Backport transposes optimization to v0.3.0 (#3994)

* Optimizer: optimize transposes in variety of circumstances (#3509)

* Optimizer: Optimize transposes in variety of circumstances

- No-op transposes
- Consecutive transposes (fuse them)
- Transposes into Gemm (fuse them into transA/transB parameter)

* touch up out of date comment

* Backporting optimizer changes

.azure_pipelines/build-pipeline.yml

@@ -1,63 +0,0 @@
-# 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

@@ -1,82 +0,0 @@
-# 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

@@ -1,134 +0,0 @@
-# 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

@@ -1,150 +0,0 @@
-# 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

@@ -1,17 +0,0 @@
-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

@@ -1,26 +0,0 @@
-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

@@ -1,62 +0,0 @@
-# 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

@@ -1,61 +0,0 @@
-# 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

@@ -1,57 +0,0 @@
-# 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

@@ -1,129 +0,0 @@
-# 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

@@ -1,14 +0,0 @@
-# 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

@@ -1,92 +0,0 @@
-# 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

@@ -1,60 +0,0 @@
-# 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

@@ -1,62 +0,0 @@
-# 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

@@ -1,224 +0,0 @@
-# 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

.bazelrc

@@ -1,26 +0,0 @@
-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 --experimental_ui_max_stdouterr_bytes=2048576
-
-# Configuration to disable tty features for environments like CI
-build:no-tty --curses no
-build:no-tty --progress_report_interval 10
-build:no-tty --show_progress_rate_limit 10
-
-# Configuration to build with GPU support
-build:gpu --define=cuda=true
-# define a separate build folder for faster switching between configs
-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 "-gpu" platform suffix.
-build --copt=-isystem --copt=bazel-out/k8-fastbuild-gpu/bin
-# rules_cuda configuration
-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 +0,0 @@
-4.2.1

.circleci/.gitignore

@@ -1,2 +0,0 @@
-*.svg
-*.png

.circleci/README.md

@@ -1,498 +0,0 @@
-Structure of CI
-===============
-
-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

.circleci/cimodel/data/binary_build_data.py

@@ -1,195 +0,0 @@
-"""
-This module models the tree of configuration variants
-for "smoketest" builds.
-
-Each subclass of ConfigNode represents a layer of the configuration hierarchy.
-These tree nodes encapsulate the logic for whether a branch of the hierarchy
-should be "pruned".
-"""
-
-from collections import OrderedDict
-
-from cimodel.lib.conf_tree import ConfigNode
-import cimodel.data.dimensions as dimensions
-
-
-LINKING_DIMENSIONS = [
-    "shared",
-    "static",
-]
-
-
-DEPS_INCLUSION_DIMENSIONS = [
-    "with-deps",
-    "without-deps",
-]
-
-
-def get_processor_arch_name(gpu_version):
-    return "cpu" if not gpu_version else (
-        "cu" + gpu_version.strip("cuda") if gpu_version.startswith("cuda") else gpu_version
-    )
-
-CONFIG_TREE_DATA = OrderedDict(
-    macos=([None], OrderedDict(
-        wheel=dimensions.STANDARD_PYTHON_VERSIONS,
-        conda=dimensions.STANDARD_PYTHON_VERSIONS,
-        libtorch=[
-            "3.7",
-        ],
-    )),
-    macos_arm64=([None], OrderedDict(
-        wheel=[
-            "3.8",
-            "3.9",
-        ],
-        conda=[
-            "3.8",
-            "3.9",
-        ],
-    )),
-    windows=(
-        # Stop building Win+CU102, see https://github.com/pytorch/pytorch/issues/65648
-        [v for v in dimensions.GPU_VERSIONS if v not in dimensions.ROCM_VERSION_LABELS and v != "cuda102"],
-        OrderedDict(
-            conda=dimensions.STANDARD_PYTHON_VERSIONS,
-        )
-    ),
-)
-
-# GCC config variants:
-#
-# All the nightlies (except libtorch with new gcc ABI) are built with devtoolset7,
-# which can only build with old gcc ABI. It is better than devtoolset3
-# because it understands avx512, which is needed for good fbgemm performance.
-#
-# Libtorch with new gcc ABI is built with gcc 5.4 on Ubuntu 16.04.
-LINUX_GCC_CONFIG_VARIANTS = OrderedDict(
-    manywheel=['devtoolset7'],
-    conda=['devtoolset7'],
-    libtorch=[
-        "devtoolset7",
-        "gcc5.4_cxx11-abi",
-    ],
-)
-
-WINDOWS_LIBTORCH_CONFIG_VARIANTS = [
-    "debug",
-    "release",
-]
-
-
-class TopLevelNode(ConfigNode):
-    def __init__(self, node_name, config_tree_data, smoke):
-        super(TopLevelNode, self).__init__(None, node_name)
-
-        self.config_tree_data = config_tree_data
-        self.props["smoke"] = smoke
-
-    def get_children(self):
-        return [OSConfigNode(self, x, c, p) for (x, (c, p)) in self.config_tree_data.items()]
-
-
-class OSConfigNode(ConfigNode):
-    def __init__(self, parent, os_name, gpu_versions, py_tree):
-        super(OSConfigNode, self).__init__(parent, os_name)
-
-        self.py_tree = py_tree
-        self.props["os_name"] = os_name
-        self.props["gpu_versions"] = gpu_versions
-
-    def get_children(self):
-        return [PackageFormatConfigNode(self, k, v) for k, v in self.py_tree.items()]
-
-
-class PackageFormatConfigNode(ConfigNode):
-    def __init__(self, parent, package_format, python_versions):
-        super(PackageFormatConfigNode, self).__init__(parent, package_format)
-
-        self.props["python_versions"] = python_versions
-        self.props["package_format"] = package_format
-
-
-    def get_children(self):
-        if self.find_prop("os_name") == "linux":
-            return [LinuxGccConfigNode(self, v) for v in LINUX_GCC_CONFIG_VARIANTS[self.find_prop("package_format")]]
-        elif self.find_prop("os_name") == "windows" and self.find_prop("package_format") == "libtorch":
-            return [WindowsLibtorchConfigNode(self, v) for v in WINDOWS_LIBTORCH_CONFIG_VARIANTS]
-        else:
-            return [ArchConfigNode(self, v) for v in self.find_prop("gpu_versions")]
-
-
-class LinuxGccConfigNode(ConfigNode):
-    def __init__(self, parent, gcc_config_variant):
-        super(LinuxGccConfigNode, self).__init__(parent, "GCC_CONFIG_VARIANT=" + str(gcc_config_variant))
-
-        self.props["gcc_config_variant"] = gcc_config_variant
-
-    def get_children(self):
-        gpu_versions = self.find_prop("gpu_versions")
-
-        # XXX devtoolset7 on CUDA 9.0 is temporarily disabled
-        # see https://github.com/pytorch/pytorch/issues/20066
-        if self.find_prop("gcc_config_variant") == 'devtoolset7':
-            gpu_versions = filter(lambda x: x != "cuda_90", gpu_versions)
-
-        # XXX disabling conda rocm build since docker images are not there
-        if self.find_prop("package_format") == 'conda':
-            gpu_versions = filter(lambda x: x not in dimensions.ROCM_VERSION_LABELS, gpu_versions)
-
-        # XXX libtorch rocm build  is temporarily disabled
-        if self.find_prop("package_format") == 'libtorch':
-            gpu_versions = filter(lambda x: x not in dimensions.ROCM_VERSION_LABELS, gpu_versions)
-
-        return [ArchConfigNode(self, v) for v in gpu_versions]
-
-
-class WindowsLibtorchConfigNode(ConfigNode):
-    def __init__(self, parent, libtorch_config_variant):
-        super(WindowsLibtorchConfigNode, self).__init__(parent, "LIBTORCH_CONFIG_VARIANT=" + str(libtorch_config_variant))
-
-        self.props["libtorch_config_variant"] = libtorch_config_variant
-
-    def get_children(self):
-        return [ArchConfigNode(self, v) for v in self.find_prop("gpu_versions")]
-
-
-class ArchConfigNode(ConfigNode):
-    def __init__(self, parent, gpu):
-        super(ArchConfigNode, self).__init__(parent, get_processor_arch_name(gpu))
-
-        self.props["gpu"] = gpu
-
-    def get_children(self):
-        return [PyVersionConfigNode(self, v) for v in self.find_prop("python_versions")]
-
-
-class PyVersionConfigNode(ConfigNode):
-    def __init__(self, parent, pyver):
-        super(PyVersionConfigNode, self).__init__(parent, pyver)
-
-        self.props["pyver"] = pyver
-
-    def get_children(self):
-        package_format = self.find_prop("package_format")
-        os_name = self.find_prop("os_name")
-
-        has_libtorch_variants = package_format == "libtorch" and os_name == "linux"
-        linking_variants = LINKING_DIMENSIONS if has_libtorch_variants else []
-
-        return [LinkingVariantConfigNode(self, v) for v in linking_variants]
-
-
-class LinkingVariantConfigNode(ConfigNode):
-    def __init__(self, parent, linking_variant):
-        super(LinkingVariantConfigNode, self).__init__(parent, linking_variant)
-
-    def get_children(self):
-        return [DependencyInclusionConfigNode(self, v) for v in DEPS_INCLUSION_DIMENSIONS]
-
-
-class DependencyInclusionConfigNode(ConfigNode):
-    def __init__(self, parent, deps_variant):
-        super(DependencyInclusionConfigNode, self).__init__(parent, deps_variant)
-
-        self.props["libtorch_variant"] = "-".join([self.parent.get_label(), self.get_label()])

.circleci/cimodel/data/binary_build_definitions.py

@@ -1,243 +0,0 @@
-from collections import OrderedDict
-
-import cimodel.data.simple.util.branch_filters as branch_filters
-import cimodel.data.binary_build_data as binary_build_data
-import cimodel.lib.conf_tree as conf_tree
-import cimodel.lib.miniutils as miniutils
-
-class Conf(object):
-    def __init__(self, os, gpu_version, pydistro, parms, smoke, libtorch_variant, gcc_config_variant, libtorch_config_variant):
-
-        self.os = os
-        self.gpu_version = gpu_version
-        self.pydistro = pydistro
-        self.parms = parms
-        self.smoke = smoke
-        self.libtorch_variant = libtorch_variant
-        self.gcc_config_variant = gcc_config_variant
-        self.libtorch_config_variant = libtorch_config_variant
-
-    def gen_build_env_parms(self):
-        elems = [self.pydistro] + self.parms + [binary_build_data.get_processor_arch_name(self.gpu_version)]
-        if self.gcc_config_variant is not None:
-            elems.append(str(self.gcc_config_variant))
-        if self.libtorch_config_variant is not None:
-            elems.append(str(self.libtorch_config_variant))
-        return elems
-
-    def gen_docker_image(self):
-        if self.gcc_config_variant == 'gcc5.4_cxx11-abi':
-            if self.gpu_version is None:
-                return miniutils.quote("pytorch/libtorch-cxx11-builder:cpu")
-            else:
-                return miniutils.quote(
-                    f"pytorch/libtorch-cxx11-builder:{self.gpu_version}"
-                )
-        if self.pydistro == "conda":
-            if self.gpu_version is None:
-                return miniutils.quote("pytorch/conda-builder:cpu")
-            else:
-                return miniutils.quote(
-                    f"pytorch/conda-builder:{self.gpu_version}"
-                )
-
-        docker_word_substitution = {
-            "manywheel": "manylinux",
-            "libtorch": "manylinux",
-        }
-
-        docker_distro_prefix = miniutils.override(self.pydistro, docker_word_substitution)
-
-        # The cpu nightlies are built on the pytorch/manylinux-cuda102 docker image
-        # TODO cuda images should consolidate into tag-base images similar to rocm
-        alt_docker_suffix = "cuda102" if not self.gpu_version else (
-            "rocm:" + self.gpu_version.strip("rocm") if self.gpu_version.startswith("rocm") else self.gpu_version)
-        docker_distro_suffix = alt_docker_suffix if self.pydistro != "conda" else (
-            "cuda" if alt_docker_suffix.startswith("cuda") else "rocm")
-        return miniutils.quote("pytorch/" + docker_distro_prefix + "-" + docker_distro_suffix)
-
-    def get_name_prefix(self):
-        return "smoke" if self.smoke else "binary"
-
-    def gen_build_name(self, build_or_test, nightly):
-
-        parts = [self.get_name_prefix(), self.os] + self.gen_build_env_parms()
-
-        if nightly:
-            parts.append("nightly")
-
-        if self.libtorch_variant:
-            parts.append(self.libtorch_variant)
-
-        if not self.smoke:
-            parts.append(build_or_test)
-
-        joined = "_".join(parts)
-        return joined.replace(".", "_")
-
-    def gen_workflow_job(self, phase, upload_phase_dependency=None, nightly=False):
-        job_def = OrderedDict()
-        job_def["name"] = self.gen_build_name(phase, nightly)
-        job_def["build_environment"] = miniutils.quote(" ".join(self.gen_build_env_parms()))
-        if self.smoke:
-            job_def["requires"] = [
-                "update_s3_htmls",
-            ]
-            job_def["filters"] = branch_filters.gen_filter_dict(
-                branches_list=["postnightly"],
-            )
-        else:
-            filter_branch = r"/.*/"
-            job_def["filters"] = branch_filters.gen_filter_dict(
-                branches_list=[filter_branch],
-                tags_list=[branch_filters.RC_PATTERN],
-            )
-        if self.libtorch_variant:
-            job_def["libtorch_variant"] = miniutils.quote(self.libtorch_variant)
-        if phase == "test":
-            if not self.smoke:
-                job_def["requires"] = [self.gen_build_name("build", nightly)]
-            if not (self.smoke and self.os == "macos") and self.os != "windows":
-                job_def["docker_image"] = self.gen_docker_image()
-
-            # fix this. only works on cuda not rocm
-            if self.os != "windows" and self.gpu_version:
-                job_def["use_cuda_docker_runtime"] = miniutils.quote("1")
-        else:
-            if self.os == "linux" and phase != "upload":
-                job_def["docker_image"] = self.gen_docker_image()
-
-        if phase == "test":
-            if self.gpu_version:
-                if self.os == "windows":
-                    job_def["executor"] = "windows-with-nvidia-gpu"
-                else:
-                    job_def["resource_class"] = "gpu.medium"
-
-        os_name = miniutils.override(self.os, {"macos": "mac"})
-        job_name = "_".join([self.get_name_prefix(), os_name, phase])
-        return {job_name : job_def}
-
-    def gen_upload_job(self, phase, requires_dependency):
-        """Generate binary_upload job for configuration
-
-        Output looks similar to:
-
-      - binary_upload:
-          name: binary_linux_manywheel_3_7m_cu113_devtoolset7_nightly_upload
-          context: org-member
-          requires: binary_linux_manywheel_3_7m_cu113_devtoolset7_nightly_test
-          filters:
-            branches:
-              only:
-                - nightly
-            tags:
-              only: /v[0-9]+(\\.[0-9]+)*-rc[0-9]+/
-          package_type: manywheel
-          upload_subfolder: cu113
-        """
-        return {
-            "binary_upload": OrderedDict({
-                "name": self.gen_build_name(phase, nightly=True),
-                "context": "org-member",
-                "requires": [self.gen_build_name(
-                    requires_dependency,
-                    nightly=True
-                )],
-                "filters": branch_filters.gen_filter_dict(
-                    branches_list=["nightly"],
-                    tags_list=[branch_filters.RC_PATTERN],
-                ),
-                "package_type": self.pydistro,
-                "upload_subfolder": binary_build_data.get_processor_arch_name(
-                    self.gpu_version,
-                ),
-            })
-        }
-
-def get_root(smoke, name):
-
-    return binary_build_data.TopLevelNode(
-        name,
-        binary_build_data.CONFIG_TREE_DATA,
-        smoke,
-    )
-
-
-def gen_build_env_list(smoke):
-
-    root = get_root(smoke, "N/A")
-    config_list = conf_tree.dfs(root)
-
-    newlist = []
-    for c in config_list:
-        conf = Conf(
-            c.find_prop("os_name"),
-            c.find_prop("gpu"),
-            c.find_prop("package_format"),
-            [c.find_prop("pyver")],
-            c.find_prop("smoke") and not (c.find_prop("os_name") == "macos_arm64"),  # don't test arm64
-            c.find_prop("libtorch_variant"),
-            c.find_prop("gcc_config_variant"),
-            c.find_prop("libtorch_config_variant"),
-        )
-        newlist.append(conf)
-
-    return newlist
-
-def predicate_exclude_macos(config):
-    return config.os == "linux" or config.os == "windows"
-
-def get_nightly_uploads():
-    configs = gen_build_env_list(False)
-    mylist = []
-    for conf in configs:
-        phase_dependency = "test" if predicate_exclude_macos(conf) else "build"
-        mylist.append(conf.gen_upload_job("upload", phase_dependency))
-
-    return mylist
-
-def get_post_upload_jobs():
-    return [
-        {
-            "update_s3_htmls": {
-                "name": "update_s3_htmls",
-                "context": "org-member",
-                "filters": branch_filters.gen_filter_dict(
-                    branches_list=["postnightly"],
-                ),
-            },
-        },
-    ]
-
-def get_nightly_tests():
-
-    configs = gen_build_env_list(False)
-    filtered_configs = filter(predicate_exclude_macos, configs)
-
-    tests = []
-    for conf_options in filtered_configs:
-        yaml_item = conf_options.gen_workflow_job("test", nightly=True)
-        tests.append(yaml_item)
-
-    return tests
-
-
-def get_jobs(toplevel_key, smoke):
-    jobs_list = []
-    configs = gen_build_env_list(smoke)
-    phase = "build" if toplevel_key == "binarybuilds" else "test"
-    for build_config in configs:
-        # don't test for macos_arm64 as it's cross compiled
-        if phase != "test" or build_config.os != "macos_arm64":
-            jobs_list.append(build_config.gen_workflow_job(phase, nightly=True))
-
-    return jobs_list
-
-
-def get_binary_build_jobs():
-    return get_jobs("binarybuilds", False)
-
-
-def get_binary_smoke_test_jobs():
-    return get_jobs("binarysmoketests", True)

.circleci/cimodel/data/dimensions.py

@@ -1,24 +0,0 @@
-PHASES = ["build", "test"]
-
-CUDA_VERSIONS = [
-    "102",
-    "111",
-    "113",
-    "115",
-]
-
-ROCM_VERSIONS = [
-    "4.3.1",
-    "4.5.2",
-]
-
-ROCM_VERSION_LABELS = ["rocm" + v for v in ROCM_VERSIONS]
-
-GPU_VERSIONS = [None] + ["cuda" + v for v in CUDA_VERSIONS] + ROCM_VERSION_LABELS
-
-STANDARD_PYTHON_VERSIONS = [
-    "3.7",
-    "3.8",
-    "3.9",
-    "3.10"
-]

.circleci/cimodel/data/pytorch_build_data.py

@@ -1,280 +0,0 @@
-from cimodel.lib.conf_tree import ConfigNode
-
-
-CONFIG_TREE_DATA = [
-]
-
-
-def get_major_pyver(dotted_version):
-    parts = dotted_version.split(".")
-    return "py" + parts[0]
-
-
-class TreeConfigNode(ConfigNode):
-    def __init__(self, parent, node_name, subtree):
-        super(TreeConfigNode, self).__init__(parent, self.modify_label(node_name))
-        self.subtree = subtree
-        self.init2(node_name)
-
-    def modify_label(self, label):
-        return label
-
-    def init2(self, node_name):
-        pass
-
-    def get_children(self):
-        return [self.child_constructor()(self, k, v) for (k, v) in self.subtree]
-
-
-class TopLevelNode(TreeConfigNode):
-    def __init__(self, node_name, subtree):
-        super(TopLevelNode, self).__init__(None, node_name, subtree)
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return DistroConfigNode
-
-
-class DistroConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["distro_name"] = node_name
-
-    def child_constructor(self):
-        distro = self.find_prop("distro_name")
-
-        next_nodes = {
-            "xenial": XenialCompilerConfigNode,
-            "bionic": BionicCompilerConfigNode,
-        }
-        return next_nodes[distro]
-
-
-class PyVerConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["pyver"] = node_name
-        self.props["abbreviated_pyver"] = get_major_pyver(node_name)
-        if node_name == "3.9":
-            self.props["abbreviated_pyver"] = "py3.9"
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return ExperimentalFeatureConfigNode
-
-
-class ExperimentalFeatureConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["experimental_feature"] = node_name
-
-    def child_constructor(self):
-        experimental_feature = self.find_prop("experimental_feature")
-
-        next_nodes = {
-            "asan": AsanConfigNode,
-            "xla": XlaConfigNode,
-            "mlc": MLCConfigNode,
-            "vulkan": VulkanConfigNode,
-            "parallel_tbb": ParallelTBBConfigNode,
-            "noarch": NoarchConfigNode,
-            "parallel_native": ParallelNativeConfigNode,
-            "onnx": ONNXConfigNode,
-            "libtorch": LibTorchConfigNode,
-            "important": ImportantConfigNode,
-            "build_only": BuildOnlyConfigNode,
-            "shard_test": ShardTestConfigNode,
-            "cuda_gcc_override": CudaGccOverrideConfigNode,
-            "pure_torch": PureTorchConfigNode,
-            "slow_gradcheck": SlowGradcheckConfigNode,
-        }
-        return next_nodes[experimental_feature]
-
-
-class SlowGradcheckConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["is_slow_gradcheck"] = True
-
-    def child_constructor(self):
-        return ExperimentalFeatureConfigNode
-
-class PureTorchConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "PURE_TORCH=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_pure_torch"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class XlaConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "XLA=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_xla"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-class MLCConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "MLC=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_mlc"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class AsanConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "Asan=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_asan"] = node_name
-
-    def child_constructor(self):
-        return ExperimentalFeatureConfigNode
-
-
-class ONNXConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "Onnx=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_onnx"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class VulkanConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "Vulkan=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_vulkan"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class ParallelTBBConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "PARALLELTBB=" + str(label)
-
-    def init2(self, node_name):
-        self.props["parallel_backend"] = "paralleltbb"
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class NoarchConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["is_noarch"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class ParallelNativeConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "PARALLELNATIVE=" + str(label)
-
-    def init2(self, node_name):
-        self.props["parallel_backend"] = "parallelnative"
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class LibTorchConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "BUILD_TEST_LIBTORCH=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_libtorch"] = node_name
-
-    def child_constructor(self):
-        return ExperimentalFeatureConfigNode
-
-
-class CudaGccOverrideConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["cuda_gcc_override"] = node_name
-
-    def child_constructor(self):
-        return ExperimentalFeatureConfigNode
-
-
-class BuildOnlyConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["build_only"] = node_name
-
-    def child_constructor(self):
-        return ExperimentalFeatureConfigNode
-
-
-class ShardTestConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["shard_test"] = node_name
-
-    def child_constructor(self):
-        return ImportantConfigNode
-
-
-class ImportantConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return "IMPORTANT=" + str(label)
-
-    def init2(self, node_name):
-        self.props["is_important"] = node_name
-
-    def get_children(self):
-        return []
-
-
-class XenialCompilerConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return label or "<unspecified>"
-
-    def init2(self, node_name):
-        self.props["compiler_name"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-
-        return XenialCompilerVersionConfigNode if self.props["compiler_name"] else PyVerConfigNode
-
-
-class BionicCompilerConfigNode(TreeConfigNode):
-    def modify_label(self, label):
-        return label or "<unspecified>"
-
-    def init2(self, node_name):
-        self.props["compiler_name"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-
-        return BionicCompilerVersionConfigNode if self.props["compiler_name"] else PyVerConfigNode
-
-
-class XenialCompilerVersionConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["compiler_version"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return PyVerConfigNode
-
-
-class BionicCompilerVersionConfigNode(TreeConfigNode):
-    def init2(self, node_name):
-        self.props["compiler_version"] = node_name
-
-    # noinspection PyMethodMayBeStatic
-    def child_constructor(self):
-        return PyVerConfigNode

.circleci/cimodel/data/pytorch_build_definitions.py

@@ -1,381 +0,0 @@
-from collections import OrderedDict
-from dataclasses import dataclass, field
-from typing import List, Optional
-
-import cimodel.data.dimensions as dimensions
-import cimodel.lib.conf_tree as conf_tree
-import cimodel.lib.miniutils as miniutils
-from cimodel.data.pytorch_build_data import CONFIG_TREE_DATA, TopLevelNode
-from cimodel.data.simple.util.branch_filters import gen_filter_dict, RC_PATTERN
-from cimodel.data.simple.util.docker_constants import gen_docker_image
-
-
-@dataclass
-class Conf:
-    distro: str
-    parms: List[str]
-    parms_list_ignored_for_docker_image: Optional[List[str]] = None
-    pyver: Optional[str] = None
-    cuda_version: Optional[str] = None
-    rocm_version: Optional[str] = None
-    # TODO expand this to cover all the USE_* that we want to test for
-    #  tesnrorrt, leveldb, lmdb, redis, opencv, mkldnn, ideep, etc.
-    # (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259453608)
-    is_xla: bool = False
-    is_vulkan: bool = False
-    is_pure_torch: bool = False
-    restrict_phases: Optional[List[str]] = None
-    gpu_resource: Optional[str] = None
-    dependent_tests: List = field(default_factory=list)
-    parent_build: Optional["Conf"] = None
-    is_libtorch: bool = False
-    is_important: bool = False
-    parallel_backend: Optional[str] = None
-    build_only: bool = False
-
-    @staticmethod
-    def is_test_phase(phase):
-        return "test" in phase
-
-    # TODO: Eliminate the special casing for docker paths
-    # In the short term, we *will* need to support special casing as docker images are merged for caffe2 and pytorch
-    def get_parms(self, for_docker):
-        leading = []
-        # We just don't run non-important jobs on pull requests;
-        # previously we also named them in a way to make it obvious
-        # if self.is_important and not for_docker:
-        #    leading.append("AAA")
-        leading.append("pytorch")
-        if self.is_xla and not for_docker:
-            leading.append("xla")
-        if self.is_vulkan and not for_docker:
-            leading.append("vulkan")
-        if self.is_libtorch and not for_docker:
-            leading.append("libtorch")
-        if self.is_pure_torch and not for_docker:
-            leading.append("pure_torch")
-        if self.parallel_backend is not None and not for_docker:
-            leading.append(self.parallel_backend)
-
-        cuda_parms = []
-        if self.cuda_version:
-            cudnn = "cudnn8" if self.cuda_version.startswith("11.") else "cudnn7"
-            cuda_parms.extend(["cuda" + self.cuda_version, cudnn])
-        if self.rocm_version:
-            cuda_parms.extend([f"rocm{self.rocm_version}"])
-        result = leading + ["linux", self.distro] + cuda_parms + self.parms
-        if not for_docker and self.parms_list_ignored_for_docker_image is not None:
-            result = result + self.parms_list_ignored_for_docker_image
-        return result
-
-    def gen_docker_image_path(self):
-        parms_source = self.parent_build or self
-        base_build_env_name = "-".join(parms_source.get_parms(True))
-        image_name, _ = gen_docker_image(base_build_env_name)
-        return miniutils.quote(image_name)
-
-    def gen_docker_image_requires(self):
-        parms_source = self.parent_build or self
-        base_build_env_name = "-".join(parms_source.get_parms(True))
-        _, requires = gen_docker_image(base_build_env_name)
-        return miniutils.quote(requires)
-
-    def get_build_job_name_pieces(self, build_or_test):
-        return self.get_parms(False) + [build_or_test]
-
-    def gen_build_name(self, build_or_test):
-        return (
-            ("_".join(map(str, self.get_build_job_name_pieces(build_or_test))))
-            .replace(".", "_")
-            .replace("-", "_")
-        )
-
-    def get_dependents(self):
-        return self.dependent_tests or []
-
-    def gen_workflow_params(self, phase):
-        parameters = OrderedDict()
-        build_job_name_pieces = self.get_build_job_name_pieces(phase)
-
-        build_env_name = "-".join(map(str, build_job_name_pieces))
-        parameters["build_environment"] = miniutils.quote(build_env_name)
-        parameters["docker_image"] = self.gen_docker_image_path()
-        if Conf.is_test_phase(phase) and self.gpu_resource:
-            parameters["use_cuda_docker_runtime"] = miniutils.quote("1")
-        if Conf.is_test_phase(phase):
-            resource_class = "large"
-            if self.gpu_resource:
-                resource_class = "gpu." + self.gpu_resource
-            if self.rocm_version is not None:
-                resource_class = "pytorch/amd-gpu"
-            parameters["resource_class"] = resource_class
-        if phase == "build" and self.rocm_version is not None:
-            parameters["resource_class"] = "xlarge"
-        if hasattr(self, 'filters'):
-            parameters['filters'] = self.filters
-        if self.build_only:
-            parameters['build_only'] = miniutils.quote(str(int(True)))
-        return parameters
-
-    def gen_workflow_job(self, phase):
-        job_def = OrderedDict()
-        job_def["name"] = self.gen_build_name(phase)
-
-        if Conf.is_test_phase(phase):
-
-            # TODO When merging the caffe2 and pytorch jobs, it might be convenient for a while to make a
-            #  caffe2 test job dependent on a pytorch build job. This way we could quickly dedup the repeated
-            #  build of pytorch in the caffe2 build job, and just run the caffe2 tests off of a completed
-            #  pytorch build job (from https://github.com/pytorch/pytorch/pull/17323#discussion_r259452641)
-
-            dependency_build = self.parent_build or self
-            job_def["requires"] = [dependency_build.gen_build_name("build")]
-            job_name = "pytorch_linux_test"
-        else:
-            job_name = "pytorch_linux_build"
-            job_def["requires"] = [self.gen_docker_image_requires()]
-
-        if not self.is_important:
-            job_def["filters"] = gen_filter_dict()
-        job_def.update(self.gen_workflow_params(phase))
-
-        return {job_name: job_def}
-
-
-# TODO This is a hack to special case some configs just for the workflow list
-class HiddenConf(object):
-    def __init__(self, name, parent_build=None, filters=None):
-        self.name = name
-        self.parent_build = parent_build
-        self.filters = filters
-
-    def gen_workflow_job(self, phase):
-        return {
-            self.gen_build_name(phase): {
-                "requires": [self.parent_build.gen_build_name("build")],
-                "filters": self.filters,
-            }
-        }
-
-    def gen_build_name(self, _):
-        return self.name
-
-class DocPushConf(object):
-    def __init__(self, name, parent_build=None, branch="master"):
-        self.name = name
-        self.parent_build = parent_build
-        self.branch = branch
-
-    def gen_workflow_job(self, phase):
-        return {
-            "pytorch_doc_push": {
-                "name": self.name,
-                "branch": self.branch,
-                "requires": [self.parent_build],
-                "context": "org-member",
-                "filters": gen_filter_dict(branches_list=["nightly"],
-                                           tags_list=RC_PATTERN)
-            }
-        }
-
-def gen_docs_configs(xenial_parent_config):
-    configs = []
-
-    configs.append(
-        HiddenConf(
-            "pytorch_python_doc_build",
-            parent_build=xenial_parent_config,
-            filters=gen_filter_dict(branches_list=["master", "nightly"],
-                                    tags_list=RC_PATTERN),
-        )
-    )
-    configs.append(
-        DocPushConf(
-            "pytorch_python_doc_push",
-            parent_build="pytorch_python_doc_build",
-            branch="site",
-        )
-    )
-
-    configs.append(
-        HiddenConf(
-            "pytorch_cpp_doc_build",
-            parent_build=xenial_parent_config,
-            filters=gen_filter_dict(branches_list=["master", "nightly"],
-                                    tags_list=RC_PATTERN),
-        )
-    )
-    configs.append(
-        DocPushConf(
-            "pytorch_cpp_doc_push",
-            parent_build="pytorch_cpp_doc_build",
-            branch="master",
-        )
-    )
-    return configs
-
-
-def get_root():
-    return TopLevelNode("PyTorch Builds", CONFIG_TREE_DATA)
-
-
-def gen_tree():
-    root = get_root()
-    configs_list = conf_tree.dfs(root)
-    return configs_list
-
-
-def instantiate_configs(only_slow_gradcheck):
-
-    config_list = []
-
-    root = get_root()
-    found_configs = conf_tree.dfs(root)
-    for fc in found_configs:
-
-        restrict_phases = None
-        distro_name = fc.find_prop("distro_name")
-        compiler_name = fc.find_prop("compiler_name")
-        compiler_version = fc.find_prop("compiler_version")
-        is_xla = fc.find_prop("is_xla") or False
-        is_asan = fc.find_prop("is_asan") or False
-        is_noarch = fc.find_prop("is_noarch") or False
-        is_onnx = fc.find_prop("is_onnx") or False
-        is_pure_torch = fc.find_prop("is_pure_torch") or False
-        is_vulkan = fc.find_prop("is_vulkan") or False
-        is_slow_gradcheck = fc.find_prop("is_slow_gradcheck") or False
-        parms_list_ignored_for_docker_image = []
-
-        if only_slow_gradcheck ^ is_slow_gradcheck:
-            continue
-
-        python_version = None
-        if compiler_name == "cuda" or compiler_name == "android":
-            python_version = fc.find_prop("pyver")
-            parms_list = [fc.find_prop("abbreviated_pyver")]
-        else:
-            parms_list = ["py" + fc.find_prop("pyver")]
-
-        cuda_version = None
-        rocm_version = None
-        if compiler_name == "cuda":
-            cuda_version = fc.find_prop("compiler_version")
-
-        elif compiler_name == "rocm":
-            rocm_version = fc.find_prop("compiler_version")
-            restrict_phases = ["build", "test1", "test2", "caffe2_test"]
-
-        elif compiler_name == "android":
-            android_ndk_version = fc.find_prop("compiler_version")
-            # TODO: do we need clang to compile host binaries like protoc?
-            parms_list.append("clang5")
-            parms_list.append("android-ndk-" + android_ndk_version)
-            android_abi = fc.find_prop("android_abi")
-            parms_list_ignored_for_docker_image.append(android_abi)
-            restrict_phases = ["build"]
-
-        elif compiler_name:
-            gcc_version = compiler_name + (fc.find_prop("compiler_version") or "")
-            parms_list.append(gcc_version)
-
-        if is_asan:
-            parms_list.append("asan")
-            python_version = fc.find_prop("pyver")
-            parms_list[0] = fc.find_prop("abbreviated_pyver")
-
-        if is_noarch:
-            parms_list_ignored_for_docker_image.append("noarch")
-
-        if is_onnx:
-            parms_list.append("onnx")
-            python_version = fc.find_prop("pyver")
-            parms_list[0] = fc.find_prop("abbreviated_pyver")
-            restrict_phases = ["build", "ort_test1", "ort_test2"]
-
-        if cuda_version:
-            cuda_gcc_version = fc.find_prop("cuda_gcc_override") or "gcc7"
-            parms_list.append(cuda_gcc_version)
-
-        is_libtorch = fc.find_prop("is_libtorch") or False
-        is_important = fc.find_prop("is_important") or False
-        parallel_backend = fc.find_prop("parallel_backend") or None
-        build_only = fc.find_prop("build_only") or False
-        shard_test = fc.find_prop("shard_test") or False
-        # TODO: fix pure_torch python test packaging issue.
-        if shard_test:
-            restrict_phases = ["build"] if restrict_phases is None else restrict_phases
-            restrict_phases.extend(["test1", "test2"])
-        if build_only or is_pure_torch:
-            restrict_phases = ["build"]
-
-        if is_slow_gradcheck:
-            parms_list_ignored_for_docker_image.append("old")
-            parms_list_ignored_for_docker_image.append("gradcheck")
-
-        gpu_resource = None
-        if cuda_version and cuda_version != "10":
-            gpu_resource = "medium"
-
-        c = Conf(
-            distro_name,
-            parms_list,
-            parms_list_ignored_for_docker_image,
-            python_version,
-            cuda_version,
-            rocm_version,
-            is_xla,
-            is_vulkan,
-            is_pure_torch,
-            restrict_phases,
-            gpu_resource,
-            is_libtorch=is_libtorch,
-            is_important=is_important,
-            parallel_backend=parallel_backend,
-            build_only=build_only,
-        )
-
-        # run docs builds on "pytorch-linux-xenial-py3.6-gcc5.4". Docs builds
-        # should run on a CPU-only build that runs on all PRs.
-        # XXX should this be updated to a more modern build? Projects are
-        #     beginning to drop python3.6
-        if (
-            distro_name == "xenial"
-            and fc.find_prop("pyver") == "3.6"
-            and cuda_version is None
-            and parallel_backend is None
-            and not is_vulkan
-            and not is_pure_torch
-            and compiler_name == "gcc"
-            and fc.find_prop("compiler_version") == "5.4"
-        ):
-            c.filters = gen_filter_dict(branches_list=r"/.*/",
-                                        tags_list=RC_PATTERN)
-            c.dependent_tests = gen_docs_configs(c)
-
-        config_list.append(c)
-
-    return config_list
-
-
-def get_workflow_jobs(only_slow_gradcheck=False):
-
-    config_list = instantiate_configs(only_slow_gradcheck)
-
-    x = []
-    for conf_options in config_list:
-
-        phases = conf_options.restrict_phases or dimensions.PHASES
-
-        for phase in phases:
-
-            # TODO why does this not have a test?
-            if Conf.is_test_phase(phase) and conf_options.cuda_version == "10":
-                continue
-
-            x.append(conf_options.gen_workflow_job(phase))
-
-        # TODO convert to recursion
-        for conf in conf_options.get_dependents():
-            x.append(conf.gen_workflow_job("test"))
-
-    return x

.circleci/cimodel/data/simple/anaconda_prune_defintions.py

@@ -1,28 +0,0 @@
-from collections import OrderedDict
-
-from cimodel.data.simple.util.branch_filters import gen_filter_dict
-from cimodel.lib.miniutils import quote
-
-
-CHANNELS_TO_PRUNE = ["pytorch-nightly", "pytorch-test"]
-PACKAGES_TO_PRUNE = "pytorch torchvision torchaudio torchtext ignite torchcsprng"
-
-
-def gen_workflow_job(channel: str):
-    return OrderedDict(
-        {
-            "anaconda_prune": OrderedDict(
-                {
-                    "name": f"anaconda-prune-{channel}",
-                    "context": quote("org-member"),
-                    "packages": quote(PACKAGES_TO_PRUNE),
-                    "channel": channel,
-                    "filters": gen_filter_dict(branches_list=["postnightly"]),
-                }
-            )
-        }
-    )
-
-
-def get_workflow_jobs():
-    return [gen_workflow_job(channel) for channel in CHANNELS_TO_PRUNE]

.circleci/cimodel/data/simple/android_definitions.py

@@ -1,103 +0,0 @@
-import cimodel.data.simple.util.branch_filters as branch_filters
-from cimodel.data.simple.util.docker_constants import (
-    DOCKER_IMAGE_NDK, DOCKER_REQUIREMENT_NDK
-)
-
-
-class AndroidJob:
-    def __init__(self,
-                 variant,
-                 template_name,
-                 is_master_only=True):
-
-        self.variant = variant
-        self.template_name = template_name
-        self.is_master_only = is_master_only
-
-    def gen_tree(self):
-
-        base_name_parts = [
-            "pytorch",
-            "linux",
-            "xenial",
-            "py3",
-            "clang5",
-            "android",
-            "ndk",
-            "r19c",
-        ] + self.variant + [
-            "build",
-        ]
-
-        full_job_name = "_".join(base_name_parts)
-        build_env_name = "-".join(base_name_parts)
-
-        props_dict = {
-            "name": full_job_name,
-            "build_environment": "\"{}\"".format(build_env_name),
-            "docker_image": "\"{}\"".format(DOCKER_IMAGE_NDK),
-            "requires": [DOCKER_REQUIREMENT_NDK]
-        }
-
-        if self.is_master_only:
-            props_dict["filters"] = branch_filters.gen_filter_dict(branch_filters.NON_PR_BRANCH_LIST)
-
-        return [{self.template_name: props_dict}]
-
-
-class AndroidGradleJob:
-    def __init__(self,
-                 job_name,
-                 template_name,
-                 dependencies,
-                 is_master_only=True,
-                 is_pr_only=False,
-                 extra_props=tuple()):
-
-        self.job_name = job_name
-        self.template_name = template_name
-        self.dependencies = dependencies
-        self.is_master_only = is_master_only
-        self.is_pr_only = is_pr_only
-        self.extra_props = dict(extra_props)
-
-    def gen_tree(self):
-
-        props_dict = {
-            "name": self.job_name,
-            "requires": self.dependencies,
-        }
-
-        if self.is_master_only:
-            props_dict["filters"] = branch_filters.gen_filter_dict(branch_filters.NON_PR_BRANCH_LIST)
-        elif self.is_pr_only:
-            props_dict["filters"] = branch_filters.gen_filter_dict(branch_filters.PR_BRANCH_LIST)
-        if self.extra_props:
-            props_dict.update(self.extra_props)
-
-        return [{self.template_name: props_dict}]
-
-
-WORKFLOW_DATA = [
-    AndroidJob(["x86_32"], "pytorch_linux_build", is_master_only=False),
-    AndroidJob(["x86_64"], "pytorch_linux_build"),
-    AndroidJob(["arm", "v7a"], "pytorch_linux_build"),
-    AndroidJob(["arm", "v8a"], "pytorch_linux_build"),
-    AndroidGradleJob(
-        "pytorch-linux-xenial-py3-clang5-android-ndk-r19c-gradle-build-x86_32",
-        "pytorch_android_gradle_build-x86_32",
-        ["pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_32_build"],
-        is_master_only=False,
-        is_pr_only=True),
-    AndroidGradleJob(
-        "pytorch-linux-xenial-py3-clang5-android-ndk-r19c-gradle-build",
-        "pytorch_android_gradle_build",
-        ["pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_32_build",
-         "pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_64_build",
-         "pytorch_linux_xenial_py3_clang5_android_ndk_r19c_arm_v7a_build",
-         "pytorch_linux_xenial_py3_clang5_android_ndk_r19c_arm_v8a_build"]),
-]
-
-
-def get_workflow_jobs():
-    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/binary_smoketest.py

@@ -1,193 +0,0 @@
-"""
-TODO: Refactor circleci/cimodel/data/binary_build_data.py to generate this file
-       instead of doing one offs here
- Binary builds (subset, to smoke test that they'll work)
-
- NB: If you modify this file, you need to also modify
- the binary_and_smoke_tests_on_pr variable in
- pytorch-ci-hud to adjust the allowed build list
- at https://github.com/ezyang/pytorch-ci-hud/blob/master/src/BuildHistoryDisplay.js
-
- Note:
- This binary build is currently broken, see https://github_com/pytorch/pytorch/issues/16710
- - binary_linux_conda_3_6_cu90_devtoolset7_build
- - binary_linux_conda_3_6_cu90_devtoolset7_test
-
- TODO
- we should test a libtorch cuda build, but they take too long
- - binary_linux_libtorch_3_6m_cu90_devtoolset7_static-without-deps_build
-"""
-
-import cimodel.lib.miniutils as miniutils
-import cimodel.data.simple.util.branch_filters
-
-
-class SmoketestJob:
-    def __init__(self,
-                 template_name,
-                 build_env_parts,
-                 docker_image,
-                 job_name,
-                 is_master_only=False,
-                 requires=None,
-                 has_libtorch_variant=False,
-                 extra_props=None):
-
-        self.template_name = template_name
-        self.build_env_parts = build_env_parts
-        self.docker_image = docker_image
-        self.job_name = job_name
-        self.is_master_only = is_master_only
-        self.requires = requires or []
-        self.has_libtorch_variant = has_libtorch_variant
-        self.extra_props = extra_props or {}
-
-    def gen_tree(self):
-
-        props_dict = {
-            "build_environment": " ".join(self.build_env_parts),
-            "name": self.job_name,
-            "requires": self.requires,
-        }
-
-        if self.docker_image:
-            props_dict["docker_image"] = self.docker_image
-
-        if self.is_master_only:
-            props_dict["filters"] = cimodel.data.simple.util.branch_filters.gen_filter_dict()
-
-        if self.has_libtorch_variant:
-            props_dict["libtorch_variant"] = "shared-with-deps"
-
-        props_dict.update(self.extra_props)
-
-        return [{self.template_name: props_dict}]
-
-
-WORKFLOW_DATA = [
-    SmoketestJob(
-        "binary_linux_build",
-        ["manywheel", "3.7m", "cu102", "devtoolset7"],
-        "pytorch/manylinux-cuda102",
-        "binary_linux_manywheel_3_7m_cu102_devtoolset7_build",
-        is_master_only=True,
-    ),
-    SmoketestJob(
-        "binary_linux_build",
-        ["libtorch", "3.7m", "cpu", "devtoolset7"],
-        "pytorch/manylinux-cuda102",
-        "binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_build",
-        is_master_only=True,
-        has_libtorch_variant=True,
-    ),
-    SmoketestJob(
-        "binary_linux_build",
-        ["libtorch", "3.7m", "cpu", "gcc5.4_cxx11-abi"],
-        "pytorch/pytorch-binary-docker-image-ubuntu16.04:latest",
-        "binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_build",
-        is_master_only=False,
-        has_libtorch_variant=True,
-    ),
-    SmoketestJob(
-        "binary_mac_build",
-        ["wheel", "3.7", "cpu"],
-        None,
-        "binary_macos_wheel_3_7_cpu_build",
-        is_master_only=True,
-    ),
-    # This job has an average run time of 3 hours o.O
-    # Now only running this on master to reduce overhead
-    SmoketestJob(
-        "binary_mac_build",
-        ["libtorch", "3.7", "cpu"],
-        None,
-        "binary_macos_libtorch_3_7_cpu_build",
-        is_master_only=True,
-    ),
-    SmoketestJob(
-        "binary_windows_build",
-        ["libtorch", "3.7", "cpu", "debug"],
-        None,
-        "binary_windows_libtorch_3_7_cpu_debug_build",
-        is_master_only=True,
-    ),
-    SmoketestJob(
-        "binary_windows_build",
-        ["libtorch", "3.7", "cpu", "release"],
-        None,
-        "binary_windows_libtorch_3_7_cpu_release_build",
-        is_master_only=True,
-    ),
-    SmoketestJob(
-        "binary_windows_build",
-        ["wheel", "3.7", "cu113"],
-        None,
-        "binary_windows_wheel_3_7_cu113_build",
-        is_master_only=True,
-    ),
-
-    SmoketestJob(
-        "binary_windows_test",
-        ["libtorch", "3.7", "cpu", "debug"],
-        None,
-        "binary_windows_libtorch_3_7_cpu_debug_test",
-        is_master_only=True,
-        requires=["binary_windows_libtorch_3_7_cpu_debug_build"],
-    ),
-    SmoketestJob(
-        "binary_windows_test",
-        ["libtorch", "3.7", "cpu", "release"],
-        None,
-        "binary_windows_libtorch_3_7_cpu_release_test",
-        is_master_only=False,
-        requires=["binary_windows_libtorch_3_7_cpu_release_build"],
-    ),
-    SmoketestJob(
-        "binary_windows_test",
-        ["wheel", "3.7", "cu113"],
-        None,
-        "binary_windows_wheel_3_7_cu113_test",
-        is_master_only=True,
-        requires=["binary_windows_wheel_3_7_cu113_build"],
-        extra_props={
-            "executor": "windows-with-nvidia-gpu",
-        },
-    ),
-
-
-
-    SmoketestJob(
-        "binary_linux_test",
-        ["manywheel", "3.7m", "cu102", "devtoolset7"],
-        "pytorch/manylinux-cuda102",
-        "binary_linux_manywheel_3_7m_cu102_devtoolset7_test",
-        is_master_only=True,
-        requires=["binary_linux_manywheel_3_7m_cu102_devtoolset7_build"],
-        extra_props={
-            "resource_class": "gpu.medium",
-            "use_cuda_docker_runtime": miniutils.quote((str(1))),
-        },
-    ),
-    SmoketestJob(
-        "binary_linux_test",
-        ["libtorch", "3.7m", "cpu", "devtoolset7"],
-        "pytorch/manylinux-cuda102",
-        "binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_test",
-        is_master_only=True,
-        requires=["binary_linux_libtorch_3_7m_cpu_devtoolset7_shared-with-deps_build"],
-        has_libtorch_variant=True,
-    ),
-    SmoketestJob(
-        "binary_linux_test",
-        ["libtorch", "3.7m", "cpu", "gcc5.4_cxx11-abi"],
-        "pytorch/pytorch-binary-docker-image-ubuntu16.04:latest",
-        "binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_test",
-        is_master_only=True,
-        requires=["binary_linux_libtorch_3_7m_cpu_gcc5_4_cxx11-abi_shared-with-deps_build"],
-        has_libtorch_variant=True,
-    ),
-]
-
-
-def get_workflow_jobs():
-    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/docker_definitions.py

@@ -1,39 +0,0 @@
-from collections import OrderedDict
-
-from cimodel.lib.miniutils import quote
-from cimodel.data.simple.util.branch_filters import gen_filter_dict, RC_PATTERN
-
-
-# NOTE: All hardcoded docker image builds have been migrated to GHA
-IMAGE_NAMES = [
-]
-
-# This entry should be an element from the list above
-# This should contain the image matching the "slow_gradcheck" entry in
-# pytorch_build_data.py
-SLOW_GRADCHECK_IMAGE_NAME = "pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"
-
-def get_workflow_jobs(images=IMAGE_NAMES, only_slow_gradcheck=False):
-    """Generates a list of docker image build definitions"""
-    ret = []
-    for image_name in images:
-        if image_name.startswith('docker-'):
-            image_name = image_name.lstrip('docker-')
-        if only_slow_gradcheck and image_name is not SLOW_GRADCHECK_IMAGE_NAME:
-            continue
-
-        parameters = OrderedDict({
-            "name": quote(f"docker-{image_name}"),
-            "image_name": quote(image_name),
-        })
-        if image_name == "pytorch-linux-xenial-py3.6-gcc5.4":
-            # pushing documentation on tags requires CircleCI to also
-            # build all the dependencies on tags, including this docker image
-            parameters['filters'] = gen_filter_dict(branches_list=r"/.*/",
-                                                    tags_list=RC_PATTERN)
-        ret.append(OrderedDict(
-            {
-                "docker_build_job": parameters
-            }
-        ))
-    return ret

.circleci/cimodel/data/simple/ios_definitions.py

@@ -1,88 +0,0 @@
-from cimodel.data.simple.util.versions import MultiPartVersion
-import cimodel.lib.miniutils as miniutils
-
-XCODE_VERSION = MultiPartVersion([12, 5, 1])
-
-
-class ArchVariant:
-    def __init__(self, name, custom_build_name=""):
-        self.name = name
-        self.custom_build_name = custom_build_name
-
-    def render(self):
-        extra_parts = [self.custom_build_name] if len(self.custom_build_name) > 0 else []
-        return "_".join([self.name] + extra_parts)
-
-
-def get_platform(arch_variant_name):
-    return "SIMULATOR" if arch_variant_name == "x86_64" else "OS"
-
-
-class IOSJob:
-    def __init__(self, xcode_version, arch_variant, is_org_member_context=True, extra_props=None):
-        self.xcode_version = xcode_version
-        self.arch_variant = arch_variant
-        self.is_org_member_context = is_org_member_context
-        self.extra_props = extra_props
-
-    def gen_name_parts(self, with_version_dots):
-
-        version_parts = self.xcode_version.render_dots_or_parts(with_version_dots)
-        build_variant_suffix = "_".join([self.arch_variant.render(), "build"])
-
-        return [
-            "pytorch",
-            "ios",
-        ] + version_parts + [
-            build_variant_suffix,
-        ]
-
-    def gen_job_name(self):
-        return "_".join(self.gen_name_parts(False))
-
-    def gen_tree(self):
-
-        platform_name = get_platform(self.arch_variant.name)
-
-        props_dict = {
-            "build_environment": "-".join(self.gen_name_parts(True)),
-            "ios_arch": self.arch_variant.name,
-            "ios_platform": platform_name,
-            "name": self.gen_job_name(),
-        }
-
-        if self.is_org_member_context:
-            props_dict["context"] = "org-member"
-
-        if self.extra_props:
-            props_dict.update(self.extra_props)
-
-        return [{"pytorch_ios_build": props_dict}]
-
-
-WORKFLOW_DATA = [
-    IOSJob(XCODE_VERSION, ArchVariant("x86_64"), is_org_member_context=False, extra_props={
-        "lite_interpreter": miniutils.quote(str(int(True)))}),
-    IOSJob(XCODE_VERSION, ArchVariant("x86_64", "full_jit"), is_org_member_context=False, extra_props={
-        "lite_interpreter": miniutils.quote(str(int(False)))}),
-    IOSJob(XCODE_VERSION, ArchVariant("arm64"), extra_props={
-        "lite_interpreter": miniutils.quote(str(int(True)))}),
-    IOSJob(XCODE_VERSION, ArchVariant("arm64", "metal"), extra_props={
-        "use_metal": miniutils.quote(str(int(True))),
-        "lite_interpreter": miniutils.quote(str(int(True)))}),
-    IOSJob(XCODE_VERSION, ArchVariant("arm64", "full_jit"), extra_props={
-        "lite_interpreter": miniutils.quote(str(int(False)))}),
-    IOSJob(XCODE_VERSION, ArchVariant("arm64", "custom"), extra_props={
-        "op_list": "mobilenetv2.yaml",
-        "lite_interpreter": miniutils.quote(str(int(True)))}),
-    IOSJob(XCODE_VERSION, ArchVariant("x86_64", "coreml"), is_org_member_context=False, extra_props={
-        "use_coreml": miniutils.quote(str(int(True))),
-        "lite_interpreter": miniutils.quote(str(int(True)))}),
-    IOSJob(XCODE_VERSION, ArchVariant("arm64", "coreml"), extra_props={
-        "use_coreml": miniutils.quote(str(int(True))),
-        "lite_interpreter": miniutils.quote(str(int(True)))}),
-]
-
-
-def get_workflow_jobs():
-    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/macos_definitions.py

@@ -1,52 +0,0 @@
-class MacOsJob:
-    def __init__(self, os_version, is_build=False, is_test=False, extra_props=tuple()):
-        # extra_props is tuple type, because mutable data structures for argument defaults
-        # is not recommended.
-        self.os_version = os_version
-        self.is_build = is_build
-        self.is_test = is_test
-        self.extra_props = dict(extra_props)
-
-    def gen_tree(self):
-        non_phase_parts = ["pytorch", "macos", self.os_version, "py3"]
-
-        extra_name_list = [name for name, exist in self.extra_props.items() if exist]
-        full_job_name_list = non_phase_parts + extra_name_list + [
-            'build' if self.is_build else None,
-            'test' if self.is_test else None,
-        ]
-
-        full_job_name = "_".join(list(filter(None, full_job_name_list)))
-
-        test_build_dependency = "_".join(non_phase_parts + ["build"])
-        extra_dependencies = [test_build_dependency] if self.is_test else []
-        job_dependencies = extra_dependencies
-
-        # Yes we name the job after itself, it needs a non-empty value in here
-        # for the YAML output to work.
-        props_dict = {"requires": job_dependencies, "name": full_job_name}
-
-        return [{full_job_name: props_dict}]
-
-
-WORKFLOW_DATA = [
-    MacOsJob("10_15", is_build=True),
-    MacOsJob("10_13", is_build=True),
-    MacOsJob(
-        "10_13",
-        is_build=False,
-        is_test=True,
-    ),
-    MacOsJob(
-        "10_13",
-        is_build=True,
-        is_test=True,
-        extra_props=tuple({
-            "lite_interpreter": True
-        }.items()),
-    )
-]
-
-
-def get_workflow_jobs():
-    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/mobile_definitions.py

@@ -1,53 +0,0 @@
-"""
-PyTorch Mobile PR builds (use linux host toolchain + mobile build options)
-"""
-
-import cimodel.lib.miniutils as miniutils
-import cimodel.data.simple.util.branch_filters
-
-
-class MobileJob:
-    def __init__(
-            self,
-            docker_image,
-            docker_requires,
-            variant_parts,
-            is_master_only=False):
-        self.docker_image = docker_image
-        self.docker_requires = docker_requires
-        self.variant_parts = variant_parts
-        self.is_master_only = is_master_only
-
-    def gen_tree(self):
-        non_phase_parts = [
-            "pytorch",
-            "linux",
-            "xenial",
-            "py3",
-            "clang5",
-            "mobile",
-        ] + self.variant_parts
-
-        full_job_name = "_".join(non_phase_parts)
-        build_env_name = "-".join(non_phase_parts)
-
-        props_dict = {
-            "build_environment": build_env_name,
-            "build_only": miniutils.quote(str(int(True))),
-            "docker_image": self.docker_image,
-            "requires": self.docker_requires,
-            "name": full_job_name,
-        }
-
-        if self.is_master_only:
-            props_dict["filters"] = cimodel.data.simple.util.branch_filters.gen_filter_dict()
-
-        return [{"pytorch_linux_build": props_dict}]
-
-
-WORKFLOW_DATA = [
-]
-
-
-def get_workflow_jobs():
-    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/nightly_android.py

@@ -1,77 +0,0 @@
-from cimodel.data.simple.util.docker_constants import (
-    DOCKER_IMAGE_NDK,
-    DOCKER_REQUIREMENT_NDK
-)
-
-
-class AndroidNightlyJob:
-    def __init__(self,
-                 variant,
-                 template_name,
-                 extra_props=None,
-                 with_docker=True,
-                 requires=None,
-                 no_build_suffix=False):
-
-        self.variant = variant
-        self.template_name = template_name
-        self.extra_props = extra_props or {}
-        self.with_docker = with_docker
-        self.requires = requires
-        self.no_build_suffix = no_build_suffix
-
-    def gen_tree(self):
-
-        base_name_parts = [
-            "pytorch",
-            "linux",
-            "xenial",
-            "py3",
-            "clang5",
-            "android",
-            "ndk",
-            "r19c",
-        ] + self.variant
-
-        build_suffix = [] if self.no_build_suffix else ["build"]
-        full_job_name = "_".join(["nightly"] + base_name_parts + build_suffix)
-        build_env_name = "-".join(base_name_parts)
-
-        props_dict = {
-            "name": full_job_name,
-            "requires": self.requires,
-            "filters": {"branches": {"only": "nightly"}},
-        }
-
-        props_dict.update(self.extra_props)
-
-        if self.with_docker:
-            props_dict["docker_image"] = DOCKER_IMAGE_NDK
-            props_dict["build_environment"] = build_env_name
-
-        return [{self.template_name: props_dict}]
-
-BASE_REQUIRES = [DOCKER_REQUIREMENT_NDK]
-
-WORKFLOW_DATA = [
-    AndroidNightlyJob(["x86_32"], "pytorch_linux_build", requires=BASE_REQUIRES),
-    AndroidNightlyJob(["x86_64"], "pytorch_linux_build", requires=BASE_REQUIRES),
-    AndroidNightlyJob(["arm", "v7a"], "pytorch_linux_build", requires=BASE_REQUIRES),
-    AndroidNightlyJob(["arm", "v8a"], "pytorch_linux_build", requires=BASE_REQUIRES),
-    AndroidNightlyJob(["android_gradle"], "pytorch_android_gradle_build",
-                      with_docker=False,
-                      requires=[
-                          "nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_32_build",
-                          "nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_x86_64_build",
-                          "nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_arm_v7a_build",
-                          "nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_arm_v8a_build"]),
-    AndroidNightlyJob(["x86_32_android_publish_snapshot"], "pytorch_android_publish_snapshot",
-                      extra_props={"context": "org-member"},
-                      with_docker=False,
-                      requires=["nightly_pytorch_linux_xenial_py3_clang5_android_ndk_r19c_android_gradle_build"],
-                      no_build_suffix=True),
-]
-
-
-def get_workflow_jobs():
-    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/nightly_ios.py

@@ -1,85 +0,0 @@
-import cimodel.data.simple.ios_definitions as ios_definitions
-import cimodel.lib.miniutils as miniutils
-
-
-class IOSNightlyJob:
-    def __init__(self,
-                 variant,
-                 is_full_jit=False,
-                 is_upload=False):
-
-        self.variant = variant
-        self.is_full_jit = is_full_jit
-        self.is_upload = is_upload
-
-    def get_phase_name(self):
-        return "upload" if self.is_upload else "build"
-
-    def get_common_name_pieces(self, with_version_dots):
-
-        extra_name_suffix = [self.get_phase_name()] if self.is_upload else []
-
-        extra_name = ["full_jit"] if self.is_full_jit else []
-
-        common_name_pieces = [
-            "ios",
-        ] + extra_name + [
-        ] + ios_definitions.XCODE_VERSION.render_dots_or_parts(with_version_dots) + [
-            "nightly",
-            self.variant,
-            "build",
-        ] + extra_name_suffix
-
-        return common_name_pieces
-
-    def gen_job_name(self):
-        return "_".join(["pytorch"] + self.get_common_name_pieces(False))
-
-    def gen_tree(self):
-        build_configs = BUILD_CONFIGS_FULL_JIT if self.is_full_jit else BUILD_CONFIGS
-        extra_requires = [x.gen_job_name() for x in build_configs] if self.is_upload else []
-
-        props_dict = {
-            "build_environment": "-".join(["libtorch"] + self.get_common_name_pieces(True)),
-            "requires": extra_requires,
-            "context": "org-member",
-            "filters": {"branches": {"only": "nightly"}},
-        }
-
-        if not self.is_upload:
-            props_dict["ios_arch"] = self.variant
-            props_dict["ios_platform"] = ios_definitions.get_platform(self.variant)
-            props_dict["name"] = self.gen_job_name()
-            props_dict["use_metal"] = miniutils.quote(str(int(True)))
-            props_dict["use_coreml"] = miniutils.quote(str(int(True)))
-
-        if self.is_full_jit:
-            props_dict["lite_interpreter"] = miniutils.quote(str(int(False)))
-
-        template_name = "_".join([
-            "binary",
-            "ios",
-            self.get_phase_name(),
-        ])
-
-        return [{template_name: props_dict}]
-
-
-BUILD_CONFIGS = [
-    IOSNightlyJob("x86_64"),
-    IOSNightlyJob("arm64"),
-]
-
-BUILD_CONFIGS_FULL_JIT = [
-    IOSNightlyJob("x86_64", is_full_jit=True),
-    IOSNightlyJob("arm64", is_full_jit=True),
-]
-
-WORKFLOW_DATA = BUILD_CONFIGS + BUILD_CONFIGS_FULL_JIT + [
-    IOSNightlyJob("binary", is_full_jit=False, is_upload=True),
-    IOSNightlyJob("binary", is_full_jit=True, is_upload=True),
-]
-
-
-def get_workflow_jobs():
-    return [item.gen_tree() for item in WORKFLOW_DATA]

.circleci/cimodel/data/simple/util/branch_filters.py

@@ -1,27 +0,0 @@
-NON_PR_BRANCH_LIST = [
-    "master",
-    r"/ci-all\/.*/",
-    r"/release\/.*/",
-]
-
-PR_BRANCH_LIST = [
-    r"/gh\/.*\/head/",
-    r"/pull\/.*/",
-]
-
-RC_PATTERN = r"/v[0-9]+(\.[0-9]+)*-rc[0-9]+/"
-
-def gen_filter_dict(
-        branches_list=NON_PR_BRANCH_LIST,
-        tags_list=None
-):
-    """Generates a filter dictionary for use with CircleCI's job filter"""
-    filter_dict = {
-        "branches": {
-            "only": branches_list,
-        },
-    }
-
-    if tags_list is not None:
-        filter_dict["tags"] = {"only": tags_list}
-    return filter_dict

.circleci/cimodel/data/simple/util/docker_constants.py

@@ -1,33 +0,0 @@
-AWS_DOCKER_HOST = "308535385114.dkr.ecr.us-east-1.amazonaws.com"
-
-def gen_docker_image(container_type):
-    return (
-        "/".join([AWS_DOCKER_HOST, "pytorch", container_type]),
-        f"docker-{container_type}",
-    )
-
-def gen_docker_image_requires(image_name):
-    return [f"docker-{image_name}"]
-
-
-DOCKER_IMAGE_BASIC, DOCKER_REQUIREMENT_BASE = gen_docker_image(
-    "pytorch-linux-xenial-py3.7-gcc5.4"
-)
-
-DOCKER_IMAGE_CUDA_10_2, DOCKER_REQUIREMENT_CUDA_10_2 = gen_docker_image(
-    "pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7"
-)
-
-DOCKER_IMAGE_GCC7, DOCKER_REQUIREMENT_GCC7 = gen_docker_image(
-    "pytorch-linux-xenial-py3.7-gcc7"
-)
-
-
-def gen_mobile_docker(specifier):
-    container_type = "pytorch-linux-xenial-py3-clang5-" + specifier
-    return gen_docker_image(container_type)
-
-
-DOCKER_IMAGE_ASAN, DOCKER_REQUIREMENT_ASAN = gen_mobile_docker("asan")
-
-DOCKER_IMAGE_NDK, DOCKER_REQUIREMENT_NDK = gen_mobile_docker("android-ndk-r19c")

.circleci/cimodel/data/simple/util/versions.py

@@ -1,34 +0,0 @@
-class MultiPartVersion:
-    def __init__(self, parts, prefix=""):
-        self.parts = parts
-        self.prefix = prefix
-
-    def prefixed_parts(self):
-        """
-        Prepends the first element of the version list
-        with the prefix string.
-        """
-        if self.parts:
-            return [self.prefix + str(self.parts[0])] + [str(part) for part in self.parts[1:]]
-        else:
-            return [self.prefix]
-
-    def render_dots(self):
-        return ".".join(self.prefixed_parts())
-
-    def render_dots_or_parts(self, with_dots):
-        if with_dots:
-            return [self.render_dots()]
-        else:
-            return self.prefixed_parts()
-
-
-class CudaVersion(MultiPartVersion):
-    def __init__(self, major, minor):
-        self.major = major
-        self.minor = minor
-
-        super().__init__([self.major, self.minor], "cuda")
-
-    def __str__(self):
-        return f"{self.major}.{self.minor}"

.circleci/cimodel/lib/conf_tree.py

@@ -1,107 +0,0 @@
-from dataclasses import dataclass, field
-from typing import Optional, Dict
-
-
-def X(val):
-    """
-    Compact way to write a leaf node
-    """
-    return val, []
-
-
-def XImportant(name):
-    """Compact way to write an important (run on PRs) leaf node"""
-    return (name, [("important", [X(True)])])
-
-
-@dataclass
-class Ver:
-    """
-    Represents a product with a version number
-    """
-    name: str
-    version: str = ""
-
-    def __str__(self):
-        return self.name + self.version
-
-
-@dataclass
-class ConfigNode:
-    parent: Optional['ConfigNode']
-    node_name: str
-    props: Dict[str, str] = field(default_factory=dict)
-
-    def get_label(self):
-        return self.node_name
-
-    # noinspection PyMethodMayBeStatic
-    def get_children(self):
-        return []
-
-    def get_parents(self):
-        return (self.parent.get_parents() + [self.parent.get_label()]) if self.parent else []
-
-    def get_depth(self):
-        return len(self.get_parents())
-
-    def get_node_key(self):
-        return "%".join(self.get_parents() + [self.get_label()])
-
-    def find_prop(self, propname, searched=None):
-        """
-        Checks if its own dictionary has
-        the property, otherwise asks parent node.
-        """
-
-        if searched is None:
-            searched = []
-
-        searched.append(self.node_name)
-
-        if propname in self.props:
-            return self.props[propname]
-        elif self.parent:
-            return self.parent.find_prop(propname, searched)
-        else:
-            # raise Exception('Property "%s" does not exist anywhere in the tree! Searched: %s' % (propname, searched))
-            return None
-
-
-def dfs_recurse(
-        node,
-        leaf_callback=lambda x: None,
-        discovery_callback=lambda x, y, z: None,
-        child_callback=lambda x, y: None,
-        sibling_index=0,
-        sibling_count=1):
-
-    discovery_callback(node, sibling_index, sibling_count)
-
-    node_children = node.get_children()
-    if node_children:
-        for i, child in enumerate(node_children):
-            child_callback(node, child)
-
-            dfs_recurse(
-                child,
-                leaf_callback,
-                discovery_callback,
-                child_callback,
-                i,
-                len(node_children),
-            )
-    else:
-        leaf_callback(node)
-
-
-def dfs(toplevel_config_node):
-
-    config_list = []
-
-    def leaf_callback(node):
-        config_list.append(node)
-
-    dfs_recurse(toplevel_config_node, leaf_callback)
-
-    return config_list

.circleci/cimodel/lib/miniutils.py

@@ -1,10 +0,0 @@
-def quote(s):
-    return sandwich('"', s)
-
-
-def sandwich(bread, jam):
-    return bread + jam + bread
-
-
-def override(word, substitutions):
-    return substitutions.get(word, word)

.circleci/cimodel/lib/miniyaml.py

@@ -1,52 +0,0 @@
-from collections import OrderedDict
-
-import cimodel.lib.miniutils as miniutils
-
-
-LIST_MARKER = "- "
-INDENTATION_WIDTH = 2
-
-
-def is_dict(data):
-    return type(data) in [dict, OrderedDict]
-
-
-def is_collection(data):
-    return is_dict(data) or type(data) is list
-
-
-def render(fh, data, depth, is_list_member=False):
-    """
-    PyYaml does not allow precise control over the quoting
-    behavior, especially for merge references.
-    Therefore, we use this custom YAML renderer.
-    """
-
-    indentation = " " * INDENTATION_WIDTH * depth
-
-    if is_dict(data):
-
-        tuples = list(data.items())
-        if type(data) is not OrderedDict:
-            tuples.sort()
-
-        for i, (k, v) in enumerate(tuples):
-            if not v:
-                continue
-            # If this dict is itself a list member, the first key gets prefixed with a list marker
-            list_marker_prefix = LIST_MARKER if is_list_member and not i else ""
-
-            trailing_whitespace = "\n" if is_collection(v) else " "
-            fh.write(indentation + list_marker_prefix + k + ":" + trailing_whitespace)
-
-            render(fh, v, depth + 1 + int(is_list_member))
-
-    elif type(data) is list:
-        for v in data:
-            render(fh, v, depth, True)
-
-    else:
-        # use empty quotes to denote an empty string value instead of blank space
-        modified_data = miniutils.quote(data) if data == "" else data
-        list_member_prefix = indentation + LIST_MARKER if is_list_member else ""
-        fh.write(list_member_prefix + str(modified_data) + "\n")

.circleci/codegen_validation/compare_normalized_yaml.sh

@@ -1,17 +0,0 @@
-#!/bin/bash -xe
-
-
-YAML_FILENAME=verbatim-sources/workflows-pytorch-ge-config-tests.yml
-DIFF_TOOL=meld
-
-
-# Allows this script to be invoked from any directory:
-cd $(dirname "$0")
-
-pushd ..
-
-
-$DIFF_TOOL $YAML_FILENAME <(./codegen_validation/normalize_yaml_fragment.py < $YAML_FILENAME)
-
-
-popd

.circleci/codegen_validation/normalize_yaml_fragment.py

@@ -1,24 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import sys
-import yaml
-
-# Need to import modules that lie on an upward-relative path
-sys.path.append(os.path.join(sys.path[0], '..'))
-
-import cimodel.lib.miniyaml as miniyaml
-
-
-def regurgitate(depth, use_pyyaml_formatter=False):
-    data = yaml.safe_load(sys.stdin)
-
-    if use_pyyaml_formatter:
-        output = yaml.dump(data, sort_keys=True)
-        sys.stdout.write(output)
-    else:
-        miniyaml.render(sys.stdout, data, depth)
-
-
-if __name__ == "__main__":
-    regurgitate(3)

.circleci/codegen_validation/overwrite_with_normalized.sh

@@ -1,15 +0,0 @@
-#!/bin/bash -xe
-
-YAML_FILENAME=$1
-
-# Allows this script to be invoked from any directory:
-cd $(dirname "$0")
-
-pushd ..
-
-TEMP_FILENAME=$(mktemp)
-
-cat $YAML_FILENAME | ./codegen_validation/normalize_yaml_fragment.py > $TEMP_FILENAME
-mv $TEMP_FILENAME $YAML_FILENAME
-
-popd

.circleci/docker/README.md

@@ -1,31 +0,0 @@
-# Docker images for Jenkins
-
-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).
-
-Docker builds are now defined with `.circleci/cimodel/data/simple/docker_definitions.py`
-
-## Contents
-
-* `build.sh` -- dispatch script to launch all builds
-* `common` -- scripts used to execute individual Docker build stages
-* `ubuntu-cuda` -- Dockerfile for Ubuntu image with CUDA support for nvidia-docker
-
-## 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
-```

.circleci/docker/android/AndroidManifest.xml

@@ -1 +0,0 @@
-<manifest package="org.pytorch.deps" />

.circleci/docker/android/build.gradle

@@ -1,66 +0,0 @@
-buildscript {
-    ext {
-        minSdkVersion = 21
-        targetSdkVersion = 28
-        compileSdkVersion = 28
-        buildToolsVersion = '28.0.3'
-
-        coreVersion = "1.2.0"
-        extJUnitVersion = "1.1.1"
-        runnerVersion = "1.2.0"
-        rulesVersion = "1.2.0"
-        junitVersion = "4.12"
-    }
-
-    repositories {
-        google()
-        mavenLocal()
-        mavenCentral()
-        jcenter()
-    }
-
-    dependencies {
-        classpath 'com.android.tools.build:gradle:4.1.2'
-        classpath 'com.vanniktech:gradle-maven-publish-plugin:0.14.2'
-    }
-}
-
-repositories {
-    google()
-    jcenter()
-}
-
-apply plugin: 'com.android.library'
-
-android {
-    compileSdkVersion rootProject.compileSdkVersion
-    buildToolsVersion rootProject.buildToolsVersion
-
-    defaultConfig {
-        minSdkVersion minSdkVersion
-        targetSdkVersion targetSdkVersion
-    }
-
-    sourceSets {
-        main {
-            manifest.srcFile 'AndroidManifest.xml'
-        }
-    }
-}
-
-dependencies {
-    implementation 'com.android.support:appcompat-v7:28.0.0'
-    implementation 'androidx.appcompat:appcompat:1.0.0'
-    implementation 'com.facebook.fbjni:fbjni-java-only:0.2.2'
-    implementation 'com.google.code.findbugs:jsr305:3.0.1'
-    implementation 'com.facebook.soloader:nativeloader:0.10.1'
-
-    implementation 'junit:junit:' + rootProject.junitVersion
-    implementation 'androidx.test:core:' + rootProject.coreVersion
-
-    implementation 'junit:junit:' + rootProject.junitVersion
-    implementation 'androidx.test:core:' + rootProject.coreVersion
-    implementation 'androidx.test.ext:junit:' + rootProject.extJUnitVersion
-    implementation 'androidx.test:rules:' + rootProject.rulesVersion
-    implementation 'androidx.test:runner:' + rootProject.runnerVersion
-}

.circleci/docker/build.sh

@@ -1,406 +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${name}" = xpy ]; then
-      vername=ANACONDA_PYTHON_VERSION
-    fi
-    # skip non-conforming fields such as "pytorch", "linux" or "xenial" without version string
-    if [ -n "${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" == *-xenial* ]]; then
-  UBUNTU_VERSION=16.04
-elif [[ "$image" == *-artful* ]]; then
-  UBUNTU_VERSION=17.10
-elif [[ "$image" == *-bionic* ]]; then
-  UBUNTU_VERSION=18.04
-elif [[ "$image" == *-focal* ]]; then
-  UBUNTU_VERSION=20.04
-elif [[ "$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"
-if [[ "$image" == *cuda* ]]; then
-  DOCKERFILE="${OS}-cuda/Dockerfile"
-elif [[ "$image" == *rocm* ]]; then
-  DOCKERFILE="${OS}-rocm/Dockerfile"
-fi
-
-TRAVIS_DL_URL_PREFIX="https://s3.amazonaws.com/travis-python-archives/binaries/ubuntu/14.04/x86_64"
-
-# 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-xenial-py3.8)
-    ANACONDA_PYTHON_VERSION=3.8
-    CMAKE_VERSION=3.10.3
-    GCC_VERSION=7
-    # Do not install PROTOBUF, DB, and VISION as a test
-    ;;
-  pytorch-linux-xenial-py3.7-gcc5.4)
-    ANACONDA_PYTHON_VERSION=3.7
-    CMAKE_VERSION=3.10.3
-    GCC_VERSION=5
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    ;;
-  pytorch-linux-xenial-py3.7-gcc7.2)
-    ANACONDA_PYTHON_VERSION=3.7
-    CMAKE_VERSION=3.10.3
-    GCC_VERSION=7
-    # Do not install PROTOBUF, DB, and VISION as a test
-    ;;
-  pytorch-linux-xenial-py3.7-gcc7)
-    ANACONDA_PYTHON_VERSION=3.7
-    CMAKE_VERSION=3.10.3
-    GCC_VERSION=7
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ;;
-  pytorch-linux-xenial-cuda10.2-cudnn7-py3-gcc7)
-    CUDA_VERSION=10.2
-    CUDNN_VERSION=7
-    ANACONDA_PYTHON_VERSION=3.7
-    CMAKE_VERSION=3.10.3
-    GCC_VERSION=7
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    ;;
-  pytorch-linux-xenial-cuda11.1-cudnn8-py3-gcc7)
-    CUDA_VERSION=11.1
-    CUDNN_VERSION=8
-    ANACONDA_PYTHON_VERSION=3.7
-    CMAKE_VERSION=3.10.3
-    GCC_VERSION=7
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    ;;
-  pytorch-linux-xenial-cuda11.3-cudnn8-py3-gcc7)
-    CUDA_VERSION=11.3.0 # Deviating from major.minor to conform to nvidia's Docker image names
-    CUDNN_VERSION=8
-    TENSORRT_VERSION=8.0.1.6
-    ANACONDA_PYTHON_VERSION=3.7
-    CMAKE_VERSION=3.10.3
-    GCC_VERSION=7
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    ;;
-  pytorch-linux-bionic-cuda11.5-cudnn8-py3-gcc7)
-    CUDA_VERSION=11.5.0
-    CUDNN_VERSION=8
-    ANACONDA_PYTHON_VERSION=3.7
-    CMAKE_VERSION=3.10.3
-    GCC_VERSION=7
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    KATEX=yes
-    ;;
-  pytorch-linux-xenial-py3-clang5-asan)
-    ANACONDA_PYTHON_VERSION=3.7
-    CLANG_VERSION=5.0
-    CMAKE_VERSION=3.13.5
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ;;
-  pytorch-linux-xenial-py3-clang7-asan)
-    ANACONDA_PYTHON_VERSION=3.7
-    CLANG_VERSION=7
-    CMAKE_VERSION=3.10.3
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ;;
-  pytorch-linux-xenial-py3-clang7-onnx)
-    ANACONDA_PYTHON_VERSION=3.7
-    CLANG_VERSION=7
-    CMAKE_VERSION=3.10.3
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ;;
-  pytorch-linux-xenial-py3-clang5-android-ndk-r19c)
-    ANACONDA_PYTHON_VERSION=3.7
-    CLANG_VERSION=5.0
-    CMAKE_VERSION=3.13.5
-    LLVMDEV=yes
-    PROTOBUF=yes
-    ANDROID=yes
-    ANDROID_NDK_VERSION=r19c
-    GRADLE_VERSION=6.8.3
-    NINJA_VERSION=1.9.0
-    ;;
-  pytorch-linux-xenial-py3.7-clang7)
-    ANACONDA_PYTHON_VERSION=3.7
-    CMAKE_VERSION=3.10.3
-    CLANG_VERSION=7
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ;;
-  pytorch-linux-bionic-py3.7-clang9)
-    ANACONDA_PYTHON_VERSION=3.7
-    CLANG_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    VULKAN_SDK_VERSION=1.2.162.1
-    SWIFTSHADER=yes
-    ;;
-  pytorch-linux-bionic-py3.8-gcc9)
-    ANACONDA_PYTHON_VERSION=3.8
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ;;
-  pytorch-linux-bionic-cuda10.2-cudnn7-py3.7-clang9)
-    CUDA_VERSION=10.2
-    CUDNN_VERSION=7
-    ANACONDA_PYTHON_VERSION=3.7
-    CLANG_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ;;
-  pytorch-linux-bionic-cuda10.2-cudnn7-py3.9-gcc7)
-    CUDA_VERSION=10.2
-    CUDNN_VERSION=7
-    ANACONDA_PYTHON_VERSION=3.9
-    GCC_VERSION=7
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ;;
-  pytorch-linux-bionic-cuda11.0-cudnn8-py3.7-gcc9)
-    CUDA_VERSION=11.0
-    CUDNN_VERSION=8
-    ANACONDA_PYTHON_VERSION=3.7
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ROCM_VERSION=3.9
-    ;;
-  pytorch-linux-bionic-rocm4.3.1-py3.7)
-    ANACONDA_PYTHON_VERSION=3.7
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ROCM_VERSION=4.3.1
-    ;;
-  pytorch-linux-bionic-rocm4.5-py3.7)
-    ANACONDA_PYTHON_VERSION=3.7
-    GCC_VERSION=9
-    PROTOBUF=yes
-    DB=yes
-    VISION=yes
-    ROCM_VERSION=4.5.2
-    ;;
-  *)
-    # 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 [[ "$image" == *xenial* ]]; then
-      CMAKE_VERSION=3.10.3
-    fi
-    if [[ "$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
-    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 [[ "$image" == *cmake* ]]; then
-      extract_version_from_image_name cmake CMAKE_VERSION
-    fi
-  ;;
-esac
-
-# Set Jenkins UID and GID if running Jenkins
-if [ -n "${JENKINS:-}" ]; then
-  JENKINS_UID=$(id -u jenkins)
-  JENKINS_GID=$(id -g jenkins)
-fi
-
-tmp_tag=$(basename "$(mktemp -u)" | tr '[:upper:]' '[:lower:]')
-
-
-# Build image
-# TODO: build-arg THRIFT is not turned on for any image, remove it once we confirm
-# it's no longer needed.
-docker build \
-       --no-cache \
-       --progress=plain \
-       --build-arg "TRAVIS_DL_URL_PREFIX=${TRAVIS_DL_URL_PREFIX}" \
-       --build-arg "BUILD_ENVIRONMENT=${image}" \
-       --build-arg "PROTOBUF=${PROTOBUF:-}" \
-       --build-arg "THRIFT=${THRIFT:-}" \
-       --build-arg "LLVMDEV=${LLVMDEV:-}" \
-       --build-arg "DB=${DB:-}" \
-       --build-arg "VISION=${VISION:-}" \
-       --build-arg "EC2=${EC2:-}" \
-       --build-arg "JENKINS=${JENKINS:-}" \
-       --build-arg "JENKINS_UID=${JENKINS_UID:-}" \
-       --build-arg "JENKINS_GID=${JENKINS_GID:-}" \
-       --build-arg "UBUNTU_VERSION=${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:-gfx900;gfx906}" \
-       -f $(dirname ${DOCKERFILE})/Dockerfile \
-       -t "$tmp_tag" \
-       "$@" \
-       .
-
-# NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-devel-ubuntu18.04-rc`,
-# for this case we will set UBUNTU_VERSION to `18.04-rc` so that the Dockerfile could
-# find the correct image. As a result, here we have to replace the
-#   "$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

.circleci/docker/build_docker.sh

@@ -1,55 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-retry () {
-    $*  || (sleep 1 && $*) || (sleep 2 && $*)
-}
-
-# If UPSTREAM_BUILD_ID is set (see trigger job), then we can
-# use it to tag this build with the same ID used to tag all other
-# base image builds. Also, we can try and pull the previous
-# image first, to avoid rebuilding layers that haven't changed.
-
-#until we find a way to reliably reuse previous build, this last_tag is not in use
-# last_tag="$(( CIRCLE_BUILD_NUM - 1 ))"
-tag="${DOCKER_TAG}"
-
-
-registry="308535385114.dkr.ecr.us-east-1.amazonaws.com"
-image="${registry}/pytorch/${IMAGE_NAME}"
-
-login() {
-  aws ecr get-authorization-token --region us-east-1 --output text --query 'authorizationData[].authorizationToken' |
-    base64 -d |
-    cut -d: -f2 |
-    docker login -u AWS --password-stdin "$1"
-}
-
-
-# Only run these steps if not on github actions
-if [[ -z "${GITHUB_ACTIONS}" ]]; then
-  # Retry on timeouts (can happen on job stampede).
-  retry login "${registry}"
-  # Logout on exit
-  trap "docker logout ${registry}" EXIT
-fi
-
-# export EC2=1
-# export JENKINS=1
-
-# Try to pull the previous image (perhaps we can reuse some layers)
-# if [ -n "${last_tag}" ]; then
-#   docker pull "${image}:${last_tag}" || true
-# fi
-
-# Build new image
-./build.sh ${IMAGE_NAME} -t "${image}:${tag}"
-
-docker push "${image}:${tag}"
-
-if [ -z "${DOCKER_SKIP_S3_UPLOAD:-}" ]; then
-  trap "rm -rf ${IMAGE_NAME}:${tag}.tar" EXIT
-  docker save -o "${IMAGE_NAME}:${tag}.tar" "${image}:${tag}"
-  aws s3 cp "${IMAGE_NAME}:${tag}.tar" "s3://ossci-linux-build/pytorch/base/${IMAGE_NAME}:${tag}.tar" --acl public-read
-fi

.circleci/docker/centos-rocm/Dockerfile

@@ -1,103 +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 ${PYTORCH_ROCM_ARCH}
-
-# Install required packages to build Caffe2
-
-# Install common dependencies (so that this step can be cached separately)
-ARG EC2
-ADD ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Update CentOS git version
-RUN yum -y remove git
-RUN yum -y remove git-*
-RUN yum -y install https://packages.endpoint.com/rhel/7/os/x86_64/endpoint-repo-1.9-1.x86_64.rpm
-RUN yum install -y git
-
-# Install devtoolset
-ARG DEVTOOLSET_VERSION
-ADD ./common/install_devtoolset.sh install_devtoolset.sh
-RUN bash ./install_devtoolset.sh && rm install_devtoolset.sh
-ENV BASH_ENV "/etc/profile"
-
-# (optional) Install non-default glibc version
-ARG GLIBC_VERSION
-ADD ./common/install_glibc.sh install_glibc.sh
-RUN if [ -n "${GLIBC_VERSION}" ]; then bash ./install_glibc.sh; fi
-RUN rm install_glibc.sh
-
-# Install user
-ADD ./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)
-ENV PATH /opt/conda/bin:$PATH
-ARG ANACONDA_PYTHON_VERSION
-ADD ./common/install_conda.sh install_conda.sh
-RUN bash ./install_conda.sh && rm install_conda.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-ADD ./common/install_protobuf.sh install_protobuf.sh
-RUN if [ -n "${PROTOBUF}" ]; then bash ./install_protobuf.sh; fi
-RUN rm install_protobuf.sh
-ENV INSTALLED_PROTOBUF ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-ADD ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-ADD ./common/install_vision.sh install_vision.sh
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh
-ENV INSTALLED_VISION ${VISION}
-
-# Install rocm
-ARG ROCM_VERSION
-ADD ./common/install_rocm.sh install_rocm.sh
-RUN bash ./install_rocm.sh
-RUN rm install_rocm.sh
-ENV PATH /opt/rocm/bin:$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
-ADD ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-ADD ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${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)
-ADD ./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"]

.circleci/docker/common/install_android.sh

@@ -1,109 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "${ANDROID_NDK}" ]
-
-_https_amazon_aws=https://ossci-android.s3.amazonaws.com
-
-apt-get update
-apt-get install -y --no-install-recommends autotools-dev autoconf unzip
-apt-get autoclean && apt-get clean
-rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-pushd /tmp
-curl -Os --retry 3 $_https_amazon_aws/android-ndk-${ANDROID_NDK}-linux-x86_64.zip
-popd
-_ndk_dir=/opt/ndk
-mkdir -p "$_ndk_dir"
-unzip -qo /tmp/android*.zip -d "$_ndk_dir"
-_versioned_dir=$(find "$_ndk_dir/" -mindepth 1 -maxdepth 1 -type d)
-mv "$_versioned_dir"/* "$_ndk_dir"/
-rmdir "$_versioned_dir"
-rm -rf /tmp/*
-
-# Install OpenJDK
-# https://hub.docker.com/r/picoded/ubuntu-openjdk-8-jdk/dockerfile/
-
-sudo apt-get update && \
-    apt-get install -y openjdk-8-jdk && \
-    apt-get install -y ant && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists/* && \
-    rm -rf /var/cache/oracle-jdk8-installer;
-
-# Fix certificate issues, found as of
-# https://bugs.launchpad.net/ubuntu/+source/ca-certificates-java/+bug/983302
-
-sudo apt-get update && \
-    apt-get install -y ca-certificates-java && \
-    apt-get clean && \
-    update-ca-certificates -f && \
-    rm -rf /var/lib/apt/lists/* && \
-    rm -rf /var/cache/oracle-jdk8-installer;
-
-export JAVA_HOME=/usr/lib/jvm/java-8-openjdk-amd64/
-
-# Installing android sdk
-# https://github.com/circleci/circleci-images/blob/staging/android/Dockerfile.m4
-
-_tmp_sdk_zip=/tmp/android-sdk-linux.zip
-_android_home=/opt/android/sdk
-
-rm -rf $_android_home
-sudo mkdir -p $_android_home
-curl --silent --show-error --location --fail --retry 3 --output /tmp/android-sdk-linux.zip $_https_amazon_aws/android-sdk-linux-tools3859397-build-tools2803-2902-platforms28-29.zip
-sudo unzip -q $_tmp_sdk_zip -d $_android_home
-rm $_tmp_sdk_zip
-
-sudo chmod -R 777 $_android_home
-
-export ANDROID_HOME=$_android_home
-export ADB_INSTALL_TIMEOUT=120
-
-export PATH="${ANDROID_HOME}/tools:${ANDROID_HOME}/tools/bin:${ANDROID_HOME}/platform-tools:${PATH}"
-echo "PATH:${PATH}"
-
-# Installing Gradle
-echo "GRADLE_VERSION:${GRADLE_VERSION}"
-_gradle_home=/opt/gradle
-sudo rm -rf $gradle_home
-sudo mkdir -p $_gradle_home
-
-curl --silent --output /tmp/gradle.zip --retry 3 $_https_amazon_aws/gradle-${GRADLE_VERSION}-bin.zip
-
-sudo unzip -q /tmp/gradle.zip -d $_gradle_home
-rm /tmp/gradle.zip
-
-sudo chmod -R 777 $_gradle_home
-
-export GRADLE_HOME=$_gradle_home/gradle-$GRADLE_VERSION
-alias gradle="${GRADLE_HOME}/bin/gradle"
-
-export PATH="${GRADLE_HOME}/bin/:${PATH}"
-echo "PATH:${PATH}"
-
-gradle --version
-
-mkdir /var/lib/jenkins/gradledeps
-cp build.gradle /var/lib/jenkins/gradledeps
-cp AndroidManifest.xml /var/lib/jenkins/gradledeps
-
-pushd /var/lib/jenkins
-
-export GRADLE_LOCAL_PROPERTIES=gradledeps/local.properties
-rm -f $GRADLE_LOCAL_PROPERTIES
-echo "sdk.dir=/opt/android/sdk" >> $GRADLE_LOCAL_PROPERTIES
-echo "ndk.dir=/opt/ndk" >> $GRADLE_LOCAL_PROPERTIES
-
-chown -R jenkins /var/lib/jenkins/gradledeps
-chgrp -R jenkins /var/lib/jenkins/gradledeps
-
-sudo -H -u jenkins $GRADLE_HOME/bin/gradle -Pandroid.useAndroidX=true -p /var/lib/jenkins/gradledeps -g /var/lib/jenkins/.gradle --refresh-dependencies --debug --stacktrace assemble
-
-chown -R jenkins /var/lib/jenkins/.gradle
-chgrp -R jenkins /var/lib/jenkins/.gradle
-
-popd
-
-rm -rf /var/lib/jenkins/.gradle/daemon

.circleci/docker/common/install_base.sh

@@ -1,129 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-install_ubuntu() {
-  # NVIDIA dockers for RC releases use tag names like `11.0-cudnn8-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 [[ "$UBUNTU_VERSION" == "18.04"* ]]; then
-    cmake3="cmake=3.10*"
-    maybe_libiomp_dev="libiomp-dev"
-  elif [[ "$UBUNTU_VERSION" == "20.04"* ]]; then
-    cmake3="cmake=3.16*"
-    maybe_libiomp_dev=""
-  else
-    cmake3="cmake=3.5*"
-    maybe_libiomp_dev="libiomp-dev"
-  fi
-
-  # Install common dependencies
-  apt-get update
-  # TODO: Some of these may not be necessary
-  ccache_deps="asciidoc docbook-xml docbook-xsl xsltproc"
-  numpy_deps="gfortran"
-  apt-get install -y --no-install-recommends \
-    $ccache_deps \
-    $numpy_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 \
-    libjpeg-dev \
-    libasound2-dev \
-    libsndfile-dev \
-    software-properties-common \
-    sudo \
-    wget \
-    vim
-
-  # 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 \
-    $numpy_deps \
-    autoconf \
-    automake \
-    bzip2 \
-    cmake \
-    cmake3 \
-    curl \
-    gcc \
-    gcc-c++ \
-    gflags-devel \
-    git \
-    glibc-devel \
-    glibc-headers \
-    glog-devel \
-    hiredis-devel \
-    libstdc++-devel \
-    libsndfile-devel \
-    make \
-    opencv-devel \
-    sudo \
-    wget \
-    vim
-
-  # Cleanup
-  yum clean all
-  rm -rf /var/cache/yum
-  rm -rf /var/lib/yum/yumdb
-  rm -rf /var/lib/yum/history
-}
-
-# Install base packages depending on the base OS
-ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-case "$ID" in
-  ubuntu)
-    install_ubuntu
-    ;;
-  centos)
-    install_centos
-    ;;
-  *)
-    echo "Unable to determine OS..."
-    exit 1
-    ;;
-esac
-
-# Install Valgrind separately since the apt-get version is too old.
-mkdir valgrind_build && cd valgrind_build
-VALGRIND_VERSION=3.16.1
-wget https://ossci-linux.s3.amazonaws.com/valgrind-${VALGRIND_VERSION}.tar.bz2
-tar -xjf valgrind-${VALGRIND_VERSION}.tar.bz2
-cd valgrind-${VALGRIND_VERSION}
-./configure --prefix=/usr/local
-make -j6
-sudo make install
-cd ../../
-rm -rf valgrind_build
-alias valgrind="/usr/local/bin/valgrind"

.circleci/docker/common/install_cache.sh

@@ -1,117 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-install_ubuntu() {
-  echo "Preparing to build sccache from source"
-  apt-get update
-  apt-get install -y cargo pkg-config libssl-dev
-  echo "Checking out sccache repo"
-  git clone https://github.com/pytorch/sccache
-  cd sccache
-  echo "Building sccache"
-  cargo build --release
-  cp target/release/sccache /opt/cache/bin
-  echo "Cleaning up"
-  cd ..
-  rm -rf sccache
-  apt-get remove -y cargo rustc
-  apt-get autoclean && apt-get clean
-}
-
-install_binary() {
-  echo "Downloading sccache binary from S3 repo"
-  curl --retry 3 https://s3.amazonaws.com/ossci-linux/sccache -o /opt/cache/bin/sccache
-}
-
-mkdir -p /opt/cache/bin
-mkdir -p /opt/cache/lib
-sed -e 's|PATH="\(.*\)"|PATH="/opt/cache/bin:\1"|g' -i /etc/environment
-export PATH="/opt/cache/bin:$PATH"
-
-# Setup compiler cache
-if [ -n "$ROCM_VERSION" ]; then
-  curl --retry 3 http://repo.radeon.com/misc/.sccache_amd/sccache -o /opt/cache/bin/sccache
-else
-  ID=$(grep -oP '(?<=^ID=).+' /etc/os-release | tr -d '"')
-  case "$ID" in
-    ubuntu)
-      install_ubuntu
-      ;;
-    *)
-      install_binary
-      ;;
-  esac
-fi
-chmod a+x /opt/cache/bin/sccache
-
-function write_sccache_stub() {
-  printf "#!/bin/sh\nif [ \$(ps -p \$PPID -o comm=) != sccache ]; then\n  exec sccache $(which $1) \"\$@\"\nelse\n  exec $(which $1) \"\$@\"\nfi" > "/opt/cache/bin/$1"
-  chmod a+x "/opt/cache/bin/$1"
-}
-
-write_sccache_stub cc
-write_sccache_stub c++
-write_sccache_stub gcc
-write_sccache_stub g++
-
-# NOTE: See specific ROCM_VERSION case below.
-if [ "x$ROCM_VERSION" = x ]; then
-  write_sccache_stub clang
-  write_sccache_stub clang++
-fi
-
-if [ -n "$CUDA_VERSION" ]; then
-  # TODO: This is a workaround for the fact that PyTorch's FindCUDA
-  # implementation cannot find nvcc if it is setup this way, because it
-  # appears to search for the nvcc in PATH, and use its path to infer
-  # where CUDA is installed.  Instead, we install an nvcc symlink outside
-  # of the PATH, and set CUDA_NVCC_EXECUTABLE so that we make use of it.
-
-  write_sccache_stub nvcc
-  mv /opt/cache/bin/nvcc /opt/cache/lib/
-fi
-
-if [ -n "$ROCM_VERSION" ]; then
-  # ROCm compiler is hcc or clang. However, it is commonly invoked via hipcc wrapper.
-  # hipcc will call either hcc or clang using an absolute path starting with /opt/rocm,
-  # causing the /opt/cache/bin to be skipped. We must create the sccache wrappers
-  # directly under /opt/rocm while also preserving the original compiler names.
-  # Note symlinks will chain as follows: [hcc or clang++] -> clang -> clang-??
-  # Final link in symlink chain must point back to original directory.
-
-  # Original compiler is moved one directory deeper. Wrapper replaces it.
-  function write_sccache_stub_rocm() {
-    OLDCOMP=$1
-    COMPNAME=$(basename $OLDCOMP)
-    TOPDIR=$(dirname $OLDCOMP)
-    WRAPPED="$TOPDIR/original/$COMPNAME"
-    mv "$OLDCOMP" "$WRAPPED"
-    printf "#!/bin/sh\nexec sccache $WRAPPED \"\$@\"" > "$OLDCOMP"
-    chmod a+x "$OLDCOMP"
-  }
-
-  if [[ -e "/opt/rocm/hcc/bin/hcc" ]]; then
-    # ROCm 3.3 or earlier.
-    mkdir /opt/rocm/hcc/bin/original
-    write_sccache_stub_rocm /opt/rocm/hcc/bin/hcc
-    write_sccache_stub_rocm /opt/rocm/hcc/bin/clang
-    write_sccache_stub_rocm /opt/rocm/hcc/bin/clang++
-    # Fix last link in symlink chain, clang points to versioned clang in prior dir
-    pushd /opt/rocm/hcc/bin/original
-    ln -s ../$(readlink clang)
-    popd
-  elif [[ -e "/opt/rocm/llvm/bin/clang" ]]; then
-    # ROCm 3.5 and beyond.
-    mkdir /opt/rocm/llvm/bin/original
-    write_sccache_stub_rocm /opt/rocm/llvm/bin/clang
-    write_sccache_stub_rocm /opt/rocm/llvm/bin/clang++
-    # Fix last link in symlink chain, clang points to versioned clang in prior dir
-    pushd /opt/rocm/llvm/bin/original
-    ln -s ../$(readlink clang)
-    popd
-  else
-    echo "Cannot find ROCm compiler."
-    exit 1
-  fi
-fi

.circleci/docker/common/install_clang.sh

@@ -1,44 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$CLANG_VERSION" ]; then
-
-  if [[ $CLANG_VERSION == 7 && $UBUNTU_VERSION == 16.04 ]]; then
-    wget -O - https://apt.llvm.org/llvm-snapshot.gpg.key | sudo apt-key add -
-    sudo apt-add-repository "deb http://apt.llvm.org/xenial/ llvm-toolchain-xenial-7 main"
-  elif [[ $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"
-  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
-  # TODO: Decide if overriding gcc as well is a good idea
-  # update-alternatives --install /usr/bin/gcc gcc /usr/bin/clang-"$CLANG_VERSION" 50
-  # update-alternatives --install /usr/bin/g++ g++ /usr/bin/clang++-"$CLANG_VERSION" 50
-  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
-
-  # 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

.circleci/docker/common/install_cmake.sh

@@ -1,19 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "$CMAKE_VERSION" ]
-
-# Remove system cmake install so it won't get used instead
-apt-get remove cmake -y
-
-# Turn 3.6.3 into v3.6
-path=$(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

.circleci/docker/common/install_conda.sh

@@ -1,137 +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 "$ANACONDA_PYTHON_VERSION" | cut -d . -f 1)
-
-  case "$MAJOR_PYTHON_VERSION" in
-    2)
-      CONDA_FILE="Miniconda2-latest-Linux-x86_64.sh"
-    ;;
-    3)
-      if [ "$ANACONDA_PYTHON_VERSION" = "3.6" ]; then
-        # Latest release of Conda that still supports python-3.6
-        CONDA_FILE="Miniconda3-py37_4.10.3-Linux-x86_64.sh"
-      else
-        CONDA_FILE="Miniconda3-latest-Linux-x86_64.sh"
-      fi
-    ;;
-    *)
-      echo "Unsupported ANACONDA_PYTHON_VERSION: $ANACONDA_PYTHON_VERSION"
-      exit 1
-      ;;
-  esac
-
-  mkdir /opt/conda
-  chown jenkins:jenkins /opt/conda
-
-  # Work around bug where devtoolset replaces sudo and breaks it.
-  if [ -n "$DEVTOOLSET_VERSION" ]; then
-    SUDO=/bin/sudo
-  else
-    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
-    $SUDO -H -u jenkins env -u SUDO_UID -u SUDO_GID -u SUDO_COMMAND -u SUDO_USER env "PATH=$PATH" "LD_LIBRARY_PATH=$LD_LIBRARY_PATH" $*
-  }
-
-  pushd /tmp
-  wget -q "${BASE_URL}/${CONDA_FILE}"
-  chmod +x "${CONDA_FILE}"
-  as_jenkins ./"${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:$PATH"
-
-  # Ensure we run conda in a directory that jenkins has write access to
-  pushd /opt/conda
-
-  # Track latest conda update
-  if [ "$ANACONDA_PYTHON_VERSION" != "3.6" ]; then
-    as_jenkins conda update -y -n base conda
-  fi
-
-  # Install correct Python version
-  as_jenkins conda install -y python="$ANACONDA_PYTHON_VERSION"
-
-  conda_install() {
-    # Ensure that the install command don't upgrade/downgrade Python
-    # This should be called as
-    #   conda_install pkg1 pkg2 ... [-c channel]
-    as_jenkins conda install -q -y python="$ANACONDA_PYTHON_VERSION" $*
-  }
-
-  # Install PyTorch conda deps, as per https://github.com/pytorch/pytorch README
-  # DO NOT install cmake here as it would install a version newer than 3.10, but
-  # we want to pin to version 3.10.
-  SCIPY_VERSION=1.1.0
-  if [ "$ANACONDA_PYTHON_VERSION" = "3.9" ]; then
-    # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
-    conda_install numpy=1.19.2 astunparse pyyaml mkl mkl-include setuptools cffi future six llvmdev=8.0.0 -c conda-forge
-    SCIPY_VERSION=1.6.0
-  elif [ "$ANACONDA_PYTHON_VERSION" = "3.8" ]; then
-    # Install llvm-8 as it is required to compile llvmlite-0.30.0 from source
-    conda_install numpy=1.18.5 astunparse pyyaml mkl mkl-include setuptools cffi future six llvmdev=8.0.0
-  elif [ "$ANACONDA_PYTHON_VERSION" = "3.7" ]; then
-    # DO NOT install dataclasses if installing python-3.7, since its part of python-3.7 core packages
-    conda_install numpy=1.18.5 astunparse pyyaml mkl mkl-include setuptools cffi future six typing_extensions
-  else
-    conda_install numpy=1.18.5 astunparse pyyaml mkl mkl-include setuptools cffi future six dataclasses typing_extensions
-  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 ${TMP%.*[0-9]}) -c pytorch
-  fi
-
-  # TODO: This isn't working atm
-  conda_install nnpack -c killeent
-
-  # Install some other packages, including those needed for Python test reporting
-  # TODO: Why is scipy pinned
-  # Pin MyPy version because new errors are likely to appear with each release
-  # Pin hypothesis to avoid flakiness: https://github.com/pytorch/pytorch/issues/31136
-  as_jenkins pip install --progress-bar off pytest \
-    scipy==$SCIPY_VERSION \
-    scikit-image \
-    psutil \
-    unittest-xml-reporting \
-    boto3==1.16.34 \
-    hypothesis==4.53.2 \
-    expecttest==0.1.3 \
-    mypy==0.812 \
-    tb-nightly
-
-  # Install numba only on python-3.8 or below
-  # For numba issue see https://github.com/pytorch/pytorch/issues/51511
-  if [[ $(python -c "import sys; print(int(sys.version_info < (3, 9)))") == "1" ]]; then
-    as_jenkins pip install --progress-bar off numba==0.54.1 "librosa>=0.6.2,<0.9.0"
-  else
-    as_jenkins pip install --progress-bar off numba==0.49.0 "librosa>=0.6.2,<0.9.0"
-  fi
-
-  # Update scikit-learn to a python-3.8 compatible version
-  if [[ $(python -c "import sys; print(int(sys.version_info >= (3, 8)))") == "1" ]]; then
-    as_jenkins pip install --progress-bar off -U scikit-learn
-  else
-    # Pinned scikit-learn due to https://github.com/scikit-learn/scikit-learn/issues/14485 (affects gcc 5.5 only)
-    as_jenkins pip install --progress-bar off scikit-learn==0.20.3
-  fi
-
-  popd
-fi

.circleci/docker/common/install_db.sh

@@ -1,49 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-install_ubuntu() {
-  apt-get update
-  apt-get install -y --no-install-recommends \
-          libhiredis-dev \
-          libleveldb-dev \
-          liblmdb-dev \
-          libsnappy-dev
-
-  # 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
-
-  yum install -y \
-      hiredis-devel \
-      leveldb-devel \
-      lmdb-devel \
-      snappy-devel
-
-  # 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

.circleci/docker/common/install_devtoolset.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "$DEVTOOLSET_VERSION" ]
-
-yum install -y centos-release-scl
-yum install -y devtoolset-$DEVTOOLSET_VERSION
-
-echo "source scl_source enable devtoolset-$DEVTOOLSET_VERSION" > "/etc/profile.d/devtoolset-$DEVTOOLSET_VERSION.sh"

.circleci/docker/common/install_gcc.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$GCC_VERSION" ]; then
-
-  # Need the official toolchain repo to get alternate packages
-  add-apt-repository ppa:ubuntu-toolchain-r/test
-  apt-get update
-  if [[ "$UBUNTU_VERSION" == "16.04" && "${GCC_VERSION:0:1}" == "5" ]]; then
-    apt-get install -y g++-5=5.4.0-6ubuntu1~16.04.12
-    update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-5 50
-    update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-5 50
-    update-alternatives --install /usr/bin/gcov gcov /usr/bin/gcov-5 50
-  else
-    apt-get install -y g++-$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
-  fi
-
-
-  # Cleanup package manager
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-fi

.circleci/docker/common/install_glibc.sh

@@ -1,34 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "$GLIBC_VERSION" ]
-if [[ -n "$CENTOS_VERSION" ]]; then
-  [ -n "$DEVTOOLSET_VERSION" ]
-fi
-
-yum install -y wget sed
-
-mkdir -p /packages && cd /packages
-wget -q http://ftp.gnu.org/gnu/glibc/glibc-$GLIBC_VERSION.tar.gz
-tar xzf glibc-$GLIBC_VERSION.tar.gz
-if [[ "$GLIBC_VERSION" == "2.26" ]]; then
-  cd glibc-$GLIBC_VERSION
-  sed -i 's/$name ne "nss_test1"/$name ne "nss_test1" \&\& $name ne "nss_test2"/' scripts/test-installation.pl
-  cd ..
-fi
-mkdir -p glibc-$GLIBC_VERSION-build && cd glibc-$GLIBC_VERSION-build
-
-if [[ -n "$CENTOS_VERSION" ]]; then
-  export PATH=/opt/rh/devtoolset-$DEVTOOLSET_VERSION/root/usr/bin:$PATH
-fi
-
-../glibc-$GLIBC_VERSION/configure --prefix=/usr CFLAGS='-Wno-stringop-truncation -Wno-format-overflow -Wno-restrict -Wno-format-truncation -g -O2'
-make -j$(nproc)
-make install
-
-# Cleanup
-rm -rf /packages
-rm -rf /var/cache/yum/*
-rm -rf /var/lib/rpm/__db.*
-yum clean all

.circleci/docker/common/install_jni.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-mkdir -p /usr/local/include
-cp jni.h /usr/local/include

.circleci/docker/common/install_katex.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-if [ -n "$KATEX" ]; then
-
-  curl -sL https://deb.nodesource.com/setup_12.x | sudo -E bash -
-  sudo apt-get install -y nodejs
-
-  curl -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
-
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-fi

.circleci/docker/common/install_lcov.sh

@@ -1,8 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-git clone --branch v1.15 https://github.com/linux-test-project/lcov.git
-pushd lcov
-sudo make install   # will be installed in /usr/local/bin/lcov
-popd

.circleci/docker/common/install_ninja.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "$NINJA_VERSION" ]
-
-url="https://github.com/ninja-build/ninja/releases/download/v${NINJA_VERSION}/ninja-linux.zip"
-
-pushd /tmp
-wget --no-verbose --output-document=ninja-linux.zip "$url"
-unzip ninja-linux.zip -d /usr/local/bin
-rm -f ninja-linux.zip
-popd

.circleci/docker/common/install_openmpi.sh

@@ -1,10 +0,0 @@
-#!/bin/bash
-
-sudo apt-get update
-# also install ssh to avoid error of:
-# --------------------------------------------------------------------------
-# The value of the MCA parameter "plm_rsh_agent" was set to a path
-# that could not be found:
-#   plm_rsh_agent: ssh : rsh
-sudo apt-get install -y ssh
-sudo apt-get install -y --allow-downgrades --allow-change-held-packages openmpi-bin libopenmpi-dev

.circleci/docker/common/install_openssl.sh

@@ -1,14 +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,$(LIBRPATH)'
-# NOTE: openssl install errors out when built with the -j option
-make -j6; make install_sw
-cd ..
-rm -rf "${OPENSSL}"

.circleci/docker/common/install_protobuf.sh

@@ -1,56 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# This function installs protobuf 3.17
-install_protobuf_317() {
-  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"
-  tar -xvz -C "$pb_dir" --strip-components 1 -f protobuf-all-3.17.3.tar.gz
-  # -j6 to balance memory usage and speed.
-  # naked `-j` seems to use too much memory.
-  pushd "$pb_dir" && ./configure && make -j6 && make -j6 check && sudo make -j6 install && sudo ldconfig
-  popd
-  rm -rf $pb_dir
-}
-
-install_ubuntu() {
-  # Ubuntu 14.04 has cmake 2.8.12 as the default option, so we will
-  # install cmake3 here and use cmake3.
-  apt-get update
-  if [[ "$UBUNTU_VERSION" == 14.04 ]]; then
-    apt-get install -y --no-install-recommends cmake3
-  fi
-
-  # Cleanup
-  apt-get autoclean && apt-get clean
-  rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
-
-  install_protobuf_317
-}
-
-install_centos() {
-  install_protobuf_317
-}
-
-# 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

.circleci/docker/common/install_rocm.sh

@@ -1,160 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-install_magma() {
-    # "install" hipMAGMA into /opt/rocm/magma by copying after build
-    git clone https://bitbucket.org/icl/magma.git
-    pushd magma
-    # fix for magma_queue memory leak issue
-    git checkout c62d700d880c7283b33fb1d615d62fc9c7f7ca21
-    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 $amdgpu_targets; do
-      echo "DEVCCFLAGS += --amdgpu-target=$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
-    make testing/testing_dgemm -j $(nproc) MKLROOT=/opt/conda
-    popd
-    mv magma /opt/rocm
-}
-
-ver() {
-    printf "%3d%03d%03d%03d" $(echo "$1" | tr '.' ' ');
-}
-
-# Map ROCm version to AMDGPU version
-declare -A AMDGPU_VERSIONS=( ["4.5.2"]="21.40.2" )
-
-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
-
-    if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
-        # Add amdgpu repository
-        UBUNTU_VERSION_NAME=`cat /etc/os-release | grep UBUNTU_CODENAME | awk -F= '{print $2}'`
-        local amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/ubuntu"
-        echo "deb [arch=amd64] ${amdgpu_baseurl} ${UBUNTU_VERSION_NAME} main" > /etc/apt/sources.list.d/amdgpu.list
-    fi
-
-    ROCM_REPO="ubuntu"
-    if [[ $(ver $ROCM_VERSION) -lt $(ver 4.2) ]]; then
-        ROCM_REPO="xenial"
-    fi
-
-    # Add rocm repository
-    wget -qO - http://repo.radeon.com/rocm/rocm.gpg.key | apt-key add -
-    local rocm_baseurl="http://repo.radeon.com/rocm/apt/${ROCM_VERSION}"
-    echo "deb [arch=amd64] ${rocm_baseurl} ${ROCM_REPO} main" > /etc/apt/sources.list.d/rocm.list
-    apt-get update --allow-insecure-repositories
-
-    DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated \
-                   rocm-dev \
-                   rocm-utils \
-                   rocm-libs \
-                   rccl \
-                   rocprofiler-dev \
-                   roctracer-dev
-
-    # precompiled miopen kernels added in ROCm 3.5; search for all unversioned packages
-    # if search fails it will abort this script; use true to avoid case where search fails
-    MIOPENKERNELS=$(apt-cache search --names-only miopenkernels | awk '{print $1}' | grep -F -v . || true)
-    if [[ "x${MIOPENKERNELS}" = x ]]; then
-      echo "miopenkernels package not available"
-    else
-      DEBIAN_FRONTEND=noninteractive apt-get install -y --allow-unauthenticated ${MIOPENKERNELS}
-    fi
-
-    install_magma
-
-    # 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`
-
-  if [[ $(ver $ROCM_VERSION) -ge $(ver 4.5) ]]; then
-      # Add amdgpu repository
-      local amdgpu_baseurl="https://repo.radeon.com/amdgpu/${AMDGPU_VERSIONS[$ROCM_VERSION]}/rhel/7.9/main/x86_64"
-      echo "[AMDGPU]" > /etc/yum.repos.d/amdgpu.repo
-      echo "name=AMDGPU" >> /etc/yum.repos.d/amdgpu.repo
-      echo "baseurl=${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
-  fi
-
-  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
-
-  install_magma
-
-  # 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

.circleci/docker/common/install_swiftshader.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "${SWIFTSHADER}" ]
-
-retry () {
-    $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
-}
-
-_https_amazon_aws=https://ossci-android.s3.amazonaws.com
-
-# SwiftShader
-_swiftshader_dir=/var/lib/jenkins/swiftshader
-_swiftshader_file_targz=swiftshader-abe07b943-prebuilt.tar.gz
-mkdir -p $_swiftshader_dir
-_tmp_swiftshader_targz="/tmp/${_swiftshader_file_targz}"
-
-curl --silent --show-error --location --fail --retry 3 \
-  --output "${_tmp_swiftshader_targz}" "$_https_amazon_aws/${_swiftshader_file_targz}"
-
-tar -C "${_swiftshader_dir}" -xzf "${_tmp_swiftshader_targz}"
-
-export VK_ICD_FILENAMES="${_swiftshader_dir}/build/Linux/vk_swiftshader_icd.json"

.circleci/docker/common/install_tensorrt.sh

@@ -1,7 +0,0 @@
-#!/bin/bash
-
-if [ -n "$TENSORRT_VERSION" ]; then
-    python3 -m pip install --upgrade setuptools pip
-    python3 -m pip install nvidia-pyindex
-    python3 -m pip install nvidia-tensorrt==${TENSORRT_VERSION} --extra-index-url https://pypi.ngc.nvidia.com
-fi

.circleci/docker/common/install_thrift.sh

@@ -1,14 +0,0 @@
-apt-get update
-apt-get install -y sudo wget libboost-dev libboost-test-dev libboost-program-options-dev libboost-filesystem-dev libboost-thread-dev libevent-dev automake libtool flex bison pkg-config g++ libssl-dev
-wget https://www-us.apache.org/dist/thrift/0.12.0/thrift-0.12.0.tar.gz
-tar -xvf thrift-0.12.0.tar.gz
-cd thrift-0.12.0
-for file in ./compiler/cpp/Makefile*; do
-  sed -i 's/\-Werror//' $file
-done
-./bootstrap.sh
-./configure --without-php --without-java --without-python --without-nodejs --without-go --without-ruby
-sudo make
-sudo make install
-cd ..
-rm thrift-0.12.0.tar.gz

.circleci/docker/common/install_user.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Mirror jenkins user in container
-echo "jenkins:x:1014:1014::/var/lib/jenkins:" >> /etc/passwd
-echo "jenkins:x:1014:" >> /etc/group
-
-# 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

.circleci/docker/common/install_vision.sh

@@ -1,45 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-install_ubuntu() {
-  apt-get update
-  apt-get install -y --no-install-recommends \
-          libopencv-dev \
-          libavcodec-dev
-
-  # 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
-
-  yum install -y \
-      opencv-devel \
-      ffmpeg-devel
-
-  # 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

.circleci/docker/common/install_vulkan_sdk.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-[ -n "${VULKAN_SDK_VERSION}" ]
-
-retry () {
-    $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
-}
-
-_vulkansdk_dir=/var/lib/jenkins/vulkansdk
-_tmp_vulkansdk_targz=/tmp/vulkansdk.tar.gz
-
-curl \
-  --silent \
-  --show-error \
-  --location \
-  --fail \
-  --retry 3 \
-  --output "${_tmp_vulkansdk_targz}" "https://ossci-android.s3.amazonaws.com/vulkansdk-linux-x86_64-${VULKAN_SDK_VERSION}.tar.gz"
-
-mkdir -p "${_vulkansdk_dir}"
-tar -C "${_vulkansdk_dir}" -xzf "${_tmp_vulkansdk_targz}" --strip-components 1
-rm -rf "${_tmp_vulkansdk_targz}"

.circleci/docker/ubuntu-cuda/Dockerfile

@@ -1,109 +0,0 @@
-ARG UBUNTU_VERSION
-ARG CUDA_VERSION
-ARG CUDNN_VERSION
-
-FROM nvidia/cuda:${CUDA_VERSION}-cudnn${CUDNN_VERSION}-devel-ubuntu${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-ARG CUDA_VERSION
-ARG CUDNN_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-ARG EC2
-ADD ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install user
-ADD ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-ADD ./common/install_katex.sh install_katex.sh
-RUN bash ./install_katex.sh && rm install_katex.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ENV PATH /opt/conda/bin:$PATH
-ARG ANACONDA_PYTHON_VERSION
-ADD ./common/install_conda.sh install_conda.sh
-RUN bash ./install_conda.sh && rm install_conda.sh
-
-# Install gcc
-ARG GCC_VERSION
-ADD ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install clang
-ARG CLANG_VERSION
-ADD ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-ADD ./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 ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-ADD ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-ADD ./common/install_vision.sh install_vision.sh
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh
-ENV INSTALLED_VISION ${VISION}
-
-ADD ./common/install_openssl.sh install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-RUN bash ./install_openssl.sh
-
-# (optional) Install TensorRT
-ARG TENSORRT_VERSION
-ADD ./common/install_tensorrt.sh install_tensorrt.sh
-RUN if [ -n "${TENSORRT_VERSION}" ]; then bash ./install_tensorrt.sh; fi
-RUN rm install_tensorrt.sh
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-ADD ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-ADD ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-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
-ADD ./common/install_jni.sh install_jni.sh
-ADD ./java/jni.h jni.h
-RUN bash ./install_jni.sh && rm install_jni.sh
-
-# Install Open MPI for CUDA
-ADD ./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
-
-USER jenkins
-CMD ["bash"]

.circleci/docker/ubuntu-rocm/.gitignore

@@ -1 +0,0 @@
-*.sh

.circleci/docker/ubuntu-rocm/Dockerfile

@@ -1,96 +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 ${PYTORCH_ROCM_ARCH}
-
-# Install common dependencies (so that this step can be cached separately)
-ARG EC2
-ADD ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-ARG CLANG_VERSION
-ADD ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# Install user
-ADD ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ENV PATH /opt/conda/bin:$PATH
-ARG ANACONDA_PYTHON_VERSION
-ADD ./common/install_conda.sh install_conda.sh
-RUN bash ./install_conda.sh && rm install_conda.sh
-
-# Install gcc
-ARG GCC_VERSION
-ADD ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-ADD ./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 ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-ADD ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-ADD ./common/install_vision.sh install_vision.sh
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh
-ENV INSTALLED_VISION ${VISION}
-
-# Install rocm
-ARG ROCM_VERSION
-ADD ./common/install_rocm.sh install_rocm.sh
-RUN bash ./install_rocm.sh
-RUN rm install_rocm.sh
-ENV PATH /opt/rocm/bin:$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 C.UTF-8
-ENV LC_ALL C.UTF-8
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-ADD ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-ADD ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${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)
-ADD ./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"]

.circleci/docker/ubuntu/Dockerfile

@@ -1,131 +0,0 @@
-ARG UBUNTU_VERSION
-
-FROM ubuntu:${UBUNTU_VERSION}
-
-ARG UBUNTU_VERSION
-
-ENV DEBIAN_FRONTEND noninteractive
-
-# Install common dependencies (so that this step can be cached separately)
-ARG EC2
-ADD ./common/install_base.sh install_base.sh
-RUN bash ./install_base.sh && rm install_base.sh
-
-# Install clang
-ARG LLVMDEV
-ARG CLANG_VERSION
-ADD ./common/install_clang.sh install_clang.sh
-RUN bash ./install_clang.sh && rm install_clang.sh
-
-# (optional) Install thrift.
-ARG THRIFT
-ADD ./common/install_thrift.sh install_thrift.sh
-RUN if [ -n "${THRIFT}" ]; then bash ./install_thrift.sh; fi
-RUN rm install_thrift.sh
-ENV INSTALLED_THRIFT ${THRIFT}
-
-# Install user
-ADD ./common/install_user.sh install_user.sh
-RUN bash ./install_user.sh && rm install_user.sh
-
-# Install katex
-ARG KATEX
-ADD ./common/install_katex.sh install_katex.sh
-RUN bash ./install_katex.sh && rm install_katex.sh
-
-# Install conda and other packages (e.g., numpy, pytest)
-ENV PATH /opt/conda/bin:$PATH
-ARG ANACONDA_PYTHON_VERSION
-ADD ./common/install_conda.sh install_conda.sh
-RUN bash ./install_conda.sh && rm install_conda.sh
-
-# Install gcc
-ARG GCC_VERSION
-ADD ./common/install_gcc.sh install_gcc.sh
-RUN bash ./install_gcc.sh && rm install_gcc.sh
-
-# Install lcov for C++ code coverage
-ADD ./common/install_lcov.sh install_lcov.sh
-RUN  bash ./install_lcov.sh && rm install_lcov.sh
-
-# (optional) Install protobuf for ONNX
-ARG PROTOBUF
-ADD ./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 ${PROTOBUF}
-
-# (optional) Install database packages like LMDB and LevelDB
-ARG DB
-ADD ./common/install_db.sh install_db.sh
-RUN if [ -n "${DB}" ]; then bash ./install_db.sh; fi
-RUN rm install_db.sh
-ENV INSTALLED_DB ${DB}
-
-# (optional) Install vision packages like OpenCV and ffmpeg
-ARG VISION
-ADD ./common/install_vision.sh install_vision.sh
-RUN if [ -n "${VISION}" ]; then bash ./install_vision.sh; fi
-RUN rm install_vision.sh
-ENV INSTALLED_VISION ${VISION}
-
-# (optional) Install Android NDK
-ARG ANDROID
-ARG ANDROID_NDK
-ARG GRADLE_VERSION
-ADD ./common/install_android.sh install_android.sh
-ADD ./android/AndroidManifest.xml AndroidManifest.xml
-ADD ./android/build.gradle build.gradle
-RUN if [ -n "${ANDROID}" ]; then bash ./install_android.sh; fi
-RUN rm install_android.sh
-RUN rm AndroidManifest.xml
-RUN rm build.gradle
-ENV INSTALLED_ANDROID ${ANDROID}
-
-# (optional) Install Vulkan SDK
-ARG VULKAN_SDK_VERSION
-ADD ./common/install_vulkan_sdk.sh install_vulkan_sdk.sh
-RUN if [ -n "${VULKAN_SDK_VERSION}" ]; then bash ./install_vulkan_sdk.sh; fi
-RUN rm install_vulkan_sdk.sh
-
-# (optional) Install swiftshader
-ARG SWIFTSHADER
-ADD ./common/install_swiftshader.sh install_swiftshader.sh
-RUN if [ -n "${SWIFTSHADER}" ]; then bash ./install_swiftshader.sh; fi
-RUN rm install_swiftshader.sh
-
-# (optional) Install non-default CMake version
-ARG CMAKE_VERSION
-ADD ./common/install_cmake.sh install_cmake.sh
-RUN if [ -n "${CMAKE_VERSION}" ]; then bash ./install_cmake.sh; fi
-RUN rm install_cmake.sh
-
-# (optional) Install non-default Ninja version
-ARG NINJA_VERSION
-ADD ./common/install_ninja.sh install_ninja.sh
-RUN if [ -n "${NINJA_VERSION}" ]; then bash ./install_ninja.sh; fi
-RUN rm install_ninja.sh
-
-ADD ./common/install_openssl.sh install_openssl.sh
-RUN bash ./install_openssl.sh
-ENV OPENSSL_ROOT_DIR /opt/openssl
-
-# Install ccache/sccache (do this last, so we get priority in PATH)
-ADD ./common/install_cache.sh install_cache.sh
-ENV PATH /opt/cache/bin:$PATH
-RUN bash ./install_cache.sh && rm install_cache.sh
-
-# Add jni.h for java host build
-ADD ./common/install_jni.sh install_jni.sh
-ADD ./java/jni.h jni.h
-RUN bash ./install_jni.sh && rm install_jni.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"]

.circleci/ensure-consistency.py

@@ -1,39 +0,0 @@
-#!/usr/bin/env python3
-
-import os
-import subprocess
-import sys
-import tempfile
-
-import generate_config_yml
-
-
-CHECKED_IN_FILE = "config.yml"
-REGENERATION_SCRIPT = "regenerate.sh"
-
-PARENT_DIR = os.path.basename(os.path.dirname(os.path.abspath(__file__)))
-README_PATH = os.path.join(PARENT_DIR, "README.md")
-
-ERROR_MESSAGE_TEMPLATE = """
-The checked-in CircleCI "%s" file does not match what was generated by the scripts.
-Please re-run the "%s" script in the "%s" directory and commit the result. See "%s" for more information.
-"""
-
-
-def check_consistency():
-
-    _, temp_filename = tempfile.mkstemp("-generated-config.yml")
-
-    with open(temp_filename, "w") as fh:
-        generate_config_yml.stitch_sources(fh)
-
-    try:
-        subprocess.check_call(["cmp", temp_filename, CHECKED_IN_FILE])
-    except subprocess.CalledProcessError:
-        sys.exit(ERROR_MESSAGE_TEMPLATE % (CHECKED_IN_FILE, REGENERATION_SCRIPT, PARENT_DIR, README_PATH))
-    finally:
-        os.remove(temp_filename)
-
-
-if __name__ == "__main__":
-    check_consistency()

.circleci/generate_config_yml.py

@@ -1,212 +0,0 @@
-#!/usr/bin/env python3
-
-"""
-This script is the source of truth for config.yml.
-Please see README.md in this directory for details.
-"""
-
-import os
-import shutil
-import sys
-from collections import namedtuple
-
-import cimodel.data.binary_build_definitions as binary_build_definitions
-import cimodel.data.simple.android_definitions
-import cimodel.data.simple.binary_smoketest
-import cimodel.data.simple.docker_definitions
-import cimodel.data.simple.mobile_definitions
-import cimodel.data.simple.nightly_android
-import cimodel.data.simple.nightly_ios
-import cimodel.data.simple.anaconda_prune_defintions
-import cimodel.lib.miniutils as miniutils
-import cimodel.lib.miniyaml as miniyaml
-
-
-class File(object):
-    """
-    Verbatim copy the contents of a file into config.yml
-    """
-
-    def __init__(self, filename):
-        self.filename = filename
-
-    def write(self, output_filehandle):
-        with open(os.path.join("verbatim-sources", self.filename)) as fh:
-            shutil.copyfileobj(fh, output_filehandle)
-
-
-class FunctionGen(namedtuple("FunctionGen", "function depth")):
-    __slots__ = ()
-
-
-class Treegen(FunctionGen):
-    """
-    Insert the content of a YAML tree into config.yml
-    """
-
-    def write(self, output_filehandle):
-        miniyaml.render(output_filehandle, self.function(), self.depth)
-
-
-class Listgen(FunctionGen):
-    """
-    Insert the content of a YAML list into config.yml
-    """
-
-    def write(self, output_filehandle):
-        miniyaml.render(output_filehandle, self.function(), self.depth)
-
-
-def horizontal_rule():
-    return "".join("#" * 78)
-
-
-class Header(object):
-    def __init__(self, title, summary=None):
-        self.title = title
-        self.summary_lines = summary or []
-
-    def write(self, output_filehandle):
-        text_lines = [self.title] + self.summary_lines
-        comment_lines = ["# " + x for x in text_lines]
-        lines = miniutils.sandwich([horizontal_rule()], comment_lines)
-
-        for line in filter(None, lines):
-            output_filehandle.write(line + "\n")
-
-def _for_all_items(items, functor) -> None:
-    if isinstance(items, list):
-        for item in items:
-            _for_all_items(item, functor)
-    if isinstance(items, dict) and len(items) == 1:
-        item_type, item = next(iter(items.items()))
-        functor(item_type, item)
-
-def filter_master_only_jobs(items):
-    def _is_master_item(item):
-        filters = item.get('filters', None)
-        branches = filters.get('branches', None) if filters is not None else None
-        branches_only = branches.get('only', None) if branches is not None else None
-        return 'master' in branches_only if branches_only is not None else False
-
-    master_deps = set()
-
-    def _save_requires_if_master(item_type, item):
-        requires = item.get('requires', None)
-        item_name = item.get("name", None)
-        if not isinstance(requires, list):
-            return
-        if _is_master_item(item) or item_name in master_deps:
-            master_deps.update([n.strip('"') for n in requires])
-
-    def _do_filtering(items):
-        if isinstance(items, list):
-            rc = [_do_filtering(item) for item in items]
-            return [item for item in rc if len(item if item is not None else []) > 0]
-        assert isinstance(items, dict) and len(items) == 1
-        item_type, item = next(iter(items.items()))
-        item_name = item.get("name", None)
-        item_name = item_name.strip('"') if item_name is not None else None
-        if not _is_master_item(item) and item_name not in master_deps:
-            return None
-        if 'filters' in item:
-            item = item.copy()
-            item.pop('filters')
-        return {item_type: item}
-
-    # Scan of dependencies twice to pick up nested required jobs
-    # I.e. jobs depending on jobs that master-only job depend on
-    _for_all_items(items, _save_requires_if_master)
-    _for_all_items(items, _save_requires_if_master)
-    return _do_filtering(items)
-
-def generate_required_docker_images(items):
-    required_docker_images = set()
-
-    def _requires_docker_image(item_type, item):
-        requires = item.get('requires', None)
-        if not isinstance(requires, list):
-            return
-        for requirement in requires:
-            requirement = requirement.replace('"', '')
-            if requirement.startswith('docker-'):
-                required_docker_images.add(requirement)
-
-    _for_all_items(items, _requires_docker_image)
-    return required_docker_images
-
-def gen_build_workflows_tree():
-    build_workflows_functions = [
-        cimodel.data.simple.android_definitions.get_workflow_jobs,
-        cimodel.data.simple.mobile_definitions.get_workflow_jobs,
-        cimodel.data.simple.binary_smoketest.get_workflow_jobs,
-        cimodel.data.simple.nightly_ios.get_workflow_jobs,
-        cimodel.data.simple.nightly_android.get_workflow_jobs,
-        cimodel.data.simple.anaconda_prune_defintions.get_workflow_jobs,
-        binary_build_definitions.get_post_upload_jobs,
-        binary_build_definitions.get_binary_smoke_test_jobs,
-    ]
-    build_jobs = [f() for f in build_workflows_functions]
-    build_jobs.extend(
-        cimodel.data.simple.docker_definitions.get_workflow_jobs(
-            # sort for consistency
-            sorted(generate_required_docker_images(build_jobs))
-        )
-    )
-    master_build_jobs = filter_master_only_jobs(build_jobs)
-
-    binary_build_functions = [
-        binary_build_definitions.get_binary_build_jobs,
-        binary_build_definitions.get_nightly_tests,
-        binary_build_definitions.get_nightly_uploads,
-    ]
-
-    return {
-        "workflows": {
-            "binary_builds": {
-                "when": r"<< pipeline.parameters.run_binary_tests >>",
-                "jobs": [f() for f in binary_build_functions],
-            },
-            "build": {
-                "when": r"<< pipeline.parameters.run_build >>",
-                "jobs": build_jobs,
-            },
-            "master_build": {
-                "when": r"<< pipeline.parameters.run_master_build >>",
-                "jobs": master_build_jobs,
-            },
-        }
-    }
-
-
-# Order of this list matters to the generated config.yml.
-YAML_SOURCES = [
-    File("header-section.yml"),
-    File("commands.yml"),
-    File("nightly-binary-build-defaults.yml"),
-    Header("Build parameters"),
-    File("build-parameters/pytorch-build-params.yml"),
-    File("build-parameters/binary-build-params.yml"),
-    File("build-parameters/promote-build-params.yml"),
-    Header("Job specs"),
-    File("job-specs/pytorch-job-specs.yml"),
-    File("job-specs/binary-job-specs.yml"),
-    File("job-specs/job-specs-custom.yml"),
-    File("job-specs/job-specs-promote.yml"),
-    File("job-specs/binary_update_htmls.yml"),
-    File("job-specs/binary-build-tests.yml"),
-    File("job-specs/docker_jobs.yml"),
-    Header("Workflows"),
-    Treegen(gen_build_workflows_tree, 0),
-    File("workflows/workflows-promote.yml"),
-]
-
-
-def stitch_sources(output_filehandle):
-    for f in YAML_SOURCES:
-        f.write(output_filehandle)
-
-
-if __name__ == "__main__":
-
-    stitch_sources(sys.stdout)

.circleci/regenerate.ps1

@@ -1,5 +0,0 @@
-cd $PSScriptRoot;
-$NewFile = New-TemporaryFile;
-python generate_config_yml.py > $NewFile.name
-(Get-Content $NewFile.name -Raw).TrimEnd().Replace("`r`n","`n") | Set-Content config.yml -Force
-Remove-Item $NewFile.name

.circleci/regenerate.sh

@@ -1,17 +0,0 @@
-#!/bin/bash -e
-
-# Allows this script to be invoked from any directory:
-cd "$(dirname "$0")"
-
-UNCOMMIT_CHANGE=$(git status -s | grep " config.yml" | wc -l | xargs)
-if [[ $UNCOMMIT_CHANGE != 0 ]]; then
-    OLD_FILE=$(mktemp)
-    cp config.yml "$OLD_FILE"
-    echo "Uncommitted change detected in .circleci/config.yml"
-    echo "It has been backed up to $OLD_FILE"
-fi
-
-NEW_FILE=$(mktemp)
-./generate_config_yml.py > "$NEW_FILE"
-cp "$NEW_FILE" config.yml
-echo "New config generated in .circleci/config.yml"

.circleci/scripts/README.md

@@ -1,4 +0,0 @@
-All the scripts in this directory are callable from `~/workspace/.circleci/scripts/foo.sh`.
-Don't try to call them as `.circleci/scripts/foo.sh`, that won't
-(necessarily) work.  See Note [Workspace for CircleCI scripts] in
-job-specs-setup.yml for more details.

.circleci/scripts/binary_checkout.sh

@@ -1,68 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-retry () {
-    $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
-}
-
-
-# This step runs on multiple executors with different envfile locations
-if [[ "$(uname)" == Darwin ]]; then
-  # macos executor (builds and tests)
-  workdir="/Users/distiller/project"
-elif [[ "$OSTYPE" == "msys" ]]; then
-  # windows executor (builds and tests)
-  rm -rf /c/w
-  ln -s "/c/Users/circleci/project" /c/w
-  workdir="/c/w"
-elif [[ -d "/home/circleci/project" ]]; then
-  # machine executor (binary tests)
-  workdir="/home/circleci/project"
-else
-  # docker executor (binary builds)
-  workdir="/"
-fi
-
-# It is very important that this stays in sync with binary_populate_env.sh
-if [[ "$OSTYPE" == "msys" ]]; then
-  # We need to make the paths as short as possible on Windows
-  export PYTORCH_ROOT="$workdir/p"
-  export BUILDER_ROOT="$workdir/b"
-else
-  export PYTORCH_ROOT="$workdir/pytorch"
-  export BUILDER_ROOT="$workdir/builder"
-fi
-
-# Try to extract PR number from branch if not already set
-if [[ -z "${CIRCLE_PR_NUMBER:-}" ]]; then
-  CIRCLE_PR_NUMBER="$(echo ${CIRCLE_BRANCH} | sed -E -n 's/pull\/([0-9]*).*/\1/p')"
-fi
-
-# Clone the Pytorch branch
-retry git clone https://github.com/pytorch/pytorch.git "$PYTORCH_ROOT"
-pushd "$PYTORCH_ROOT"
-if [[ -n "${CIRCLE_PR_NUMBER:-}" ]]; then
-  # "smoke" binary build on PRs
-  git fetch --force origin "pull/${CIRCLE_PR_NUMBER}/head:remotes/origin/pull/${CIRCLE_PR_NUMBER}"
-  git reset --hard "$CIRCLE_SHA1"
-  git checkout -q -B "$CIRCLE_BRANCH"
-  git reset --hard "$CIRCLE_SHA1"
-elif [[ -n "${CIRCLE_SHA1:-}" ]]; then
-  # Scheduled workflows & "smoke" binary build on master on PR merges
-  git reset --hard "$CIRCLE_SHA1"
-  git checkout -q -B master
-else
-  echo "Can't tell what to checkout"
-  exit 1
-fi
-retry git submodule update --init --recursive --jobs 0
-echo "Using Pytorch from "
-git --no-pager log --max-count 1
-popd
-
-# Clone the Builder master repo
-retry git clone -q https://github.com/pytorch/builder.git -b release/1.11 "$BUILDER_ROOT"
-pushd "$BUILDER_ROOT"
-echo "Using builder from "
-git --no-pager log --max-count 1
-popd

.circleci/scripts/binary_install_miniconda.sh

@@ -1,44 +0,0 @@
-#!/bin/bash
-
-set -eux -o pipefail
-
-# This step runs on multiple executors with different envfile locations
-if [[ "$(uname)" == Darwin ]]; then
-  envfile="/Users/distiller/project/env"
-elif [[ -d "/home/circleci/project" ]]; then
-  # machine executor (binary tests)
-  envfile="/home/circleci/project/env"
-else
-  # docker executor (binary builds)
-  envfile="/env"
-fi
-
-# TODO this is super hacky and ugly. Basically, the binary_update_html job does
-# not have an env file, since it does not call binary_populate_env.sh, since it
-# does not have a BUILD_ENVIRONMENT. So for this one case, which we detect by a
-# lack of an env file, we manually export the environment variables that we
-# need to install miniconda
-if [[ ! -f "$envfile" ]]; then
-  MINICONDA_ROOT="/home/circleci/project/miniconda"
-  workdir="/home/circleci/project"
-  retry () {
-      $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)
-  }
-  export -f retry
-else
-  source "$envfile"
-fi
-
-conda_sh="$workdir/install_miniconda.sh"
-if [[ "$(uname)" == Darwin ]]; then
-  curl --retry 3 -o "$conda_sh" https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
-else
-  curl --retry 3 -o "$conda_sh" https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
-fi
-chmod +x "$conda_sh"
-"$conda_sh" -b -p "$MINICONDA_ROOT"
-rm -f "$conda_sh"
-
-# We can't actually add miniconda to the PATH in the envfile, because that
-# breaks 'unbuffer' in Mac jobs. This is probably because conda comes with
-# a tclsh, which then gets inserted before the tclsh needed in /usr/bin

.circleci/scripts/binary_ios_build.sh

@@ -1,47 +0,0 @@
-#!/bin/bash
-set -ex -o pipefail
-
-echo ""
-echo "DIR: $(pwd)"
-WORKSPACE=/Users/distiller/workspace
-PROJ_ROOT=/Users/distiller/project
-export TCLLIBPATH="/usr/local/lib"
-
-# Install conda
-curl --retry 3 -o ~/conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
-chmod +x ~/conda.sh
-/bin/bash ~/conda.sh -b -p ~/anaconda
-export PATH="~/anaconda/bin:${PATH}"
-source ~/anaconda/bin/activate
-
-# Install dependencies
-conda install numpy ninja pyyaml mkl mkl-include setuptools cmake cffi requests typing_extensions --yes
-conda install -c conda-forge valgrind --yes
-export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
-
-# sync submodules
-cd ${PROJ_ROOT}
-git submodule sync
-git submodule update --init --recursive --jobs 0
-
-# run build script
-chmod a+x ${PROJ_ROOT}/scripts/build_ios.sh
-echo "########################################################"
-cat ${PROJ_ROOT}/scripts/build_ios.sh
-echo "########################################################"
-echo "IOS_ARCH: ${IOS_ARCH}"
-echo "IOS_PLATFORM: ${IOS_PLATFORM}"
-echo "USE_PYTORCH_METAL: ${USE_PYTORCH_METAL}"
-echo "USE_COREML_DELEGATE: ${USE_COREML_DELEGATE}"
-export IOS_ARCH=${IOS_ARCH}
-export IOS_PLATFORM=${IOS_PLATFORM}
-export USE_PYTORCH_METAL=${USE_PYTORCH_METAL}
-export USE_COREML_DELEGATE=${USE_COREML_DELEGATE}
-unbuffer ${PROJ_ROOT}/scripts/build_ios.sh 2>&1 | ts
-
-#store the binary
-cd ${WORKSPACE}
-DEST_DIR=${WORKSPACE}/ios
-mkdir -p ${DEST_DIR}
-cp -R ${PROJ_ROOT}/build_ios/install ${DEST_DIR}
-mv ${DEST_DIR}/install ${DEST_DIR}/${IOS_ARCH}

.circleci/scripts/binary_ios_test.sh

@@ -1,30 +0,0 @@
-#!/bin/bash
-set -ex -o pipefail
-
-echo ""
-echo "DIR: $(pwd)"
-PROJ_ROOT=/Users/distiller/project
-cd ${PROJ_ROOT}/ios/TestApp
-# install fastlane
-sudo gem install bundler && bundle install
-# install certificates
-echo "${IOS_CERT_KEY_2022}" >> cert.txt
-base64 --decode cert.txt -o Certificates.p12
-rm cert.txt
-bundle exec fastlane install_root_cert
-bundle exec fastlane install_dev_cert
-# install the provisioning profile
-PROFILE=PyTorch_CI_2022.mobileprovision
-PROVISIONING_PROFILES=~/Library/MobileDevice/Provisioning\ Profiles
-mkdir -pv "${PROVISIONING_PROFILES}"
-cd "${PROVISIONING_PROFILES}"
-echo "${IOS_SIGN_KEY_2022}" >> cert.txt
-base64 --decode cert.txt -o ${PROFILE}
-rm cert.txt
-# run the ruby build script
-if ! [ -x "$(command -v xcodebuild)" ]; then
-    echo 'Error: xcodebuild is not installed.'
-    exit 1
-fi
-PROFILE=PyTorch_CI_2022
-ruby ${PROJ_ROOT}/scripts/xcode_build.rb -i ${PROJ_ROOT}/build_ios/install -x ${PROJ_ROOT}/ios/TestApp/TestApp.xcodeproj -p ${IOS_PLATFORM} -c ${PROFILE} -t ${IOS_DEV_TEAM_ID}

.circleci/scripts/binary_ios_upload.sh

@@ -1,75 +0,0 @@
-#!/bin/bash
-set -ex -o pipefail
-
-echo ""
-echo "DIR: $(pwd)"
-WORKSPACE=/Users/distiller/workspace
-PROJ_ROOT=/Users/distiller/project
-ARTIFACTS_DIR=${WORKSPACE}/ios
-ls ${ARTIFACTS_DIR}
-ZIP_DIR=${WORKSPACE}/zip
-mkdir -p ${ZIP_DIR}/install/lib
-mkdir -p ${ZIP_DIR}/src
-# copy header files
-cp -R ${ARTIFACTS_DIR}/arm64/include ${ZIP_DIR}/install/
-# build a FAT bianry
-cd ${ZIP_DIR}/install/lib
-target_libs=(libc10.a libclog.a libcpuinfo.a libeigen_blas.a libpthreadpool.a libpytorch_qnnpack.a libtorch_cpu.a libtorch.a libXNNPACK.a)
-for lib in ${target_libs[*]}
-do
-    if [ -f "${ARTIFACTS_DIR}/x86_64/lib/${lib}" ] && [ -f "${ARTIFACTS_DIR}/arm64/lib/${lib}" ]; then
-        libs=("${ARTIFACTS_DIR}/x86_64/lib/${lib}" "${ARTIFACTS_DIR}/arm64/lib/${lib}")
-        lipo -create "${libs[@]}" -o ${ZIP_DIR}/install/lib/${lib}
-    fi
-done
-lipo -i ${ZIP_DIR}/install/lib/*.a
-echo "BUILD_LITE_INTERPRETER: ${BUILD_LITE_INTERPRETER}"
-# copy the umbrella header and license
-if [ "${BUILD_LITE_INTERPRETER}" == "1" ]; then
-    cp ${PROJ_ROOT}/ios/LibTorch-Lite.h ${ZIP_DIR}/src/
-else
-    cp ${PROJ_ROOT}/ios/LibTorch.h ${ZIP_DIR}/src/
-fi
-cp ${PROJ_ROOT}/LICENSE ${ZIP_DIR}/
-# zip the library
-export DATE="$(date -u +%Y%m%d)"
-export IOS_NIGHTLY_BUILD_VERSION="1.11.0.${DATE}"
-if [ "${BUILD_LITE_INTERPRETER}" == "1" ]; then
-    # libtorch_lite_ios_nightly_1.11.0.20210810.zip
-    ZIPFILE="libtorch_lite_ios_nightly_${IOS_NIGHTLY_BUILD_VERSION}.zip"
-else
-    ZIPFILE="libtorch_ios_nightly_build.zip"
-fi
-cd ${ZIP_DIR}
-#for testing
-touch version.txt
-echo "${IOS_NIGHTLY_BUILD_VERSION}" > version.txt
-zip -r ${ZIPFILE} install src version.txt LICENSE
-# upload to aws
-# Install conda then 'conda install' awscli
-curl --retry 3 -o ~/conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
-chmod +x ~/conda.sh
-/bin/bash ~/conda.sh -b -p ~/anaconda
-export PATH="~/anaconda/bin:${PATH}"
-source ~/anaconda/bin/activate
-conda install -c conda-forge awscli --yes
-set +x
-export AWS_ACCESS_KEY_ID=${AWS_S3_ACCESS_KEY_FOR_PYTORCH_BINARY_UPLOAD}
-export AWS_SECRET_ACCESS_KEY=${AWS_S3_ACCESS_SECRET_FOR_PYTORCH_BINARY_UPLOAD}
-set +x
-# echo "AWS KEY: ${AWS_ACCESS_KEY_ID}"
-# echo "AWS SECRET: ${AWS_SECRET_ACCESS_KEY}"
-aws s3 cp ${ZIPFILE} s3://ossci-ios-build/ --acl public-read
-
-if [ "${BUILD_LITE_INTERPRETER}" == "1" ]; then
-    # create a new LibTorch-Lite-Nightly.podspec from the template
-    echo "cp ${PROJ_ROOT}/ios/LibTorch-Lite-Nightly.podspec.template ${PROJ_ROOT}/ios/LibTorch-Lite-Nightly.podspec"
-    cp ${PROJ_ROOT}/ios/LibTorch-Lite-Nightly.podspec.template ${PROJ_ROOT}/ios/LibTorch-Lite-Nightly.podspec
-
-    # update pod version
-    sed -i '' -e "s/IOS_NIGHTLY_BUILD_VERSION/${IOS_NIGHTLY_BUILD_VERSION}/g" ${PROJ_ROOT}/ios/LibTorch-Lite-Nightly.podspec
-    cat ${PROJ_ROOT}/ios/LibTorch-Lite-Nightly.podspec
-
-    # push the new LibTorch-Lite-Nightly.podspec to CocoaPods
-    pod trunk push --verbose --allow-warnings --use-libraries --skip-import-validation ${PROJ_ROOT}/ios/LibTorch-Lite-Nightly.podspec
-fi

.circleci/scripts/binary_linux_build.sh

@@ -1,34 +0,0 @@
-#!/bin/bash
-
-echo "RUNNING ON $(uname -a) WITH $(nproc) CPUS AND $(free -m)"
-set -eux -o pipefail
-source /env
-
-# Because most Circle executors only have 20 CPUs, using more causes OOMs w/ Ninja and nvcc parallelization
-MEMORY_LIMIT_MAX_JOBS=18
-NUM_CPUS=$(( $(nproc) - 2 ))
-
-# Defaults here for **binary** linux builds so they can be changed in one place
-export MAX_JOBS=${MAX_JOBS:-$(( ${NUM_CPUS} > ${MEMORY_LIMIT_MAX_JOBS} ? ${MEMORY_LIMIT_MAX_JOBS} : ${NUM_CPUS} ))}
-
-if [[ "${DESIRED_CUDA}" =~ cu11[0-9] ]]; then
-  export BUILD_SPLIT_CUDA="ON"
-fi
-
-# Parse the parameters
-if [[ "$PACKAGE_TYPE" == 'conda' ]]; then
-  build_script='conda/build_pytorch.sh'
-elif [[ "$DESIRED_CUDA" == cpu ]]; then
-  build_script='manywheel/build_cpu.sh'
-elif [[ "$DESIRED_CUDA" == *"rocm"* ]]; then
-  build_script='manywheel/build_rocm.sh'
-else
-  build_script='manywheel/build.sh'
-fi
-
-if [[ "$CIRCLE_BRANCH" == "master" ]] || [[ "$CIRCLE_BRANCH" == release/* ]]; then
-  export BUILD_DEBUG_INFO=1
-fi
-
-# Build the package
-SKIP_ALL_TESTS=1 "/builder/$build_script"

.circleci/scripts/binary_linux_test.sh

@@ -1,118 +0,0 @@
-#!/bin/bash
-
-OUTPUT_SCRIPT=${OUTPUT_SCRIPT:-/home/circleci/project/ci_test_script.sh}
-
-# only source if file exists
-if [[ -f /home/circleci/project/env ]]; then
-  source /home/circleci/project/env
-fi
-cat >"${OUTPUT_SCRIPT}" <<EOL
-# =================== The following code will be executed inside Docker container ===================
-set -eux -o pipefail
-
-retry () {
-    "\$@"  || (sleep 1 && "\$@") || (sleep 2 && "\$@")
-}
-
-# Source binary env file here if exists
-if [[ -e "${BINARY_ENV_FILE:-/nofile}" ]]; then
-  source "${BINARY_ENV_FILE:-/nofile}"
-fi
-
-python_nodot="\$(echo $DESIRED_PYTHON | tr -d m.u)"
-
-# Set up Python
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  retry conda create -qyn testenv python="$DESIRED_PYTHON"
-  source activate testenv >/dev/null
-elif [[ "$PACKAGE_TYPE" != libtorch ]]; then
-  python_path="/opt/python/cp\$python_nodot-cp\${python_nodot}"
-  # Prior to Python 3.8 paths were suffixed with an 'm'
-  if [[ -d  "\${python_path}/bin" ]]; then
-    export PATH="\${python_path}/bin:\$PATH"
-  elif [[ -d "\${python_path}m/bin" ]]; then
-    export PATH="\${python_path}m/bin:\$PATH"
-  fi
-fi
-
-EXTRA_CONDA_FLAGS=""
-NUMPY_PIN=""
-PROTOBUF_PACKAGE="defaults::protobuf"
-if [[ "\$python_nodot" = *310* ]]; then
-  EXTRA_CONDA_FLAGS="-c=conda-forge"
-  # There's an issue with conda channel priority where it'll randomly pick 1.19 over 1.20
-  # we set a lower boundary here just to be safe
-  NUMPY_PIN=">=1.21.2"
-  PROTOBUF_PACKAGE="protobuf>=3.19.0"
-fi
-
-if [[ "\$python_nodot" = *39*  ]]; then
-  EXTRA_CONDA_FLAGS="-c=conda-forge"
-  # There's an issue with conda channel priority where it'll randomly pick 1.19 over 1.20
-  # we set a lower boundary here just to be safe
-  NUMPY_PIN=">=1.20"
-fi
-
-if [[ "$DESIRED_CUDA" == "cu112" || "$DESIRED_CUDA" == "cu115" ]]; then
-  EXTRA_CONDA_FLAGS="-c=conda-forge"
-fi
-
-# Move debug wheels out of the the package dir so they don't get installed
-mkdir -p /tmp/debug_final_pkgs
-mv /final_pkgs/debug-*.zip /tmp/debug_final_pkgs || echo "no debug packages to move"
-
-# Install the package
-# These network calls should not have 'retry's because they are installing
-# locally and aren't actually network calls
-# TODO there is duplicated and inconsistent test-python-env setup across this
-#   file, builder/smoke_test.sh, and builder/run_tests.sh, and also in the
-#   conda build scripts themselves. These should really be consolidated
-pkg="/final_pkgs/\$(ls /final_pkgs)"
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  (
-    # For some reason conda likes to re-activate the conda environment when attempting this install
-    # which means that a deactivate is run and some variables might not exist when that happens,
-    # namely CONDA_MKL_INTERFACE_LAYER_BACKUP from libblas so let's just ignore unbound variables when
-    # it comes to the conda installation commands
-    set +u
-    retry conda install \${EXTRA_CONDA_FLAGS} -yq \
-      "numpy\${NUMPY_PIN}" \
-      future \
-      mkl>=2018 \
-      ninja \
-      dataclasses \
-      typing-extensions \
-      ${PROTOBUF_PACKAGE} \
-      six
-    if [[ "$DESIRED_CUDA" == 'cpu' ]]; then
-      retry conda install -c pytorch -y cpuonly
-    else
-      # DESIRED_CUDA is in format cu90 or cu102
-      if [[ "${#DESIRED_CUDA}" == 4 ]]; then
-        cu_ver="${DESIRED_CUDA:2:1}.${DESIRED_CUDA:3}"
-      else
-        cu_ver="${DESIRED_CUDA:2:2}.${DESIRED_CUDA:4}"
-      fi
-      retry conda install \${EXTRA_CONDA_FLAGS} -yq -c nvidia -c pytorch "cudatoolkit=\${cu_ver}"
-    fi
-    conda install \${EXTRA_CONDA_FLAGS} -y "\$pkg" --offline
-  )
-elif [[ "$PACKAGE_TYPE" != libtorch ]]; then
-  pip install "\$pkg"
-  retry pip install -q future numpy protobuf typing-extensions six
-fi
-if [[ "$PACKAGE_TYPE" == libtorch ]]; then
-  pkg="\$(ls /final_pkgs/*-latest.zip)"
-  unzip "\$pkg" -d /tmp
-  cd /tmp/libtorch
-fi
-
-# Test the package
-/builder/check_binary.sh
-
-# =================== The above code will be executed inside Docker container ===================
-EOL
-echo
-echo
-echo "The script that will run in the next step is:"
-cat "${OUTPUT_SCRIPT}"

.circleci/scripts/binary_macos_build.sh

@@ -1,28 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-source "/Users/distiller/project/env"
-mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR"
-
-# For some reason `unbuffer` breaks if we change the PATH here, so we
-# write a script with the PATH change in it and unbuffer the whole
-# thing
-build_script="$workdir/build_script.sh"
-touch "$build_script"
-chmod +x "$build_script"
-
-# Build
-cat >"$build_script" <<EOL
-export PATH="$workdir/miniconda/bin:$PATH"
-if [[ "$CIRCLE_BRANCH" == "nightly" ]]; then
-  export USE_PYTORCH_METAL_EXPORT=1
-  export USE_COREML_DELEGATE=1
-fi
-if [[ "$PACKAGE_TYPE" == conda ]]; then
-  "$workdir/builder/conda/build_pytorch.sh"
-else
-  export TORCH_PACKAGE_NAME="$(echo $TORCH_PACKAGE_NAME | tr '-' '_')"
-  "$workdir/builder/wheel/build_wheel.sh"
-fi
-EOL
-unbuffer "$build_script" | ts

.circleci/scripts/binary_macos_test.sh

@@ -1,34 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-source "/Users/distiller/project/env"
-export "PATH=$workdir/miniconda/bin:$PATH"
-pkg="$workdir/final_pkgs/$(ls $workdir/final_pkgs)"
-
-# Create a new test env
-# TODO cut all this out into a separate test job and have an entirely different
-# miniconda
-if [[ "$PACKAGE_TYPE" != libtorch ]]; then
-  source deactivate || true
-  conda create -qyn test python="$DESIRED_PYTHON"
-  source activate test >/dev/null
-fi
-
-# Install the package
-if [[ "$PACKAGE_TYPE" == libtorch ]]; then
-  pkg="$(ls $workdir/final_pkgs/*-latest.zip)"
-  unzip "$pkg" -d /tmp
-  cd /tmp/libtorch
-elif [[ "$PACKAGE_TYPE" == conda ]]; then
-  conda install -y "$pkg"
-else
-  pip install "$pkg" -v
-fi
-
-# Test
-if [[ "$PACKAGE_TYPE" == libtorch ]]; then
-  $workdir/builder/check_binary.sh
-else
-  pushd "$workdir/pytorch"
-  $workdir/builder/run_tests.sh "$PACKAGE_TYPE" "$DESIRED_PYTHON" "$DESIRED_CUDA"
-fi

.circleci/scripts/binary_populate_env.sh

@@ -1,236 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-export TZ=UTC
-
-tagged_version() {
-  # Grabs version from either the env variable CIRCLE_TAG
-  # or the pytorch git described version
-  if [[ "$OSTYPE" == "msys" &&  -z "${IS_GHA:-}" ]]; then
-    GIT_DIR="${workdir}/p/.git"
-  else
-    GIT_DIR="${workdir}/pytorch/.git"
-  fi
-  GIT_DESCRIBE="git --git-dir ${GIT_DIR} describe --tags --match v[0-9]*.[0-9]*.[0-9]*"
-  if [[ -n "${CIRCLE_TAG:-}" ]]; then
-    echo "${CIRCLE_TAG}"
-  elif [[ ! -d "${GIT_DIR}" ]]; then
-    echo "Abort, abort! Git dir ${GIT_DIR} does not exists!"
-    kill $$
-  elif ${GIT_DESCRIBE} --exact >/dev/null; then
-    ${GIT_DESCRIBE}
-  else
-    return 1
-  fi
-}
-
-# These are only relevant for CircleCI
-# TODO: Remove these later once migrated fully to GHA
-if [[ -z ${IS_GHA:-} ]]; then
-  # We need to write an envfile to persist these variables to following
-  # steps, but the location of the envfile depends on the circleci executor
-  if [[ "$(uname)" == Darwin ]]; then
-    # macos executor (builds and tests)
-    workdir="/Users/distiller/project"
-  elif [[ "$OSTYPE" == "msys" ]]; then
-    # windows executor (builds and tests)
-    workdir="/c/w"
-  elif [[ -d "/home/circleci/project" ]]; then
-    # machine executor (binary tests)
-    workdir="/home/circleci/project"
-  else
-    # docker executor (binary builds)
-    workdir="/"
-  fi
-  envfile="$workdir/env"
-  touch "$envfile"
-  chmod +x "$envfile"
-
-  # Parse the BUILD_ENVIRONMENT to package type, python, and cuda
-  configs=($BUILD_ENVIRONMENT)
-  export PACKAGE_TYPE="${configs[0]}"
-  export DESIRED_PYTHON="${configs[1]}"
-  export DESIRED_CUDA="${configs[2]}"
-  if [[ "${OSTYPE}" == "msys" ]]; then
-    export DESIRED_DEVTOOLSET=""
-    export LIBTORCH_CONFIG="${configs[3]:-}"
-    if [[ "$LIBTORCH_CONFIG" == 'debug' ]]; then
-      export DEBUG=1
-    fi
-  else
-    export DESIRED_DEVTOOLSET="${configs[3]:-}"
-  fi
-else
-  envfile=${BINARY_ENV_FILE:-/tmp/env}
-  if [[ -n "${PYTORCH_ROOT}"  ]]; then
-    workdir=$(dirname "${PYTORCH_ROOT}")
-  else
-    # docker executor (binary builds)
-    workdir="/"
-  fi
-fi
-
-if [[ "$PACKAGE_TYPE" == 'libtorch' ]]; then
-  export BUILD_PYTHONLESS=1
-fi
-
-# Pick docker image
-export DOCKER_IMAGE=${DOCKER_IMAGE:-}
-if [[ -z "$DOCKER_IMAGE" ]]; then
-  if [[ "$PACKAGE_TYPE" == conda ]]; then
-    export DOCKER_IMAGE="pytorch/conda-cuda"
-  elif [[ "$DESIRED_CUDA" == cpu ]]; then
-    export DOCKER_IMAGE="pytorch/manylinux-cpu"
-  else
-    export DOCKER_IMAGE="pytorch/manylinux-cuda${DESIRED_CUDA:2}"
-  fi
-fi
-
-USE_GOLD_LINKER="OFF"
-# GOLD linker can not be used if CUPTI is statically linked into PyTorch, see https://github.com/pytorch/pytorch/issues/57744
-if [[ ${DESIRED_CUDA} == "cpu" ]]; then
-  USE_GOLD_LINKER="ON"
-fi
-
-USE_WHOLE_CUDNN="OFF"
-# Link whole cuDNN for CUDA-11.1 to include fp16 fast kernels
-if [[  "$(uname)" == "Linux" && "${DESIRED_CUDA}" == "cu111" ]]; then
-  USE_WHOLE_CUDNN="ON"
-fi
-
-# Default to nightly, since that's where this normally uploads to
-PIP_UPLOAD_FOLDER='nightly/'
-# We put this here so that OVERRIDE_PACKAGE_VERSION below can read from it
-export DATE="$(date -u +%Y%m%d)"
-#TODO: We should be pulling semver version from the base version.txt
-BASE_BUILD_VERSION="1.11.0.dev$DATE"
-# Change BASE_BUILD_VERSION to git tag when on a git tag
-# Use 'git -C' to make doubly sure we're in the correct directory for checking
-# the git tag
-if tagged_version >/dev/null; then
-  # Switch upload folder to 'test/' if we are on a tag
-  PIP_UPLOAD_FOLDER='test/'
-  # Grab git tag, remove prefixed v and remove everything after -
-  # Used to clean up tags that are for release candidates like v1.6.0-rc1
-  # Turns tag v1.6.0-rc1 -> v1.6.0
-  BASE_BUILD_VERSION="$(tagged_version | sed -e 's/^v//' -e 's/-.*$//')"
-fi
-if [[ "$(uname)" == 'Darwin' ]] || [[ "$PACKAGE_TYPE" == conda ]]; then
-  export PYTORCH_BUILD_VERSION="${BASE_BUILD_VERSION}"
-else
-  export PYTORCH_BUILD_VERSION="${BASE_BUILD_VERSION}+$DESIRED_CUDA"
-fi
-export PYTORCH_BUILD_NUMBER=1
-
-
-JAVA_HOME=
-BUILD_JNI=OFF
-if [[ "$PACKAGE_TYPE" == libtorch ]]; then
-  POSSIBLE_JAVA_HOMES=()
-  POSSIBLE_JAVA_HOMES+=(/usr/local)
-  POSSIBLE_JAVA_HOMES+=(/usr/lib/jvm/java-8-openjdk-amd64)
-  POSSIBLE_JAVA_HOMES+=(/Library/Java/JavaVirtualMachines/*.jdk/Contents/Home)
-  # Add the Windows-specific JNI path
-  POSSIBLE_JAVA_HOMES+=("$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 $JH"
-      JAVA_HOME="$JH"
-      BUILD_JNI=ON
-      break
-    fi
-  done
-  if [ -z "$JAVA_HOME" ]; then
-    echo "Did not find jni.h"
-  fi
-fi
-
-cat >"$envfile" <<EOL
-# =================== The following code will be executed inside Docker container ===================
-export TZ=UTC
-echo "Running on $(uname -a) at $(date)"
-
-export PACKAGE_TYPE="$PACKAGE_TYPE"
-export DESIRED_PYTHON="${DESIRED_PYTHON:-}"
-export DESIRED_CUDA="$DESIRED_CUDA"
-export LIBTORCH_VARIANT="${LIBTORCH_VARIANT:-}"
-export BUILD_PYTHONLESS="${BUILD_PYTHONLESS:-}"
-if [[ "${OSTYPE}" == "msys" ]]; then
-  export LIBTORCH_CONFIG="${LIBTORCH_CONFIG:-}"
-  if [[ "${LIBTORCH_CONFIG:-}" == 'debug' ]]; then
-    export DEBUG=1
-  fi
-  export DESIRED_DEVTOOLSET=""
-else
-  export DESIRED_DEVTOOLSET="${DESIRED_DEVTOOLSET:-}"
-fi
-
-export DATE="$DATE"
-export NIGHTLIES_DATE_PREAMBLE=1.11.0.dev
-export PYTORCH_BUILD_VERSION="$PYTORCH_BUILD_VERSION"
-export PYTORCH_BUILD_NUMBER="$PYTORCH_BUILD_NUMBER"
-export OVERRIDE_PACKAGE_VERSION="$PYTORCH_BUILD_VERSION"
-
-# TODO: We don't need this anymore IIUC
-export TORCH_PACKAGE_NAME='torch'
-export TORCH_CONDA_BUILD_FOLDER='pytorch-nightly'
-export ANACONDA_USER='pytorch'
-
-export USE_FBGEMM=1
-export JAVA_HOME=$JAVA_HOME
-export BUILD_JNI=$BUILD_JNI
-export PIP_UPLOAD_FOLDER="$PIP_UPLOAD_FOLDER"
-export DOCKER_IMAGE="$DOCKER_IMAGE"
-
-
-export USE_GOLD_LINKER="${USE_GOLD_LINKER}"
-export USE_GLOO_WITH_OPENSSL="ON"
-export USE_WHOLE_CUDNN="${USE_WHOLE_CUDNN}"
-# =================== The above code will be executed inside Docker container ===================
-EOL
-
-# nproc doesn't exist on darwin
-if [[ "$(uname)" != Darwin ]]; then
-  # Because most Circle executors only have 20 CPUs, using more causes OOMs w/ Ninja and nvcc parallelization
-  MEMORY_LIMIT_MAX_JOBS=18
-  NUM_CPUS=$(( $(nproc) - 2 ))
-
-  # Defaults here for **binary** linux builds so they can be changed in one place
-  export MAX_JOBS=${MAX_JOBS:-$(( ${NUM_CPUS} > ${MEMORY_LIMIT_MAX_JOBS} ? ${MEMORY_LIMIT_MAX_JOBS} : ${NUM_CPUS} ))}
-
-  cat >>"$envfile" <<EOL
-  export MAX_JOBS="${MAX_JOBS}"
-EOL
-fi
-
-if [[ -z "${IS_GHA:-}" ]]; then
-  cat >>"$envfile" <<EOL
-  export workdir="$workdir"
-  export MAC_PACKAGE_WORK_DIR="$workdir"
-  if [[ "$OSTYPE" == "msys" ]]; then
-    export PYTORCH_ROOT="$workdir/p"
-    export BUILDER_ROOT="$workdir/b"
-  else
-    export PYTORCH_ROOT="$workdir/pytorch"
-    export BUILDER_ROOT="$workdir/builder"
-  fi
-  export MINICONDA_ROOT="$workdir/miniconda"
-  export PYTORCH_FINAL_PACKAGE_DIR="$workdir/final_pkgs"
-
-  export CIRCLE_TAG="${CIRCLE_TAG:-}"
-  export CIRCLE_SHA1="$CIRCLE_SHA1"
-  export CIRCLE_PR_NUMBER="${CIRCLE_PR_NUMBER:-}"
-  export CIRCLE_BRANCH="$CIRCLE_BRANCH"
-  export CIRCLE_WORKFLOW_ID="$CIRCLE_WORKFLOW_ID"
-EOL
-fi
-
-echo 'retry () {' >> "$envfile"
-echo '    $*  || (sleep 1 && $*) || (sleep 2 && $*) || (sleep 4 && $*) || (sleep 8 && $*)' >> "$envfile"
-echo '}' >> "$envfile"
-echo 'export -f retry' >> "$envfile"
-
-cat "$envfile"

.circleci/scripts/binary_run_in_docker.sh

@@ -1,29 +0,0 @@
-#!/bin/bash
-
-# This section is used in the binary_test and smoke_test jobs. It expects
-# 'binary_populate_env' to have populated /home/circleci/project/env and it
-# expects another section to populate /home/circleci/project/ci_test_script.sh
-# with the code to run in the docker
-
-# Expect all needed environment variables to be written to this file
-source /home/circleci/project/env
-echo "Running the following code in Docker"
-cat /home/circleci/project/ci_test_script.sh
-echo
-echo
-set -eux -o pipefail
-
-# Expect actual code to be written to this file
-chmod +x /home/circleci/project/ci_test_script.sh
-
-VOLUME_MOUNTS="-v /home/circleci/project/:/circleci_stuff -v /home/circleci/project/final_pkgs:/final_pkgs -v ${PYTORCH_ROOT}:/pytorch -v ${BUILDER_ROOT}:/builder"
-# Run the docker
-if [ -n "${USE_CUDA_DOCKER_RUNTIME:-}" ]; then
-  export id=$(docker run --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --gpus all ${VOLUME_MOUNTS} -t -d "${DOCKER_IMAGE}")
-else
-  export id=$(docker run --cap-add=SYS_PTRACE --security-opt seccomp=unconfined ${VOLUME_MOUNTS} -t -d "${DOCKER_IMAGE}")
-fi
-
-# Execute the test script that was populated by an earlier section
-export COMMAND='((echo "source /circleci_stuff/env && /circleci_stuff/ci_test_script.sh") | docker exec -i "$id" bash) 2>&1'
-echo ${COMMAND} > ./command.sh && unbuffer bash ./command.sh | ts

.circleci/scripts/binary_upload.sh

@@ -1,102 +0,0 @@
-#!/usr/bin/env bash
-
-set -euo pipefail
-
-PACKAGE_TYPE=${PACKAGE_TYPE:-conda}
-
-PKG_DIR=${PKG_DIR:-/tmp/workspace/final_pkgs}
-
-# Designates whether to submit as a release candidate or a nightly build
-# Value should be `test` when uploading release candidates
-# currently set within `designate_upload_channel`
-UPLOAD_CHANNEL=${UPLOAD_CHANNEL:-nightly}
-# Designates what subfolder to put packages into
-UPLOAD_SUBFOLDER=${UPLOAD_SUBFOLDER:-cpu}
-UPLOAD_BUCKET="s3://pytorch"
-BACKUP_BUCKET="s3://pytorch-backup"
-
-DRY_RUN=${DRY_RUN:-enabled}
-# Don't actually do work unless explicit
-ANACONDA="true anaconda"
-AWS_S3_CP="aws s3 cp --dryrun"
-if [[ "${DRY_RUN}" = "disabled" ]]; then
-  ANACONDA="anaconda"
-  AWS_S3_CP="aws s3 cp"
-fi
-
-do_backup() {
-  local backup_dir
-  backup_dir=$1
-  (
-    pushd /tmp/workspace
-    set -x
-    ${AWS_S3_CP} --recursive . "${BACKUP_BUCKET}/${CIRCLE_TAG}/${backup_dir}/"
-  )
-}
-
-conda_upload() {
-  (
-    set -x
-    ${ANACONDA} \
-      upload  \
-      ${PKG_DIR}/*.tar.bz2 \
-      -u "pytorch-${UPLOAD_CHANNEL}" \
-      --label main \
-      --no-progress \
-      --force
-  )
-}
-
-s3_upload() {
-  local extension
-  local pkg_type
-  extension="$1"
-  pkg_type="$2"
-  s3_dir="${UPLOAD_BUCKET}/${pkg_type}/${UPLOAD_CHANNEL}/${UPLOAD_SUBFOLDER}/"
-  (
-    for pkg in ${PKG_DIR}/*.${extension}; do
-      (
-        set -x
-        ${AWS_S3_CP} --no-progress --acl public-read "${pkg}" "${s3_dir}"
-      )
-    done
-  )
-}
-
-# Install dependencies (should be a no-op if previously installed)
-conda install -yq anaconda-client
-pip install -q awscli
-
-case "${PACKAGE_TYPE}" in
-  conda)
-    conda_upload
-    # Fetch  platform (eg. win-64, linux-64, etc.) from index file
-    # Because there's no actual conda command to read this
-    subdir=$(\
-      tar -xOf ${PKG_DIR}/*.bz2 info/index.json \
-        | grep subdir  \
-        | cut -d ':' -f2 \
-        | sed -e 's/[[:space:]]//' -e 's/"//g' -e 's/,//' \
-    )
-    BACKUP_DIR="conda/${subdir}"
-    ;;
-  libtorch)
-    s3_upload "zip" "libtorch"
-    BACKUP_DIR="libtorch/${UPLOAD_CHANNEL}/${UPLOAD_SUBFOLDER}"
-    ;;
-  # wheel can either refer to wheel/manywheel
-  *wheel)
-    s3_upload "whl" "whl"
-    BACKUP_DIR="whl/${UPLOAD_CHANNEL}/${UPLOAD_SUBFOLDER}"
-    ;;
-  *)
-    echo "ERROR: unknown package type: ${PACKAGE_TYPE}"
-    exit 1
-    ;;
-esac
-
-# CIRCLE_TAG is defined by upstream circleci,
-# this can be changed to recognize tagged versions
-if [[ -n "${CIRCLE_TAG:-}" ]]; then
-  do_backup "${BACKUP_DIR}"
-fi

.circleci/scripts/binary_windows_build.sh

@@ -1,77 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-source "${BINARY_ENV_FILE:-/c/w/env}"
-mkdir -p "$PYTORCH_FINAL_PACKAGE_DIR"
-
-export CUDA_VERSION="${DESIRED_CUDA/cu/}"
-export USE_SCCACHE=1
-export SCCACHE_BUCKET=ossci-compiler-cache-windows
-export NIGHTLIES_PYTORCH_ROOT="$PYTORCH_ROOT"
-export VC_YEAR=2019
-
-if [[ "${DESIRED_CUDA}" == *"cu11"* ]]; then
-    export BUILD_SPLIT_CUDA=ON
-fi
-
-echo "Free Space for CUDA DEBUG BUILD"
-if [[ "${CIRCLECI:-}" == 'true' ]]; then
-    if [[ -d "C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Community" ]]; then
-        rm -rf "C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Community"
-    fi
-
-    if [[ -d "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0" ]]; then
-        rm -rf "C:\\Program Files (x86)\\Microsoft Visual Studio 14.0"
-    fi
-
-    if [[ -d "C:\\Program Files (x86)\\Microsoft.NET" ]]; then
-        rm -rf "C:\\Program Files (x86)\\Microsoft.NET"
-    fi
-
-    if [[ -d "C:\\Program Files\\dotnet" ]]; then
-        rm -rf "C:\\Program Files\\dotnet"
-    fi
-
-    if [[ -d "C:\\Program Files (x86)\\dotnet" ]]; then
-        rm -rf "C:\\Program Files (x86)\\dotnet"
-    fi
-
-    if [[ -d "C:\\Program Files (x86)\\Microsoft SQL Server" ]]; then
-        rm -rf "C:\\Program Files (x86)\\Microsoft SQL Server"
-    fi
-
-    if [[ -d "C:\\Program Files (x86)\\Xamarin" ]]; then
-        rm -rf "C:\\Program Files (x86)\\Xamarin"
-    fi
-
-    if [[ -d "C:\\Program Files (x86)\\Google" ]]; then
-        rm -rf "C:\\Program Files (x86)\\Google"
-    fi
-    set +x
-    export AWS_ACCESS_KEY_ID=${CIRCLECI_AWS_ACCESS_KEY_FOR_SCCACHE_S3_BUCKET_V4:-}
-    export AWS_SECRET_ACCESS_KEY=${CIRCLECI_AWS_SECRET_KEY_FOR_SCCACHE_S3_BUCKET_V4:-}
-    set -x
-    if [[ -d "C:\\ProgramData\\Microsoft\\VisualStudio\\Packages\\_Instances" ]]; then
-        mv "C:\\ProgramData\\Microsoft\\VisualStudio\\Packages\\_Instances" .
-        rm -rf "C:\\ProgramData\\Microsoft\\VisualStudio\\Packages"
-        mkdir -p "C:\\ProgramData\\Microsoft\\VisualStudio\\Packages"
-        mv _Instances "C:\\ProgramData\\Microsoft\\VisualStudio\\Packages"
-    fi
-    if [[ -d "C:\\Microsoft" ]]; then
-        # don't use quotes here
-        rm -rf /c/Microsoft/AndroidNDK*
-    fi
-fi
-
-echo "Free space on filesystem before build:"
-df -h
-
-pushd "$BUILDER_ROOT"
-if [[ "$PACKAGE_TYPE" == 'conda' ]]; then
-    ./windows/internal/build_conda.bat
-elif [[ "$PACKAGE_TYPE" == 'wheel' || "$PACKAGE_TYPE" == 'libtorch' ]]; then
-    ./windows/internal/build_wheels.bat
-fi
-
-echo "Free space on filesystem after build:"
-df -h

.circleci/scripts/binary_windows_test.sh

@@ -1,13 +0,0 @@
-#!/bin/bash
-set -eux -o pipefail
-
-source "${BINARY_ENV_FILE:-/c/w/env}"
-
-export CUDA_VERSION="${DESIRED_CUDA/cu/}"
-export VC_YEAR=2019
-
-pushd "$BUILDER_ROOT"
-
-./windows/internal/smoke_test.bat
-
-popd