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https://github.com/zebrajr/pytorch.git
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08126c9153
Summary: It is often that the conversion from torch operator to onnx operator requires input rank/dtype/shape to be known. Previously, the conversion depends on tracer to provide these info, leaving a gap in conversion of scripted modules. We are extending the export with support from onnx shape inference. If enabled, onnx shape inference will be called whenever an onnx node is created. This is the first PR introducing the initial look of the feature. More and more cases will be supported following this PR. * Added pass to run onnx shape inference on a given node. The node has to have namespace `onnx`. * Moved helper functions from `export.cpp` to a common place for re-use. * This feature is currently experimental, and can be turned on through flag `onnx_shape_inference` in internal api `torch.onnx._export`. * Currently skipping ONNX Sequence ops, If/Loop and ConstantOfShape due to limitations. Support will be added in the future. Pull Request resolved: https://github.com/pytorch/pytorch/pull/40628 Reviewed By: mrshenli Differential Revision: D22709746 Pulled By: bzinodev fbshipit-source-id: b52aeeae00667e66e0b0c1144022f7af9a8b2948
26 lines
703 B
Bash
Executable File
26 lines
703 B
Bash
Executable File
#!/bin/bash
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set -e
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echo "Running pre-commit flake8"
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python tools/flake8_hook.py
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if [ $(which clang-tidy) ]
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then
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echo "Running pre-commit clang-tidy"
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python tools/clang_tidy.py \
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--paths torch/csrc \
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--diff HEAD \
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-g"-torch/csrc/jit/passes/onnx/helper.cpp" \
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-g"-torch/csrc/jit/passes/onnx/shape_type_inference.cpp" \
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-g"-torch/csrc/jit/serialization/onnx.cpp" \
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-g"-torch/csrc/jit/serialization/export.cpp" \
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-g"-torch/csrc/jit/serialization/import.cpp" \
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-j
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else
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echo "WARNING: Couldn't find clang-tidy executable."
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echo " Please install it if you want local clang-tidy checks."
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fi
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echo "Running pre-commit clang-format"
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tools/git-clang-format HEAD~ --force
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