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Add 64-bit indices support to csrmm2 #58899

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Tracked by #60854
aocsa opened this issue May 25, 2021 · 1 comment
Closed
Tracked by #60854

Add 64-bit indices support to csrmm2 #58899

aocsa opened this issue May 25, 2021 · 1 comment
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@IvanYashchuk
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module: sparse Related to torch.sparse open source triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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Sparse tensors

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@aocsa
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aocsa commented May 25, 2021 •
edited

Motivation

NVIDIA documentation mentions that with cusparseSpMM cuSparse API CUSPARSE_INDEX_64I can be used with CUSPARSE_SPMM_CSR_ALG2/CUSPARSE_SPMM_CSR_ALG3 options, currently it uses CUSPARSE_CSRMM_ALG1.

See: https://docs.nvidia.com/cuda/cusparse/index.html

Support for 64-bit indices will improve performance if csrmm2 could work also with int64_t csr indices.

pytorch/aten/src/ATen/native/sparse/cuda/SparseCUDABlas.cu

Line 200 in e4be80c

template<> void csrmm2<float>(

The task boils down to generalizing template<typename T> void _csrmm2... to template<typename T, typename I> void _csrmm2 where I is index type.

cc @aocsa @nikitaved @pearu @mruberry @IvanYashchuk
@aocsa aocsa added this to To do in Sparse tensors via automation May 25, 2021
@aocsa aocsa added module: sparse Related to torch.sparse open source labels May 25, 2021
@aocsa aocsa mentioned this issue May 25, 2021
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@gchanan gchanan added the triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module label May 25, 2021
This was referenced May 27, 2021
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@IvanYashchuk IvanYashchuk mentioned this issue Jun 28, 2021
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@IvanYashchuk IvanYashchuk self-assigned this Jul 6, 2021
@pearu pearu moved this from To do to To Do: CSR in Sparse tensors Aug 10, 2021
@IvanYashchuk
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torch.addmm now works without conversions with both 32-bit and 64-bit indices with #60838.

Sparse tensors automation moved this from To Do: CSR to Done Oct 19, 2021
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module: sparse Related to torch.sparse open source triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module
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Sparse tensors
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