CUTLASS 2.7 (#318)
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
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# NVIDIA CUTLASS Changelog
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# CUTLASS 2.x
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## [2.7.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.7.0) (2021-09-24)
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* Mainloop fusion for GEMM: [summation over A or B](/examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu)
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* [Strided DGRAD (optimized iterators)](/include/cutlass/conv/kernel/default_conv2d_dgrad.h)
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* [Half-precision GELU_taylor activation functions](/include/cutlass/epilogue/thread/activation.h#L196)
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* Use these when accumulation and epilogue compute types are all `cutlass::half_t`
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* Tuning and bug fixes to [fused GEMM + GEMM example](/examples/13_two_tensor_op_fusion/)
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* Support for smaller than 128b aligned Convolutions: [see examples](test/unit/conv/device/conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f16_sm80.cu#L272)
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* Caching of results to accelerate Convolution [unit tests](test/unit/conv/device/cache_testbed_output.h)
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* Can be enabled or disabled by running `cmake .. -DCUTLASS_TEST_ENABLE_CACHED_RESULTS=OFF`
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* Corrections and bug fixes reported by the CUTLASS community
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* Thank you for filing these issues!
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## [2.6.1](https://github.com/NVIDIA/cutlass/releases/tag/v2.6.1) (2021-09-03)
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* Arbitrary padding and striding for CUTLASS Strided DGRAD Convolution operator (Analytic Iterators)
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