mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
CI fixes
Summary: Update `main` build to latest CircleCI image - Ubuntu 2020.04. Avoid torch.logical_or and logical_and for PyTorch 1.4 compatibility. Also speed up the test run with Pytorch 1.4.0 (which has no ninja) by not setting NVCC_FLAGS for it. Reviewed By: theschnitz Differential Revision: D27262327 fbshipit-source-id: ddc359d134b1dc755f8b20bd3f33bb080cb3a0e1
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@ -3,4 +3,7 @@
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# Run this script before committing config.yml to verify it is valid yaml.
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python -c 'import yaml; yaml.safe_load(open("config.yml"))' && echo OK
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python -c 'import yaml; yaml.safe_load(open("config.yml"))' && echo OK - valid yaml
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msg="circleci not installed so can't check schema"
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command -v circleci > /dev/null && (cd ..; circleci config validate) || echo "$msg"
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@ -18,12 +18,12 @@ setupcuda: &setupcuda
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working_directory: ~/
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command: |
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# download and install nvidia drivers, cuda, etc
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wget --no-verbose --no-clobber -P ~/nvidia-downloads http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
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sudo sh ~/nvidia-downloads/cuda_10.2.89_440.33.01_linux.run --silent
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wget --no-verbose --no-clobber -P ~/nvidia-downloads https://developer.download.nvidia.com/compute/cuda/11.2.2/local_installers/cuda_11.2.2_460.32.03_linux.run
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sudo sh ~/nvidia-downloads/cuda_11.2.2_460.32.03_linux.run --silent
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echo "Done installing CUDA."
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pyenv versions
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nvidia-smi
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pyenv global 3.7.0
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pyenv global 3.9.1
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gpu: &gpu
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environment:
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@ -64,7 +64,7 @@ jobs:
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main:
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<<: *gpu
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machine:
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image: ubuntu-1604:201903-01
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image: ubuntu-2004:202101-01
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steps:
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- checkout
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- <<: *setupcuda
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@ -86,10 +86,10 @@ jobs:
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- run:
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name: build
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command: |
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export LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64
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export LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.2/lib64
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export CUB_HOME=$(realpath ../cub-1.10.0)
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python3 setup.py build_ext --inplace
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- run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python -m unittest discover -v -s tests
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- run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.2/lib64 python -m unittest discover -v -s tests
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- run: python3 setup.py bdist_wheel
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binary_linux_wheel:
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@ -186,7 +186,7 @@ jobs:
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{ docker login -u="$DOCKERHUB_USERNAME" -p="$DOCKERHUB_TOKEN" ; } 2> /dev/null
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export DOCKER_IMAGE=pytorch/conda-cuda
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DOCKER_IMAGE=pytorch/conda-cuda
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echo Pulling docker image $DOCKER_IMAGE
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docker pull $DOCKER_IMAGE
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- run:
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@ -196,8 +196,9 @@ jobs:
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cd ${HOME}/project/
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export DOCKER_IMAGE=pytorch/conda-cuda
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export VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e CU_VERSION"
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DOCKER_IMAGE=pytorch/conda-cuda
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export JUST_TESTRUN=1
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VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e CU_VERSION -e JUST_TESTRUN"
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docker run --gpus all --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${DOCKER_IMAGE} ./packaging/build_conda.sh
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@ -18,12 +18,12 @@ setupcuda: &setupcuda
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working_directory: ~/
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command: |
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# download and install nvidia drivers, cuda, etc
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wget --no-verbose --no-clobber -P ~/nvidia-downloads http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
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sudo sh ~/nvidia-downloads/cuda_10.2.89_440.33.01_linux.run --silent
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wget --no-verbose --no-clobber -P ~/nvidia-downloads https://developer.download.nvidia.com/compute/cuda/11.2.2/local_installers/cuda_11.2.2_460.32.03_linux.run
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sudo sh ~/nvidia-downloads/cuda_11.2.2_460.32.03_linux.run --silent
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echo "Done installing CUDA."
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pyenv versions
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nvidia-smi
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pyenv global 3.7.0
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pyenv global 3.9.1
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gpu: &gpu
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environment:
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@ -64,7 +64,7 @@ jobs:
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main:
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<<: *gpu
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machine:
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image: ubuntu-1604:201903-01
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image: ubuntu-2004:202101-01
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steps:
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- checkout
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- <<: *setupcuda
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@ -86,10 +86,10 @@ jobs:
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- run:
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name: build
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command: |
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export LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64
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export LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.2/lib64
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export CUB_HOME=$(realpath ../cub-1.10.0)
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python3 setup.py build_ext --inplace
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- run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python -m unittest discover -v -s tests
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- run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.2/lib64 python -m unittest discover -v -s tests
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- run: python3 setup.py bdist_wheel
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binary_linux_wheel:
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@ -186,7 +186,7 @@ jobs:
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{ docker login -u="$DOCKERHUB_USERNAME" -p="$DOCKERHUB_TOKEN" ; } 2> /dev/null
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export DOCKER_IMAGE=pytorch/conda-cuda
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DOCKER_IMAGE=pytorch/conda-cuda
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echo Pulling docker image $DOCKER_IMAGE
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docker pull $DOCKER_IMAGE
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- run:
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@ -196,8 +196,9 @@ jobs:
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cd ${HOME}/project/
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export DOCKER_IMAGE=pytorch/conda-cuda
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export VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e CU_VERSION"
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DOCKER_IMAGE=pytorch/conda-cuda
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export JUST_TESTRUN=1
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VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e CU_VERSION -e JUST_TESTRUN"
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docker run --gpus all --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${DOCKER_IMAGE} ./packaging/build_conda.sh
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@ -131,6 +131,8 @@ def generate_upload_workflow(*, base_workflow_name, btype, cu_version, filter_br
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def indent(indentation, data_list):
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if len(data_list) == 0:
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return ""
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return ("\n" + " " * indentation).join(
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yaml.dump(data_list, default_flow_style=False).splitlines()
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)
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setup_conda_pytorch_constraint
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setup_conda_cudatoolkit_constraint
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setup_visual_studio_constraint
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if [[ "$JUST_TESTRUN" == "1" ]]
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then
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# We are not building for other users, we
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# are only trying to see if the tests pass.
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# So save time by only building for our own GPU.
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unset NVCC_FLAGS
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fi
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# shellcheck disable=SC2086
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conda build $CONDA_CHANNEL_FLAGS ${TEST_FLAG:-} -c bottler -c defaults -c conda-forge -c fvcore -c iopath --no-anaconda-upload --python "$PYTHON_VERSION" packaging/pytorch3d
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@ -397,15 +397,15 @@ def clip_faces(
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# pyre-ignore[16]:
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faces_unculled = ~faces_culled
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# Case 1: no clipped verts or culled faces
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cases1_unclipped = torch.logical_and(faces_num_clipped_verts == 0, faces_unculled)
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cases1_unclipped = (faces_num_clipped_verts == 0) & faces_unculled
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case1_unclipped_idx = cases1_unclipped.nonzero(as_tuple=True)[0]
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# Case 2: all verts clipped
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case2_unclipped = torch.logical_or(faces_num_clipped_verts == 3, faces_culled)
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case2_unclipped = (faces_num_clipped_verts == 3) | faces_culled
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# Case 3: two verts clipped
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case3_unclipped = torch.logical_and(faces_num_clipped_verts == 2, faces_unculled)
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case3_unclipped = (faces_num_clipped_verts == 2) & faces_unculled
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case3_unclipped_idx = case3_unclipped.nonzero(as_tuple=True)[0]
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# Case 4: one vert clipped
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case4_unclipped = torch.logical_and(faces_num_clipped_verts == 1, faces_unculled)
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case4_unclipped = (faces_num_clipped_verts == 1) & faces_unculled
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case4_unclipped_idx = case4_unclipped.nonzero(as_tuple=True)[0]
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# faces_unclipped_to_clipped_idx is an (F) dim tensor storing the index of each
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@ -158,6 +158,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
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def test_pluggable_load_cube(self):
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"""
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This won't work on Windows due to NamedTemporaryFile being reopened.
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Use the testpath package instead?
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"""
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ply_file = "\n".join(CUBE_PLY_LINES)
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io = IO()
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