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4 Commits
| Author | SHA1 | Message | Date | |
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Sameer Deshmukh
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5fb1142702 |
Add CSR (compressed sparse row) layout for sparse tensors (#50937)
Summary: Implement compressed sparse row format. Derived from the GCS implementation at https://github.com/pytorch/pytorch/pull/44190 Pull Request resolved: https://github.com/pytorch/pytorch/pull/50937 Reviewed By: mrshenli Differential Revision: D27439865 Pulled By: ezyang fbshipit-source-id: 3ba3dcb9679505b980ff6a5f513e913bbae2fb1d |
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Alexander
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39f50f468d |
matmul performance benchmarks (#51647)
Summary: Minor PR following up the previous PR about sparse benchmarking utils https://github.com/pytorch/pytorch/pull/48397 Fixes https://github.com/pytorch/pytorch/issues/44634: Performance benchmarks for matrix-matrix and matrix-vector ops (dense-sparse, sparse-sparse, and compare to dense-dense) I ran all benchmarks on an 2xRTX8000 machine with AMD 2970WX 24-cores for `DLMC/magnitude_pruning` dataset with different sparsity levels. --- <details><summary> forward tests (expand for details). </summary> - `sparse@sparse` ``` [------------------------------- cpu:matmul-forward -------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ------------------------------------------------------------------------- torch:dense@dense | 108.1 | 100.5 | 101.3 | 108.4 | 98.4 | 187.4 torch:sparse@sparse | 659.1 | 368.8 | 156.5 | 53.3 | 26.8 | 14.9 scipy:sparse@sparse | 565.1 | 233.9 | 130.2 | 23.1 | 21.6 | 15.2 Times are in milliseconds (ms). [----------------------------------- cuda:matmul-forward -----------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ---------------------------------------------------------------------------------- torch:dense@dense | 2243.5 | 4392.5 | 4419.8 | 2272.3 | 4433.9 | 8920.1 torch:sparse@sparse | 21369.2 | 11877.6 | 7339.2 | 1787.2 | 1335.1 | 845.7 Times are in microseconds (us). ``` - `sparse@dense` ``` [------------------------------- cpu:matmul-forward -------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ------------------------------------------------------------------------- torch:dense@dense | 105.8 | 103.8 | 103.0 | 104.4 | 104.4 | 197.0 torch:sparse@dense | 119.9 | 102.4 | 84.0 | 19.7 | 16.8 | 11.6 scipy:sparse@dense | 906.5 | 799.6 | 697.8 | 182.2 | 165.5 | 135.4 Times are in milliseconds (ms). [------------------------- cuda:matmul-forward --------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: --------------------------------------------------------------- torch:dense@dense | 2.2 | 4.4 | 4.4 | 2.3 | 4.5 | 2.3 torch:sparse@dense | 5.7 | 6.6 | 4.5 | 1.4 | 1.4 | 1.3 Times are in milliseconds (ms). ``` - `sparse@vector` ``` [----------------------------------- cpu:matmul-forward ----------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: -------------------------------------------------------------------------------- torch:dense@vector | 510.6 | 505.8 | 759.6 | 782.1 | 682.4 | 764.6 torch:sparse@vector | 10122.8 | 6241.1 | 7935.6 | 2076.3 | 1049.5 | 826.3 scipy:sparse@vector | 1756.7 | 1033.9 | 678.2 | 343.5 | 168.5 | 65.4 Times are in microseconds (us). [-------------------------------- cuda:matmul-forward --------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ---------------------------------------------------------------------------- torch:dense@vector | 36.1 | 21.5 | 21.6 | 21.5 | 21.6 | 21.5 torch:sparse@vector | 1099.2 | 1289.4 | 775.7 | 327.1 | 285.4 | 274.0 Times are in microseconds (us). ``` </details> --- <details><summary> backward tests (expand for details). </summary> - `sparse@sparse` ``` [--------------------------------- cpu:matmul-backward ---------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ------------------------------------------------------------------------------ torch:dense@dense | 246.1 | 315.0 | 306.9 | 168.6 | 290.6 | 146.9 torch:sparse@sparse | 6417.5 | 4393.7 | 3012.7 | 1029.4 | 908.0 | 650.7 Times are in microseconds (us). [----------------------------- cuda:matmul-backward -----------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ----------------------------------------------------------------------- torch:dense@dense | 6.7 | 13.3 | 13.3 | 6.9 | 13.5 | 6.9 torch:sparse@sparse | 143.7 | 143.4 | 119.6 | 29.5 | 29.1 | 10.9 Times are in microseconds (us). ``` - `sparse@dense` ``` [------------------------------ cpu:matmul-backward -------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ------------------------------------------------------------------------- torch:dense@dense | 185.9 | 304.8 | 305.8 | 169.9 | 308.7 | 168.4 torch:sparse@dense | 407.9 | 345.8 | 274.6 | 114.2 | 163.6 | 230.5 Times are in milliseconds (ms). [--------------------------- cuda:matmul-backward --------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ------------------------------------------------------------------ torch:dense@dense | 6.7 | 13.3 | 13.3 | 6.9 | 13.4 | 6.9 torch:sparse@dense | 16.7 | 19.0 | 15.1 | 6.3 | 8.2 | 12.7 Times are in milliseconds (ms). ``` </details> Kindly review this PR. cc mruberry, ngimel Pull Request resolved: https://github.com/pytorch/pytorch/pull/51647 Reviewed By: albanD Differential Revision: D27007809 Pulled By: mruberry fbshipit-source-id: 8c1922cb3280027ca5e3eef31bfa20500c548cfd |
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Sam Estep
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8c798e0622 |
Forbid trailing whitespace (#53406)
Summary: Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857 These are the only hand-written parts of this diff: - the addition to `.github/workflows/lint.yml` - the file endings changed in these four files (to appease FB-internal land-blocking lints): - `GLOSSARY.md` - `aten/src/ATen/core/op_registration/README.md` - `scripts/README.md` - `torch/csrc/jit/codegen/fuser/README.md` The rest was generated by running this command (on macOS): ``` git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//' ``` I looked over the auto-generated changes and didn't see anything that looked problematic. Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406 Test Plan: This run (after adding the lint but before removing existing trailing spaces) failed: - https://github.com/pytorch/pytorch/runs/2043032377 This run (on the tip of this PR) succeeded: - https://github.com/pytorch/pytorch/runs/2043296348 Reviewed By: walterddr, seemethere Differential Revision: D26856620 Pulled By: samestep fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97 |
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Alexander
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44ce0b8883 |
Sparse-sparse matrix multiplication (CPU/CUDA) (#39526)
Summary: This PR implements matrix multiplication support for 2-d sparse tensors using the COO sparse format. The current implementation of `torch.sparse.mm` support this configuration, `torch.sparse.mm(sparse_matrix1, sparse_matrix2.to_dense())`, but this could spend a lot of memory when sparse_matrix2's shape is large. This implementation extends `torch.sparse.mm` function to support `torch.sparse.mm(sparse_matrix1, sparse_matrix2)` Resolves #[20988](https://github.com/pytorch/pytorch/issues/20988) for CPU/CUDA. - [x] sparse matmul - [x] CPU/CUDA C++ implementation - [x] unittests - [x] update torch.sparse.mm documentation - [x] autograd support The CPU sparse-sparse matmul was implemented taking as a reference this work "Sparse Matrix Multiplication Package (SMMP)". The GPU sparse-sparse matmul is based on cuSparse, there is specific code for CUSPARSE when CUSPARSE_VERSION >= 11 and old version of CUSPARSE. Both CPU/CUDA rely on the sparse-sparse matmul algorithm using the CSR indices format as it is one of the fastest algorithm. Here it is the latest benchmark (script is here) results for torch.sparse.mm (CUDA) and torch.sparse.mm (CPU) and scipy, values are float32 scalars: size | density | sparse.mm(CUDA) | sparse.mm(CPU) | scipy_coo_matmul -- | -- | -- | -- | -- (32, 10000) | 0.01 | 822.7 | 79.4 | 704.1 (32, 10000) | 0.05 | 1741.1 | 402.6 | 1155.3 (32, 10000) | 0.1 | 2956.8 | 840.8 | 1885.4 (32, 10000) | 0.25 | 6417.7 | 2832.3 | 4665.2 (512, 10000) | 0.01 | 1010.2 | 3941.3 | 26937.7 (512, 10000) | 0.05 | 2216.2 | 26903.8 | 57343.7 (512, 10000) | 0.1 | 4868.4 | 87773.7 | 117477.0 (512, 10000) | 0.25 | 16639.3 | 608105.0 | 624290.4 (1024, 10000) | 0.01 | 1224.8 | 13088.1 | 110379.2 (1024, 10000) | 0.05 | 3897.5 | 94783.9 | 236541.8 (1024, 10000) | 0.1 | 10559.1 | 405312.5 | 525483.4 (1024, 10000) | 0.25 | 57456.3 | 2424337.5 | 2729318.7 A new backward algorithm was implemented using only `sparse @ sparse` and `sparse_mask` operations. Here is some benchmarking: ``` [------------------------- sparse.mm-backward -------------------------] | sparse.backward | dense.backward ----------------------------------------------------------------------- (32, 10000) | 0.01 | 13.5 | 2.4 (32, 10000) | 0.05 | 52.3 | 2.4 (512, 10000) | 0.01 | 1016.8 | 491.5 (512, 10000) | 0.05 | 1604.3 | 492.3 (1024, 10000) | 0.01 | 2384.1 | 1963.7 (1024, 10000) | 0.05 | 3965.8 | 1951.9 ``` I added new benchmark tests. Now I am using a real dataset used in recent studies [1, 2] with different sparsity levels. ``` [---------------------------------- matmul ---------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ------------------------------------------------------------------ (cpu) torch | 5.4 | 5.4 | 5.2 | 5.3 | 5.3 | 5.4 torch.sparse | 122.2 | 51.9 | 27.5 | 11.4 | 4.9 | 1.8 scipy | 150.1 | 87.4 | 69.2 | 56.8 | 38.4 | 17.1 (cuda) torch | 1.3 | 1.1 | 1.1 | 1.1 | 1.1 | 1.1 torch.sparse | 20.0 | 8.4 | 5.1 | 2.5 | 1.5 | 1.1 [----------------------------------- backward -----------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ----------------------------------------------------------------------- (cpu) torch | 17.7 | 17.9 | 17.7 | 17.7 | 17.6 | 17.9 torch.sparse | 672.9 | 432.6 | 327.5 | 230.8 | 176.7 | 116.7 (cuda) torch | 3.8 | 3.6 | 3.5 | 3.5 | 3.6 | 3.5 torch.sparse | 68.8 | 46.2 | 35.6 | 24.2 | 17.8 | 11.9 Times are in milliseconds (ms). ``` In summary, I can say that the new `sparse @ sparse` backward algorithm is better as it is more about saving space than performance. Moreover, it is better than other options tested before. ## **References** 1. Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen. **Sparse GPU Kernels for Deep Learning.** Proceedings of the International Conference for High Performance Computing, 2020. [https://github.com/google-research/google-research/tree/master/sgk](https://github.com/google-research/google-research/tree/master/sgk) 2. Trevor Gale, Erich Elsen, Sara Hooker. **The State of Sparsity in Deep Neural Networks.** [https://github.com/google-research/google-research/tree/master/state_of_sparsity](https://github.com/google-research/google-research/tree/master/state_of_sparsity) Pull Request resolved: https://github.com/pytorch/pytorch/pull/39526 Reviewed By: mruberry Differential Revision: D25661239 Pulled By: ngimel fbshipit-source-id: b515ecd66d25f347d637e159d51aa45fb43b6938 |