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CUTLASS 2.7 (#318)

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CUTLASS 2.7

Mainloop fusion for GEMM: summation over A or B
Strided DGRAD (optimized iterators)
Half-precision GELU_taylor activation functions
Use these when accumulation and epilogue compute types are all cutlass::half_t
Tuning and bug fixes to fused GEMM + GEMM example
Support for smaller than 128b aligned Convolutions: see examples
Caching of results to accelerate Convolution unit tests
Can be enabled or disabled by running cmake .. -DCUTLASS_TEST_ENABLE_CACHED_RESULTS=OFF
Corrections and bug fixes reported by the CUTLASS community
Thank you for filing these issues!

authored-by: Haicheng Wu haichengw@nvidia.com, Manish Gupta manigupta@nvidia.com, Dustyn Blasig dblasig@nvidia.com, Andrew Kerr akerr@nvidia.com
This commit is contained in:
Manish Gupta
2021-09-20 11:02:22 -07:00
committed by GitHub
parent 9ac255863f
commit 2e07c4cc2f
62 changed files with 5611 additions and 186 deletions
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include/cutlass/gemm/kernel/default_gemm_with_k_reduction.h
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@@ -126,7 +126,7 @@ struct DefaultGemmWithKReduction {
ThreadblockShape, typename Mma::Operator, kPartitionsK, EpilogueOutputOp,
EpilogueOutputOp::kCount>::Epilogue;
/// Define the epilogue
/// Define the epilogue of the reduction vector
using EpilogueGemmKReduction =
typename cutlass::epilogue::threadblock::EpilogueGemmKReduction<
ElementAccumulator, ElementC, ThreadblockShape, typename Mma::Operator, kReduceKForA>;