at runtime
-  for kdb in /opt/rocm/share/miopen/db/*.kdb
-  do
-      sqlite3 $kdb "PRAGMA journal_mode=off; PRAGMA VACUUM;"
-  done
-
-  # Cleanup
-  yum clean all
-  rm -rf /var/cache/yum
-  rm -rf /var/lib/yum/yumdb
-  rm -rf /var/lib/yum/history
-}
-
-# Install Python packages depending on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-  ubuntu)
-    install_ubuntu
-    ;;
-  centos)
-    install_centos
-    ;;
-  *)
-    echo "Unable to determine OS..."
-    exit 1
-    ;;
-esac

.ci/docker/common/install_rocm_magma.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# "install" hipMAGMA into /opt/rocm/magma by copying after build
-git clone https://bitbucket.org/icl/magma.git
-pushd magma
-
-# Version 2.7.2 + ROCm related updates
-git checkout a1625ff4d9bc362906bd01f805dbbe12612953f6
-
-cp make.inc-examples/make.inc.hip-gcc-mkl make.inc
-echo 'LIBDIR += -L$(MKLROOT)/lib' >> make.inc
-echo 'LIB += -Wl,--enable-new-dtags -Wl,--rpath,/opt/rocm/lib -Wl,--rpath,$(MKLROOT)/lib -Wl,--rpath,/opt/rocm/magma/lib' >> make.inc
-echo 'DEVCCFLAGS += --gpu-max-threads-per-block=256' >> make.inc
-export PATH="${PATH}:/opt/rocm/bin"
-if [[ -n "$PYTORCH_ROCM_ARCH" ]]; then
-  amdgpu_targets=`echo $PYTORCH_ROCM_ARCH | sed 's/;/ /g'`
-else
-  amdgpu_targets=`rocm_agent_enumerator | grep -v gfx000 | sort -u | xargs`
-fi
-for arch in $amdgpu_targets; do
-  echo "DEVCCFLAGS += --offload-arch=$arch" >> make.inc
-done
-# hipcc with openmp flag may cause isnan() on __device__ not to be found; depending on context, compiler may attempt to match with host definition
-sed -i 's/^FOPENMP/#FOPENMP/g' make.inc
-make -f make.gen.hipMAGMA -j $(nproc)
-LANG=C.UTF-8 make lib/libmagma.so -j $(nproc) MKLROOT=/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION
-make testing/testing_dgemm -j $(nproc) MKLROOT=/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION
-popd
-mv magma /opt/rocm

.ci/docker/common/install_triton.sh

@@ -1,72 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-
-get_conda_version() {
-  as_jenkins conda list -n py_$ANACONDA_PYTHON_VERSION | grep -w $* | head -n 1 | awk '{print $2}'
-}
-
-conda_reinstall() {
-  as_jenkins conda install -q -n py_$ANACONDA_PYTHON_VERSION -y --force-reinstall $*
-}
-
-if [ -n "${ROCM_VERSION}" ]; then
-  TRITON_REPO="https://github.com/openai/triton"
-  TRITON_TEXT_FILE="triton-rocm"
-elif [ -n "${XPU_VERSION}" ]; then
-  TRITON_REPO="https://github.com/intel/intel-xpu-backend-for-triton"
-  TRITON_TEXT_FILE="triton-xpu"
-else
-  TRITON_REPO="https://github.com/openai/triton"
-  TRITON_TEXT_FILE="triton"
-fi
-
-# The logic here is copied from .ci/pytorch/common_utils.sh
-TRITON_PINNED_COMMIT=$(get_pinned_commit ${TRITON_TEXT_FILE})
-
-if [ -n "${UBUNTU_VERSION}" ];then
-    apt update
-    apt-get install -y gpg-agent
-fi
-
-if [ -n "${CONDA_CMAKE}" ]; then
-  # Keep the current cmake and numpy version here, so we can reinstall them later
-  CMAKE_VERSION=$(get_conda_version cmake)
-  NUMPY_VERSION=$(get_conda_version numpy)
-fi
-
-if [ -z "${MAX_JOBS}" ]; then
-    export MAX_JOBS=$(nproc)
-fi
-
-if [ -n "${UBUNTU_VERSION}" ] && [ -n "${GCC_VERSION}" ] && [[ "${GCC_VERSION}" == "7" ]]; then
-  # Triton needs at least gcc-9 to build
-  apt-get install -y g++-9
-
-  CXX=g++-9 pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-elif [ -n "${UBUNTU_VERSION}" ] && [ -n "${CLANG_VERSION}" ]; then
-  # Triton needs <filesystem> which surprisingly is not available with clang-9 toolchain
-  add-apt-repository -y ppa:ubuntu-toolchain-r/test
-  apt-get install -y g++-9
-
-  CXX=g++-9 pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-else
-  pip_install "git+${TRITON_REPO}@${TRITON_PINNED_COMMIT}#subdirectory=python"
-fi
-
-if [ -n "${CONDA_CMAKE}" ]; then
-  # TODO: This is to make sure that the same cmake and numpy version from install conda
-  # script is used. Without this step, the newer cmake version (3.25.2) downloaded by
-  # triton build step via pip will fail to detect conda MKL. Once that issue is fixed,
-  # this can be removed.
-  #
-  # The correct numpy version also needs to be set here because conda claims that it
-  # causes inconsistent environment.  Without this, conda will attempt to install the
-  # latest numpy version, which fails ASAN tests with the following import error: Numba
-  # needs NumPy 1.20 or less.
-  conda_reinstall cmake="${CMAKE_VERSION}"
-  # Note that we install numpy with pip as conda might not have the version we want
-  pip_install --force-reinstall numpy=="${NUMPY_VERSION}"
-fi

.ci/docker/common/install_ucc.sh

@@ -1,53 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [[ -d "/usr/local/cuda/" ]];  then
-  with_cuda=/usr/local/cuda/
-else
-  with_cuda=no
-fi
-
-function install_ucx() {
-  set -ex
-  git clone --recursive https://github.com/openucx/ucx.git
-  pushd ucx
-  git checkout ${UCX_COMMIT}
-  git submodule update --init --recursive
-
-  ./autogen.sh
-  ./configure --prefix=$UCX_HOME      \
-      --enable-mt                     \
-      --with-cuda=$with_cuda          \
-      --enable-profiling              \
-      --enable-stats
-  time make -j
-  sudo make install
-
-  popd
-  rm -rf ucx
-}
-
-function install_ucc() {
-  set -ex
-  git clone --recursive https://github.com/openucx/ucc.git
-  pushd ucc
-  git checkout ${UCC_COMMIT}
-  git submodule update --init --recursive
-
-  ./autogen.sh
-  # We only run distributed tests on Tesla M60 and A10G
-  NVCC_GENCODE="-gencode=arch=compute_52,code=sm_52 -gencode=arch=compute_86,code=compute_86"
-  ./configure --prefix=$UCC_HOME          \
-    --with-ucx=$UCX_HOME                  \
-    --with-cuda=$with_cuda                \
-    --with-nvcc-gencode="${NVCC_GENCODE}"
-  time make -j
-  sudo make install
-
-  popd
-  rm -rf ucc
-}
-
-install_ucx
-install_ucc

.ci/docker/common/install_user.sh

@@ -1,33 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Mirror jenkins user in container
-# jenkins user as ec2-user should have the same user-id
-echo "jenkins:x:1000:1000::/var/lib/jenkins:" >> /etc/passwd
-echo "jenkins:x:1000:" >> /etc/group
-# Needed on focal or newer
-echo "jenkins:*:19110:0:99999:7:::" >>/etc/shadow
-
-# Create $HOME
-mkdir -p /var/lib/jenkins
-chown jenkins:jenkins /var/lib/jenkins
-mkdir -p /var/lib/jenkins/.ccache
-chown jenkins:jenkins /var/lib/jenkins/.ccache
-
-# Allow writing to /usr/local (for make install)
-chown jenkins:jenkins /usr/local
-
-# Allow sudo
-# TODO: Maybe we shouldn't
-echo 'jenkins ALL=(ALL) NOPASSWD:ALL' > /etc/sudoers.d/jenkins
-
-# Work around bug where devtoolset replaces sudo and breaks it.
-if [ -n "$DEVTOOLSET_VERSION" ]; then
-  SUDO=/bin/sudo
-else
-  SUDO=sudo
-fi
-
-# Test that sudo works
-$SUDO -u jenkins $SUDO -v

.ci/docker/common/install_xpu.sh

@@ -1,114 +0,0 @@
-#!/bin/bash
-set -xe
-
-
-# Intel® software for general purpose GPU capabilities.
-# Refer to https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html
-
-# Users should update to the latest version as it becomes available
-
-function install_ubuntu() {
-    apt-get update -y
-    apt-get install -y gpg-agent wget
-
-    # Set up the repository. To do this, download the key to the system keyring
-    wget -qO - https://repositories.intel.com/gpu/intel-graphics.key \
-        | gpg --dearmor --output /usr/share/keyrings/intel-graphics.gpg
-    wget -qO - https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB \
-        | gpg --dearmor --output /usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg
-
-    # Add the signed entry to APT sources and configure the APT client to use the Intel repository
-    echo "deb [arch=amd64 signed-by=/usr/share/keyrings/intel-graphics.gpg] \
-        https://repositories.intel.com/gpu/ubuntu jammy/lts/2350 unified" \
-        | tee /etc/apt/sources.list.d/intel-gpu-jammy.list
-    echo "deb [signed-by=/usr/share/keyrings/intel-for-pytorch-gpu-dev-keyring.gpg] \
-        https://apt.repos.intel.com/intel-for-pytorch-gpu-dev all main" \
-        | tee /etc/apt/sources.list.d/intel-for-pytorch-gpu-dev.list
-
-    # Update the packages list and repository index
-    apt-get update
-
-    # The xpu-smi packages
-    apt-get install -y flex bison xpu-smi
-    # Compute and Media Runtimes
-    apt-get install -y \
-        intel-opencl-icd intel-level-zero-gpu level-zero \
-        intel-media-va-driver-non-free libmfx1 libmfxgen1 libvpl2 \
-        libegl-mesa0 libegl1-mesa libegl1-mesa-dev libgbm1 libgl1-mesa-dev libgl1-mesa-dri \
-        libglapi-mesa libgles2-mesa-dev libglx-mesa0 libigdgmm12 libxatracker2 mesa-va-drivers \
-        mesa-vdpau-drivers mesa-vulkan-drivers va-driver-all vainfo hwinfo clinfo
-    # Development Packages
-    apt-get install -y libigc-dev intel-igc-cm libigdfcl-dev libigfxcmrt-dev level-zero-dev
-    # Install Intel Support Packages
-    if [ -n "$XPU_VERSION" ]; then
-        apt-get install -y intel-for-pytorch-gpu-dev-${XPU_VERSION}
-    else
-        apt-get install -y intel-for-pytorch-gpu-dev
-    fi
-
-    # Cleanup
-    apt-get autoclean && apt-get clean
-    rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-}
-
-function install_centos() {
-    dnf install -y 'dnf-command(config-manager)'
-    dnf config-manager --add-repo \
-        https://repositories.intel.com/gpu/rhel/8.6/production/2328/unified/intel-gpu-8.6.repo
-    # To add the EPEL repository needed for DKMS
-    dnf -y install https://dl.fedoraproject.org/pub/epel/epel-release-latest-8.noarch.rpm
-        # https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm
-
-    # Create the YUM repository file in the /temp directory as a normal user
-    tee > /tmp/oneAPI.repo << EOF
-[oneAPI]
-name=Intel® oneAPI repository
-baseurl=https://yum.repos.intel.com/oneapi
-enabled=1
-gpgcheck=1
-repo_gpgcheck=1
-gpgkey=https://yum.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
-EOF
-
-    # Move the newly created oneAPI.repo file to the YUM configuration directory /etc/yum.repos.d
-    mv /tmp/oneAPI.repo /etc/yum.repos.d
-
-    # The xpu-smi packages
-    dnf install -y flex bison xpu-smi
-    # Compute and Media Runtimes
-    dnf install -y \
-        intel-opencl intel-media intel-mediasdk libmfxgen1 libvpl2\
-        level-zero intel-level-zero-gpu mesa-dri-drivers mesa-vulkan-drivers \
-        mesa-vdpau-drivers libdrm mesa-libEGL mesa-libgbm mesa-libGL \
-        mesa-libxatracker libvpl-tools intel-metrics-discovery \
-        intel-metrics-library intel-igc-core intel-igc-cm \
-        libva libva-utils intel-gmmlib libmetee intel-gsc intel-ocloc hwinfo clinfo
-    # Development packages
-    dnf install -y --refresh \
-        intel-igc-opencl-devel level-zero-devel intel-gsc-devel libmetee-devel \
-        level-zero-devel
-    # Install Intel® oneAPI Base Toolkit
-    dnf install intel-basekit -y
-
-    # Cleanup
-    dnf clean all
-    rm -rf /var/cache/yum
-    rm -rf /var/lib/yum/yumdb
-    rm -rf /var/lib/yum/history
-}
-
-
-# The installation depends on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-    ubuntu)
-        install_ubuntu
-    ;;
-    centos)
-        install_centos
-    ;;
-    *)
-        echo "Unable to determine OS..."
-        exit 1
-    ;;
-esac

.ci/docker/linter-cuda/Dockerfile

@@ -1,44 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install missing libomp-dev
-RUN apt-get update && apt-get install -y --no-install-recommends libomp-dev && apt-get autoclean && apt-get clean && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Install cuda and cudnn
-ARG CUDA_VERSION
-RUN wget -q https://raw.githubusercontent.com/pytorch/builder/main/common/install_cuda.sh -O install_cuda.sh
-RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh
-ENV DESIRED_CUDA ${CUDA_VERSION}
-ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
-
-# Note that Docker build forbids copying file outside the build context
-COPY ./common/install_linter.sh install_linter.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_linter.sh
-RUN rm install_linter.sh common_utils.sh
-
-USER jenkins
-CMD ["bash"]

.ci/docker/linter/Dockerfile

@@ -1,34 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Note that Docker build forbids copying file outside the build context
-COPY ./common/install_linter.sh install_linter.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_linter.sh
-RUN rm install_linter.sh common_utils.sh
-
-USER jenkins
-CMD ["bash"]

.ci/docker/requirements-ci.txt

@@ -1,314 +0,0 @@
-# Python dependencies required for unit tests
-
-#awscli==1.6 #this breaks some platforms
-#Description: AWS command line interface
-#Pinned versions: 1.6
-#test that import:
-
-boto3==1.19.12
-#Description: AWS SDK for python
-#Pinned versions: 1.19.12, 1.16.34
-#test that import:
-
-click
-#Description: Command Line Interface Creation Kit
-#Pinned versions:
-#test that import:
-
-coremltools==5.0b5 ; python_version < "3.12"
-#Description: Apple framework for ML integration
-#Pinned versions: 5.0b5
-#test that import:
-
-#dataclasses #this breaks some platforms
-#Description: Provides decorators for auto adding special methods to user classes
-#Pinned versions:
-#test that import:
-
-dill==0.3.7
-#Description: dill extends pickle with serializing and de-serializing for most built-ins
-#Pinned versions: 0.3.7
-#test that import: dynamo/test_replay_record.py test_dataloader.py test_datapipe.py test_serialization.py
-
-expecttest==0.1.6
-#Description: method for writing tests where test framework auto populates
-# the expected output based on previous runs
-#Pinned versions: 0.1.6
-#test that import:
-
-flatbuffers==2.0
-#Description: cross platform serialization library
-#Pinned versions: 2.0
-#test that import:
-
-hypothesis==5.35.1
-# Pin hypothesis to avoid flakiness: https://github.com/pytorch/pytorch/issues/31136
-#Description: advanced library for generating parametrized tests
-#Pinned versions: 3.44.6, 4.53.2
-#test that import: test_xnnpack_integration.py, test_pruning_op.py, test_nn.py
-
-junitparser==2.1.1
-#Description: unitparser handles JUnit/xUnit Result XML files
-#Pinned versions: 2.1.1
-#test that import:
-
-lark==0.12.0
-#Description: parser
-#Pinned versions: 0.12.0
-#test that import:
-
-librosa>=0.6.2 ; python_version < "3.11"
-#Description: A python package for music and audio analysis
-#Pinned versions: >=0.6.2
-#test that import: test_spectral_ops.py
-
-#mkl #this breaks linux-bionic-rocm4.5-py3.7
-#Description: Intel oneAPI Math Kernel Library
-#Pinned versions:
-#test that import: test_profiler.py, test_public_bindings.py, test_testing.py,
-#test_nn.py, test_mkldnn.py, test_jit.py, test_fx_experimental.py,
-#test_autograd.py
-
-#mkl-devel
-# see mkl
-
-#mock
-#Description: A testing library that allows you to replace parts of your
-#system under test with mock objects
-#Pinned versions:
-#test that import: test_modules.py, test_nn.py,
-#test_testing.py
-
-#MonkeyType # breaks pytorch-xla-linux-bionic-py3.7-clang8
-#Description: collects runtime types of function arguments and return
-#values, and can automatically generate stub files
-#Pinned versions:
-#test that import:
-
-mypy==1.9.0
-# Pin MyPy version because new errors are likely to appear with each release
-#Description: linter
-#Pinned versions: 1.9.0
-#test that import: test_typing.py, test_type_hints.py
-
-networkx==2.8.8
-#Description: creation, manipulation, and study of
-#the structure, dynamics, and functions of complex networks
-#Pinned versions: 2.8.8
-#test that import: functorch
-
-#ninja
-#Description: build system.  Note that it install from
-#here breaks things so it is commented out
-#Pinned versions: 1.10.0.post1
-#test that import: run_test.py, test_cpp_extensions_aot.py,test_determination.py
-
-numba==0.49.0 ; python_version < "3.9"
-numba==0.54.1 ; python_version == "3.9"
-numba==0.55.2 ; python_version == "3.10"
-#Description: Just-In-Time Compiler for Numerical Functions
-#Pinned versions: 0.54.1, 0.49.0, <=0.49.1
-#test that import: test_numba_integration.py
-#For numba issue see https://github.com/pytorch/pytorch/issues/51511
-
-#numpy
-#Description: Provides N-dimensional arrays and linear algebra
-#Pinned versions: 1.20
-#test that import: test_view_ops.py, test_unary_ufuncs.py, test_type_promotion.py,
-#test_type_info.py, test_torch.py, test_tensorexpr_pybind.py, test_tensorexpr.py,
-#test_tensorboard.py, test_tensor_creation_ops.py, test_static_runtime.py,
-#test_spectral_ops.py, test_sort_and_select.py, test_shape_ops.py,
-#test_segment_reductions.py, test_reductions.py, test_pruning_op.py,
-#test_overrides.py, test_numpy_interop.py, test_numba_integration.py
-#test_nn.py, test_namedtensor.py, test_linalg.py, test_jit_cuda_fuser.py,
-#test_jit.py, test_indexing.py, test_datapipe.py, test_dataloader.py,
-#test_binary_ufuncs.py
-
-#onnxruntime
-#Description: scoring engine for Open Neural Network Exchange (ONNX) models
-#Pinned versions: 1.9.0
-#test that import:
-
-opt-einsum==3.3
-#Description: Python library to optimize tensor contraction order, used in einsum
-#Pinned versions: 3.3
-#test that import: test_linalg.py
-
-optree==0.11.0
-#Description: A library for tree manipulation
-#Pinned versions: 0.11.0
-#test that import: test_vmap.py, test_aotdispatch.py, test_dynamic_shapes.py,
-#test_pytree.py, test_ops.py, test_control_flow.py, test_modules.py,
-#common_utils.py, test_eager_transforms.py, test_python_dispatch.py,
-#test_expanded_weights.py, test_decomp.py, test_overrides.py, test_masked.py,
-#test_ops.py, test_prims.py, test_subclass.py, test_functionalization.py,
-#test_schema_check.py, test_profiler_tree.py, test_meta.py, test_torchxla_num_output.py,
-#test_utils.py, test_proxy_tensor.py, test_memory_profiler.py, test_view_ops.py,
-#test_pointwise_ops.py, test_dtensor_ops.py, test_torchinductor.py, test_fx.py,
-#test_fake_tensor.py, test_mps.py
-
-pillow==10.3.0
-#Description:  Python Imaging Library fork
-#Pinned versions: 10.3.0
-#test that import:
-
-protobuf==3.20.2
-#Description:  Google’s data interchange format
-#Pinned versions: 3.20.1
-#test that import: test_tensorboard.py
-
-psutil
-#Description: information on running processes and system utilization
-#Pinned versions:
-#test that import: test_profiler.py, test_openmp.py, test_dataloader.py
-
-pytest==7.3.2
-#Description: testing framework
-#Pinned versions:
-#test that import: test_typing.py, test_cpp_extensions_aot.py, run_test.py
-
-pytest-xdist==3.3.1
-#Description: plugin for running pytest in parallel
-#Pinned versions:
-#test that import:
-
-pytest-flakefinder==1.1.0
-#Description: plugin for rerunning tests a fixed number of times in pytest
-#Pinned versions: 1.1.0
-#test that import:
-
-pytest-rerunfailures>=10.3
-#Description: plugin for rerunning failure tests in pytest
-#Pinned versions:
-#test that import:
-
-#pytest-benchmark
-#Description: fixture for benchmarking code
-#Pinned versions: 3.2.3
-#test that import:
-
-#pytest-sugar
-#Description: shows failures and errors instantly
-#Pinned versions:
-#test that import:
-
-xdoctest==1.1.0
-#Description: runs doctests in pytest
-#Pinned versions: 1.1.0
-#test that import:
-
-pygments==2.15.0
-#Description: support doctest highlighting
-#Pinned versions: 2.12.0
-#test that import: the doctests
-
-#PyYAML
-#Description: data serialization format
-#Pinned versions:
-#test that import:
-
-#requests
-#Description: HTTP library
-#Pinned versions:
-#test that import: test_type_promotion.py
-
-#rich
-#Description: rich text and beautiful formatting in the terminal
-#Pinned versions: 10.9.0
-#test that import:
-
-scikit-image==0.19.3 ; python_version < "3.10"
-scikit-image==0.20.0 ; python_version >= "3.10"
-#Description: image processing routines
-#Pinned versions:
-#test that import: test_nn.py
-
-#scikit-learn
-#Description: machine learning package
-#Pinned versions: 0.20.3
-#test that import:
-
-scipy==1.10.1 ; python_version <= "3.11"
-scipy==1.12.0 ; python_version == "3.12"
-# Pin SciPy because of failing distribution tests (see #60347)
-#Description: scientific python
-#Pinned versions: 1.10.1
-#test that import: test_unary_ufuncs.py, test_torch.py,test_tensor_creation_ops.py
-#test_spectral_ops.py, test_sparse_csr.py, test_reductions.py,test_nn.py
-#test_linalg.py, test_binary_ufuncs.py
-
-#tabulate
-#Description: Pretty-print tabular data
-#Pinned versions:
-#test that import:
-
-tb-nightly==2.13.0a20230426
-#Description: TensorBoard
-#Pinned versions:
-#test that import:
-
-# needed by torchgen utils
-typing-extensions
-#Description: type hints for python
-#Pinned versions:
-#test that import:
-
-#virtualenv
-#Description: virtual environment for python
-#Pinned versions:
-#test that import:
-
-unittest-xml-reporting<=3.2.0,>=2.0.0
-#Description: saves unit test results to xml
-#Pinned versions:
-#test that import:
-
-#lintrunner is supported on aarch64-linux only from 0.12.4 version
-lintrunner==0.12.5
-#Description: all about linters!
-#Pinned versions: 0.12.5
-#test that import:
-
-rockset==1.0.3
-#Description: queries Rockset
-#Pinned versions: 1.0.3
-#test that import:
-
-ghstack==0.8.0
-#Description: ghstack tool
-#Pinned versions: 0.8.0
-#test that import:
-
-jinja2==3.1.4
-#Description: jinja2 template engine
-#Pinned versions: 3.1.4
-#test that import:
-
-pytest-cpp==2.3.0
-#Description: This is used by pytest to invoke C++ tests
-#Pinned versions: 2.3.0
-#test that import:
-
-z3-solver==4.12.2.0
-#Description: The Z3 Theorem Prover Project
-#Pinned versions:
-#test that import:
-
-tensorboard==2.13.0
-#Description: Also included in .ci/docker/requirements-docs.txt
-#Pinned versions:
-#test that import: test_tensorboard
-
-pywavelets==1.4.1 ; python_version < "3.12"
-pywavelets==1.5.0 ; python_version >= "3.12"
-#Description: This is a requirement of scikit-image, we need to pin
-# it here because 1.5.0 conflicts with numpy 1.21.2 used in CI
-#Pinned versions: 1.4.1
-#test that import:
-
-lxml==5.0.0.
-#Description: This is a requirement of unittest-xml-reporting
-
-# Python-3.9 binaries
-
-PyGithub==2.3.0

.ci/docker/requirements-docs.txt

@@ -1,49 +0,0 @@
-sphinx==5.3.0
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 5.3.0
--e git+https://github.com/pytorch/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
-
-# TODO: sphinxcontrib.katex 0.9.0 adds a local KaTeX server to speed up pre-rendering
-# but it doesn't seem to work and hangs around idly. The initial thought is probably
-# something related to Docker setup. We can investigate this later
-sphinxcontrib.katex==0.8.6
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 0.8.6
-
-matplotlib==3.5.3
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 3.5.3
-
-tensorboard==2.13.0
-#Description: This is used to generate PyTorch docs
-#Pinned versions: 2.13.0
-
-breathe==4.34.0
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 4.34.0
-
-exhale==0.2.3
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.2.3
-
-docutils==0.16
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.16
-
-bs4==0.0.1
-#Description: This is used to generate PyTorch C++ docs
-#Pinned versions: 0.0.1
-
-IPython==8.12.0
-#Description: This is used to generate PyTorch functorch docs
-#Pinned versions: 8.12.0
-
-myst-nb==0.17.2
-#Description: This is used to generate PyTorch functorch docs
-#Pinned versions: 0.13.2
-
-# The following are required to build torch.distributed.elastic.rendezvous.etcd* docs
-python-etcd==0.4.5
-sphinx-copybutton==0.5.0
-sphinx-panels==0.4.1
-myst-parser==0.18.1

.ci/docker/triton_version.txt

@@ -1 +0,0 @@
-3.0.0

.ci/docker/ubuntu-cuda/Dockerfile

@@ -1,158 +0,0 @@
-ARG UBUNTU_VERSION
-ARG CUDA_VERSION
-ARG IMAGE_NAME
-
-FROM ${IMAGE_NAME}
-
-ARG UBUNTU_VERSION
-ARG CUDA_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
-RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-ARG CONDA_CMAKE
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install clang
-ARG CLANG_VERSION
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-COPY ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# (optional) Install UCC
-ARG UCX_COMMIT
-ARG UCC_COMMIT
-ENV UCX_COMMIT $UCX_COMMIT
-ENV UCC_COMMIT $UCC_COMMIT
-ENV UCX_HOME /usr
-ENV UCC_HOME /usr
-ADD ./common/install_ucc.sh install_ucc.sh
-RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
-RUN rm install_ucc.sh
-
-COPY ./common/install_openssl.sh install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-RUN bash ./install_openssl.sh
-ENV OPENSSL_DIR /opt/openssl
-
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton.txt triton.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton.txt triton_version.txt
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-# See https://github.com/pytorch/pytorch/issues/82174
-# TODO(sdym@fb.com):
-# check if this is needed after full off Xenial migration
-ENV CARGO_NET_GIT_FETCH_WITH_CLI true
-RUN bash ./install_cache.sh && rm install_cache.sh
-ENV CMAKE_CUDA_COMPILER_LAUNCHER=/opt/cache/bin/sccache
-
-# Add jni.h for java host build
-COPY ./common/install_jni.sh install_jni.sh
-COPY ./java/jni.h jni.h
-RUN bash ./install_jni.sh && rm install_jni.sh
-
-# Install Open MPI for CUDA
-COPY ./common/install_openmpi.sh install_openmpi.sh
-RUN if [ -n "${CUDA_VERSION}" ]; then bash install_openmpi.sh; fi
-RUN rm install_openmpi.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-# AWS specific CUDA build guidance
-ENV TORCH_CUDA_ARCH_LIST Maxwell
-ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
-ENV CUDA_PATH /usr/local/cuda
-
-# Install LLVM dev version (Defined in the pytorch/builder github repository)
-COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
-
-# Install CUDNN
-ARG CUDNN_VERSION
-ARG CUDA_VERSION
-COPY ./common/install_cudnn.sh install_cudnn.sh
-RUN if [ -n "${CUDNN_VERSION}" ]; then bash install_cudnn.sh; fi
-RUN rm install_cudnn.sh
-
-# Install CUSPARSELT
-ARG CUDA_VERSION
-COPY ./common/install_cusparselt.sh install_cusparselt.sh
-RUN bash install_cusparselt.sh
-RUN rm install_cusparselt.sh
-
-# Delete /usr/local/cuda-11.X/cuda-11.X symlinks
-RUN if [ -h /usr/local/cuda-11.6/cuda-11.6 ]; then rm /usr/local/cuda-11.6/cuda-11.6; fi
-RUN if [ -h /usr/local/cuda-11.7/cuda-11.7 ]; then rm /usr/local/cuda-11.7/cuda-11.7; fi
-RUN if [ -h /usr/local/cuda-12.1/cuda-12.1 ]; then rm /usr/local/cuda-12.1/cuda-12.1; fi
-RUN if [ -h /usr/local/cuda-12.4/cuda-12.4 ]; then rm /usr/local/cuda-12.4/cuda-12.4; fi
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ubuntu-rocm/Dockerfile

@@ -1,125 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Set AMD gpu targets to build for
-ARG PYTORCH_ROCM_ARCH
-ENV PYTORCH_ROCM_ARCH ${PYTORCH_ROCM_ARCH}
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-ARG CLANG_VERSION
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-COPY requirements-ci.txt /opt/conda/requirements-ci.txt
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-COPY ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# Install rocm
-ARG ROCM_VERSION
-COPY ./common/install_rocm.sh install_rocm.sh
-RUN bash ./install_rocm.sh
-RUN rm install_rocm.sh
-COPY ./common/install_rocm_magma.sh install_rocm_magma.sh
-RUN bash ./install_rocm_magma.sh
-RUN rm install_rocm_magma.sh
-ENV ROCM_PATH /opt/rocm
-ENV PATH /opt/rocm/bin:$PATH
-ENV PATH /opt/rocm/hcc/bin:$PATH
-ENV PATH /opt/rocm/hip/bin:$PATH
-ENV PATH /opt/rocm/opencl/bin:$PATH
-ENV PATH /opt/rocm/llvm/bin:$PATH
-ENV MAGMA_HOME /opt/rocm/magma
-ENV LANG C.UTF-8
-ENV LC_ALL C.UTF-8
-
-# Install amdsmi
-COPY ./common/install_amdsmi.sh install_amdsmi.sh
-RUN bash ./install_amdsmi.sh
-RUN rm install_amdsmi.sh
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton-rocm.txt triton-rocm.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton-rocm.txt triton_version.txt
-
-# Install AOTriton
-COPY ./aotriton_version.txt aotriton_version.txt
-COPY ./common/common_utils.sh common_utils.sh
-COPY ./common/install_aotriton.sh install_aotriton.sh
-RUN ["/bin/bash", "-c", "./install_aotriton.sh /opt/rocm && rm -rf install_aotriton.sh aotriton_version.txt common_utils.sh"]
-ENV AOTRITON_INSTALLED_PREFIX /opt/rocm/aotriton
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ubuntu-xpu/Dockerfile

@@ -1,118 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-ARG CLANG_VERSION
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
-RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ARG DOCS
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-ENV DOCS=$DOCS
-COPY requirements-ci.txt requirements-docs.txt /opt/conda/
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt /opt/conda/requirements-docs.txt
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install lcov for C++ code coverage
-COPY ./common/install_lcov.sh install_lcov.sh
-RUN  bash ./install_lcov.sh && rm install_lcov.sh
-
-COPY ./common/install_openssl.sh install_openssl.sh
-RUN bash ./install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-ENV OPENSSL_DIR /opt/openssl
-RUN rm install_openssl.sh
-
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
-# Install XPU Dependencies
-ARG XPU_VERSION
-COPY ./common/install_xpu.sh install_xpu.sh
-RUN bash ./install_xpu.sh && rm install_xpu.sh
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton-xpu.txt triton-xpu.txt
-COPY triton_version.txt triton_version.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton-xpu.txt triton_version.txt
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-# Install LLVM dev version (Defined in the pytorch/builder github repository)
-COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
-
-USER jenkins
-CMD ["bash"]

.ci/docker/ubuntu/Dockerfile

@@ -1,203 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-ARG CLANG_VERSION
-
-# Install common dependencies (so that this step can be cached separately)
-COPY ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-COPY ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# Install user
-COPY ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-COPY ./common/install_docs_reqs.sh install_docs_reqs.sh
-RUN bash ./install_docs_reqs.sh && rm install_docs_reqs.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ARG ANACONDA_PYTHON_VERSION
-ARG CONDA_CMAKE
-ARG DOCS
-ENV ANACONDA_PYTHON_VERSION=$ANACONDA_PYTHON_VERSION
-ENV PATH /opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/bin:/opt/conda/bin:$PATH
-ENV DOCS=$DOCS
-COPY requirements-ci.txt requirements-docs.txt /opt/conda/
-COPY ./common/install_conda.sh install_conda.sh
-COPY ./common/common_utils.sh common_utils.sh
-RUN bash ./install_conda.sh && rm install_conda.sh common_utils.sh /opt/conda/requirements-ci.txt /opt/conda/requirements-docs.txt
-RUN if [ -n "${UNINSTALL_DILL}" ]; then pip uninstall -y dill; fi
-
-# Install gcc
-ARG GCC_VERSION
-COPY ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install lcov for C++ code coverage
-COPY ./common/install_lcov.sh install_lcov.sh
-RUN  bash ./install_lcov.sh && rm install_lcov.sh
-
-# Install cuda and cudnn
-ARG CUDA_VERSION
-RUN wget -q https://raw.githubusercontent.com/pytorch/builder/main/common/install_cuda.sh -O install_cuda.sh
-RUN bash ./install_cuda.sh ${CUDA_VERSION} && rm install_cuda.sh
-ENV DESIRED_CUDA ${CUDA_VERSION}
-ENV PATH /usr/local/nvidia/bin:/usr/local/cuda/bin:$PATH
-
-# (optional) Install UCC
-ARG UCX_COMMIT
-ARG UCC_COMMIT
-ENV UCX_COMMIT $UCX_COMMIT
-ENV UCC_COMMIT $UCC_COMMIT
-ENV UCX_HOME /usr
-ENV UCC_HOME /usr
-ADD ./common/install_ucc.sh install_ucc.sh
-RUN if [ -n "${UCX_COMMIT}" ] && [ -n "${UCC_COMMIT}" ]; then bash ./install_ucc.sh; fi
-RUN rm install_ucc.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-COPY ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-COPY ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV
-ARG VISION
-COPY ./common/install_vision.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh cache_vision_models.sh common_utils.sh
-ENV INSTALLED_VISION ${VISION}
-
-# (optional) Install Android NDK
-ARG ANDROID
-ARG ANDROID_NDK
-ARG GRADLE_VERSION
-COPY ./common/install_android.sh ./common/cache_vision_models.sh ./common/common_utils.sh ./
-COPY ./android/AndroidManifest.xml AndroidManifest.xml
-COPY ./android/build.gradle build.gradle
-RUN if [ -n "${ANDROID}" ]; then bash ./install_android.sh; fi
-RUN rm install_android.sh cache_vision_models.sh common_utils.sh
-RUN rm AndroidManifest.xml
-RUN rm build.gradle
-ENV INSTALLED_ANDROID ${ANDROID}
-
-# (optional) Install Vulkan SDK
-ARG VULKAN_SDK_VERSION
-COPY ./common/install_vulkan_sdk.sh install_vulkan_sdk.sh
-RUN if [ -n "${VULKAN_SDK_VERSION}" ]; then bash ./install_vulkan_sdk.sh; fi
-RUN rm install_vulkan_sdk.sh
-
-# (optional) Install swiftshader
-ARG SWIFTSHADER
-COPY ./common/install_swiftshader.sh install_swiftshader.sh
-RUN if [ -n "${SWIFTSHADER}" ]; then bash ./install_swiftshader.sh; fi
-RUN rm install_swiftshader.sh
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-COPY ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-COPY ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-COPY ./common/install_openssl.sh install_openssl.sh
-RUN bash ./install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-ENV OPENSSL_DIR /opt/openssl
-RUN rm install_openssl.sh
-
-ARG INDUCTOR_BENCHMARKS
-COPY ./common/install_inductor_benchmark_deps.sh install_inductor_benchmark_deps.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/huggingface.txt huggingface.txt
-COPY ci_commit_pins/timm.txt timm.txt
-RUN if [ -n "${INDUCTOR_BENCHMARKS}" ]; then bash ./install_inductor_benchmark_deps.sh; fi
-RUN rm install_inductor_benchmark_deps.sh common_utils.sh timm.txt huggingface.txt
-
-ARG TRITON
-# Install triton, this needs to be done before sccache because the latter will
-# try to reach out to S3, which docker build runners don't have access
-COPY ./common/install_triton.sh install_triton.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/triton.txt triton.txt
-RUN if [ -n "${TRITON}" ]; then bash ./install_triton.sh; fi
-RUN rm install_triton.sh common_utils.sh triton.txt
-
-ARG EXECUTORCH
-# Build and install executorch
-COPY ./common/install_executorch.sh install_executorch.sh
-COPY ./common/common_utils.sh common_utils.sh
-COPY ci_commit_pins/executorch.txt executorch.txt
-RUN if [ -n "${EXECUTORCH}" ]; then bash ./install_executorch.sh; fi
-RUN rm install_executorch.sh common_utils.sh executorch.txt
-
-ARG ONNX
-# Install ONNX dependencies
-COPY ./common/install_onnx.sh ./common/common_utils.sh ./
-RUN if [ -n "${ONNX}" ]; then bash ./install_onnx.sh; fi
-RUN rm install_onnx.sh common_utils.sh
-
-# (optional) Build ACL
-ARG ACL
-COPY ./common/install_acl.sh install_acl.sh
-RUN if [ -n "${ACL}" ]; then bash ./install_acl.sh; fi
-RUN rm install_acl.sh
-ENV INSTALLED_ACL ${ACL}
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-ARG SKIP_SCCACHE_INSTALL
-COPY ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN if [ -z "${SKIP_SCCACHE_INSTALL}" ]; then bash ./install_cache.sh; fi
-RUN rm install_cache.sh
-
-# Add jni.h for java host build
-COPY ./common/install_jni.sh install_jni.sh
-COPY ./java/jni.h jni.h
-RUN bash ./install_jni.sh && rm install_jni.sh
-
-# Install Open MPI for CUDA
-COPY ./common/install_openmpi.sh install_openmpi.sh
-RUN if [ -n "${CUDA_VERSION}" ]; then bash install_openmpi.sh; fi
-RUN rm install_openmpi.sh
-
-# Include BUILD_ENVIRONMENT environment variable in image
-ARG BUILD_ENVIRONMENT
-ENV BUILD_ENVIRONMENT ${BUILD_ENVIRONMENT}
-
-# Install LLVM dev version (Defined in the pytorch/builder github repository)
-ARG SKIP_LLVM_SRC_BUILD_INSTALL
-COPY --from=pytorch/llvm:9.0.1 /opt/llvm /opt/llvm
-RUN if [ -n "${SKIP_LLVM_SRC_BUILD_INSTALL}" ]; then set -eu; rm -rf /opt/llvm; fi
-
-# AWS specific CUDA build guidance
-ENV TORCH_CUDA_ARCH_LIST Maxwell
-ENV TORCH_NVCC_FLAGS "-Xfatbin -compress-all"
-ENV CUDA_PATH /usr/local/cuda
-
-USER jenkins
-CMD ["bash"]

.ci/onnx/README.md

@@ -1,14 +0,0 @@
-# Jenkins
-
-The scripts in this directory are the entrypoint for testing ONNX exporter.
-
-The environment variable `BUILD_ENVIRONMENT` is expected to be set to
-the build environment you intend to test. It is a hint for the build
-and test scripts to configure Caffe2 a certain way and include/exclude
-tests. Docker images, they equal the name of the image itself. For
-example: `py2-cuda9.0-cudnn7-ubuntu16.04`. The Docker images that are
-built on Jenkins and are used in triggered builds already have this
-environment variable set in their manifest. Also see
-`./docker/jenkins/*/Dockerfile` and search for `BUILD_ENVIRONMENT`.
-
-Our Jenkins installation is located at https://ci.pytorch.org/jenkins/.

.ci/onnx/common.sh

@@ -1,23 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-source "$(dirname "${BASH_SOURCE[0]}")/../pytorch/common_utils.sh"
-
-LOCAL_DIR=$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)
-ROOT_DIR=$(cd "$LOCAL_DIR"/../.. && pwd)
-TEST_DIR="$ROOT_DIR/test"
-pytest_reports_dir="${TEST_DIR}/test-reports/python"
-
-# Figure out which Python to use
-PYTHON="$(which python)"
-if [[ "${BUILD_ENVIRONMENT}" =~ py((2|3)\.?[0-9]?\.?[0-9]?) ]]; then
-  PYTHON=$(which "python${BASH_REMATCH[1]}")
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
-    # HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
-    unset HIP_PLATFORM
-fi
-
-mkdir -p "$pytest_reports_dir" || true

.ci/onnx/test.sh

@@ -1,29 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-# Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
-WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
-cleanup_workspace() {
-  echo "sudo may print the following warning message that can be ignored. The chown command will still run."
-  echo "    sudo: setrlimit(RLIMIT_STACK): Operation not permitted"
-  echo "For more details refer to https://github.com/sudo-project/sudo/issues/42"
-  sudo chown -R "$WORKSPACE_ORIGINAL_OWNER_ID" /var/lib/jenkins/workspace
-}
-# Disable shellcheck SC2064 as we want to parse the original owner immediately.
-# shellcheck disable=SC2064
-trap_add cleanup_workspace EXIT
-sudo chown -R jenkins /var/lib/jenkins/workspace
-git config --global --add safe.directory /var/lib/jenkins/workspace
-
-if [[ "$BUILD_ENVIRONMENT" == *onnx* ]]; then
-  # TODO: This can be removed later once vision is also part of the Docker image
-  pip install -q --user --no-use-pep517 "git+https://github.com/pytorch/vision.git@$(cat .github/ci_commit_pins/vision.txt)"
-  # JIT C++ extensions require ninja, so put it into PATH.
-  export PATH="/var/lib/jenkins/.local/bin:$PATH"
-  # NB: ONNX test is fast (~15m) so it's ok to retry it few more times to avoid any flaky issue, we
-  # need to bring this to the standard PyTorch run_test eventually. The issue will be tracked in
-  # https://github.com/pytorch/pytorch/issues/98626
-  "$ROOT_DIR/scripts/onnx/test.sh"
-fi

.ci/pytorch/README.md

@@ -1,42 +0,0 @@
-This directory contains scripts for our continuous integration.
-
-One important thing to keep in mind when reading the scripts here is
-that they are all based off of Docker images, which we build for each of
-the various system configurations we want to run on Jenkins.  This means
-it is very easy to run these tests yourself:
-
-1. Figure out what Docker image you want.  The general template for our
-   images look like:
-   ``registry.pytorch.org/pytorch/pytorch-$BUILD_ENVIRONMENT:$DOCKER_VERSION``,
-   where ``$BUILD_ENVIRONMENT`` is one of the build environments
-   enumerated in
-   [pytorch-dockerfiles](https://github.com/pytorch/pytorch/blob/master/.ci/docker/build.sh). The dockerfile used by jenkins can be found under the `.ci` [directory](https://github.com/pytorch/pytorch/blob/master/.ci/docker)
-
-2. Run ``docker run -it -u jenkins $DOCKER_IMAGE``, clone PyTorch and
-   run one of the scripts in this directory.
-
-The Docker images are designed so that any "reasonable" build commands
-will work; if you look in [build.sh](build.sh) you will see that it is a
-very simple script.  This is intentional.  Idiomatic build instructions
-should work inside all of our Docker images.  You can tweak the commands
-however you need (e.g., in case you want to rebuild with DEBUG, or rerun
-the build with higher verbosity, etc.).
-
-We have to do some work to make this so.  Here is a summary of the
-mechanisms we use:
-
-- We install binaries to directories like `/usr/local/bin` which
-  are automatically part of your PATH.
-
-- We add entries to the PATH using Docker ENV variables (so
-  they apply when you enter Docker) and `/etc/environment` (so they
-  continue to apply even if you sudo), instead of modifying
-  `PATH` in our build scripts.
-
-- We use `/etc/ld.so.conf.d` to register directories containing
-  shared libraries, instead of modifying `LD_LIBRARY_PATH` in our
-  build scripts.
-
-- We reroute well known paths like `/usr/bin/gcc` to alternate
-  implementations with `update-alternatives`, instead of setting
-  `CC` and `CXX` in our implementations.

.ci/pytorch/build.sh

@@ -1,393 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Required environment variable: $BUILD_ENVIRONMENT
-# (This is set by default in the Docker images we build, so you don't
-# need to set it yourself.
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-# shellcheck source=./common-build.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
-
-if [[ "$BUILD_ENVIRONMENT" == *-mobile-*build* ]]; then
-  exec "$(dirname "${BASH_SOURCE[0]}")/build-mobile.sh" "$@"
-fi
-
-echo "Python version:"
-python --version
-
-echo "GCC version:"
-gcc --version
-
-echo "CMake version:"
-cmake --version
-
-echo "Environment variables:"
-env
-
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
-  # Use jemalloc during compilation to mitigate https://github.com/pytorch/pytorch/issues/116289
-  export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libjemalloc.so.2
-  echo "NVCC version:"
-  nvcc --version
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *cuda11* ]]; then
-  if [[ "$BUILD_ENVIRONMENT" != *cuda11.3* && "$BUILD_ENVIRONMENT" != *clang* ]]; then
-    # TODO: there is a linking issue when building with UCC using clang,
-    # disable it for now and to be fix later.
-    # TODO: disable UCC temporarily to enable CUDA 12.1 in CI
-    export USE_UCC=1
-    export USE_SYSTEM_UCC=1
-  fi
-fi
-
-if [[ ${BUILD_ENVIRONMENT} == *"parallelnative"* ]]; then
-  export ATEN_THREADING=NATIVE
-fi
-
-# Enable LLVM dependency for TensorExpr testing
-if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-  export USE_LLVM=/opt/rocm/llvm
-  export LLVM_DIR=/opt/rocm/llvm/lib/cmake/llvm
-else
-  export USE_LLVM=/opt/llvm
-  export LLVM_DIR=/opt/llvm/lib/cmake/llvm
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *executorch* ]]; then
-  # To build test_edge_op_registration
-  export BUILD_EXECUTORCH=ON
-  export USE_CUDA=0
-fi
-
-if ! which conda; then
-  # In ROCm CIs, we are doing cross compilation on build machines with
-  # intel cpu and later run tests on machines with amd cpu.
-  # Also leave out two builds to make sure non-mkldnn builds still work.
-  if [[ "$BUILD_ENVIRONMENT" != *rocm* ]]; then
-    export USE_MKLDNN=1
-  else
-    export USE_MKLDNN=0
-  fi
-else
-  # CMAKE_PREFIX_PATH precedences
-  # 1. $CONDA_PREFIX, if defined. This follows the pytorch official build instructions.
-  # 2. /opt/conda/envs/py_${ANACONDA_PYTHON_VERSION}, if ANACONDA_PYTHON_VERSION defined.
-  #    This is for CI, which defines ANACONDA_PYTHON_VERSION but not CONDA_PREFIX.
-  # 3. $(conda info --base). The fallback value of pytorch official build
-  #    instructions actually refers to this.
-  #    Commonly this is /opt/conda/
-  if [[ -v CONDA_PREFIX ]]; then
-    export CMAKE_PREFIX_PATH=${CONDA_PREFIX}
-  elif [[ -v ANACONDA_PYTHON_VERSION ]]; then
-    export CMAKE_PREFIX_PATH="/opt/conda/envs/py_${ANACONDA_PYTHON_VERSION}"
-  else
-    # already checked by `! which conda`
-    CMAKE_PREFIX_PATH="$(conda info --base)"
-    export CMAKE_PREFIX_PATH
-  fi
-
-  # Workaround required for MKL library linkage
-  # https://github.com/pytorch/pytorch/issues/119557
-  if [ "$ANACONDA_PYTHON_VERSION" = "3.12" ]; then
-    export CMAKE_LIBRARY_PATH="/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/lib/"
-    export CMAKE_INCLUDE_PATH="/opt/conda/envs/py_$ANACONDA_PYTHON_VERSION/include/"
-  fi
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *aarch64* ]]; then
-  export USE_MKLDNN=1
-  export USE_MKLDNN_ACL=1
-  export ACL_ROOT_DIR=/ComputeLibrary
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *libtorch* ]]; then
-  POSSIBLE_JAVA_HOMES=()
-  POSSIBLE_JAVA_HOMES+=(/usr/local)
-  POSSIBLE_JAVA_HOMES+=(/usr/lib/jvm/java-8-openjdk-amd64)
-  POSSIBLE_JAVA_HOMES+=(/Library/Java/JavaVirtualMachines/*.jdk/Contents/Home)
-  # Add the Windows-specific JNI
-  POSSIBLE_JAVA_HOMES+=("$PWD/.circleci/windows-jni/")
-  for JH in "${POSSIBLE_JAVA_HOMES[@]}" ; do
-    if [[ -e "$JH/include/jni.h" ]] ; then
-      # Skip if we're not on Windows but haven't found a JAVA_HOME
-      if [[ "$JH" == "$PWD/.circleci/windows-jni/" && "$OSTYPE" != "msys" ]] ; then
-        break
-      fi
-      echo "Found jni.h under $JH"
-      export JAVA_HOME="$JH"
-      export BUILD_JNI=ON
-      break
-    fi
-  done
-  if [ -z "$JAVA_HOME" ]; then
-    echo "Did not find jni.h"
-  fi
-fi
-
-# Use special scripts for Android builds
-if [[ "${BUILD_ENVIRONMENT}" == *-android* ]]; then
-  export ANDROID_NDK=/opt/ndk
-  build_args=()
-  if [[ "${BUILD_ENVIRONMENT}" == *-arm-v7a* ]]; then
-    build_args+=("-DANDROID_ABI=armeabi-v7a")
-  elif [[ "${BUILD_ENVIRONMENT}" == *-arm-v8a* ]]; then
-    build_args+=("-DANDROID_ABI=arm64-v8a")
-  elif [[ "${BUILD_ENVIRONMENT}" == *-x86_32* ]]; then
-    build_args+=("-DANDROID_ABI=x86")
-  elif [[ "${BUILD_ENVIRONMENT}" == *-x86_64* ]]; then
-    build_args+=("-DANDROID_ABI=x86_64")
-  fi
-  if [[ "${BUILD_ENVIRONMENT}" == *vulkan* ]]; then
-    build_args+=("-DUSE_VULKAN=ON")
-  fi
-  build_args+=("-DUSE_LITE_INTERPRETER_PROFILER=OFF")
-  exec ./scripts/build_android.sh "${build_args[@]}" "$@"
-fi
-
-if [[ "$BUILD_ENVIRONMENT" != *android* && "$BUILD_ENVIRONMENT" == *vulkan* ]]; then
-  export USE_VULKAN=1
-  # shellcheck disable=SC1091
-  source /var/lib/jenkins/vulkansdk/setup-env.sh
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-  # hcc used to run out of memory, silently exiting without stopping
-  # the build process, leaving undefined symbols in the shared lib,
-  # causing undefined symbol errors when later running tests.
-  # We used to set MAX_JOBS to 4 to avoid, but this is no longer an issue.
-  if [ -z "$MAX_JOBS" ]; then
-    export MAX_JOBS=$(($(nproc) - 1))
-  fi
-
-  if [[ -n "$CI" && -z "$PYTORCH_ROCM_ARCH" ]]; then
-      # Set ROCM_ARCH to gfx906 for CI builds, if user doesn't override.
-      echo "Limiting PYTORCH_ROCM_ARCH to gfx906 for CI builds"
-      export PYTORCH_ROCM_ARCH="gfx906"
-  fi
-
-  # hipify sources
-  python tools/amd_build/build_amd.py
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *xpu* ]]; then
-  # shellcheck disable=SC1091
-  source /opt/intel/oneapi/compiler/latest/env/vars.sh
-  export USE_XPU=1
-fi
-
-# sccache will fail for CUDA builds if all cores are used for compiling
-# gcc 7 with sccache seems to have intermittent OOM issue if all cores are used
-if [ -z "$MAX_JOBS" ]; then
-  if { [[ "$BUILD_ENVIRONMENT" == *cuda* ]] || [[ "$BUILD_ENVIRONMENT" == *gcc7* ]]; } && which sccache > /dev/null; then
-    export MAX_JOBS=$(($(nproc) - 1))
-  fi
-fi
-
-# TORCH_CUDA_ARCH_LIST must be passed from an environment variable
-if [[ "$BUILD_ENVIRONMENT" == *cuda* && -z "$TORCH_CUDA_ARCH_LIST" ]]; then
-  echo "TORCH_CUDA_ARCH_LIST must be defined"
-  exit 1
-fi
-
-# We only build FlashAttention files for CUDA 8.0+, and they require large amounts of
-# memory to build and will OOM
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]] && [[ "$TORCH_CUDA_ARCH_LIST" == *"8.6"* || "$TORCH_CUDA_ARCH_LIST" == *"8.0"* ]]; then
-  echo "WARNING: FlashAttention files require large amounts of memory to build and will OOM"
-  echo "Setting MAX_JOBS=(nproc-2)/3 to reduce memory usage"
-  export MAX_JOBS="$(( $(nproc --ignore=2) / 3 ))"
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *clang* ]]; then
-  export CC=clang
-  export CXX=clang++
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *-clang*-asan* ]]; then
-  export LDSHARED="clang --shared"
-  export USE_CUDA=0
-  export USE_ASAN=1
-  export UBSAN_FLAGS="-fno-sanitize-recover=all;-fno-sanitize=float-divide-by-zero;-fno-sanitize=float-cast-overflow"
-  unset USE_LLVM
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *no-ops* ]]; then
-  export USE_PER_OPERATOR_HEADERS=0
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *-pch* ]]; then
-    export USE_PRECOMPILED_HEADERS=1
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" == *linux-focal-py3.7-gcc7-build*  ]]; then
-  export USE_GLOO_WITH_OPENSSL=ON
-fi
-
-if [[ "${BUILD_ENVIRONMENT}" != *android* && "${BUILD_ENVIRONMENT}" != *cuda* ]]; then
-  export BUILD_STATIC_RUNTIME_BENCHMARK=ON
-fi
-
-# Do not change workspace permissions for ROCm CI jobs
-# as it can leave workspace with bad permissions for cancelled jobs
-if [[ "$BUILD_ENVIRONMENT" != *rocm* ]]; then
-  # Workaround for dind-rootless userid mapping (https://github.com/pytorch/ci-infra/issues/96)
-  WORKSPACE_ORIGINAL_OWNER_ID=$(stat -c '%u' "/var/lib/jenkins/workspace")
-  cleanup_workspace() {
-    echo "sudo may print the following warning message that can be ignored. The chown command will still run."
-    echo "    sudo: setrlimit(RLIMIT_STACK): Operation not permitted"
-    echo "For more details refer to https://github.com/sudo-project/sudo/issues/42"
-    sudo chown -R "$WORKSPACE_ORIGINAL_OWNER_ID" /var/lib/jenkins/workspace
-  }
-  # Disable shellcheck SC2064 as we want to parse the original owner immediately.
-  # shellcheck disable=SC2064
-  trap_add cleanup_workspace EXIT
-  sudo chown -R jenkins /var/lib/jenkins/workspace
-  git config --global --add safe.directory /var/lib/jenkins/workspace
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *-bazel-* ]]; then
-  set -e
-
-  get_bazel
-  install_sccache_nvcc_for_bazel
-
-  # Leave 1 CPU free and use only up to 80% of memory to reduce the change of crashing
-  # the runner
-  BAZEL_MEM_LIMIT="--local_ram_resources=HOST_RAM*.8"
-  BAZEL_CPU_LIMIT="--local_cpu_resources=HOST_CPUS-1"
-
-  if [[ "$CUDA_VERSION" == "cpu" ]]; then
-    # Build torch, the Python module, and tests for CPU-only
-    tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" --config=cpu-only :torch :torch/_C.so :all_tests
-  else
-    tools/bazel build --config=no-tty "${BAZEL_MEM_LIMIT}" "${BAZEL_CPU_LIMIT}" //...
-  fi
-else
-  # check that setup.py would fail with bad arguments
-  echo "The next three invocations are expected to fail with invalid command error messages."
-  ( ! get_exit_code python setup.py bad_argument )
-  ( ! get_exit_code python setup.py clean] )
-  ( ! get_exit_code python setup.py clean bad_argument )
-
-  if [[ "$BUILD_ENVIRONMENT" != *libtorch* ]]; then
-    # rocm builds fail when WERROR=1
-    # XLA test build fails when WERROR=1
-    # set only when building other architectures
-    # or building non-XLA tests.
-    if [[ "$BUILD_ENVIRONMENT" != *rocm*  &&
-          "$BUILD_ENVIRONMENT" != *xla* ]]; then
-      if [[ "$BUILD_ENVIRONMENT" != *py3.8* ]]; then
-        # Install numpy-2.0 release candidate for builds
-        # Which should be backward compatible with Numpy-1.X
-        python -mpip install --pre numpy==2.0.0rc1
-      fi
-      WERROR=1 python setup.py bdist_wheel
-    else
-      if [[ "$BUILD_ENVIRONMENT" == *xla* ]]; then
-        source .ci/pytorch/install_cache_xla.sh
-      fi
-      python setup.py bdist_wheel
-    fi
-    pip_install_whl "$(echo dist/*.whl)"
-
-    # TODO: I'm not sure why, but somehow we lose verbose commands
-    set -x
-
-    assert_git_not_dirty
-    # Copy ninja build logs to dist folder
-    mkdir -p dist
-    if [ -f build/.ninja_log ]; then
-      cp build/.ninja_log dist
-    fi
-
-    if [[ "$BUILD_ENVIRONMENT" == *rocm* ]]; then
-      # remove sccache wrappers post-build; runtime compilation of MIOpen kernels does not yet fully support them
-      sudo rm -f /opt/cache/bin/cc
-      sudo rm -f /opt/cache/bin/c++
-      sudo rm -f /opt/cache/bin/gcc
-      sudo rm -f /opt/cache/bin/g++
-      pushd /opt/rocm/llvm/bin
-      if [[ -d original ]]; then
-        sudo mv original/clang .
-        sudo mv original/clang++ .
-      fi
-      sudo rm -rf original
-      popd
-    fi
-
-    CUSTOM_TEST_ARTIFACT_BUILD_DIR=${CUSTOM_TEST_ARTIFACT_BUILD_DIR:-"build/custom_test_artifacts"}
-    CUSTOM_TEST_USE_ROCM=$([[ "$BUILD_ENVIRONMENT" == *rocm* ]] && echo "ON" || echo "OFF")
-    CUSTOM_TEST_MODULE_PATH="${PWD}/cmake/public"
-    mkdir -pv "${CUSTOM_TEST_ARTIFACT_BUILD_DIR}"
-
-    # Build custom operator tests.
-    CUSTOM_OP_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/custom-op-build"
-    CUSTOM_OP_TEST="$PWD/test/custom_operator"
-    python --version
-    SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-    mkdir -p "$CUSTOM_OP_BUILD"
-    pushd "$CUSTOM_OP_BUILD"
-    cmake "$CUSTOM_OP_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
-          -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
-    make VERBOSE=1
-    popd
-    assert_git_not_dirty
-
-    # Build jit hook tests
-    JIT_HOOK_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/jit-hook-build"
-    JIT_HOOK_TEST="$PWD/test/jit_hooks"
-    python --version
-    SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-    mkdir -p "$JIT_HOOK_BUILD"
-    pushd "$JIT_HOOK_BUILD"
-    cmake "$JIT_HOOK_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
-          -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
-    make VERBOSE=1
-    popd
-    assert_git_not_dirty
-
-    # Build custom backend tests.
-    CUSTOM_BACKEND_BUILD="${CUSTOM_TEST_ARTIFACT_BUILD_DIR}/custom-backend-build"
-    CUSTOM_BACKEND_TEST="$PWD/test/custom_backend"
-    python --version
-    mkdir -p "$CUSTOM_BACKEND_BUILD"
-    pushd "$CUSTOM_BACKEND_BUILD"
-    cmake "$CUSTOM_BACKEND_TEST" -DCMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" -DPython_EXECUTABLE="$(which python)" \
-          -DCMAKE_MODULE_PATH="$CUSTOM_TEST_MODULE_PATH" -DUSE_ROCM="$CUSTOM_TEST_USE_ROCM"
-    make VERBOSE=1
-    popd
-    assert_git_not_dirty
-  else
-    # Test no-Python build
-    echo "Building libtorch"
-
-    # This is an attempt to mitigate flaky libtorch build OOM error. By default, the build parallelization
-    # is set to be the number of CPU minus 2. So, let's try a more conservative value here. A 4xlarge has
-    # 16 CPUs
-    MAX_JOBS=$(nproc --ignore=4)
-    export MAX_JOBS
-
-    # NB: Install outside of source directory (at the same level as the root
-    # pytorch folder) so that it doesn't get cleaned away prior to docker push.
-    BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
-    mkdir -p ../cpp-build/caffe2
-    pushd ../cpp-build/caffe2
-    WERROR=1 VERBOSE=1 DEBUG=1 python "$BUILD_LIBTORCH_PY"
-    popd
-  fi
-fi
-
-if [[ "$BUILD_ENVIRONMENT" != *libtorch* && "$BUILD_ENVIRONMENT" != *bazel* ]]; then
-  # export test times so that potential sharded tests that'll branch off this build will use consistent data
-  # don't do this for libtorch as libtorch is C++ only and thus won't have python tests run on its build
-  python tools/stats/export_test_times.py
-fi
-
-# snadampal: skipping it till sccache support added for aarch64
-# https://github.com/pytorch/pytorch/issues/121559
-if [[ "$BUILD_ENVIRONMENT" != *aarch64* ]]; then
-  print_sccache_stats
-fi

.ci/pytorch/common-build.sh

@@ -1,59 +0,0 @@
-#!/bin/bash
-# Required environment variables:
-#   $BUILD_ENVIRONMENT (should be set by your Docker image)
-
-if [[ "$BUILD_ENVIRONMENT" != *win-* ]]; then
-    # Save the absolute path in case later we chdir (as occurs in the gpu perf test)
-    script_dir="$( cd "$(dirname "${BASH_SOURCE[0]}")" || exit ; pwd -P )"
-
-    if which sccache > /dev/null; then
-        # Save sccache logs to file
-        sccache --stop-server > /dev/null  2>&1 || true
-        rm -f ~/sccache_error.log || true
-
-        function sccache_epilogue() {
-            echo "::group::Sccache Compilation Log"
-            echo '=================== sccache compilation log ==================='
-            python "$script_dir/print_sccache_log.py" ~/sccache_error.log 2>/dev/null || true
-            echo '=========== If your build fails, please take a look at the log above for possible reasons ==========='
-            sccache --show-stats
-            sccache --stop-server || true
-            echo "::endgroup::"
-        }
-
-        # Register the function here so that the error log can be printed even when
-        # sccache fails to start, i.e. timeout error
-        trap_add sccache_epilogue EXIT
-
-        if [[ -n "${SKIP_SCCACHE_INITIALIZATION:-}" ]]; then
-            # sccache --start-server seems to hang forever on self hosted runners for GHA
-            # so let's just go ahead and skip the --start-server altogether since it seems
-            # as though sccache still gets used even when the sscache server isn't started
-            # explicitly
-            echo "Skipping sccache server initialization, setting environment variables"
-            export SCCACHE_IDLE_TIMEOUT=0
-            export SCCACHE_ERROR_LOG=~/sccache_error.log
-            export RUST_LOG=sccache::server=error
-        elif [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
-            SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 sccache --start-server
-        else
-            # increasing SCCACHE_IDLE_TIMEOUT so that extension_backend_test.cpp can build after this PR:
-            # https://github.com/pytorch/pytorch/pull/16645
-            SCCACHE_ERROR_LOG=~/sccache_error.log SCCACHE_IDLE_TIMEOUT=0 RUST_LOG=sccache::server=error sccache --start-server
-        fi
-
-        # Report sccache stats for easier debugging. It's ok if this commands
-        # timeouts and fails on MacOS
-        sccache --zero-stats || true
-    fi
-
-    if which ccache > /dev/null; then
-        # Report ccache stats for easier debugging
-        ccache --zero-stats
-        ccache --show-stats
-        function ccache_epilogue() {
-            ccache --show-stats
-        }
-        trap_add ccache_epilogue EXIT
-    fi
-fi

.ci/pytorch/common.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-# Common setup for all Jenkins scripts
-# shellcheck source=./common_utils.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common_utils.sh"
-set -ex
-
-# Required environment variables:
-#   $BUILD_ENVIRONMENT (should be set by your Docker image)
-
-# Figure out which Python to use for ROCm
-if [[ "${BUILD_ENVIRONMENT}" == *rocm* ]]; then
-  # HIP_PLATFORM is auto-detected by hipcc; unset to avoid build errors
-  unset HIP_PLATFORM
-  export PYTORCH_TEST_WITH_ROCM=1
-  # temporary to locate some kernel issues on the CI nodes
-  export HSAKMT_DEBUG_LEVEL=4
-  # improve rccl performance for distributed tests
-  export HSA_FORCE_FINE_GRAIN_PCIE=1
-fi
-
-# TODO: Renable libtorch testing for MacOS, see https://github.com/pytorch/pytorch/issues/62598
-# shellcheck disable=SC2034
-BUILD_TEST_LIBTORCH=0

.ci/pytorch/common_utils.sh

@@ -1,240 +0,0 @@
-#!/bin/bash
-
-# Common util **functions** that can be sourced in other scripts.
-
-# note: printf is used instead of echo to avoid backslash
-# processing and to properly handle values that begin with a '-'.
-
-log() { printf '%s\n' "$*"; }
-error() { log "ERROR: $*" >&2; }
-fatal() { error "$@"; exit 1; }
-
-retry () {
-    "$@" || (sleep 10 && "$@") || (sleep 20 && "$@") || (sleep 40 && "$@")
-}
-
-# compositional trap taken from https://stackoverflow.com/a/7287873/23845
-# appends a command to a trap
-#
-# - 1st arg:  code to add
-# - remaining args:  names of traps to modify
-#
-trap_add() {
-    trap_add_cmd=$1; shift || fatal "${FUNCNAME[0]} usage error"
-    for trap_add_name in "$@"; do
-        trap -- "$(
-            # helper fn to get existing trap command from output
-            # of trap -p
-            extract_trap_cmd() { printf '%s\n' "$3"; }
-            # print existing trap command with newline
-            eval "extract_trap_cmd $(trap -p "${trap_add_name}")"
-            # print the new trap command
-            printf '%s\n' "${trap_add_cmd}"
-        )" "${trap_add_name}" \
-            || fatal "unable to add to trap ${trap_add_name}"
-    done
-}
-# set the trace attribute for the above function.  this is
-# required to modify DEBUG or RETURN traps because functions don't
-# inherit them unless the trace attribute is set
-declare -f -t trap_add
-
-function assert_git_not_dirty() {
-    # TODO: we should add an option to `build_amd.py` that reverts the repo to
-    #       an unmodified state.
-    if [[ "$BUILD_ENVIRONMENT" != *rocm* ]] && [[ "$BUILD_ENVIRONMENT" != *xla* ]] ; then
-        git_status=$(git status --porcelain | grep -v '?? third_party' || true)
-        if [[ $git_status ]]; then
-            echo "Build left local git repository checkout dirty"
-            echo "git status --porcelain:"
-            echo "${git_status}"
-            exit 1
-        fi
-    fi
-}
-
-function pip_install_whl() {
-  # This is used to install PyTorch and other build artifacts wheel locally
-  # without using any network connection
-  python3 -mpip install --no-index --no-deps "$@"
-}
-
-function pip_install() {
-  # retry 3 times
-  # old versions of pip don't have the "--progress-bar" flag
-  pip install --progress-bar off "$@" || pip install --progress-bar off "$@" || pip install --progress-bar off "$@" ||\
-  pip install "$@" || pip install "$@" || pip install "$@"
-}
-
-function pip_uninstall() {
-  # uninstall 2 times
-  pip uninstall -y "$@" || pip uninstall -y "$@"
-}
-
-function get_exit_code() {
-  set +e
-  "$@"
-  retcode=$?
-  set -e
-  return $retcode
-}
-
-function get_bazel() {
-  # Download and use the cross-platform, dependency-free Python
-  # version of Bazelisk to fetch the platform specific version of
-  # Bazel to use from .bazelversion.
-  retry curl --location --output tools/bazel \
-    https://raw.githubusercontent.com/bazelbuild/bazelisk/v1.16.0/bazelisk.py
-  shasum --algorithm=1 --check \
-    <(echo 'd4369c3d293814d3188019c9f7527a948972d9f8  tools/bazel')
-  chmod u+x tools/bazel
-}
-
-# This function is bazel specific because of the bug
-# in the bazel that requires some special paths massaging
-# as a workaround. See
-# https://github.com/bazelbuild/bazel/issues/10167
-function install_sccache_nvcc_for_bazel() {
-  sudo mv /usr/local/cuda/bin/nvcc /usr/local/cuda/bin/nvcc-real
-
-  # Write the `/usr/local/cuda/bin/nvcc`
-  cat << EOF | sudo tee /usr/local/cuda/bin/nvcc
-#!/bin/sh
-if [ \$(env -u LD_PRELOAD ps -p \$PPID -o comm=) != sccache ]; then
-  exec sccache /usr/local/cuda/bin/nvcc "\$@"
-else
-  exec external/local_cuda/cuda/bin/nvcc-real "\$@"
-fi
-EOF
-
-  sudo chmod +x /usr/local/cuda/bin/nvcc
-}
-
-function install_monkeytype {
-  # Install MonkeyType
-  pip_install MonkeyType
-}
-
-
-function get_pinned_commit() {
-  cat .github/ci_commit_pins/"${1}".txt
-}
-
-function install_torchaudio() {
-  local commit
-  commit=$(get_pinned_commit audio)
-  if [[ "$1" == "cuda" ]]; then
-    # TODO: This is better to be passed as a parameter from _linux-test workflow
-    # so that it can be consistent with what is set in build
-    TORCH_CUDA_ARCH_LIST="8.0;8.6" pip_install --no-use-pep517 --user "git+https://github.com/pytorch/audio.git@${commit}"
-  else
-    pip_install --no-use-pep517 --user "git+https://github.com/pytorch/audio.git@${commit}"
-  fi
-
-}
-
-function install_torchtext() {
-  local data_commit
-  local text_commit
-  data_commit=$(get_pinned_commit data)
-  text_commit=$(get_pinned_commit text)
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/data.git@${data_commit}"
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/text.git@${text_commit}"
-}
-
-function install_torchvision() {
-  local orig_preload
-  local commit
-  commit=$(get_pinned_commit vision)
-  orig_preload=${LD_PRELOAD}
-  if [ -n "${LD_PRELOAD}" ]; then
-    # Silence dlerror to work-around glibc ASAN bug, see https://sourceware.org/bugzilla/show_bug.cgi?id=27653#c9
-    echo 'char* dlerror(void) { return "";}'|gcc -fpic -shared -o "${HOME}/dlerror.so" -x c -
-    LD_PRELOAD=${orig_preload}:${HOME}/dlerror.so
-  fi
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/vision.git@${commit}"
-  if [ -n "${LD_PRELOAD}" ]; then
-    LD_PRELOAD=${orig_preload}
-  fi
-}
-
-function install_tlparse() {
-  pip_install --user "tlparse==0.3.7"
-  PATH="$(python -m site --user-base)/bin:$PATH"
-}
-
-function install_torchrec_and_fbgemm() {
-  local torchrec_commit
-  torchrec_commit=$(get_pinned_commit torchrec)
-  local fbgemm_commit
-  fbgemm_commit=$(get_pinned_commit fbgemm)
-  pip_uninstall torchrec-nightly
-  pip_uninstall fbgemm-gpu-nightly
-  pip_install setuptools-git-versioning scikit-build pyre-extensions
-  # See https://github.com/pytorch/pytorch/issues/106971
-  CUDA_PATH=/usr/local/cuda-12.1 pip_install --no-use-pep517 --user "git+https://github.com/pytorch/FBGEMM.git@${fbgemm_commit}#egg=fbgemm-gpu&subdirectory=fbgemm_gpu"
-  pip_install --no-use-pep517 --user "git+https://github.com/pytorch/torchrec.git@${torchrec_commit}"
-}
-
-function clone_pytorch_xla() {
-  if [[ ! -d ./xla ]]; then
-    git clone --recursive --quiet https://github.com/pytorch/xla.git
-    pushd xla
-    # pin the xla hash so that we don't get broken by changes to xla
-    git checkout "$(cat ../.github/ci_commit_pins/xla.txt)"
-    git submodule sync
-    git submodule update --init --recursive
-    popd
-  fi
-}
-
-function checkout_install_torchdeploy() {
-  local commit
-  commit=$(get_pinned_commit multipy)
-  pushd ..
-  git clone --recurse-submodules https://github.com/pytorch/multipy.git
-  pushd multipy
-  git checkout "${commit}"
-  python multipy/runtime/example/generate_examples.py
-  BUILD_CUDA_TESTS=1 pip install -e .
-  popd
-  popd
-}
-
-function test_torch_deploy(){
- pushd ..
- pushd multipy
- ./multipy/runtime/build/test_deploy
- ./multipy/runtime/build/test_deploy_gpu
- popd
- popd
-}
-
-function checkout_install_torchbench() {
-  local commit
-  commit=$(get_pinned_commit torchbench)
-  git clone https://github.com/pytorch/benchmark torchbench
-  pushd torchbench
-  git checkout "$commit"
-
-  if [ "$1" ]; then
-    python install.py --continue_on_fail models "$@"
-  else
-    # Occasionally the installation may fail on one model but it is ok to continue
-    # to install and test other models
-    python install.py --continue_on_fail
-  fi
-  popd
-}
-
-function print_sccache_stats() {
-  echo 'PyTorch Build Statistics'
-  sccache --show-stats
-
-  if [[ -n "${OUR_GITHUB_JOB_ID}" ]]; then
-    sccache --show-stats --stats-format json | jq .stats \
-      > "sccache-stats-${BUILD_ENVIRONMENT}-${OUR_GITHUB_JOB_ID}.json"
-  else
-    echo "env var OUR_GITHUB_JOB_ID not set, will not write sccache stats to json"
-  fi
-}

.ci/pytorch/create_test_cert.py

@@ -1,124 +0,0 @@
-from datetime import datetime, timedelta
-from tempfile import mkdtemp
-
-from cryptography import x509
-from cryptography.hazmat.primitives import hashes, serialization
-from cryptography.hazmat.primitives.asymmetric import rsa
-from cryptography.x509.oid import NameOID
-
-temp_dir = mkdtemp()
-print(temp_dir)
-
-
-def genrsa(path):
-    key = rsa.generate_private_key(
-        public_exponent=65537,
-        key_size=2048,
-    )
-    with open(path, "wb") as f:
-        f.write(
-            key.private_bytes(
-                encoding=serialization.Encoding.PEM,
-                format=serialization.PrivateFormat.TraditionalOpenSSL,
-                encryption_algorithm=serialization.NoEncryption(),
-            )
-        )
-    return key
-
-
-def create_cert(path, C, ST, L, O, key):
-    subject = issuer = x509.Name(
-        [
-            x509.NameAttribute(NameOID.COUNTRY_NAME, C),
-            x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, ST),
-            x509.NameAttribute(NameOID.LOCALITY_NAME, L),
-            x509.NameAttribute(NameOID.ORGANIZATION_NAME, O),
-        ]
-    )
-    cert = (
-        x509.CertificateBuilder()
-        .subject_name(subject)
-        .issuer_name(issuer)
-        .public_key(key.public_key())
-        .serial_number(x509.random_serial_number())
-        .not_valid_before(datetime.utcnow())
-        .not_valid_after(
-            # Our certificate will be valid for 10 days
-            datetime.utcnow()
-            + timedelta(days=10)
-        )
-        .add_extension(
-            x509.BasicConstraints(ca=True, path_length=None),
-            critical=True,
-        )
-        .sign(key, hashes.SHA256())
-    )
-    # Write our certificate out to disk.
-    with open(path, "wb") as f:
-        f.write(cert.public_bytes(serialization.Encoding.PEM))
-    return cert
-
-
-def create_req(path, C, ST, L, O, key):
-    csr = (
-        x509.CertificateSigningRequestBuilder()
-        .subject_name(
-            x509.Name(
-                [
-                    # Provide various details about who we are.
-                    x509.NameAttribute(NameOID.COUNTRY_NAME, C),
-                    x509.NameAttribute(NameOID.STATE_OR_PROVINCE_NAME, ST),
-                    x509.NameAttribute(NameOID.LOCALITY_NAME, L),
-                    x509.NameAttribute(NameOID.ORGANIZATION_NAME, O),
-                ]
-            )
-        )
-        .sign(key, hashes.SHA256())
-    )
-    with open(path, "wb") as f:
-        f.write(csr.public_bytes(serialization.Encoding.PEM))
-    return csr
-
-
-def sign_certificate_request(path, csr_cert, ca_cert, private_ca_key):
-    cert = (
-        x509.CertificateBuilder()
-        .subject_name(csr_cert.subject)
-        .issuer_name(ca_cert.subject)
-        .public_key(csr_cert.public_key())
-        .serial_number(x509.random_serial_number())
-        .not_valid_before(datetime.utcnow())
-        .not_valid_after(
-            # Our certificate will be valid for 10 days
-            datetime.utcnow()
-            + timedelta(days=10)
-            # Sign our certificate with our private key
-        )
-        .sign(private_ca_key, hashes.SHA256())
-    )
-    with open(path, "wb") as f:
-        f.write(cert.public_bytes(serialization.Encoding.PEM))
-    return cert
-
-
-ca_key = genrsa(temp_dir + "/ca.key")
-ca_cert = create_cert(
-    temp_dir + "/ca.pem",
-    "US",
-    "New York",
-    "New York",
-    "Gloo Certificate Authority",
-    ca_key,
-)
-
-pkey = genrsa(temp_dir + "/pkey.key")
-csr = create_req(
-    temp_dir + "/csr.csr",
-    "US",
-    "California",
-    "San Francisco",
-    "Gloo Testing Company",
-    pkey,
-)
-
-cert = sign_certificate_request(temp_dir + "/cert.pem", csr, ca_cert, ca_key)

.ci/pytorch/docker-build-test.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-docker build -t pytorch .

.ci/pytorch/docs-test.sh

@@ -1,9 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-echo "Testing pytorch docs"
-
-cd docs
-TERM=vt100 make doctest

.ci/pytorch/functorch_doc_push_script.sh

@@ -1,40 +0,0 @@
-#!/bin/bash
-
-# This is where the local pytorch install in the docker image is located
-pt_checkout="/var/lib/jenkins/workspace"
-source "$pt_checkout/.ci/pytorch/common_utils.sh"
-echo "functorch_doc_push_script.sh: Invoked with $*"
-
-set -ex
-
-version=${DOCS_VERSION:-nightly}
-echo "version: $version"
-
-# Build functorch docs
-pushd $pt_checkout/functorch/docs
-make html
-popd
-
-git clone https://github.com/pytorch/functorch -b gh-pages --depth 1 functorch_ghpages
-pushd functorch_ghpages
-
-if [ "$version" == "main" ]; then
-  version=nightly
-fi
-
-git rm -rf "$version" || true
-mv "$pt_checkout/functorch/docs/build/html" "$version"
-
-git add "$version" || true
-git status
-git config user.email "soumith+bot@pytorch.org"
-git config user.name "pytorchbot"
-# If there aren't changes, don't make a commit; push is no-op
-git commit -m "Generate Python docs from pytorch/pytorch@${GITHUB_SHA}" || true
-git status
-
-if [[ "${WITH_PUSH:-}" == true ]]; then
-  git push -u origin gh-pages
-fi
-
-popd

.ci/pytorch/install_cache_xla.sh

@@ -1,37 +0,0 @@
-#!/bin/bash
-
-# Script for installing sccache on the xla build job, which uses xla's docker
-# image and doesn't have sccache installed on it.  This is mostly copied from
-# .ci/docker/install_cache.sh.  Changes are: removing checks that will always
-# return the same thing, ex checks for for rocm, CUDA, and changing the path
-# where sccache is installed, and not changing /etc/environment.
-
-set -ex
-
-install_binary() {
-  echo "Downloading sccache binary from S3 repo"
-  curl --retry 3 https://s3.amazonaws.com/ossci-linux/sccache -o /tmp/cache/bin/sccache
-}
-
-mkdir -p /tmp/cache/bin
-mkdir -p /tmp/cache/lib
-export PATH="/tmp/cache/bin:$PATH"
-
-install_binary
-chmod a+x /tmp/cache/bin/sccache
-
-function write_sccache_stub() {
-  # Unset LD_PRELOAD for ps because of asan + ps issues
-  # https://gcc.gnu.org/bugzilla/show_bug.cgi?id=90589
-  # shellcheck disable=SC2086
-  # shellcheck disable=SC2059
-  printf "#!/bin/sh\nif [ \$(env -u LD_PRELOAD ps -p \$PPID -o comm=) != sccache ]; then\n  exec sccache $(which $1) \"\$@\"\nelse\n  exec $(which $1) \"\$@\"\nfi" > "/tmp/cache/bin/$1"
-  chmod a+x "/tmp/cache/bin/$1"
-}
-
-write_sccache_stub cc
-write_sccache_stub c++
-write_sccache_stub gcc
-write_sccache_stub g++
-write_sccache_stub clang
-write_sccache_stub clang++

.ci/pytorch/macos-build.sh

@@ -1,92 +0,0 @@
-#!/bin/bash
-
-# shellcheck disable=SC2034
-# shellcheck source=./macos-common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/macos-common.sh"
-# shellcheck source=./common-build.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
-
-# Build PyTorch
-if [ -z "${CI}" ]; then
-  export DEVELOPER_DIR=/Applications/Xcode9.app/Contents/Developer
-fi
-
-# This helper function wraps calls to binaries with sccache, but only if they're not already wrapped with sccache.
-# For example, `clang` will be `sccache clang`, but `sccache clang` will not become `sccache sccache clang`.
-# The way this is done is by detecting the command of the parent pid of the current process and checking whether
-# that is sccache, and wrapping sccache around the process if its parent were not already sccache.
-function write_sccache_stub() {
-  output=$1
-  binary=$(basename "${output}")
-
-  printf "#!/bin/sh\nif [ \$(ps auxc \$(ps auxc -o ppid \$\$ | grep \$\$ | rev | cut -d' ' -f1 | rev) | tr '\\\\n' ' ' | rev | cut -d' ' -f2 | rev) != sccache ]; then\n  exec sccache %s \"\$@\"\nelse\n  exec %s \"\$@\"\nfi" "$(which "${binary}")" "$(which "${binary}")" > "${output}"
-  chmod a+x "${output}"
-}
-
-if which sccache > /dev/null; then
-  # Create temp directory for sccache shims
-  tmp_dir=$(mktemp -d)
-  trap 'rm -rfv ${tmp_dir}' EXIT
-  write_sccache_stub "${tmp_dir}/clang++"
-  write_sccache_stub "${tmp_dir}/clang"
-
-  export PATH="${tmp_dir}:$PATH"
-fi
-
-cross_compile_arm64() {
-  # Cross compilation for arm64
-  # Explicitly set USE_DISTRIBUTED=0 to align with the default build config on mac. This also serves as the sole CI config that tests
-  # that building with USE_DISTRIBUTED=0 works at all. See https://github.com/pytorch/pytorch/issues/86448
-  USE_DISTRIBUTED=0 CMAKE_OSX_ARCHITECTURES=arm64 MACOSX_DEPLOYMENT_TARGET=11.0 USE_MKLDNN=OFF USE_QNNPACK=OFF WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
-}
-
-compile_arm64() {
-  # Compilation for arm64
-  # TODO: Compile with OpenMP support (but this causes CI regressions as cross-compilation were done with OpenMP disabled)
-  USE_DISTRIBUTED=0 USE_OPENMP=1 MACOSX_DEPLOYMENT_TARGET=11.0 WERROR=1 BUILD_TEST=OFF USE_PYTORCH_METAL=1 python setup.py bdist_wheel
-}
-
-compile_x86_64() {
-  USE_DISTRIBUTED=0 WERROR=1 python setup.py bdist_wheel --plat-name=macosx_10_9_x86_64
-}
-
-build_lite_interpreter() {
-    echo "Testing libtorch (lite interpreter)."
-
-    CPP_BUILD="$(pwd)/../cpp_build"
-    # Ensure the removal of the tmp directory
-    trap 'rm -rfv ${CPP_BUILD}' EXIT
-    rm -rf "${CPP_BUILD}"
-    mkdir -p "${CPP_BUILD}/caffe2"
-
-    # It looks libtorch need to be built in "${CPP_BUILD}/caffe2 folder.
-    BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
-    pushd "${CPP_BUILD}/caffe2" || exit
-    VERBOSE=1 DEBUG=1 python "${BUILD_LIBTORCH_PY}"
-    popd || exit
-
-    "${CPP_BUILD}/caffe2/build/bin/test_lite_interpreter_runtime"
-}
-
-print_cmake_info
-
-if [[ ${BUILD_ENVIRONMENT} = *arm64* ]]; then
-  if [[ $(uname -m) == "arm64" ]]; then
-    compile_arm64
-  else
-    cross_compile_arm64
-  fi
-elif [[ ${BUILD_ENVIRONMENT} = *lite-interpreter* ]]; then
-  export BUILD_LITE_INTERPRETER=1
-  build_lite_interpreter
-else
-  compile_x86_64
-fi
-
-if which sccache > /dev/null; then
-  print_sccache_stats
-fi
-
-python tools/stats/export_test_times.py
-
-assert_git_not_dirty

.ci/pytorch/macos-common.sh

@@ -1,33 +0,0 @@
-#!/bin/bash
-
-# Common prelude for macos-build.sh and macos-test.sh
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-sysctl -a | grep machdep.cpu
-
-# These are required for both the build job and the test job.
-# In the latter to test cpp extensions.
-export MACOSX_DEPLOYMENT_TARGET=11.1
-export CXX=clang++
-export CC=clang
-
-print_cmake_info() {
-  CMAKE_EXEC=$(which cmake)
-  echo "$CMAKE_EXEC"
-
-  CONDA_INSTALLATION_DIR=$(dirname "$CMAKE_EXEC")
-  # Print all libraries under cmake rpath for debugging
-  ls -la "$CONDA_INSTALLATION_DIR/../lib"
-
-  export CMAKE_EXEC
-  # Explicitly add conda env lib folder to cmake rpath to address the flaky issue
-  # where cmake dependencies couldn't be found. This seems to point to how conda
-  # links $CMAKE_EXEC to its package cache when cloning a new environment
-  install_name_tool -add_rpath @executable_path/../lib "${CMAKE_EXEC}" || true
-  # Adding the rpath will invalidate cmake signature, so signing it again here
-  # to trust the executable. EXC_BAD_ACCESS (SIGKILL (Code Signature Invalid))
-  # with an exit code 137 otherwise
-  codesign -f -s - "${CMAKE_EXEC}" || true
-}

.ci/pytorch/macos-test.sh

@@ -1,169 +0,0 @@
-#!/bin/bash
-
-# shellcheck disable=SC2034
-# shellcheck source=./macos-common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/macos-common.sh"
-
-if [[ -n "$CONDA_ENV" ]]; then
-  # Use binaries under conda environment
-  export PATH="$CONDA_ENV/bin":$PATH
-fi
-
-# Test that OpenMP is enabled for non-arm64 build
-if [[ ${BUILD_ENVIRONMENT} != *arm64* ]]; then
-  pushd test
-  if [[ ! $(python -c "import torch; print(int(torch.backends.openmp.is_available()))") == "1" ]]; then
-    echo "Build should have OpenMP enabled, but torch.backends.openmp.is_available() is False"
-    exit 1
-  fi
-  popd
-fi
-
-setup_test_python() {
-  # The CircleCI worker hostname doesn't resolve to an address.
-  # This environment variable makes ProcessGroupGloo default to
-  # using the address associated with the loopback interface.
-  export GLOO_SOCKET_IFNAME=lo0
-  echo "Ninja version: $(ninja --version)"
-  echo "Python version: $(which python) ($(python --version))"
-
-  # Increase default limit on open file handles from 256 to 1024
-  ulimit -n 1024
-}
-
-test_python_all() {
-  setup_test_python
-
-  time python test/run_test.py --verbose --exclude-jit-executor
-
-  assert_git_not_dirty
-}
-
-test_python_shard() {
-  if [[ -z "$NUM_TEST_SHARDS" ]]; then
-    echo "NUM_TEST_SHARDS must be defined to run a Python test shard"
-    exit 1
-  fi
-
-  setup_test_python
-
-  time python test/run_test.py --verbose --exclude-jit-executor --exclude-distributed-tests --shard "$1" "$NUM_TEST_SHARDS"
-
-  assert_git_not_dirty
-}
-
-test_libtorch() {
-  # C++ API
-
-  if [[ "$BUILD_TEST_LIBTORCH" == "1" ]]; then
-    # NB: Install outside of source directory (at the same level as the root
-    # pytorch folder) so that it doesn't get cleaned away prior to docker push.
-    # But still clean it before we perform our own build.
-
-    echo "Testing libtorch"
-
-    CPP_BUILD="$PWD/../cpp-build"
-    rm -rf "$CPP_BUILD"
-    mkdir -p "$CPP_BUILD"/caffe2
-
-    BUILD_LIBTORCH_PY=$PWD/tools/build_libtorch.py
-    pushd "$CPP_BUILD"/caffe2
-    VERBOSE=1 DEBUG=1 python "$BUILD_LIBTORCH_PY"
-    popd
-
-    MNIST_DIR="${PWD}/test/cpp/api/mnist"
-    python tools/download_mnist.py --quiet -d "${MNIST_DIR}"
-
-    # Unfortunately it seems like the test can't load from miniconda3
-    # without these paths being set
-    export DYLD_LIBRARY_PATH="$DYLD_LIBRARY_PATH:$PWD/miniconda3/lib"
-    export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:$PWD/miniconda3/lib"
-    TORCH_CPP_TEST_MNIST_PATH="${MNIST_DIR}" CPP_TESTS_DIR="${CPP_BUILD}/caffe2/bin" python test/run_test.py --cpp --verbose -i cpp/test_api
-
-    assert_git_not_dirty
-  fi
-}
-
-test_custom_backend() {
-  print_cmake_info
-
-  echo "Testing custom backends"
-  pushd test/custom_backend
-  rm -rf build && mkdir build
-  pushd build
-  SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
-  make VERBOSE=1
-  popd
-
-  # Run Python tests and export a lowered module.
-  python test_custom_backend.py -v
-  python backend.py --export-module-to=model.pt
-  # Run C++ tests using the exported module.
-  build/test_custom_backend ./model.pt
-  rm -f ./model.pt
-  popd
-  assert_git_not_dirty
-}
-
-test_custom_script_ops() {
-  print_cmake_info
-
-  echo "Testing custom script operators"
-  pushd test/custom_operator
-  # Build the custom operator library.
-  rm -rf build && mkdir build
-  pushd build
-  SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
-  make VERBOSE=1
-  popd
-
-  # Run tests Python-side and export a script module.
-  python test_custom_ops.py -v
-  python model.py --export-script-module=model.pt
-  # Run tests C++-side and load the exported script module.
-  build/test_custom_ops ./model.pt
-  popd
-  assert_git_not_dirty
-}
-
-test_jit_hooks() {
-  print_cmake_info
-
-  echo "Testing jit hooks in cpp"
-  pushd test/jit_hooks
-  # Build the custom operator library.
-  rm -rf build && mkdir build
-  pushd build
-  SITE_PACKAGES="$(python -c 'from distutils.sysconfig import get_python_lib; print(get_python_lib())')"
-  CMAKE_PREFIX_PATH="$SITE_PACKAGES/torch" "${CMAKE_EXEC}" ..
-  make VERBOSE=1
-  popd
-
-  # Run tests Python-side and export a script module.
-  python model.py --export-script-module=model
-  # Run tests C++-side and load the exported script module.
-  build/test_jit_hooks ./model
-  popd
-  assert_git_not_dirty
-}
-
-install_tlparse
-
-if [[ $NUM_TEST_SHARDS -gt 1 ]]; then
-  test_python_shard "${SHARD_NUMBER}"
-  if [[ "${SHARD_NUMBER}" == 1 ]]; then
-    test_libtorch
-    test_custom_script_ops
-  elif [[ "${SHARD_NUMBER}" == 2 ]]; then
-    test_jit_hooks
-    test_custom_backend
-  fi
-else
-  test_python_all
-  test_libtorch
-  test_custom_script_ops
-  test_jit_hooks
-  test_custom_backend
-fi

.ci/pytorch/multigpu-test.sh

@@ -1,62 +0,0 @@
-#!/bin/bash
-
-# Required environment variable: $BUILD_ENVIRONMENT
-# (This is set by default in the Docker images we build, so you don't
-# need to set it yourself.
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-echo "Testing pytorch"
-time python test/run_test.py --include test_cuda_multigpu test_cuda_primary_ctx --verbose
-
-# Disabling tests to see if they solve timeout issues; see https://github.com/pytorch/pytorch/issues/70015
-# python tools/download_mnist.py --quiet -d test/cpp/api/mnist
-# OMP_NUM_THREADS=2 TORCH_CPP_TEST_MNIST_PATH="test/cpp/api/mnist" build/bin/test_api
-time python test/run_test.py --verbose -i distributed/test_c10d_common
-time python test/run_test.py --verbose -i distributed/test_c10d_gloo
-time python test/run_test.py --verbose -i distributed/test_c10d_nccl
-time python test/run_test.py --verbose -i distributed/test_c10d_spawn_gloo
-time python test/run_test.py --verbose -i distributed/test_c10d_spawn_nccl
-time python test/run_test.py --verbose -i distributed/test_cuda_p2p
-time python test/run_test.py --verbose -i distributed/test_store
-time python test/run_test.py --verbose -i distributed/test_pg_wrapper
-time python test/run_test.py --verbose -i distributed/rpc/cuda/test_tensorpipe_agent
-# FSDP tests
-for f in test/distributed/fsdp/*.py ; do time python test/run_test.py --verbose -i "${f#*/}" ; done
-# ShardedTensor tests
-time python test/run_test.py --verbose -i distributed/checkpoint/test_checkpoint
-time python test/run_test.py --verbose -i distributed/checkpoint/test_file_system_checkpoint
-time python test/run_test.py --verbose -i distributed/_shard/sharding_spec/test_sharding_spec
-time python test/run_test.py --verbose -i distributed/_shard/sharding_plan/test_sharding_plan
-time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor
-time python test/run_test.py --verbose -i distributed/_shard/sharded_tensor/test_sharded_tensor_reshard
-
-# functional collective tests
-time python test/run_test.py --verbose -i distributed/test_functional_api
-
-# DTensor tests
-time python test/run_test.py --verbose -i distributed/_tensor/test_random_ops
-time python test/run_test.py --verbose -i distributed/_tensor/test_dtensor_compile
-
-# DeviceMesh test
-time python test/run_test.py --verbose -i distributed/test_device_mesh
-
-# DTensor/TP tests
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_ddp_2d_parallel
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_fsdp_2d_parallel
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_examples
-time python test/run_test.py --verbose -i distributed/tensor/parallel/test_tp_random_state
-
-# FSDP2 tests
-time python test/run_test.py --verbose -i distributed/_composable/fsdp/test_fully_shard_training -- -k test_2d_mlp_with_nd_mesh
-
-# Pipelining composability tests
-time python test/run_test.py --verbose -i distributed/pipelining/test_composability.py
-
-# Other tests
-time python test/run_test.py --verbose -i test_cuda_primary_ctx
-time python test/run_test.py --verbose -i test_optim -- -k test_forloop_goes_right_direction_multigpu
-time python test/run_test.py --verbose -i test_optim -- -k test_mixed_device_dtype
-time python test/run_test.py --verbose -i test_foreach -- -k test_tensors_grouping
-assert_git_not_dirty

.ci/pytorch/perf_test/compare_with_baseline.py

@@ -1,90 +0,0 @@
-import argparse
-import json
-import math
-import sys
-
-parser = argparse.ArgumentParser()
-parser.add_argument(
-    "--test-name", dest="test_name", action="store", required=True, help="test name"
-)
-parser.add_argument(
-    "--sample-stats",
-    dest="sample_stats",
-    action="store",
-    required=True,
-    help="stats from sample",
-)
-parser.add_argument(
-    "--update",
-    action="store_true",
-    help="whether to update baseline using stats from sample",
-)
-args = parser.parse_args()
-
-test_name = args.test_name
-
-if "cpu" in test_name:
-    backend = "cpu"
-elif "gpu" in test_name:
-    backend = "gpu"
-
-data_file_path = f"../{backend}_runtime.json"
-
-with open(data_file_path) as data_file:
-    data = json.load(data_file)
-
-if test_name in data:
-    mean = float(data[test_name]["mean"])
-    sigma = float(data[test_name]["sigma"])
-else:
-    # Let the test pass if baseline number doesn't exist
-    mean = sys.maxsize
-    sigma = 0.001
-
-print("population mean: ", mean)
-print("population sigma: ", sigma)
-
-# Let the test pass if baseline number is NaN (which happened in
-# the past when we didn't have logic for catching NaN numbers)
-if math.isnan(mean) or math.isnan(sigma):
-    mean = sys.maxsize
-    sigma = 0.001
-
-sample_stats_data = json.loads(args.sample_stats)
-
-sample_mean = float(sample_stats_data["mean"])
-sample_sigma = float(sample_stats_data["sigma"])
-
-print("sample mean: ", sample_mean)
-print("sample sigma: ", sample_sigma)
-
-if math.isnan(sample_mean):
-    raise Exception("""Error: sample mean is NaN""")  # noqa: TRY002
-elif math.isnan(sample_sigma):
-    raise Exception("""Error: sample sigma is NaN""")  # noqa: TRY002
-
-z_value = (sample_mean - mean) / sigma
-
-print("z-value: ", z_value)
-
-if z_value >= 3:
-    raise Exception(  # noqa: TRY002
-        f"""\n
-z-value >= 3, there is high chance of perf regression.\n
-To reproduce this regression, run
-`cd .ci/pytorch/perf_test/ && bash {test_name}.sh` on your local machine
-and compare the runtime before/after your code change.
-"""
-    )
-else:
-    print("z-value < 3, no perf regression detected.")
-    if args.update:
-        print("We will use these numbers as new baseline.")
-        new_data_file_path = f"../new_{backend}_runtime.json"
-        with open(new_data_file_path) as new_data_file:
-            new_data = json.load(new_data_file)
-        new_data[test_name] = {}
-        new_data[test_name]["mean"] = sample_mean
-        new_data[test_name]["sigma"] = max(sample_sigma, sample_mean * 0.1)
-        with open(new_data_file_path, "w") as new_data_file:
-            json.dump(new_data, new_data_file, indent=4)

.ci/pytorch/perf_test/update_commit_hash.py

@@ -1,13 +0,0 @@
-import json
-import sys
-
-data_file_path = sys.argv[1]
-commit_hash = sys.argv[2]
-
-with open(data_file_path) as data_file:
-    data = json.load(data_file)
-
-data["commit"] = commit_hash
-
-with open(data_file_path, "w") as data_file:
-    json.dump(data, data_file)

.ci/pytorch/print_sccache_log.py

@@ -1,17 +0,0 @@
-import sys
-
-log_file_path = sys.argv[1]
-
-with open(log_file_path) as f:
-    lines = f.readlines()
-
-for line in lines:
-    # Ignore errors from CPU instruction set, symbol existing testing,
-    # or compilation error formatting
-    ignored_keywords = [
-        "src.c",
-        "CheckSymbolExists.c",
-        "test_compilation_error_formatting",
-    ]
-    if all(keyword not in line for keyword in ignored_keywords):
-        print(line)

.ci/pytorch/short-perf-test-cpu.sh

@@ -1,71 +0,0 @@
-#!/bin/bash
-
-SCRIPT_PARENT_DIR=$(dirname "${BASH_SOURCE[0]}")
-
-# shellcheck source=.ci/pytorch/common.sh
-source "$SCRIPT_PARENT_DIR/common.sh"
-
-cd .ci/pytorch/perf_test
-
-echo "Running CPU perf test for PyTorch..."
-
-pip install -q awscli
-
-# Set multipart_threshold to be sufficiently high, so that `aws s3 cp` is not a multipart read
-# More info at https://github.com/aws/aws-cli/issues/2321
-aws configure set default.s3.multipart_threshold 5GB
-UPSTREAM_DEFAULT_BRANCH="$(git remote show https://github.com/pytorch/pytorch.git | awk '/HEAD branch/ {print $NF}')"
-
-if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
-    # Get current default branch commit hash
-    DEFAULT_BRANCH_COMMIT_ID=$(git log --format="%H" -n 1)
-    export DEFAULT_BRANCH_COMMIT_ID
-fi
-
-# Find the default branch commit to test against
-git remote add upstream https://github.com/pytorch/pytorch.git
-git fetch upstream
-IFS=$'\n'
-while IFS='' read -r commit_id; do
-    if aws s3 ls s3://ossci-perf-test/pytorch/cpu_runtime/"${commit_id}".json; then
-        LATEST_TESTED_COMMIT=${commit_id}
-        break
-    fi
-done < <(git rev-list upstream/"$UPSTREAM_DEFAULT_BRANCH")
-aws s3 cp s3://ossci-perf-test/pytorch/cpu_runtime/"${LATEST_TESTED_COMMIT}".json cpu_runtime.json
-
-if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
-    # Prepare new baseline file
-    cp cpu_runtime.json new_cpu_runtime.json
-    python update_commit_hash.py new_cpu_runtime.json "${DEFAULT_BRANCH_COMMIT_ID}"
-fi
-
-# Include tests
-# shellcheck source=./perf_test/test_cpu_speed_mini_sequence_labeler.sh
-. ./test_cpu_speed_mini_sequence_labeler.sh
-# shellcheck source=./perf_test/test_cpu_speed_mnist.sh
-. ./test_cpu_speed_mnist.sh
-# shellcheck source=./perf_test/test_cpu_speed_torch.sh
-. ./test_cpu_speed_torch.sh
-# shellcheck source=./perf_test/test_cpu_speed_torch_tensor.sh
-. ./test_cpu_speed_torch_tensor.sh
-
-# Run tests
-export TEST_MODE="compare_with_baseline"
-if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
-    export TEST_MODE="compare_and_update"
-fi
-
-# Operator tests
-run_test test_cpu_speed_torch ${TEST_MODE}
-run_test test_cpu_speed_torch_tensor ${TEST_MODE}
-
-# Sample model tests
-run_test test_cpu_speed_mini_sequence_labeler 20 ${TEST_MODE}
-run_test test_cpu_speed_mnist 20 ${TEST_MODE}
-
-if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
-    # This could cause race condition if we are testing the same default branch commit twice,
-    # but the chance of them executing this line at the same time is low.
-    aws s3 cp new_cpu_runtime.json s3://ossci-perf-test/pytorch/cpu_runtime/"${DEFAULT_BRANCH_COMMIT_ID}".json --acl public-read
-fi

.ci/pytorch/short-perf-test-gpu.sh

@@ -1,76 +0,0 @@
-#!/bin/bash
-
-# shellcheck source=./common.sh
-source "$(dirname "${BASH_SOURCE[0]}")/common.sh"
-
-pushd .ci/pytorch/perf_test
-
-echo "Running GPU perf test for PyTorch..."
-
-# Trying to uninstall PyYAML can cause problem. Workaround according to:
-# https://github.com/pypa/pip/issues/5247#issuecomment-415571153
-pip install -q awscli --ignore-installed PyYAML
-
-# Set multipart_threshold to be sufficiently high, so that `aws s3 cp` is not a multipart read
-# More info at https://github.com/aws/aws-cli/issues/2321
-aws configure set default.s3.multipart_threshold 5GB
-UPSTREAM_DEFAULT_BRANCH="$(git remote show https://github.com/pytorch/pytorch.git | awk '/HEAD branch/ {print $NF}')"
-
-if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
-    # Get current default branch commit hash
-    DEFAULT_BRANCH_COMMIT_ID=$(git log --format="%H" -n 1)
-    export DEFAULT_BRANCH_COMMIT_ID
-fi
-
-# Find the default branch commit to test against
-git remote add upstream https://github.com/pytorch/pytorch.git
-git fetch upstream
-IFS=$'\n'
-while IFS='' read -r commit_id; do
-    if aws s3 ls s3://ossci-perf-test/pytorch/gpu_runtime/"${commit_id}".json; then
-        LATEST_TESTED_COMMIT=${commit_id}
-        break
-    fi
-done < <(git rev-list upstream/"$UPSTREAM_DEFAULT_BRANCH")
-aws s3 cp s3://ossci-perf-test/pytorch/gpu_runtime/"${LATEST_TESTED_COMMIT}".json gpu_runtime.json
-
-if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
-    # Prepare new baseline file
-    cp gpu_runtime.json new_gpu_runtime.json
-    python update_commit_hash.py new_gpu_runtime.json "${DEFAULT_BRANCH_COMMIT_ID}"
-fi
-
-# Include tests
-# shellcheck source=./perf_test/test_gpu_speed_mnist.sh
-. ./test_gpu_speed_mnist.sh
-# shellcheck source=./perf_test/test_gpu_speed_word_language_model.sh
-. ./test_gpu_speed_word_language_model.sh
-# shellcheck source=./perf_test/test_gpu_speed_cudnn_lstm.sh
-. ./test_gpu_speed_cudnn_lstm.sh
-# shellcheck source=./perf_test/test_gpu_speed_lstm.sh
-. ./test_gpu_speed_lstm.sh
-# shellcheck source=./perf_test/test_gpu_speed_mlstm.sh
-. ./test_gpu_speed_mlstm.sh
-
-# Run tests
-if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
-    run_test test_gpu_speed_mnist 20 compare_and_update
-    run_test test_gpu_speed_word_language_model 20 compare_and_update
-    run_test test_gpu_speed_cudnn_lstm 20 compare_and_update
-    run_test test_gpu_speed_lstm 20 compare_and_update
-    run_test test_gpu_speed_mlstm 20 compare_and_update
-else
-    run_test test_gpu_speed_mnist 20 compare_with_baseline
-    run_test test_gpu_speed_word_language_model 20 compare_with_baseline
-    run_test test_gpu_speed_cudnn_lstm 20 compare_with_baseline
-    run_test test_gpu_speed_lstm 20 compare_with_baseline
-    run_test test_gpu_speed_mlstm 20 compare_with_baseline
-fi
-
-if [[ "$COMMIT_SOURCE" == "$UPSTREAM_DEFAULT_BRANCH" ]]; then
-    # This could cause race condition if we are testing the same default branch commit twice,
-    # but the chance of them executing this line at the same time is low.
-    aws s3 cp new_gpu_runtime.json s3://ossci-perf-test/pytorch/gpu_runtime/"${DEFAULT_BRANCH_COMMIT_ID}".json --acl public-read
-fi
-
-popd

.ci/pytorch/win-build.sh

@@ -1,47 +0,0 @@
-#!/bin/bash
-
-# If you want to rebuild, run this with REBUILD=1
-# If you want to build with CUDA, run this with USE_CUDA=1
-# If you want to build without CUDA, run this with USE_CUDA=0
-
-if [ ! -f setup.py ]; then
-  echo "ERROR: Please run this build script from PyTorch root directory."
-  exit 1
-fi
-
-SCRIPT_PARENT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
-# shellcheck source=./common.sh
-source "$SCRIPT_PARENT_DIR/common.sh"
-# shellcheck source=./common-build.sh
-source "$SCRIPT_PARENT_DIR/common-build.sh"
-
-export TMP_DIR="${PWD}/build/win_tmp"
-TMP_DIR_WIN=$(cygpath -w "${TMP_DIR}")
-export TMP_DIR_WIN
-export PYTORCH_FINAL_PACKAGE_DIR=${PYTORCH_FINAL_PACKAGE_DIR:-/c/w/build-results}
-if [[ -n "$PYTORCH_FINAL_PACKAGE_DIR" ]]; then
-    mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR" || true
-fi
-
-export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
-
-set +ex
-grep -E -R 'PyLong_(From|As)(Unsigned|)Long\(' --exclude=python_numbers.h --exclude=eval_frame.c torch/
-PYLONG_API_CHECK=$?
-if [[ $PYLONG_API_CHECK == 0 ]]; then
-  echo "Usage of PyLong_{From,As}{Unsigned}Long API may lead to overflow errors on Windows"
-  echo "because \`sizeof(long) == 4\` and \`sizeof(unsigned long) == 4\`."
-  echo "Please include \"torch/csrc/utils/python_numbers.h\" and use the correspoding APIs instead."
-  echo "PyLong_FromLong -> THPUtils_packInt32 / THPUtils_packInt64"
-  echo "PyLong_AsLong -> THPUtils_unpackInt (32-bit) / THPUtils_unpackLong (64-bit)"
-  echo "PyLong_FromUnsignedLong -> THPUtils_packUInt32 / THPUtils_packUInt64"
-  echo "PyLong_AsUnsignedLong -> THPUtils_unpackUInt32 / THPUtils_unpackUInt64"
-  exit 1
-fi
-set -ex
-
-"$SCRIPT_HELPERS_DIR"/build_pytorch.bat
-
-assert_git_not_dirty
-
-echo "BUILD PASSED"

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

@@ -1,142 +0,0 @@
-if "%DEBUG%" == "1" (
-  set BUILD_TYPE=debug
-) ELSE (
-  set BUILD_TYPE=release
-)
-
-set PATH=C:\Program Files\CMake\bin;C:\Program Files\7-Zip;C:\ProgramData\chocolatey\bin;C:\Program Files\Git\cmd;C:\Program Files\Amazon\AWSCLI;C:\Program Files\Amazon\AWSCLI\bin;%PATH%
-
-:: This inflates our log size slightly, but it is REALLY useful to be
-:: able to see what our cl.exe commands are (since you can actually
-:: just copy-paste them into a local Windows setup to just rebuild a
-:: single file.)
-:: log sizes are too long, but leaving this here incase someone wants to use it locally
-:: set CMAKE_VERBOSE_MAKEFILE=1
-
-
-set INSTALLER_DIR=%SCRIPT_HELPERS_DIR%\installation-helpers
-
-call %INSTALLER_DIR%\install_magma.bat
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
-
-call %INSTALLER_DIR%\install_sccache.bat
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
-
-:: Miniconda has been installed as part of the Windows AMI with all the dependencies.
-:: We just need to activate it here
-call %INSTALLER_DIR%\activate_miniconda3.bat
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
-
-call pip install mkl-include==2021.4.0 mkl-devel==2021.4.0
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
-
-:: Override VS env here
-pushd .
-if "%VC_VERSION%" == "" (
-    call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64
-) else (
-    call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%VC_VERSION%
-)
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
-@echo on
-popd
-
-if not "%USE_CUDA%"=="1" goto cuda_build_end
-
-set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v%CUDA_VERSION%
-
-if x%CUDA_VERSION:.=%==x%CUDA_VERSION% (
-    echo CUDA version %CUDA_VERSION% format isn't correct, which doesn't contain '.'
-    goto fail
-)
-rem version transformer, for example 10.1 to 10_1.
-if x%CUDA_VERSION:.=%==x%CUDA_VERSION% (
-    echo CUDA version %CUDA_VERSION% format isn't correct, which doesn't contain '.'
-    goto fail
-)
-set VERSION_SUFFIX=%CUDA_VERSION:.=_%
-set CUDA_PATH_V%VERSION_SUFFIX%=%CUDA_PATH%
-
-set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
-set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
-set CUDNN_ROOT_DIR=%CUDA_PATH%
-set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
-
-set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
-set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
-set CUDNN_ROOT_DIR=%CUDA_PATH%
-set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
-
-:cuda_build_end
-
-set DISTUTILS_USE_SDK=1
-set PATH=%TMP_DIR_WIN%\bin;%PATH%
-
-:: The latest Windows CUDA test is running on AWS G5 runner with A10G GPU
-if "%TORCH_CUDA_ARCH_LIST%" == "" set TORCH_CUDA_ARCH_LIST=8.6
-
-:: The default sccache idle timeout is 600, which is too short and leads to intermittent build errors.
-set SCCACHE_IDLE_TIMEOUT=0
-set SCCACHE_IGNORE_SERVER_IO_ERROR=1
-sccache --stop-server
-sccache --start-server
-sccache --zero-stats
-set CMAKE_C_COMPILER_LAUNCHER=sccache
-set CMAKE_CXX_COMPILER_LAUNCHER=sccache
-
-set CMAKE_GENERATOR=Ninja
-
-if "%USE_CUDA%"=="1" (
-  :: randomtemp is used to resolve the intermittent build error related to CUDA.
-  :: code: https://github.com/peterjc123/randomtemp-rust
-  :: issue: https://github.com/pytorch/pytorch/issues/25393
-  ::
-  :: CMake requires a single command as CUDA_NVCC_EXECUTABLE, so we push the wrappers
-  :: randomtemp.exe and sccache.exe into a batch file which CMake invokes.
-  curl -kL https://github.com/peterjc123/randomtemp-rust/releases/download/v0.4/randomtemp.exe --output %TMP_DIR_WIN%\bin\randomtemp.exe
-  if errorlevel 1 goto fail
-  if not errorlevel 0 goto fail
-  echo @"%TMP_DIR_WIN%\bin\randomtemp.exe" "%TMP_DIR_WIN%\bin\sccache.exe" "%CUDA_PATH%\bin\nvcc.exe" %%* > "%TMP_DIR%/bin/nvcc.bat"
-  cat %TMP_DIR%/bin/nvcc.bat
-  set CUDA_NVCC_EXECUTABLE=%TMP_DIR%/bin/nvcc.bat
-  for /F "usebackq delims=" %%n in (`cygpath -m "%CUDA_PATH%\bin\nvcc.exe"`) do set CMAKE_CUDA_COMPILER=%%n
-  set CMAKE_CUDA_COMPILER_LAUNCHER=%TMP_DIR%/bin/randomtemp.exe;%TMP_DIR%\bin\sccache.exe
-)
-
-:: Print all existing environment variable for debugging
-set
-
-python setup.py bdist_wheel
-if errorlevel 1 goto fail
-if not errorlevel 0 goto fail
-sccache --show-stats
-python -c "import os, glob; os.system('python -mpip install --no-index --no-deps ' + glob.glob('dist/*.whl')[0])"
-(
-  if "%BUILD_ENVIRONMENT%"=="" (
-    echo NOTE: To run `import torch`, please make sure to activate the conda environment by running `call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3` in Command Prompt before running Git Bash.
-  ) else (
-    copy /Y "dist\*.whl" "%PYTORCH_FINAL_PACKAGE_DIR%"
-
-    :: export test times so that potential sharded tests that'll branch off this build will use consistent data
-    python tools/stats/export_test_times.py
-    robocopy /E ".additional_ci_files" "%PYTORCH_FINAL_PACKAGE_DIR%\.additional_ci_files"
-
-    :: Also save build/.ninja_log as an artifact
-    copy /Y "build\.ninja_log" "%PYTORCH_FINAL_PACKAGE_DIR%\"
-  )
-)
-
-sccache --show-stats --stats-format json | jq .stats > sccache-stats-%BUILD_ENVIRONMENT%-%OUR_GITHUB_JOB_ID%.json
-sccache --stop-server
-
-exit /b 0
-
-:fail
-exit /b 1

.ci/pytorch/win-test-helpers/installation-helpers/activate_miniconda3.bat

@@ -1,26 +0,0 @@
-if "%BUILD_ENVIRONMENT%"=="" (
-  set CONDA_PARENT_DIR=%CD%
-) else (
-  set CONDA_PARENT_DIR=C:\Jenkins
-)
-
-
-:: Be conservative here when rolling out the new AMI with conda. This will try
-:: to install conda as before if it couldn't find the conda installation. This
-:: can be removed eventually after we gain enough confidence in the AMI
-if not exist %CONDA_PARENT_DIR%\Miniconda3 (
-  set INSTALL_FRESH_CONDA=1
-)
-
-if "%INSTALL_FRESH_CONDA%"=="1" (
-  curl --retry 3 --retry-all-errors -k https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe --output %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe
-  if errorlevel 1 exit /b
-  if not errorlevel 0 exit /b
-
-  %TMP_DIR_WIN%\Miniconda3-latest-Windows-x86_64.exe /InstallationType=JustMe /RegisterPython=0 /S /AddToPath=0 /D=%CONDA_PARENT_DIR%\Miniconda3
-  if errorlevel 1 exit /b
-  if not errorlevel 0 exit /b
-)
-
-:: Activate conda so that we can use its commands, i.e. conda, python, pip
-call %CONDA_PARENT_DIR%\Miniconda3\Scripts\activate.bat %CONDA_PARENT_DIR%\Miniconda3

.ci/pytorch/win-test-helpers/installation-helpers/install_sccache.bat

@@ -1,13 +0,0 @@
-mkdir %TMP_DIR_WIN%\bin
-
-if "%REBUILD%"=="" (
-  IF EXIST %TMP_DIR_WIN%\bin\sccache.exe (
-    taskkill /im sccache.exe /f /t || ver > nul
-    del %TMP_DIR_WIN%\bin\sccache.exe || ver > nul
-  )
-  if "%BUILD_ENVIRONMENT%"=="" (
-    curl --retry 3 --retry-all-errors -k https://s3.amazonaws.com/ossci-windows/sccache-v0.7.4.exe --output %TMP_DIR_WIN%\bin\sccache.exe
-  ) else (
-    aws s3 cp s3://ossci-windows/sccache-v0.7.4.exe %TMP_DIR_WIN%\bin\sccache.exe
-  )
-)

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

@@ -1,54 +0,0 @@
-set PATH=C:\Program Files\CMake\bin;C:\Program Files\7-Zip;C:\ProgramData\chocolatey\bin;C:\Program Files\Git\cmd;C:\Program Files\Amazon\AWSCLI;C:\Program Files\Amazon\AWSCLI\bin;%PATH%
-
-:: Install Miniconda3
-set INSTALLER_DIR=%SCRIPT_HELPERS_DIR%\installation-helpers
-
-:: Miniconda has been installed as part of the Windows AMI with all the dependencies.
-:: We just need to activate it here
-call %INSTALLER_DIR%\activate_miniconda3.bat
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-
-:: PyTorch is now installed using the standard wheel on Windows into the conda environment.
-:: However, the test scripts are still frequently referring to the workspace temp directory
-:: build\torch. Rather than changing all these references, making a copy of torch folder
-:: from conda to the current workspace is easier. The workspace will be cleaned up after
-:: the job anyway
-xcopy /s %CONDA_PARENT_DIR%\Miniconda3\Lib\site-packages\torch %TMP_DIR_WIN%\build\torch\
-
-pushd .
-if "%VC_VERSION%" == "" (
-    call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64
-) else (
-    call "C:\Program Files (x86)\Microsoft Visual Studio\%VC_YEAR%\%VC_PRODUCT%\VC\Auxiliary\Build\vcvarsall.bat" x64 -vcvars_ver=%VC_VERSION%
-)
-if errorlevel 1 exit /b
-if not errorlevel 0 exit /b
-@echo on
-popd
-
-set DISTUTILS_USE_SDK=1
-
-if not "%USE_CUDA%"=="1" goto cuda_build_end
-
-set CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v%CUDA_VERSION%
-
-rem version transformer, for example 10.1 to 10_1.
-set VERSION_SUFFIX=%CUDA_VERSION:.=_%
-set CUDA_PATH_V%VERSION_SUFFIX%=%CUDA_PATH%
-
-set CUDNN_LIB_DIR=%CUDA_PATH%\lib\x64
-set CUDA_TOOLKIT_ROOT_DIR=%CUDA_PATH%
-set CUDNN_ROOT_DIR=%CUDA_PATH%
-set NVTOOLSEXT_PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt
-set PATH=%CUDA_PATH%\bin;%CUDA_PATH%\libnvvp;%PATH%
-set NUMBAPRO_CUDALIB=%CUDA_PATH%\bin
-set NUMBAPRO_LIBDEVICE=%CUDA_PATH%\nvvm\libdevice
-set NUMBAPRO_NVVM=%CUDA_PATH%\nvvm\bin\nvvm64_32_0.dll
-
-:cuda_build_end
-
-set PYTHONPATH=%TMP_DIR_WIN%\build;%PYTHONPATH%
-
-:: Print all existing environment variable for debugging
-set

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

@@ -1,54 +0,0 @@
-call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
-if errorlevel 1 exit /b 1
-
-:: Save the current working directory so that we can go back there
-set CWD=%cd%
-
-set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\bin
-set PATH=C:\Program Files\NVIDIA Corporation\NvToolsExt\bin\x64;%TMP_DIR_WIN%\build\torch\lib;%PATH%
-
-set TORCH_CPP_TEST_MNIST_PATH=%CWD%\test\cpp\api\mnist
-python tools\download_mnist.py --quiet -d %TORCH_CPP_TEST_MNIST_PATH%
-
-python test\run_test.py --cpp --verbose -i cpp/test_api
-if errorlevel 1 exit /b 1
-if not errorlevel 0 exit /b 1
-
-cd %TMP_DIR_WIN%\build\torch\test
-for /r "." %%a in (*.exe) do (
-    call :libtorch_check "%%~na" "%%~fa"
-    if errorlevel 1 goto fail
-)
-
-goto :eof
-
-:libtorch_check
-
-cd %CWD%
-set CPP_TESTS_DIR=%TMP_DIR_WIN%\build\torch\test
-
-:: Skip verify_api_visibility as it a compile level test
-if "%~1" == "verify_api_visibility" goto :eof
-
-echo Running "%~2"
-if "%~1" == "c10_intrusive_ptr_benchmark" (
-  :: NB: This is not a gtest executable file, thus couldn't be handled by pytest-cpp
-  call "%~2"
-  goto :eof
-)
-
-python test\run_test.py --cpp --verbose -i "cpp/%~1"
-if errorlevel 1 (
-  echo %1 failed with exit code %errorlevel%
-  goto fail
-)
-if not errorlevel 0 (
-  echo %1 failed with exit code %errorlevel%
-  goto fail
-)
-
-:eof
-exit /b 0
-
-:fail
-exit /b 1

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

@@ -1,12 +0,0 @@
-call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
-
-echo Copying over test times file
-robocopy /E "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.additional_ci_files" "%PROJECT_DIR_WIN%\.additional_ci_files"
-
-pushd test
-
-echo Run jit_profiling tests
-python run_test.py --include test_jit_legacy test_jit_fuser_legacy --verbose
-if ERRORLEVEL 1 exit /b 1
-
-popd

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

@@ -1,37 +0,0 @@
-call %SCRIPT_HELPERS_DIR%\setup_pytorch_env.bat
-:: exit the batch once there's an error
-if not errorlevel 0 (
-  echo "setup pytorch env failed"
-  echo %errorlevel%
-  exit /b
-)
-
-pushd test
-
-set GFLAGS_EXE="C:\Program Files (x86)\Windows Kits\10\Debuggers\x64\gflags.exe"
-if "%SHARD_NUMBER%" == "1" (
-  if exist %GFLAGS_EXE% (
-    echo Some smoke tests
-    %GFLAGS_EXE% /i python.exe +sls
-    python %SCRIPT_HELPERS_DIR%\run_python_nn_smoketests.py
-    if ERRORLEVEL 1 goto fail
-
-    %GFLAGS_EXE% /i python.exe -sls
-    if ERRORLEVEL 1 goto fail
-  )
-)
-
-echo Copying over test times file
-robocopy /E "%PYTORCH_FINAL_PACKAGE_DIR_WIN%\.additional_ci_files" "%PROJECT_DIR_WIN%\.additional_ci_files"
-
-echo Run nn tests
-python run_test.py --exclude-jit-executor --exclude-distributed-tests --shard "%SHARD_NUMBER%" "%NUM_TEST_SHARDS%" --verbose
-if ERRORLEVEL 1 goto fail
-
-popd
-
-:eof
-exit /b 0
-
-:fail
-exit /b 1

.ci/pytorch/win-test.sh

@@ -1,73 +0,0 @@
-#!/bin/bash
-set -ex
-
-SCRIPT_PARENT_DIR=$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )
-# shellcheck source=./common.sh
-source "$SCRIPT_PARENT_DIR/common.sh"
-
-export TMP_DIR="${PWD}/build/win_tmp"
-TMP_DIR_WIN=$(cygpath -w "${TMP_DIR}")
-export TMP_DIR_WIN
-export PROJECT_DIR="${PWD}"
-PROJECT_DIR_WIN=$(cygpath -w "${PROJECT_DIR}")
-export PROJECT_DIR_WIN
-export TEST_DIR="${PWD}/test"
-TEST_DIR_WIN=$(cygpath -w "${TEST_DIR}")
-export TEST_DIR_WIN
-export PYTORCH_FINAL_PACKAGE_DIR="${PYTORCH_FINAL_PACKAGE_DIR:-/c/w/build-results}"
-PYTORCH_FINAL_PACKAGE_DIR_WIN=$(cygpath -w "${PYTORCH_FINAL_PACKAGE_DIR}")
-export PYTORCH_FINAL_PACKAGE_DIR_WIN
-
-mkdir -p "$TMP_DIR"/build/torch
-
-export SCRIPT_HELPERS_DIR=$SCRIPT_PARENT_DIR/win-test-helpers
-
-if [[ "$TEST_CONFIG" = "force_on_cpu" ]]; then
-  # run the full test suite for force_on_cpu test
-  export USE_CUDA=0
-fi
-
-if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
-  # Used so that only cuda/rocm specific versions of tests are generated
-  # mainly used so that we're not spending extra cycles testing cpu
-  # devices on expensive gpu machines
-  export PYTORCH_TESTING_DEVICE_ONLY_FOR="cuda"
-fi
-
-# TODO: Move both of them to Windows AMI
-python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==2.13.0
-
-# Install Z3 optional dependency for Windows builds.
-python -m pip install z3-solver==4.12.2.0
-
-run_tests() {
-    # Run nvidia-smi if available
-    for path in '/c/Program Files/NVIDIA Corporation/NVSMI/nvidia-smi.exe' /c/Windows/System32/nvidia-smi.exe; do
-        if [[ -x "$path" ]]; then
-            "$path" || echo "true";
-            break
-        fi
-    done
-
-    if [[ $NUM_TEST_SHARDS -eq 1 ]]; then
-        "$SCRIPT_HELPERS_DIR"/test_python_shard.bat
-        "$SCRIPT_HELPERS_DIR"/test_custom_script_ops.bat
-        "$SCRIPT_HELPERS_DIR"/test_custom_backend.bat
-        "$SCRIPT_HELPERS_DIR"/test_libtorch.bat
-    else
-        "$SCRIPT_HELPERS_DIR"/test_python_shard.bat
-        if [[ "${SHARD_NUMBER}" == 1 && $NUM_TEST_SHARDS -gt 1 ]]; then
-            "$SCRIPT_HELPERS_DIR"/test_libtorch.bat
-            if [[ "${USE_CUDA}" == "1" ]]; then
-              "$SCRIPT_HELPERS_DIR"/test_python_jit_legacy.bat
-            fi
-        elif [[ "${SHARD_NUMBER}" == 2 && $NUM_TEST_SHARDS -gt 1 ]]; then
-            "$SCRIPT_HELPERS_DIR"/test_custom_backend.bat
-            "$SCRIPT_HELPERS_DIR"/test_custom_script_ops.bat
-        fi
-    fi
-}
-
-run_tests
-assert_git_not_dirty
-echo "TEST PASSED"

.circleci/README.md

@@ -1,4 +1,498 @@
-Warning
-=======
+Structure of CI
+===============
 
-PyTorch migration from CircleCI to github actions has been completed. All continuous integration & deployment workflows are defined in  `.github/workflows` folder
+setup job:
+1. Does a git checkout
+2. Persists CircleCI scripts (everything in `.circleci`) into a workspace.  Why?
+   We don't always do a Git checkout on all subjobs, but we usually
+   still want to be able to call scripts one way or another in a subjob.
+   Persisting files this way lets us have access to them without doing a
+   checkout.  This workspace is conventionally mounted on `~/workspace`
+   (this is distinguished from `~/project`, which is the conventional
+   working directory that CircleCI will default to starting your jobs
+   in.)
+3. Write out the commit message to `.circleci/COMMIT_MSG`.  This is so
+   we can determine in subjobs if we should actually run the jobs or
+   not, even if there isn't a Git checkout.
+
+
+
+
+CircleCI configuration generator
+================================
+
+One may no longer make changes to the `.circleci/config.yml` file directly.
+Instead, one must edit these Python scripts or files in the `verbatim-sources/` directory.
+
+
+Usage
+----------
+
+1. Make changes to these scripts.
+2. Run the `regenerate.sh` script in this directory and commit the script changes and the resulting change to `config.yml`.
+
+You'll see a build failure on GitHub if the scripts don't agree with the checked-in version.
+
+
+Motivation
+----------
+
+These scripts establish a single, authoritative source of documentation for the CircleCI configuration matrix.
+The documentation, in the form of diagrams, is automatically generated and cannot drift out of sync with the YAML content.
+
+Furthermore, consistency is enforced within the YAML config itself, by using a single source of data to generate
+multiple parts of the file.
+
+* Facilitates one-off culling/enabling of CI configs for testing PRs on special targets
+
+Also see https://github.com/pytorch/pytorch/issues/17038
+
+
+Future direction
+----------------
+
+### Declaring sparse config subsets
+See comment [here](https://github.com/pytorch/pytorch/pull/17323#pullrequestreview-206945747):
+
+In contrast with a full recursive tree traversal of configuration dimensions,
+> in the future I think we actually want to decrease our matrix somewhat and have only a few mostly-orthogonal builds that taste as many different features as possible on PRs, plus a more complete suite on every PR and maybe an almost full suite nightly/weekly (we don't have this yet). Specifying PR jobs in the future might be easier to read with an explicit list when we come to this.
+
+----------------
+----------------
+
+# How do the binaries / nightlies / releases work?
+
+### What is a binary?
+
+A binary or package (used interchangeably) is a pre-built collection of c++ libraries, header files, python bits, and other files. We build these and distribute them so that users do not need to install from source.
+
+A **binary configuration** is a collection of
+
+* release or nightly
+    * releases are stable, nightlies are beta and built every night
+* python version
+    * linux: 3.5m, 3.6m 3.7m (mu is wide unicode or something like that. It usually doesn't matter but you should know that it exists)
+    * macos: 3.6, 3.7, 3.8
+    * windows: 3.6, 3.7, 3.8
+* cpu version
+    * cpu, cuda 9.0, cuda 10.0
+    * The supported cuda versions occasionally change
+* operating system
+    * Linux - these are all built on CentOS. There haven't been any problems in the past building on CentOS and using on Ubuntu
+    * MacOS
+    * Windows - these are built on Azure pipelines
+* devtoolset version (gcc compiler version)
+    * This only matters on Linux cause only Linux uses gcc. tldr is gcc made a backwards incompatible change from gcc 4.8 to gcc 5, because it had to change how it implemented std::vector and std::string
+
+### Where are the binaries?
+
+The binaries are built in CircleCI. There are nightly binaries built every night at 9pm PST (midnight EST) and release binaries corresponding to Pytorch releases, usually every few months.
+
+We have 3 types of binary packages
+
+* pip packages - nightlies are stored on s3 (pip install -f \<a s3 url\>). releases are stored in a pip repo (pip install torch) (ask Soumith about this)
+* conda packages - nightlies and releases are both stored in a conda repo. Nighty packages have a '_nightly' suffix
+* libtorch packages - these are zips of all the c++ libraries, header files, and sometimes dependencies. These are c++ only
+    * shared with dependencies (the only supported option for Windows)
+    * static with dependencies
+    * shared without dependencies
+    * static without dependencies
+
+All binaries are built in CircleCI workflows except Windows. There are checked-in workflows (committed into the .circleci/config.yml) to build the nightlies every night. Releases are built by manually pushing a PR that builds the suite of release binaries (overwrite the config.yml to build the release)
+
+# CircleCI structure of the binaries
+
+Some quick vocab:
+
+* A \**workflow** is a CircleCI concept; it is a DAG of '**jobs**'. ctrl-f 'workflows' on https://github.com/pytorch/pytorch/blob/master/.circleci/config.yml to see the workflows.
+* **jobs** are a sequence of '**steps**'
+* **steps** are usually just a bash script or a builtin CircleCI command. *All steps run in new environments, environment variables declared in one script DO NOT persist to following steps*
+* CircleCI has a **workspace**, which is essentially a cache between steps of the *same job* in which you can store artifacts between steps.
+
+## How are the workflows structured?
+
+The nightly binaries have 3 workflows. We have one job (actually 3 jobs:  build, test, and upload) per binary configuration
+
+1. binary_builds
+    1. every day midnight EST
+    2. linux: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/linux-binary-build-defaults.yml
+    3. macos: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/macos-binary-build-defaults.yml
+    4. For each binary configuration, e.g. linux_conda_3.7_cpu there is a
+        1. binary_linux_conda_3.7_cpu_build
+            1. Builds the build. On linux jobs this uses the 'docker executor'.
+            2. Persists the package to the workspace
+        2. binary_linux_conda_3.7_cpu_test
+            1. Loads the package to the workspace
+            2. Spins up a docker image (on Linux), mapping the package and code repos into the docker
+            3. Runs some smoke tests in the docker
+            4. (Actually, for macos this is a step rather than a separate job)
+        3. binary_linux_conda_3.7_cpu_upload
+            1. Logs in to aws/conda
+            2. Uploads the package
+2. update_s3_htmls
+    1. every day 5am EST
+    2. https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/binary_update_htmls.yml
+    3. See below for what these are for and why they're needed
+    4. Three jobs that each examine the current contents of aws and the conda repo and update some html files in s3
+3. binarysmoketests
+    1. every day
+    2. https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-build-smoke-tests-defaults.yml
+    3. For each binary configuration, e.g. linux_conda_3.7_cpu there is a
+        1. smoke_linux_conda_3.7_cpu
+            1. Downloads the package from the cloud, e.g. using the official pip or conda instructions
+            2. Runs the smoke tests
+
+## How are the jobs structured?
+
+The jobs are in https://github.com/pytorch/pytorch/tree/master/.circleci/verbatim-sources. Jobs are made of multiple steps. There are some shared steps used by all the binaries/smokes. Steps of these jobs are all delegated to scripts in https://github.com/pytorch/pytorch/tree/master/.circleci/scripts .
+
+* Linux jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/linux-binary-build-defaults.yml
+    * binary_linux_build.sh
+    * binary_linux_test.sh
+    * binary_linux_upload.sh
+* MacOS jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/macos-binary-build-defaults.yml
+    * binary_macos_build.sh
+    * binary_macos_test.sh
+    * binary_macos_upload.sh
+* Update html jobs: https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/binary_update_htmls.yml
+    * These delegate from the pytorch/builder repo
+    * https://github.com/pytorch/builder/blob/master/cron/update_s3_htmls.sh
+    * https://github.com/pytorch/builder/blob/master/cron/upload_binary_sizes.sh
+* Smoke jobs (both linux and macos): https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-build-smoke-tests-defaults.yml
+    * These delegate from the pytorch/builder repo
+    * https://github.com/pytorch/builder/blob/master/run_tests.sh
+    * https://github.com/pytorch/builder/blob/master/smoke_test.sh
+    * https://github.com/pytorch/builder/blob/master/check_binary.sh
+* Common shared code (shared across linux and macos): https://github.com/pytorch/pytorch/blob/master/.circleci/verbatim-sources/nightly-binary-build-defaults.yml
+    * binary_checkout.sh - checks out pytorch/builder repo. Right now this also checks out pytorch/pytorch, but it shouldn't. pytorch/pytorch should just be shared through the workspace. This can handle being run before binary_populate_env.sh
+    * binary_populate_env.sh - parses BUILD_ENVIRONMENT into the separate env variables that make up a binary configuration. Also sets lots of default values, the date, the version strings, the location of folders in s3, all sorts of things. This generally has to be run before other steps.
+    * binary_install_miniconda.sh - Installs miniconda, cross platform. Also hacks this for the update_binary_sizes job that doesn't have the right env variables
+    * binary_run_in_docker.sh - Takes a bash script file (the actual test code) from a hardcoded location, spins up a docker image, and runs the script inside the docker image
+
+### **Why do the steps all refer to scripts?**
+
+CircleCI creates a  final yaml file by inlining every <<* segment, so if we were to keep all the code in the config.yml itself then the config size would go over 4 MB and cause infra problems.
+
+### **What is binary_run_in_docker for?**
+
+So, CircleCI has several executor types: macos, machine, and docker are the ones we use. The 'machine' executor gives you two cores on some linux vm. The 'docker' executor gives you considerably more cores (nproc was 32 instead of 2 back when I tried in February). Since the dockers are faster, we try to run everything that we can in dockers. Thus
+
+* linux build jobs use the docker executor. Running them on the docker executor was at least 2x faster than running them on the machine executor
+* linux test jobs use the machine executor in order for them to properly interface with GPUs since docker executors cannot execute with attached GPUs
+* linux upload jobs use the machine executor. The upload jobs are so short that it doesn't really matter what they use
+* linux smoke test jobs use the machine executor for the same reason as the linux test jobs
+
+binary_run_in_docker.sh is a way to share the docker start-up code between the binary test jobs and the binary smoke test jobs
+
+### **Why does binary_checkout also checkout pytorch? Why shouldn't it?**
+
+We want all the nightly binary jobs to run on the exact same git commit, so we wrote our own checkout logic to ensure that the same commit was always picked. Later circleci changed that to use a single pytorch checkout and persist it through the workspace (they did this because our config file was too big, so they wanted to take a lot of the setup code into scripts, but the scripts needed the code repo to exist to be called, so they added a prereq step called 'setup' to checkout the code and persist the needed scripts to the workspace). The changes to the binary jobs were not properly tested, so they all broke from missing pytorch code no longer existing. We hotfixed the problem by adding the pytorch checkout back to binary_checkout, so now there's two checkouts of pytorch on the binary jobs. This problem still needs to be fixed, but it takes careful tracing of which code is being called where.
+
+# Azure Pipelines structure of the binaries
+
+TODO: fill in stuff
+
+## How are the workflows structured?
+
+TODO: fill in stuff
+
+## How are the jobs structured?
+
+TODO: fill in stuff
+
+# Code structure of the binaries (circleci agnostic)
+
+## Overview
+
+The code that runs the binaries lives in two places, in the normal [github.com/pytorch/pytorch](http://github.com/pytorch/pytorch), but also in [github.com/pytorch/builder](http://github.com/pytorch/builder), which is a repo that defines how all the binaries are built. The relevant code is
+
+
+```
+# All code needed to set-up environments for build code to run in,
+# but only code that is specific to the current CI system
+pytorch/pytorch
+- .circleci/                # Folder that holds all circleci related stuff
+  - config.yml              # GENERATED file that actually controls all circleci behavior
+  - verbatim-sources        # Used to generate job/workflow sections in ^
+  - scripts/                # Code needed to prepare circleci environments for binary build scripts
+
+- setup.py                  # Builds pytorch. This is wrapped in pytorch/builder
+- cmake files               # used in normal building of pytorch
+
+# All code needed to prepare a binary build, given an environment
+# with all the right variables/packages/paths.
+pytorch/builder
+
+# Given an installed binary and a proper python env, runs some checks
+# to make sure the binary was built the proper way. Checks things like
+# the library dependencies, symbols present, etc.
+- check_binary.sh
+
+# Given an installed binary, runs python tests to make sure everything
+# is in order. These should be de-duped. Right now they both run smoke
+# tests, but are called from different places. Usually just call some
+# import statements, but also has overlap with check_binary.sh above
+- run_tests.sh
+- smoke_test.sh
+
+# Folders that govern how packages are built. See paragraphs below
+
+- conda/
+  - build_pytorch.sh          # Entrypoint. Delegates to proper conda build folder
+  - switch_cuda_version.sh    # Switches activate CUDA installation in Docker
+  - pytorch-nightly/          # Build-folder
+- manywheel/
+  - build_cpu.sh              # Entrypoint for cpu builds
+  - build.sh                  # Entrypoint for CUDA builds
+  - build_common.sh           # Actual build script that ^^ call into
+- wheel/
+  - build_wheel.sh            # Entrypoint for wheel builds
+- windows/
+  - build_pytorch.bat         # Entrypoint for wheel builds on Windows
+```
+
+Every type of package has an entrypoint build script that handles the all the important logic.
+
+## Conda
+
+Linux, MacOS and Windows use the same code flow for the conda builds.
+
+Conda packages are built with conda-build, see https://conda.io/projects/conda-build/en/latest/resources/commands/conda-build.html
+
+Basically, you pass `conda build` a build folder (pytorch-nightly/ above) that contains a build script and a meta.yaml. The meta.yaml specifies in what python environment to build the package in, and what dependencies the resulting package should have, and the build script gets called in the env to build the thing.
+tl;dr on conda-build is
+
+1. Creates a brand new conda environment, based off of deps in the meta.yaml
+    1. Note that environment variables do not get passed into this build env unless they are specified in the meta.yaml
+    2. If the build fails this environment will stick around. You can activate it for much easier debugging. The “General Python” section below explains what exactly a python “environment” is.
+2. Calls build.sh in the environment
+3. Copies the finished package to a new conda env, also specified by the meta.yaml
+4. Runs some simple import tests (if specified in the meta.yaml)
+5. Saves the finished package as a tarball
+
+The build.sh we use is essentially a wrapper around `python setup.py build`, but it also manually copies in some of our dependent libraries into the resulting tarball and messes with some rpaths.
+
+The entrypoint file `builder/conda/build_conda.sh` is complicated because
+
+* It works for Linux, MacOS and Windows
+    * The mac builds used to create their own environments, since they all used to be on the same machine. There’s now a lot of extra logic to handle conda envs. This extra machinery could be removed
+* It used to handle testing too, which adds more logic messing with python environments too. This extra machinery could be removed.
+
+## Manywheels (linux pip and libtorch packages)
+
+Manywheels are pip packages for linux distros. Note that these manywheels are not actually manylinux compliant.
+
+`builder/manywheel/build_cpu.sh` and `builder/manywheel/build.sh` (for CUDA builds) just set different env vars and then call into `builder/manywheel/build_common.sh`
+
+The entrypoint file `builder/manywheel/build_common.sh` is really really complicated because
+
+* This used to handle building for several different python versions at the same time. The loops have been removed, but there's still unnecessary folders and movements here and there.
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This used to handle testing the pip packages too. This is why there’s testing code at the end that messes with python installations and stuff
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This also builds libtorch packages
+    * This should really be separate. libtorch packages are c++ only and have no python. They should not share infra with all the python specific stuff in this file.
+* There is a lot of messing with rpaths. This is necessary, but could be made much much simpler if the above issues were fixed.
+
+## Wheels (MacOS pip and libtorch packages)
+
+The entrypoint file `builder/wheel/build_wheel.sh` is complicated because
+
+* The mac builds used to all run on one machine (we didn’t have autoscaling mac machines till circleci). So this script handled siloing itself by setting-up and tearing-down its build env and siloing itself into its own build directory.
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This also builds libtorch packages
+    * Ditto the comment above. This should definitely be separated out.
+
+Note that the MacOS Python wheels are still built in conda environments. Some of the dependencies present during build also come from conda.
+
+## Windows Wheels (Windows pip and libtorch packages)
+
+The entrypoint file `builder/windows/build_pytorch.bat` is complicated because
+
+* This used to handle building for several different python versions at the same time. This is why there are loops everywhere
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This used to handle testing the pip packages too. This is why there’s testing code at the end that messes with python installations and stuff
+    * The script is never used this way anymore. This extra machinery could be removed.
+* This also builds libtorch packages
+    * This should really be separate. libtorch packages are c++ only and have no python. They should not share infra with all the python specific stuff in this file.
+
+Note that the Windows Python wheels are still built in conda environments. Some of the dependencies present during build also come from conda.
+
+## General notes
+
+### Note on run_tests.sh, smoke_test.sh, and check_binary.sh
+
+* These should all be consolidated
+* These must run on all OS types: MacOS, Linux, and Windows
+* These all run smoke tests at the moment. They inspect the packages some, maybe run a few import statements. They DO NOT run the python tests nor the cpp tests. The idea is that python tests on master and PR merges will catch all breakages. All these tests have to do is make sure the special binary machinery didn’t mess anything up.
+* There are separate run_tests.sh and smoke_test.sh because one used to be called by the smoke jobs and one used to be called by the binary test jobs (see circleci structure section above). This is still true actually, but these could be united into a single script that runs these checks, given an installed pytorch package.
+
+### Note on libtorch
+
+Libtorch packages are built in the wheel build scripts: manywheel/build_*.sh for linux and build_wheel.sh for mac. There are several things wrong with this
+
+* It’s confusing. Most of those scripts deal with python specifics.
+* The extra conditionals everywhere severely complicate the wheel build scripts
+* The process for building libtorch is different from the official instructions (a plain call to cmake, or a call to a script)
+
+### Note on docker images / Dockerfiles
+
+All linux builds occur in docker images. The docker images are
+
+* pytorch/conda-cuda
+    * Has ALL CUDA versions installed. The script pytorch/builder/conda/switch_cuda_version.sh sets /usr/local/cuda to a symlink to e.g. /usr/local/cuda-10.0 to enable different CUDA builds
+    * Also used for cpu builds
+* pytorch/manylinux-cuda90
+* pytorch/manylinux-cuda100
+    * Also used for cpu builds
+
+The Dockerfiles are available in pytorch/builder, but there is no circleci job or script to build these docker images, and they cannot be run locally (unless you have the correct local packages/paths). Only Soumith can build them right now.
+
+### General Python
+
+* This is still a good explanation of python installations https://caffe2.ai/docs/faq.html#why-do-i-get-import-errors-in-python-when-i-try-to-use-caffe2
+
+# How to manually rebuild the binaries
+
+tl;dr make a PR that looks like https://github.com/pytorch/pytorch/pull/21159
+
+Sometimes we want to push a change to master and then rebuild all of today's binaries after that change. As of May 30, 2019 there isn't a way to manually run a workflow in the UI. You can manually re-run a workflow, but it will use the exact same git commits as the first run and will not include any changes. So we have to make a PR and then force circleci to run the binary workflow instead of the normal tests. The above PR is an example of how to do this; essentially you copy-paste the binarybuilds workflow steps into the default workflow steps. If you need to point the builder repo to a different commit then you'd need to change https://github.com/pytorch/pytorch/blob/master/.circleci/scripts/binary_checkout.sh#L42-L45 to checkout what you want.
+
+## How to test changes to the binaries via .circleci
+
+Writing PRs that test the binaries is annoying, since the default circleci jobs that run on PRs are not the jobs that you want to run. Likely, changes to the binaries will touch something under .circleci/ and require that .circleci/config.yml be regenerated (.circleci/config.yml controls all .circleci behavior, and is generated using `.circleci/regenerate.sh` in python 3.7). But you also need to manually hardcode the binary jobs that you want to test into the .circleci/config.yml workflow, so you should actually make at least two commits, one for your changes and one to temporarily hardcode jobs. See https://github.com/pytorch/pytorch/pull/22928 as an example of how to do this.
+
+```sh
+# Make your changes
+touch .circleci/verbatim-sources/nightly-binary-build-defaults.yml
+
+# Regenerate the yaml, has to be in python 3.7
+.circleci/regenerate.sh
+
+# Make a commit
+git add .circleci *
+git commit -m "My real changes"
+git push origin my_branch
+
+# Now hardcode the jobs that you want in the .circleci/config.yml workflows section
+# Also eliminate ensure-consistency and should_run_job checks
+# e.g. https://github.com/pytorch/pytorch/commit/2b3344bfed8772fe86e5210cc4ee915dee42b32d
+
+# Make a commit you won't keep
+git add .circleci
+git commit -m "[DO NOT LAND] testing binaries for above changes"
+git push origin my_branch
+
+# Now you need to make some changes to the first commit.
+git rebase -i HEAD~2 # mark the first commit as 'edit'
+
+# Make the changes
+touch .circleci/verbatim-sources/nightly-binary-build-defaults.yml
+.circleci/regenerate.sh
+
+# Ammend the commit and recontinue
+git add .circleci
+git commit --amend
+git rebase --continue
+
+# Update the PR, need to force since the commits are different now
+git push origin my_branch --force
+```
+
+The advantage of this flow is that you can make new changes to the base commit and regenerate the .circleci without having to re-write which binary jobs you want to test on. The downside is that all updates will be force pushes.
+
+## How to build a binary locally
+
+### Linux
+
+You can build Linux binaries locally easily using docker.
+
+```sh
+# Run the docker
+# Use the correct docker image, pytorch/conda-cuda used here as an example
+#
+# -v path/to/foo:path/to/bar makes path/to/foo on your local machine (the
+#    machine that you're running the command on) accessible to the docker
+#    container at path/to/bar. So if you then run `touch path/to/bar/baz`
+#    in the docker container then you will see path/to/foo/baz on your local
+#    machine. You could also clone the pytorch and builder repos in the docker.
+#
+# If you know how, add ccache as a volume too and speed up everything
+docker run \
+    -v your/pytorch/repo:/pytorch \
+    -v your/builder/repo:/builder \
+    -v where/you/want/packages/to/appear:/final_pkgs \
+    -it pytorch/conda-cuda /bin/bash
+
+# Export whatever variables are important to you. All variables that you'd
+# possibly need are in .circleci/scripts/binary_populate_env.sh
+# You should probably always export at least these 3 variables
+export PACKAGE_TYPE=conda
+export DESIRED_PYTHON=3.6
+export DESIRED_CUDA=cpu
+
+# Call the entrypoint
+# `|& tee foo.log` just copies all stdout and stderr output to foo.log
+# The builds generate lots of output so you probably need this when
+# building locally.
+/builder/conda/build_pytorch.sh |& tee build_output.log
+```
+
+**Building CUDA binaries on docker**
+
+You can build CUDA binaries on CPU only machines, but you can only run CUDA binaries on CUDA machines. This means that you can build a CUDA binary on a docker on your laptop if you so choose (though it’s gonna take a long time).
+
+For Facebook employees, ask about beefy machines that have docker support and use those instead of your laptop; it will be 5x as fast.
+
+### MacOS
+
+There’s no easy way to generate reproducible hermetic MacOS environments. If you have a Mac laptop then you can try emulating the .circleci environments as much as possible, but you probably have packages in /usr/local/, possibly installed by brew, that will probably interfere with the build. If you’re trying to repro an error on a Mac build in .circleci and you can’t seem to repro locally, then my best advice is actually to iterate on .circleci    :/
+
+But if you want to try, then I’d recommend
+
+```sh
+# Create a new terminal
+# Clear your LD_LIBRARY_PATH and trim as much out of your PATH as you
+# know how to do
+
+# Install a new miniconda
+# First remove any other python or conda installation from your PATH
+# Always install miniconda 3, even if building for Python <3
+new_conda="~/my_new_conda"
+conda_sh="$new_conda/install_miniconda.sh"
+curl -o "$conda_sh" https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
+chmod +x "$conda_sh"
+"$conda_sh" -b -p "$MINICONDA_ROOT"
+rm -f "$conda_sh"
+export PATH="~/my_new_conda/bin:$PATH"
+
+# Create a clean python env
+# All MacOS builds use conda to manage the python env and dependencies
+# that are built with, even the pip packages
+conda create -yn binary python=2.7
+conda activate binary
+
+# Export whatever variables are important to you. All variables that you'd
+# possibly need are in .circleci/scripts/binary_populate_env.sh
+# You should probably always export at least these 3 variables
+export PACKAGE_TYPE=conda
+export DESIRED_PYTHON=3.6
+export DESIRED_CUDA=cpu
+
+# Call the entrypoint you want
+path/to/builder/wheel/build_wheel.sh
+```
+
+N.B. installing a brand new miniconda is important. This has to do with how conda installations work. See the “General Python” section above, but tldr; is that
+
+1. You make the ‘conda’ command accessible by prepending `path/to/conda_root/bin` to your PATH.
+2. You make a new env and activate it, which then also gets prepended to your PATH. Now you have `path/to/conda_root/envs/new_env/bin:path/to/conda_root/bin:$PATH`
+3. Now say you (or some code that you ran) call python executable `foo`
+    1. if you installed `foo` in `new_env`, then `path/to/conda_root/envs/new_env/bin/foo` will get called, as expected.
+    2. But if you forgot to installed `foo` in `new_env` but happened to previously install it in your root conda env (called ‘base’), then unix/linux will still find `path/to/conda_root/bin/foo` . This is dangerous, since `foo` can be a different version than you want; `foo` can even be for an incompatible python version!
+
+Newer conda versions and proper python hygiene can prevent this, but just install a new miniconda to be safe.
+
+### Windows
+
+TODO: fill in