Faster sparse_dense on GPUs #6580
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This new sparse_dense requires a padded matrix, so a new op `sparse_dense_padded` has been added. AlterOpLayout should transform `sparse_dense` to `sparse_dense_padded` when possible on the gpu.
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@tkonolige : Thanks for the PR! The data looks quite impressive 👍 |
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@ANSHUMAN87 Right now TVM does not do any testing for performance regressions. The hard part in setting up performance testing is that is varies from run to run and machine to machine. |
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@tkonolige : I understand your concern clearly. However it was just a thought. Even if run to run or machine to machine difference, the relative reference would be same. But may be we don't have to do as part of this PR :) |
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Have you considered your syntax errors? |
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@vinx13 @antinucleon @Laurawly @jwfromm @ajtulloch I think this is ready for review. |
vinx13
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Laurawly
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Oct 2, 2020
ANSHUMAN87
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Oct 4, 2020
ANSHUMAN87
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Oct 4, 2020
ANSHUMAN87
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Oct 8, 2020
ANSHUMAN87
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ANSHUMAN87
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jroesch
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Oct 9, 2020
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@tkonolige @tqchen This commits fails in the master branch. see the CI: https://github.com/apache/incubator-tvm/commits/master It introduces a flaky test that blocks two of my PRs. |
#6658 has resolved the issue i think. |
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@merrymercy I think that was the diagnostics |
TusharKanekiDey
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Oct 13, 2020
* Faster sparse_dense on GPUs. This new sparse_dense requires a padded matrix, so a new op `sparse_dense_padded` has been added. AlterOpLayout should transform `sparse_dense` to `sparse_dense_padded` when possible on the gpu. * formatting * more formatting * Check that alteroplayout is definedbefore using it * check if FTVMAlterOpLayout exists before using it * formatting * restore message passing * Fix sparse_dense and sparse_dense_padded docs * Fix old sparse_dense, autotvm and sparse_dense dont play well together * Remove unused imports * clarify warp count in cuda_transpose * Document multidimensional access * Warn users not to use sparse_dense_padded * rename nn.sparse_dense_padded to nn.internal.sparse_dense_padded
TusharKanekiDey
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Oct 14, 2020
* Faster sparse_dense on GPUs. This new sparse_dense requires a padded matrix, so a new op `sparse_dense_padded` has been added. AlterOpLayout should transform `sparse_dense` to `sparse_dense_padded` when possible on the gpu. * formatting * more formatting * Check that alteroplayout is definedbefore using it * check if FTVMAlterOpLayout exists before using it * formatting * restore message passing * Fix sparse_dense and sparse_dense_padded docs * Fix old sparse_dense, autotvm and sparse_dense dont play well together * Remove unused imports * clarify warp count in cuda_transpose * Document multidimensional access * Warn users not to use sparse_dense_padded * rename nn.sparse_dense_padded to nn.internal.sparse_dense_padded
TusharKanekiDey
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Oct 15, 2020
* Faster sparse_dense on GPUs. This new sparse_dense requires a padded matrix, so a new op `sparse_dense_padded` has been added. AlterOpLayout should transform `sparse_dense` to `sparse_dense_padded` when possible on the gpu. * formatting * more formatting * Check that alteroplayout is definedbefore using it * check if FTVMAlterOpLayout exists before using it * formatting * restore message passing * Fix sparse_dense and sparse_dense_padded docs * Fix old sparse_dense, autotvm and sparse_dense dont play well together * Remove unused imports * clarify warp count in cuda_transpose * Document multidimensional access * Warn users not to use sparse_dense_padded * rename nn.sparse_dense_padded to nn.internal.sparse_dense_padded
TusharKanekiDey
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Oct 15, 2020
* Faster sparse_dense on GPUs. This new sparse_dense requires a padded matrix, so a new op `sparse_dense_padded` has been added. AlterOpLayout should transform `sparse_dense` to `sparse_dense_padded` when possible on the gpu. * formatting * more formatting * Check that alteroplayout is definedbefore using it * check if FTVMAlterOpLayout exists before using it * formatting * restore message passing * Fix sparse_dense and sparse_dense_padded docs * Fix old sparse_dense, autotvm and sparse_dense dont play well together * Remove unused imports * clarify warp count in cuda_transpose * Document multidimensional access * Warn users not to use sparse_dense_padded * rename nn.sparse_dense_padded to nn.internal.sparse_dense_padded
TusharKanekiDey
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Oct 16, 2020
* Faster sparse_dense on GPUs. This new sparse_dense requires a padded matrix, so a new op `sparse_dense_padded` has been added. AlterOpLayout should transform `sparse_dense` to `sparse_dense_padded` when possible on the gpu. * formatting * more formatting * Check that alteroplayout is definedbefore using it * check if FTVMAlterOpLayout exists before using it * formatting * restore message passing * Fix sparse_dense and sparse_dense_padded docs * Fix old sparse_dense, autotvm and sparse_dense dont play well together * Remove unused imports * clarify warp count in cuda_transpose * Document multidimensional access * Warn users not to use sparse_dense_padded * rename nn.sparse_dense_padded to nn.internal.sparse_dense_padded
trevor-m
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Oct 19, 2020
* Faster sparse_dense on GPUs. This new sparse_dense requires a padded matrix, so a new op `sparse_dense_padded` has been added. AlterOpLayout should transform `sparse_dense` to `sparse_dense_padded` when possible on the gpu. * formatting * more formatting * Check that alteroplayout is definedbefore using it * check if FTVMAlterOpLayout exists before using it * formatting * restore message passing * Fix sparse_dense and sparse_dense_padded docs * Fix old sparse_dense, autotvm and sparse_dense dont play well together * Remove unused imports * clarify warp count in cuda_transpose * Document multidimensional access * Warn users not to use sparse_dense_padded * rename nn.sparse_dense_padded to nn.internal.sparse_dense_padded
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I've written a faster sparse_dense for GPUs using tir. This sparse_dense requires a padded matrix, so I've added a new op sparse_dense_padded. AlterOpLayout should transform sparse_dense to sparse_dense_padded when using a gpu.
This new sparse_dense improves prunebert performance from 155.41ms mean to 7.75ms mean. In general, this implementation is faster than cublas dense on matrices with density < 0.05 and is often faster than cusparse for machine learning workloads.