Provide CUDA support for the binarize() operation on sparse matrices #601
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| # here just to allow CUDA extension to overload this function with correct type casting | ||
| binarize(x, T::DataType) = binarize(x) |
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| # here just to allow CUDA extension to overload this function with correct type casting | |
| binarize(x, T::DataType) = binarize(x) | |
| binarize(x, T::DataType) = map(y -> y > 0 ? one(T) : zero(T), x) |
or something similar
|
The segmentation fault in GNNGGraphs CUDA CI should be investigated |
Co-authored-by: Carlo Lucibello <[email protected]>
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| function GNNGraphs.binarize(Mat::CUSPARSE.CuSparseMatrixCSC) | ||
| bin_vals = fill!(similar(nonzeros(Mat), Bool), true) | ||
| return CUSPARSE.CuSparseMatrixCSC(Mat.colPtr, rowvals(Mat), bin_vals, size(Mat)) | ||
| end |
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| function GNNGraphs.binarize(Mat::CUSPARSE.CuSparseMatrixCSC) | |
| bin_vals = fill!(similar(nonzeros(Mat), Bool), true) | |
| return CUSPARSE.CuSparseMatrixCSC(Mat.colPtr, rowvals(Mat), bin_vals, size(Mat)) | |
| end |
This could be removed in favor of a single method with signature `binarize(Mat::CUSPARSE.CuSparseMatrixCSC, T::DataType = Bool). Same for the main method in GNNGraphs I guess.
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Made binarize() work on CUDA with sparse adjmat of type CUSPACE.CuSparseMatrixCSC.
Also, added a specialization of binarize() that takes an eltype as an additional arg and directly creates a binarized adjmat of that, avoiding potentially costly subsequent conversions.
However, note that for now this implementation won't be called during the fwd pass, as the CUDA propagate specialization falls back on the default gather/scatter approach, which doesn't use the adjecency matrix.
All tests on CPU and GPU correctly passed, except for 1 broken in "ChebConv GPU", but it is also broken on master.