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Merge remote-tracking branch 'upstream/master' into pooling
2 parents d5cfe5a + 006cfeb commit 07fc24d

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‎.circleci/config.yml‎

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@@ -11,6 +11,9 @@ parameters:
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run_binary_tests:
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type: boolean
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default: false
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run_build:
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type: boolean
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default: true
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docker_config_defaults: &docker_config_defaults
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user: jenkins
@@ -9762,6 +9765,7 @@ workflows:
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only:
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- postnightly
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executor: windows-with-nvidia-gpu
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when: << pipeline.parameters.run_build >>
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ecr_gc:
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triggers:
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- schedule:

‎.circleci/generate_config_yml.py‎

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@@ -112,7 +112,10 @@ def gen_build_workflows_tree():
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"when": r"<< pipeline.parameters.run_binary_tests >>",
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"jobs": [f() for f in binary_build_functions],
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},
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"build": {"jobs": [f() for f in build_workflows_functions]},
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"build": {
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"when": r"<< pipeline.parameters.run_build >>",
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"jobs": [f() for f in build_workflows_functions]
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},
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}
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}
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‎.circleci/scripts/binary_linux_test.sh‎

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@@ -51,7 +51,14 @@ if [[ "$PACKAGE_TYPE" == conda ]]; then
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else
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cu_ver="${DESIRED_CUDA:2:2}.${DESIRED_CUDA:4}"
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fi
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retry conda install \${EXTRA_CONDA_FLAGS} -yq -c nvidia -c pytorch "cudatoolkit=\${cu_ver}"
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(
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# For some reason conda likes to re-activate the conda environment when attempting this install
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# which means that a deactivate is run and some variables might not exist when that happens,
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# namely CONDA_MKL_INTERFACE_LAYER_BACKUP from libblas so let's just ignore unbound variables when
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# it comes to the conda installation commands
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set +u
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retry conda install \${EXTRA_CONDA_FLAGS} -yq -c nvidia -c pytorch "cudatoolkit=\${cu_ver}"
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)
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fi
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elif [[ "$PACKAGE_TYPE" != libtorch ]]; then
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pip install "\$pkg"

‎.circleci/scripts/windows_cuda_install.sh‎

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#!/bin/bash
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set -eux -o pipefail
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if [[ "$CUDA_VERSION" =~ ^10.* ]]; then
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cuda_major_version=${CUDA_VERSION%.*}
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if [[ "$cuda_major_version" == "10" ]]; then
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cuda_installer_name="cuda_10.1.243_426.00_win10"
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msbuild_project_dir="CUDAVisualStudioIntegration/extras/visual_studio_integration/MSBuildExtensions"
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cuda_install_packages="nvcc_10.1 cuobjdump_10.1 nvprune_10.1 cupti_10.1 cublas_10.1 cublas_dev_10.1 cudart_10.1 cufft_10.1 cufft_dev_10.1 curand_10.1 curand_dev_10.1 cusolver_10.1 cusolver_dev_10.1 cusparse_10.1 cusparse_dev_10.1 nvgraph_10.1 nvgraph_dev_10.1 npp_10.1 npp_dev_10.1 nvrtc_10.1 nvrtc_dev_10.1 nvml_dev_10.1"
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elif [[ "$CUDA_VERSION" =~ ^11.* ]]; then
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elif [[ "$cuda_major_version" == "11" ]]; then
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cuda_installer_name="cuda_11.1.0_456.43_win10"
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msbuild_project_dir="visual_studio_integration/CUDAVisualStudioIntegration/extras/visual_studio_integration/MSBuildExtensions"
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cuda_install_packages="nvcc_11.1 cuobjdump_11.1 nvprune_11.1 nvprof_11.1 cupti_11.1 cublas_11.1 cublas_dev_11.1 cudart_11.1 cufft_11.1 cufft_dev_11.1 curand_11.1 curand_dev_11.1 cusolver_11.1 cusolver_dev_11.1 cusparse_11.1 cusparse_dev_11.1 npp_11.1 npp_dev_11.1 nvrtc_11.1 nvrtc_dev_11.1 nvml_dev_11.1"
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exit 1
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fi
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if [[ "$CUDA_VERSION" =~ ^11.* && "${JOB_EXECUTOR}" == "windows-with-nvidia-gpu" ]]; then
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if [[ "$cuda_major_version" == "11" && "${JOB_EXECUTOR}" == "windows-with-nvidia-gpu" ]]; then
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cuda_install_packages="${cuda_install_packages} Display.Driver"
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fi
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‎.circleci/scripts/windows_cudnn_install.sh‎

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#!/bin/bash
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set -eux -o pipefail
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if [[ "$CUDA_VERSION" =~ ^10.* ]]; then
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cuda_major_version=${CUDA_VERSION%.*}
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if [[ "$cuda_major_version" == "10" ]]; then
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cudnn_installer_name="cudnn-${CUDA_VERSION}-windows10-x64-v7.6.4.38"
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elif [[ "$CUDA_VERSION" =~ ^11.* ]]; then
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elif [[ "$cuda_major_version" == "11" ]]; then
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cudnn_installer_name="cudnn-${CUDA_VERSION}-windows-x64-v8.0.5.39"
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else
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echo "CUDNN for CUDA_VERSION $CUDA_VERSION is not supported yet"

‎.circleci/verbatim-sources/header-section.yml‎

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run_binary_tests:
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type: boolean
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default: false
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run_build:
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type: boolean
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default: true
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docker_config_defaults: &docker_config_defaults
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user: jenkins

‎.github/pytorch-circleci-labels.yml‎

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- release/.*
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tags:
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- v[0-9]+(\.[0-9]+)*-rc[0-9]+
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set_to_false:
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- run_build

‎.github/workflows/update_s3_htmls.yml‎

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@@ -9,6 +9,7 @@ on:
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jobs:
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update-html:
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runs-on: ubuntu-latest
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if: ${{ github.repository_owner == 'pytorch' }}
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strategy:
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matrix:
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prefix: ["whl", "whl/test", "whl/nightly"]

‎.gitignore‎

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.coverage
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coverage.xml
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.dmypy.json
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.gradle
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.hypothesis
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.mypy_cache

‎.jenkins/pytorch/README.md‎

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@@ -10,9 +10,9 @@ it is very easy to run these tests yourself:
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``registry.pytorch.org/pytorch/pytorch-$BUILD_ENVIRONMENT:$DOCKER_VERSION``,
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where ``$BUILD_ENVIRONMENT`` is one of the build environments
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enumerated in
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[pytorch-dockerfiles](https://github.com/pietern/pytorch-dockerfiles/blob/master/build.sh)
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[pytorch-dockerfiles](https://github.com/pytorch/pytorch/blob/master/.circleci/docker/build.sh). The dockerfile used by jenkins can be found under the `.circle` [directory](https://github.com/pytorch/pytorch/blob/master/.circleci/docker)
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2. Run ``docker -it -u jenkins $DOCKER_IMAGE``, clone PyTorch and
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2. Run ``docker run -it -u jenkins $DOCKER_IMAGE``, clone PyTorch and
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run one of the scripts in this directory.
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The Docker images are designed so that any "reasonable" build commands
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build scripts.
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- We reroute well known paths like `/usr/bin/gcc` to alternate
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implementations with `update-alternatives, instead of setting
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implementations with `update-alternatives`, instead of setting
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`CC` and `CXX` in our implementations.

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